During the second half of August 2022, a dust intrusion event occurred, when dust that originated from the dry regions of Kalmyk steppe and the Precaspian plain was transported over the eastern region of Romania. The dry soil in these regions was the result of the severe drought over a long period of time, accentuated by early heat waves in May and June 2022. The dust event was captured by MODIS images. Smoke trains originating from fires in the north of the Azov were also detected, but these did not reach Romania. Optical parametres from AERONET was used to confirm the dust event. To determine the trajectory of the particles, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used in this paper. The Ensemble median model was used to highlight the presence and concentration of dust in eastern part of Romania. Aerosols were detected between 0 and 4 km, according to radar and ceilometer data of the REXDAN cloud remote sensing facility in Galați, Romania. This dust intrusion event was the result of the dominant easterly cir-culation caused by the extension of East European High to the northeast of the continent, which transported the dust towards eastern part of Romania for more than 2 days. Moreover, the tor-rential rains between August 22nd and 24th did not clear the atmosphere of dust, since the intense easterly circulation kept carrying the dust into the Moldavian area.

In this study, we discuss the thermospheric density response to the solar activity and QBO. By using the wavelet power spectrum method, the thermospheric density from 1967 to 2013 shows ~11-year period, semiannual and annual variations, while the seasonal variation is usually more significant under high solar activity conditions. Importantly, we investigate the possible link between the density and the QBO, with the aid of GAMDM model and different density residual method. As a result, the difference between the measured density and empirical model seemingly has QBO signal, but the ratio of them revels that the QBO signal does not exist in the thermospheric density. Comprehensively, we thought the stratospheric QBO cannot impact on the thermosphere, and more data and numerical modeling are needed for further validation.

From December 2021 to May 2022, MeteoSwiss and Meteomatics conducted a proof of concept to demonstrate the capability of Meteodrones to provide data of sufficient quality and reliability on a routine operational basis. Over 6 months, Meteodrones MM-670 were operated automati-cally 8 times per night at Payerne, Switzerland. 864 meteorological profiles were measured and compared to co-located standard measurements including radiosoundings and remote-sensing instruments. To our knowledge, it is the first time that Meteodrone’s atmospheric profiles are evaluated in such an extensive campaign. The paper highlights two case studies that showcase the performance and challenges of measur-ing temperature, humidity, and wind with a Meteodrone. It also focuses on the overall quality of the drone measurements. Throughout the campaign, the availability of Meteodrone measure-ments was 75.7%, with 82.2% of the flights reaching the nominal altitude of 2000m above sea level. To assess the quality of the Meteodrone measurements, the radiosondes were used as a refer-ence, comparing them to the WMO (World Meteorological Organization) requirements [1]. The temperature measurements by the Meteodrone met the "breakthrough" target, while the humid-ity and wind profiles met the "threshold" target for high-resolution numerical weather predic-tion. The temperature measurement quality was comparable to that of a microwave radiometer, and the humidity quality was similar to that obtained from a Raman Lidar. However, the wind measurements by a Doppler Lidar were more accurate than the estimation provided by the Me-teodrone. During this campaign, the data quality could be improved and at the end of the campaign, Me-teodrone quality reached the “goal” target for temperature and the “breakthrough” target for humidity. This campaign marks a significant step towards the operational use of automatic drones for me-teorological applications.

This study analyzes the potential response of the seasonal cycle of heatwave (HWDI), dry (CDD) and wet (CWD) spells indices over West Africa for the near (2031-2060) and the far (2071-2100) future periods, under RCP4.5 and RCP8.5 scenarios using CORDEX simulations. Although some relative biases during the historical period (1976-2005), the CORDEX simulations and their ensemble mean outperform the seasonal variability of the above indices over three defined sub-regions of West Africa (i.e., Guinea gulf, west and east Sahel). They have shown significant correlation coefficients and less RMSE. They project an increase in heatwave days for both near and far future periods over whole west Africa region under both RCP scenarios. In addition, the Sahel regions will face to a decrease in wet spells days from March to November, whereas, the Gulf of Guinea will face to a decrease during all the year, except CCCLM simulation which indicates an increase during the retreat phase of the monsoon (October to December). The results also have shown an increase in dry spells over Sahel regions, more pronounced during March-November period, whereas, over Guinea gulf, the increase is observed over the entire year. On the other hand, the months of increasing dry spells and decreasing wet spells coincide, suggesting that countries in these regions could be exposed simultaneously to dry season associated with a high risk of drought and heatwave under future climate conditions.

Extreme value analysis (EVA) is well-established to derive hydrometeorological design values for infrastructure that has to withstand extreme events. Since there is evidence of increased extremes with higher hazard potential under climate change, alterations of EVA are introduced for which statistical properties are no longer assumed to be constant but vary over time. In this study, both stationary and non-stationary EVA models are used to derive design life levels (DLLs) of daily precipitation in the pre-alpine Oberland region of Southern Germany, an orographically complex region characterized by heavy precipitation events and climate change. As EVA is fraught with uncertainties, it is crucial to quantify its methodological impacts: two theoretical distributions (i.e., Generalized Extreme Value (GEV) and Generalized Pareto (GP) distribution), four different parameter estimation techniques, (i.e., Maximum Likelihood Estimation (MLE), L-moments, Generalized Maximum Likelihood Estimation (GMLE), and Bayesian estimation method) are compared. Discrepancies due to the parameter estimation methods may reach up to 45 % of the mean absolute value, while differences between stationary and non-stationary models are of the same magnitude (differences in design levels up to 40%). Despite the underlying large methodological uncertainties, there is a robust tendency for increased DLLs in the Oberland region towards the end of this century based on the (non-)stationary models.

This study investigates the ionospheric variations resulting from thunderstorms that occurred between 00:00 UTC and 08:00 UTC on November 10, 2018, in the central region of Argentina, site of the RELAMPAGO-CACTI Project (Remote sensing of Electrification, Lightning, and Mesoscale/microscale Processes with Adaptive Ground Observations; Clouds, Aerosols, and Complex Terrain Interactions). The data used were the Total Electron Content (TEC), which was computed from Global Navigation Satellite System (GNSS) measurements provided by the Argentinian Continuous Satellite Monitoring Network (RAMSAC by its Spanish acronym) stations and the atmospheric electrical activity data that were provided by the Earth Networks Total Lightning Network (ENTLN). We observed that the generated disturbances have periods less than or equal to 100 minutes and peak-to-peak DVTEC amplitude values reaching up to 1.35 TECU (1 Total electron content unit =1016electrons/m2). We also noted that these atmospheric gravity waves, which show the highest peak-to-peak amplitudes, occur during periods of intense lightning activity. Finally, we found that for a day with Atmospheric Electrical Activity (AEA), the peak-to-peak amplitudes of the waves are approximately 2.91 times greater than the peak-to-peak amplitudes of a day without AEA.

Urumqi City, located in the northwest of China, is a city with high indoor radon concentration in nationwide indoor radon survey in China. This study focuses on the assessment of indoor radon level and its potential sources in this city. Indoor radon measure using RAD7 and solid nuclear track detector (SSNTD), soil gas radon by RAD7 and determining specific activity of uranium and radium in soil samples by a high pure germanium spectrometer were performed from 2021 to 2023. The results reveal a wide range of indoor radon concentrations in Urumqi, with anomalies above 400 Bq/m3 in some dwellings. The arithmetical and geometric mean values of indoor radon concentration in Urumqi are 90.24 ± 83.04 Bq/m3 and 66.25 Bq/m3, respectively. The geometric mean values of radon and thoron by SSNTD is 110 Bq/m3 and 109 Bq/m3. The analysis suggest that the activity of faults and fault structures are the main factors influencing indoor radon levels, with frequent earthquakes contributing to higher average radon concentrations. Additionally, investigations into indoor radon and building material radioactivity in detailed are recommended for buildings with radon anomalies and the source of indoor radon anomalies.

This paper deals with the analysis of the temperatures in a group of 36 African countries. By looking at the maximum, minimum and the range (the difference between the maximum and the minimum) and using a long memory model based on fractional integration and cointegration, we first show that all series display a long memory pattern, with a significant positive time trend in 29 countries for the maximum temperatures and in 33 for the minimum ones. Looking at the range, the estimated value for the order of integration is smaller than the one based on maximum or minimum temperatures in 17 countries. Performing fractional cointegration tests between the maximum and minimum temperatures, our results indicate that the two series cointegrate in the classical sense (i.e., with a short memory equilibrium relationship) in a group of 11 countries, and there is another group of eight countries displaying cointegration in a fractional sense. The remaining 17 countries with no evidence of cointegration are therefore at the very high risk of climate change due to the absence of long-term comovement in their maximum and minimum temperatures. Findings in this paper are of tremendous interpretations and relevance for the analysis and climate projections in Africa.

This study aims to identify the optimal combination of microphysics (MP) and cumulus (CU) parameterization schemes for accurately simulating heavy to violet rainfall events associated with Tropical Cyclones (TCs) and atmospheric disturbances in Thailand using the coupled Weather Research and Forecasting (WRF) and Regional Oceanic Model (ROMS), hereafter referred to as WRF-ROMS. Three CU schemes, namely Betts–Miller–Janjic (BMJ), Grell 3D Ensemble (G3), and Kain–Fritsch (KF), along with three MP schemes, namely Eta (ETA), Purdue Lin (LIN), and WRF Single-moment 3-class (WSM3), are selected for the sensitivity analysis. Seven instances of heavy to violent rainfall in Thailand, occurring during summer season of 2020 and associated with tropical storms and atmospheric disturbances, are simulated using all possible combinations of the chosen physics schemes. The simulated rain intensities are compared against observations from the National Hydroinformatics Data Center. Performance was assessed using the Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) metrics. The models showed proficiency in predicting light to moderate rainfall, with certain combinations performing better in specific rainfall categories. However, forecasting heavy and violent rainfall proved challenging for all models and lead-time forecasts. Specific combinations, particularly those incorporating the KF scheme, demonstrated superior prediction of heavy to violent rainfall. The FAR values increased with lead-time and rain intensity, and the KF scheme combinations showed improved predictions of intense rainfall with lower FAR values. The CSI values indicated comparable performance between the control model and combination models across light to heavy rain categories, with the KF scheme showing better predictions for longer lead-times. However, accurately predicting intense rainfall remained limited. These findings highlight the need for further improvements, including refining model parameters and exploring advanced techniques to enhance accuracy and skill, particularly for longer-term forecasts. Sub-seasonal to seasonal prediction should be considered to extend forecast capabilities.

Mountain fire can become more complex than fires at lower elevation due to the complex interaction of fire, topography, and weather. The Gell River Fire in Tasmania, Australia occurred in rugged terrain where there are abundant fire sensitive vegetation communities, as well as the presence of infrastructure including high-voltage transmission lines. The fire began at the end of December 2018 and lasted a few months, with a final burnt area of approximately 350km² despite significant fire suppression effort. The fire was investigated by employing wind vector maps, numerical weather model vertical sounding charts (NWMVS) and Prototype 2, which is an integrated fire simulator and can detect lateral fire channeling (LFC). Our analysis of the fire found its spread was likely to be introduced into a valley by forced channeling (FC), which is modified synoptic wind, and showed rapid spread in the valley. The simulated fire also showed wider spread than the observed data in the valley, with the simulated fire impacting highly sensitive vegetation communities on the fringes of the valley. This alludes to some potential conclusions: (1) The loss of fire sensitive vegetation would have increased if fire suppression activity had not been conducted. (2) Spotting fires could be produced by LFC because these spotting fire would allow spreading fire in a shorter period. (3) Heterogeneity of vegetation, such as combination of buttongrass and forest, could help carry fire rapidly in the valley with LFC. Fire can propagate faster in buttongrass than in forests while the forests allow the spotting fire.

Although mountain areas account for approximately one fifth of the terrestrial surface, there has been less research focused on fire in these areas compared to lowlands. Mountain fires have distinct behavior due to dynamic winds interacting with the terrain, which can influence the fireline intensity and propagation. For the sake of fire safety of fire crews, it is essential to know how difficult to control the fire is in the mountain regions, with fireline intensity providing a useful indicator of risk and suppressibility. We studied one of the major disasters, wildfire, in Australia in such a highland by using the Great Pine Tier Fire, which occurred 15^th January in 2019, ending up burning approximately 511.86km². Weather and fire intensity at pseudo weather stations located at key points of fire progression were analyzed by wind vector maps and numerical weather model vertical sounding (NWMVS). Fire propagation was then simulated in Prototype 2, a fire simulator capable of detecting the potential for lateral fire channeling (LFC), and simulating fireline intensity using Australian vegetation sub-models. We found that the synoptic wind appeared to be modified by the interaction with the terrain in windward and the fire intensified the most in its leeward. In practice, the fire moved out of the valley axis and up its sidehill by following the wind which had been modified by local vertex of the curved valley axis before reaching this location.

The socioeconomic sectors increasingly rely on prompt and affordable tools to predict climatic conditions. This study uses a decision tree classifier model to identify similar monthly ENSO 3.4 indices from March 2001 to February 2011 through monthly historical ENSO3.4 indices (December 1950 – February 2001). Historical monthly ENSO3.4 indices were used to extract available high-resolution monthly historical precipitation grids (1950-2001) for Mexico and constructed monthly ensembles (March 2001 – February 2011 hindcasts). Formerly, a precipitation categorization of monthly observed and simulated precipitation (2001-2011 hindcasts) was employed. Thereafter, global vector data and spatial map assessments were performed seasonally (winter, spring, summer, and fall). Global KSS and HSS metrics indicated the model approach has skill, especially in the spring and fall. Spatial metrics exposed large areas with hit rates > 0.60 in winter and spring, though in winter and spring (dry seasons), it was due to the precipitation categorization hindcast and observed were within the first classification. Lower hit rates, but still acceptable (> 0.40), were detected in summer and fall (during the rainy season and at the end, respectively) because more precipitation classifications were tested and prone to miss. Spatial Spearman correlations provided specific areas of significant model performances and found that high hit rates during the winter and spring are spurious. Meanwhile, it was also found that during the fall the model approach is reliable in most of Mexico, followed by the spring, and in constraint areas in summer and winter across the country. It also appears that dominant ENSO neutral events followed by balanced ENSO cooling and warming events do not prefer the model skill. Overall, the empirical method shows promise (low-cost human and computational resources if ENSO indices expose certainty in its Lead months to forecast) and has the potential to be used in other developing countries influenced by El Niño phenomena.

In the overall context of national eco-conservation civilization development and considering the hills in the Beijing-Tianjin-Hebei-Shanxi region, the local forest coverage rate is gradually on the rise, and so is the risk of forest fire. Every year, satellites can monitor and detect hundreds of fire spots on average. In recent years, serious fire took place for several times, leading to loss of life and personal injury. Then the fire-fighting work consumed huge amount of labor and material resources, exerting negative social impacts in many ways. Thus, the analysis of fire climate background in this region and the forecast based on fire weather indexes constitute an important part in the forest fire risk management. Located in the tropical monsoon climate zone, four provinces and municipalities of Beijing, Tianjin, Hebei and Shanxi are similar in terms of climate characteristics. This study tries to analyze the characteristics about fire spots under satellite monitoring, fire climate and fire weather index. The probability distribution analysis of weather index about fire spots in history and key meteorological observation stations as well as the weather index analysis in the early stage of typical fire indicate that the Fine Fuel Moisture Content (FFMC) and the Initial Spread Index (ISI) in the Fire Weather Index (FWI) system is applicable to the fire risk analysis in the Beijing-Tianjin-Hebei-Shanxi region, which can also effectively indicate the fuel moisture and spread conditions. As regards to the average level in Beijing-Tianjin-Hebei-Shanxi, when the FFMC is smaller than 93.5 and the ISI is also smaller than 5.0, there is a low risk of fire; when the FFMC is above 94 and the ISI is larger than 10.0, there is a high risk of fire; and the fire risk will be extremely high if the FFMC is over 96 and the ISI is above 13.0. In the practical fire-fighting management, the fire danger class can be released in reference to the ISI classes and threshold values of key meteorological observation stations under analysis provided by this study.

Background: We studied Riveaux Road Fire, which was ignited by multiple lightning strikes in January 2019 and burnt more than 637.19 km² in southern Tasmania, Australia. Aims: We focused on fire weather, such as identification of dynamic wind and vegetation type, in a valley of the study area. Methods: We employed two methods: numerical weather model vertical sounding (NWMVS) and the use of a fire simulator, to quantify and examine the contribution of dynamic winds to fire behaviour. The NWMVSs allow rapid diagnosis of changes in wind, temperature, dew point temperature and cloud coverage. Prototype 2 is a fire simulator based on the specification of Australian Fire Danger Rating System (AFDRS). Key results: We found fires to be guided by terrain-forced channelling primarily and by downslope wind conditionally in the valleys. In addition, the fire intensity periodically changed with the magnitude of surface wind, in buttongrass moorland, in which the fire often smoulders, during the fire period according to the satellite image. Conclusions and Implications: Therefore, there should be caution for not only terrain and dynamic wind but also vegetation type during fire spread in rugged terrain.

Arid- and semi-arid Central Asia is particularly sensitive to climate change. The changes in extreme precipitation in Central Asia stemming from climate warming are the subject of intense debate within the scientific community. This study employed a Morlet wavelet analysis to examine the annual occurrence number of extreme precipitation in Central Asia from May to September during the period of 1951–2005. Their modulating planetary-scale climate modes were identified by using a linear regression analysis. Two major scales of the temporal variability were derived: 2–3.9 yr and 4–6 yr. The dominant variability was 2–3.9-yr scale and was associated with the negative phase of the Polar/Eurasia (POL) pattern. The 4–6-yr scale provided a secondary contribution and was closely linked to the negative phase of the North Atlantic Oscillation (NAO). These planetary climate modes acted as precursors of extreme precipitation over Central Asia. The negative phase of POL directly contributed to a negative height anomaly over Central Asia, which was intimately related to extreme precipitation. In contrast, the negative NAO phase possibly manifested as a Rossby wave source, which was subsequently exported to Central Asia through a negative–positive–negative Rossby wave train.

Air quality and environmental fairness have always been an area of prime interest across the globe. The significance low-cost air quality sensing and practices spikes during the time of pan-demic and epidemics when the air becomes a threat to living beings especially human beings. The gradual innovation and enrichment in low-cost air quality sensing sensors, nodes or devices, and systems are exponentially increasing for the last three decades. This work reviews the major contributions in a) low-cost scalable air quality assessment; b) low-cost air quality sensors, sensing approaches and technologies; c) low-cost state-of-the-art gas sensors fabrication methods (MEMS and CMOS); d) low-cost gas sensors measurement configurations and assemblies; and e) low-cost air quality sensors calibration and testing systems. A systematic review of past work with a goal to assist end-users, public health facilities, state agencies, researchers, scientists and air quality protection agencies has been rendered in this work. Starting from sensors electrodes to IoT based mobile smart nodes; all have been introduced in this article.

Strong convective storms can be a serious threat to modern society, influencing both the economy and human life. Thunderstorms can bring much-needed rain after a dry spell, but the nature of thunderstorm mostly causes damage due to high wind associated with it and heavy intensity rain that mostly causes quick overlandflow and flashfloods. The paper focused on the 2011 Pukkelpop event in Belgium where due to heavy thunderstorm a lot of financial damage occurred, and five casualties. In this paper we look into the basic elements that enforces the creation of a thunderstorm like CAPE and windshear. A representative grid has been taken from the Alaro-0 model of 12.5 km resolution for the analysis of the thunderstorm over Belgium which is detailed out in the methodology. The model runs of Alaro-0 from Royal Meteorological Institute of Belgium (RMI) is used for historical and future years for CAPE and Shear. We look into STEnv which is a composite number that can provide some insight on occurrence of thunderstorm, and STEnv is when CAPE*Shear exceeds certain threshold. In this study the threshold has been chosen as 97%. Only summer months have been taken into account into this study as summer(s) has the most thunderstorm in Belgium. The analysis of CAPE and STEnv over historical period (1977-2005), near future (2041-2069) and far future (2071-2099) shows significant increase in occurrence of thunderstorm in the 2071-2099s, specially in extreme cases such as 97% and 99% quantile. CAPE (J/Kg) increases in all future scenarios compared to the historical period. In 2071-2099s, CAPE increases from 2100 J/Kg to 2300 J/Kg (90% quantile) and from 3500 J/Kg to 3900 J/Kg (99% quantile) compared to the historical period. The number of STEnv increases from 300 to 378 in top 10% storm case (90% quantile), 158 to 218 in top 5% storm (95% quantile), 97 to 153 in top 3% storm (97% quantile) and 33 to 66 in top 1% storm (99% quantile) with a 90% confidence interval overall. Shear mostly remains unchanged. The study also shows spatial variation of CAPE, shear and STEnv over Belgium for present and future scenarios in different extremities (90%, 95%, 97% and 99% quantiles). There is a good indication that increase in CAPE translates to increase in STEnv, which is an accepted measure to predict occurrence of thunderstorm. Future climate change also amplifies the occurrence of thunderstorm, which is coherent with relevant studies. The uncertainty related to the study can be reduced with more detailed dataset and involvement of numerical weather prediction models beyond data statistics.

The wind observations for multiple levels (40–200 m) have been conducted for a long time (2016–2020) on Jeju Island of South Korea. This study aims at understanding the vertical and temporal characteristics of lower atmosphere. Jeju Island is a region located at mid-latitude and is affected by seasonal monsoon wind. The maximum wind speed appears in the lower layer during day time and is delayed in the upper layer during latter time in diurnal cycle. In summer season, the surface layer increases up to 160 m during day time via dominant solar radiation effect, which is higher than those for other seasons. However, the maximum wind speed in winter season appears irregularly among altitudes, and the surface layer is ~100 m, which is lower than that in summer season. It can be attributed to the increase in the mean wind speed in diurnal cycle caused by the strong northwestern wind for winter season. These results imply that the relationship between near-surface and higher altitudes is primarily affected by solar radiation and seasonal monsoon winds. These results are expected to contribute to site selection criteria for wind farms and to the assessment concerning planetary boundary layer modeling.

The intercomparison among different atmospheric monitoring systems is key for instrument calibration and validation. Common cases involve satellites, radiosonde, radio occultation (e.g. GNSS-RO) and atmospheric model outputs (e.g. ERA5 reanalysis). Since instruments and/or measures are not perfectly collocated, the miss-collocation uncertainty must be considered in the related intercomparison uncertainty budgets.

The quantile estimation of extreme wind speed is needed in various environmental fields such as climatology, design of structures, renewable energy sources and agricultural operations. These calculations are crucial for the coding of wind speed. In this study, the required wind speed series of 16 stations in Khyber Pakhtunkhwa, Pakistan, was obtained from the NASA official website and measured in meters per second (m/s) at a 10-meter distance. A Regional Frequency Analysis of 16 AMWS stations was performed using L-moments. The quantile estimates of extreme wind speed are needed for various areas of interest using Regional Frequency Analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meter per second (m/s). A Regional Frequency Analysis of AMWS using L-moments is performed utilizing wind speed data from sixteen sites (16) in Pakistan's Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions are chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing Relative Bias (RB) and Relative Root Mean Square Error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

Drought events are evident effects of climate change around the globe and yield several socio-economic impacts. Such effects are even more relevant for South America (SA) since different activities essential for the continent, like agriculture and energy generation, depend highly on water resources. Thus, this study aimed to evaluate future changes in precipitation and hydro-logical droughts occurrence in SA through climate projections from eight global climate models (GCMs) of CMIP6. To this end, statistical downscaling was applied to the projections with the Quantile Delta Mapping technique, and the method proved to be efficient in reducing systematic biases and preserving GCMs’ trends. For the following decades, the results show considerable and statistically significant reductions in precipitation over most of SA, especially during the austral spring, with the most intense signal under the SSP5-8.5 forcing scenario. Furthermore, GCMs showed mixed signals about projections of the frequency and intensity of drought events. Still, they indicated agreement regarding the increase in duration and severity of events over all of SA and a substantial proportion of moderate and severe events over most of Brazil during the 21st century. These results can be helpful for better management of water resources by deci-sion-makers and energy planners.

Climate change is a threat to mining and other industries, especially those involving water supply and management by inducing or amplifying some climatic parameters such as changes in precipitation regimes and temperature extremes. Using the latest NASA NEX-GDDP-CMIP6 datasets, this study quantifies the level of climate change that may affect the development of two mine sites (Site1 and Site2) in northeast Kazakhstan. The study analyses the daily precipitation and maximum and minimum temperature a of a number of global circulation models (GCM) over three future time periods, 2040s, 2060s and 2080s, under two shared socioeconomic pathway (SSP) scenarios, SSP245 and SSP585, against the baseline period 1981- 2014. The analyses revealed that: (1) Both maximum and minimum temperature will increase under both SSP in those time periods, with the rate of change for minimum temperature being higher than maximum temperature. (2) The mean annual precipitation will increase by an average rate of 7% and 10.5% in 2040s for SSP245 and 17.5% and 7.5% for SSP585 in 2080s at Site1 and Site2, respectively. It is also observed that summer months will experience drier condition whilst all other months will increase in precipitation. (3) The values of 24-hour precipitation with 10-year return period will also increase under both SSP scenarios and future time periods for most of the studied GCM and at both mine sites. These predicted changes should be considered as design criteria adjustments for project water supply and water management structures.

Volcanic emissions (ash, gas, aerosols) dispersed in the atmosphere during explosive eruptions generate hazards affecting aviation, human health, air quality and the environment. We document for the first time the contamination of airspace by very fine volcanic ash due to sequences of tran-sient ash plumes from Mount Etna. The atmospheric dispersal of sub-10 μm (PM10) ash is mod-elled using the WRF-Chem model coupled online with meteorology and aerosols and offline with Mass Eruption Rates (MER) derived from near-vent Doppler radar measurements and inferred plume altitudes. We analyse two sequences of paroxysms with widely varied volcanological conditions and contrasted meteorological synoptic patterns in October–December 2013 and on 3-5 December 2015. We analyse the PM10 ash dispersal simulation maps in terms of time-averaged columnar ash density, concentration at specified flight levels averaged over the entire sequence interval, and daily average concentration during selected paroxysm days at these flight levels. The very fine ash from such eruption sequences is shown to easily contaminate the airspace around the volcano within a radius of about 1000 km in a matter of a few days. Synoptic patterns with relatively weak tropospheric currents lead to the accumulation of PM10 ash at a re-gional scale all around Etna. In this context, closely interspersed paroxysms tend to accumulate very fine ash more diffusively in the lower troposphere and in stretched ash clouds higher up in the troposphere. Low-pressure, high-winds weather systems tend to stretch ash clouds into ~100 km-wide clouds forming large-scale vortices 800-1600 km in diameter. Daily average PM10 ash concentrations commonly exceed the aviation hazard threshold up to 1000 km downwind from the volcano and up to the upper troposphere for intense paroxysms. Vertical distributions show ash cloud thicknesses in the range 0.7–3 km, and PM10 sometimes stagnating at ground level, represent a potential health hazard.

The evaluation of suitability for bioclimatic viticulture indices (BVIs) zones, as with any other crop, requires a well-founded knowledge of the spatial variability in climate data, which is also used to assess different grapevine cultivars and to delimit appropriate regions for vineyard production under the current climate and projected climate change. BVIs, on the other hand, are typically calculated using a small number of locations linked by climatic station sites or applied using a coarsely-resolution distributed dataset. Furthermore, often the methods applied to precisely delineate symmetrical regions are not appropriate to generate accurate maps. To provide an analysis using three temperature-based indices and the hydrothermal coefficient (HTC), quantifying their spatial variation, and representing the spatial patterns of each BVI throughout the Jabal Al Arab viticulture—one of the most important Syrian viticulture regions in the Eastern Mediterranean—daily temperature data from 15 meteorological stations and 57 rain gauges, considering the 1984–2014 period, and downscaled future scenarios (RCPs based on CanESM2), considering the 2016–2100 period, were utilized. The statistical method of each BVI was analyzed, and, later, they were mapped by using GIS and a hybrid interpolation (regression-kriging) approach. The regression fitting method revealed that the sum accumulation of heat and the hydro-thermal index during the growing season were highly related to elevation and distance to the seacoast variables; however, the viticulture zones varied spatially depending on which index was used and under suggested future scenarios compared to the current climate. The spatial distribution of climate in the Jabal Al Arab regions exhibits significant variability. The findings suggest that climate change projections indicate a prevalence of warmer conditions in the future. Under the RCP scenarios, the territory can be categorized into up to three bioclimatic classes for both the Heat Index (HI) and the Winter Index-Growing Degree Days (WI-GDD), in contrast to the current climate which has six classes. These results provide valuable insights into the suitability of viticulture within each climatic region and facilitate the identification of homogeneous zones. The utilization of consistent bioclimatic indices and an advanced hybrid interpolation method has enabled the delineation and comparison of bioclimatic variables of Jabal Al Arab with other regions worldwide. Such comparisons should be considered when selecting grapevine varieties and assessing the potential for grape production.

The mountains in northern Xinjiang of China were studied during the snowmelt season. Multi-source fusion live data Chinese Land Data Assimilation System (CLDAS,0.05°× 0.05°,hourly data) were used as real data, and Central Meteorological Observatory guidance forecast (SCMOC,0.05°× 0.05 °,forecasting the following 10 days in 3 h intervals) was used as forecast data, both issued by the China Meteorological Administration. The dynamic linear regression and the average filter correction algorithms were selected to revise the original forecast products SCMOC.Based on conventional temperature forecast information,we designed four temperature rise prediction algorithms for essential factors affecting snowmelt.Temperature rise prediction algorithms included the daily maximum temperature algorithm, daily temperature rise range algorithm, snowmelt temperature algorithm, and daily snowmelt duration algorithm.Four temperature rise prediction algorithms values were calculated of each prediction product.Root-mean-square error algorithm and temperature prediction accuracy algorithm were used to compare and test each prediction algorithm value from the time sequence and spatial distribution.Comprehensive tests show that the forecast product revised by the average filter algorithm was superior to revised by the dynamic linear regression algorithm as well as the original forecast product.Through these algorithms, the more suitable temperature rise forecast value for each grid point in the study area can be obtained at different prediction times. The comprehensive and accurate temperature forecast value in mountainous snowmelt season can provide an accurate theoretical basis for effective prediction of the runoff in snowmelt areas and prevention of snowmelt flooding.

Against the backdrop of global climate change, the frequency of drought events is increasing, leading to significant impacts on human society and development. Therefore, it is crucial to study the propagation patterns and trends of drought characteristics over a long-time scale. The main objective of this study is to delineate the dynamics of drought characteristics by examining their propagation patterns in China from 1951 to 2020. In this study, precipitation data from meteorological stations across mainland China were used. A comprehensive dataset consisting of 700 stations over the past 70 years was collected and analyzed. To ensure data accuracy, the GPCC database was employed for data correction and gap filling. Long-term drought evolution was assessed using both the SPI-12 and SPEI-12 indices to detect drought characteristics. Two Moran indices were applied to identify propagation patterns, and the MK analysis method along with the Theil-Sen slope estimator were utilized to track historical trends of these indices. The findings of this study reveal the following key results: (i) Based on the SPI-12, the main areas of China that are prone to drought are mostly concentrated around the Hu Huanyong Line. Indicating a tendency towards drying based on the decadal change analysis. (ii) The distribution of drought-prone areas in China, as indicated by the SPEI-12, is extensive and broadly distributed, with a correlation to urbanization and population density. These drought-prone areas are gradually expanding. (iii) Between 2010 and 2011, China experienced the most severe drought event in nearly 70 years, affecting nearly 50% of the country's area with a high degree of severity. This event may be attributed to atmospheric circulation variability, exacerbated by the impact of urbanization on precipitation and drought. (iv) The frequency of drought occurrence in China gradually decreases from south to north, with the northeast and northern regions being less affected. However, areas with less frequent droughts experience longer and more severe drought durations. In conclusion, this study provides valuable insights into the characteristics and propagation patterns of drought in China, offering essential information for the development of effective strategies to mitigate the impacts of drought events.

Drought is one natural disaster with the greatest impact worldwide. Southern Africa (SA) is susceptible and vulnerable to drought due to its type of climate. In the last four decades, droughts have occurred more frequently, with increasing intensity and impacts on ecosystems, agriculture and health. The work consisted of a systematic literature review on the drought regime’s characteristics in the SA, under current and future climatic conditions, conducted on the Web of Science and Scopus platforms, using the PRISMA2020 methodology, with usual and appropriate inclusion and exclusion criteria to minimize/eliminate the risk of bias, which lead to 41 documents published after the year 2000. The number of publications on the drought regime in SA is still very small. The country with the most drought situations studied is South Africa and the fewer studies are Angola and Namibia. The analysis revealed that the main driver of drought in SA is the ocean-atmosphere interactions including the El Niño Southern Oscillation. The documents used drought indices, evaluating drought descriptors for some regions, but it was not possible to identify one publication that reports the complete study of the drought regime, including the spatial and temporal distribution of all drought descriptors in SA.

Air pollution in urban centers depends directly on anthropogenic activities. The objective of the present study was to predict tropospheric ozone levels from temperature data in the city of Durango, Dgo. Temperature and tropospheric ozone values were obtained from 18 sampling points in the urban area of the city of Durango, of which 15 were obtained by collecting from the Ventusky service and the rest from three fixed monitoring stations established in the city. These values were interpolated in order to determine the spatial values for the entire city. A correlation analysis was performed for three different periods in the day established as follows (24:00-03:00-06:00, 09:00-12:00-15:00 and 15:00-18:00-21:00). Subsequently, a linear regression analysis was performed by time period. The results showed a higher positive correlation between ozone concentration and temperature from 15:00 hrs to 21:00 hrs; likewise, this period showed a higher goodness of fit in the ozone prediction (R2=0.99; RMSE: 1.12 ppbv). Temperature allows spatial prediction of ozone concentrations in urban areas with acceptable accuracy.

Due to the special terrain of Urumqi (in the valley area), which often triggers strong foehn weather, and causes huge losses to local people's lives and social economy. This study uses in-situ surface meteorological variables (including the hourly temperature, pressure, humidity, wind, etc.) from the Urumqi Meteorological Station (downstream station, DS) and the Dabancheng Meteorological Station (upstream station, US) in the Middle Tianshan Valley and NCEP/NCAR reanalysis data from 2008 to 2022. A dataset of the foehn weather process at DS in the past 15 years is established and the variation patterns of each meteorological variable during the process of foehn is analyzed. Based on the physical mechanism of the occurrence of foehn, a three-element identification standard for foehn in Urumqi is established by comparing and analyzing the variation of wind direction (WD), wind speed (WS), and the difference of potential temperature (Δθ) between DS and US during the period of foehn and non-foehn from 2013 to 2022, namely: 94°≤ 2-minute average WD ≤168°, 2-minute average WS ≥2.0 m/s, and Δθ between DS and US ≥ 0.29 K. According to the test and evaluation, the three-element identification standard has an accuracy of 82.96%, and a hit rate of 89.50% for the occurrence of foehn in Urumqi from 2008 to 2012. Moreover, the hit rate of foehn identification is 100% for strong wind or above (i.e., 2-minute average WS ≥10.8 m/s) WS. In addition, combined with two predictors of sea-level pressure difference (ΔP) and Δθ between DS and US, forecasting foehn can be more accurately predicted than a single forecasting factor. When ΔP ≤ -12 hPa and Δθ≥5 K, the probability of the occurrence of foehn is more than 90%. This study provides certain reference and application value for the weather forecasting operation of foehn.

40 years of cloud observations are available globally from satellites allowing derivation of climate data records (CDRs) for climate change studies. The aim of this study is to investigate how stable these cloud CDRs are and whether they qualify stability requirements recommended by the WMO’s Global Climate Observing System (GCOS). We also investigate robust trends in global total cloud amount (CA) and cloud top temperature (CTT) that are significant and common across all CDRs. The latest versions of four global cloud CDRs, namely CLARA-A3, ESA Cloud CCI, PATMOS-x and ISCCP-HGM, are analysed. This assessment finds that all three AVHRR based cloud CDRs satisfy even the strictest GCOS stability requirements for CA and CTT when averaged globally. While CLARA-A3 is most stable in global averages, PATMOS-x offers the most stable CDR spatially. While we find these results highly encouraging, there however remain large spatial differences in the stability of and across the CDRs. All four CDRs continue to agree on the statistically significant decrease in global cloud amount over the last four decades, although this decrease is now weaker compared to the previous assessments. This decreasing trend is stabilizing or even reversing in the last two decades; the latter is seen also in MODIS-Aqua and CALIPSO GEWEX datasets. Statistically significant trends in CTT are observed in global averages in the AVHRR-based CDRs, but the spatial agreement in the sign and the magnitude of the trends is weaker compared to those in CA. We also present maps of Common Stability Coverage and Common Trend Coverage that could provide a valuable metric to carry out an ensemble-based analysis of the CDRs.

In order to better understand the extent to which global climate variability is linked to the frequency and intensity of heat waves and overall changes in temperature throughout the United States (US), correlations between long-term monthly mean, minimum, and maximum temperatures measured at sites throughout the contiguous US and low-frequency variability of multiple climate indices (CIs) are analyzed over the period 1948 to 2018 using correlation analysis. The Pearson’s correlation coefficient is used to assess correlation strength, while Leave-One-Out Cross-Validation and a bootstrapping technique (p-value) are used to address potential serial and spurious correlation and assess the significance of each correlation. Three parameters defined the sliding windows over which surface temperature and CI values were averaged: window size, lag time between the temperature and CI windows, and the beginning month of the temperature window. A 60-month sliding window size and 0 lag time resulted in the strongest correlations overall; beginning months were optimized on an individual site basis. Strong (r >= 0.60) and significant (p-value <= 0.05) correlations were identified. The Western Hemisphere Warm Pool (WHWP) and El Niño/Southern Oscillation (ENSO) exhibited the strongest links to temperatures in the western US, tropical Atlantic sea surface temperatures to temperatures in the central US, the WHWP to temperatures throughout much of the eastern US, and atmospheric patterns over the northern Atlantic to temperatures in the Northeast and Southeast. The final results were compared to results from previous studies focused on precipitation and coastal sea levels. Regional consistency was found regarding links between the northern Atlantic and overall weather and coastal sea levels in the Northeast and Southeast as well as on weather in the upper Midwest. Though the MJO and WHWP revealed dominant links with precipitation and temperature, respectively, throughout the West, ENSO revealed consistent links to sea levels and surface temperatures along the West Coast. These results help focus future research regarding specific mechanisms of climate variability that appear the exhibit strong links to US regional weather and sea level variability and prediction.

Accurate seasonal weather forecasts are vital across a broad spectrum of applications, bearing significant environmental and socioeconomic implications. This importance renders the subject a matter of primary interest to a wide range of stakeholders, including the general public, agricultural sector, emergency responders, financial institutions, and policy strategists. The need for precision in long-term predictive models necessitates the development of innovative methodologies. These methodologies should be capable of deciphering atmospheric patterns and mechanisms with detail, especially at a local level, without the resource constraints that dynamical downscaling imposes. In response to this expanding demand, this study presents a novel solution, combining a stochastic deep learning methodology, specifically Generative Adversarial Networks (GANs), with a Digital Elevation Model (DEM). The cornerstone of the proposed model is the enhancement of gridded temperature fields from seasonal forecasts. The area of focus was the Hellenic region, wherein the spatial resolution is amplified from a coarse 1° x 1° grid to an impressively detailed 0.1° x 0.1° grid. This offers a transformative perspective for interpreting and employing this crucial meteorological data. The results suggest that the downscaled fields adequately approximate the actual spatial distribution, although the predicted values tend to slightly overestimate and in some cases underestimate the original ones. This study underlines the potential of this approach to significantly enhance the resolution and utility of weather forecasts, thereby contributing to a variety of sectors dependent on reliable meteorological data.

Based on the daily observation data of 2254 meteorological stations in China from 1961 to 2021 after homogenization and correction, the extreme value analysis method that combines relative threshold and absolute threshold was used to identify extreme climate events in the mainland of China, namely extreme high temperature, extreme low temperature, extreme drought, extreme precipitation and extreme typhoon events. The frequency and linear variation trends of individual extreme events in the past 60 years were investigated. On this basis, the multi extreme events composite risk grade index (MXCI) was constructed, which can objectively reflect the comprehensive characteristics of multi extreme climate events in different regions. The results show that the high value areas of MXCI were mainly located in Southeast China and Southwest China. The MXCI presented a decreasing trend in most of the western and southeastern regions of China, while an increasing trend from Southwest China to the south of Northeast China, due to the frequent occurrence of extreme high temperature, extreme drought and other extreme events in these regions.

Empirical Orthogonal Functions (EOF) analysis between the anomalies of Lake Titicaca water levels (WLs) and the Pacific Interdecadal Oscillation (IPO) reveals a correlation coefficient of 0.71 between the WLs and the principal component (PC1). These findings demonstrate the high accuracy of the IPO in representing the positive and negative phases of the water levels. By utilizing wavelet functions, we identified multi-decadal variability with periods ranging between 20 and 30 years. The statistical techniques employed indicate a relationship between IPO and ENSO; however, they operate on different timescales of climate variability. The IPO exhibits longer-term variability, typically spanning 20-30 years, while ENSO events occur on average every 2-7 years. Nevertheless, the IPO has the ability to modulate the strength and frequency of El Niño and La Niña events, and vice versa. During the positive phase of the IPO, there is a tendency for more frequent and intense El Niño events, accompanied by fewer La Niña events. This can be attributed to warmer sea surface temperatures in the central and eastern Pacific, which create favorable conditions for El Niño development. Conversely, during the negative phase of the IPO, there is a tendency for more frequent and intense La Niña events, with fewer occurrences of El Niño events, due to cooler sea surface temperatures in the central and eastern Pacific.

Influenced by the quasi-stationary Mei-yu front and the development of mesoscale low vortices on the Mei-yu front, a torrential rain event occurred in Suzhou on July 6, 2020. The 24-hour rainfall recorded by the automatic station exceeded 200 millimeters, with the highest hourly intensity surpassing 70 millimeters, accompanied by thunderstorms and strong winds. This heavy precipitation process was characterized by suddenness, extremity, long duration, high precipitation efficiency, and backward propagation. Using various data sources such as the European ERA5 reanalysis data, ground-based automatic stations, sounding, Doppler weather radar, and wind profile radar, this paper analyzed in detail the circulation characteristics and radar echo structures of this torrential rain event, and further studied the triggering, feature evolution, and maintenance mechanisms of the heavy precipitation.The results show: (1) The continuously strengthening low-level jet stream propagates downward forming an ultra-low-level jet stream. Heavy precipitation is located on the left front side of the jet exit area, where strong convergence and lifting in the boundary layer overlap with the ascending branch of the secondary circulation above the shear line, resulting in heavy precipitation. (2) A supercell storm with intense precipitation produced this heavy rainfall event, characterized by a low centroid of strong echo; echo-free vault, bounded weak echo region, and other supercell structural features. During this event, the high reflectivity factor areas corresponded to high KDP value areas, indicating a large amount of liquid water content. Vertically, the KDP column can track the rainwater mixing ratio well, and its location corresponds to the vicinity of the short-term heavy precipitation occurrence. The high KDP value area mainly consists of numerous precipitation particles, and the vertical vortex wind field structure causes rapid growth of precipitation particles through continuous rolling. Furthermore, it can be found through correlation coefficients (CC) that there are always high-value areas for CC since they correspond to areas of intense precipitation. (3) Continuous triggering of mesoscale convective cloud clusters on the east-west ground convergence line leads to new development and forms a "train effect." Strong frontogenesis in both horizontal and vertical directions triggers and sustains this heavy precipitation event. The significant increase in liquid water and water vapor density has indicative significance for the occurrence of heavy precipitation.

Globally, the intensity and irregularity of weather and climate extremes are increasing due to climate change. In Ethiopia, the occurrence of extreme events has been increasing, reporting severe impacts on environment which led to losses of lives and livelihood of societies. In this study, Heat Wave Magnitude Index daily (HWMId) was used to analysis heat wave magnitude in the Middle Awash Basin of Ethiopia. Gauge data obtained from Ethiopian Meteorological Institution (EMI) for 1981-2022 period and the future projection was taken from Coupled Model Intercomparison Project Phase 6 (CMIP6) under two socioeconomic pathways (SSP 2 and SSP 5) scenarios. The findings clearly showed that the area aggregated annual temperature anomaly increasing each year, 2015 was one of the warmest year on record with an anomaly of +1.8 °C. Severe to extreme heat wave recorded particularly during the last 10 years. For the future projection, under SSP 2-4.5 forcing scenario, the annual average air temperature projected to be warmer, which increasing 1.7 ℃ to 1.8 ℃ during mid-century and 2.3 ℃ to 2.4 ℃ end of century. Meanwhile, for SSP 5-8.5 forcing, the projection indicated an increment of 2.2 ℃ to 2.5 ℃ under mid-century and 4.4 ℃ to 4.8 ℃ end of century. Concerning the severity of heat wave, extreme to very extreme heat wave projected under SSP 2-4.5 forcing scenario and supper extreme heat wave projected under SSP 5-8.5 forcing scenario, respectively. The increase in extreme events may have a negative impact on health, water availability and food security. Therefore, the result of this study are essential for making wise decisions and for developing suitable strategies for climate change adaptation and mitigation that could minimize the risk of unusually extreme weather events.

Freezing/thawing indices are important indicators of dynamics of frozen ground on the Qinghai-Tibet Plateau (QTP), especially for areas with limited observations. Based on the numerical outputs of Community Land Surface Model version 4.5 (CLM4.5) from 1961 to 2010, this study compared the spatial and temporal variations between air freezing/thawing indices (2 m above the ground) and ground surface freezing/thawing indices in permafrost and seasonally frozen ground (SFG) across the QTP, after presenting changes of frozen ground distribution in each decade in the context of warming and wetting. The results indicate that an area of 0.60×106 km2 permafrost of QTP degraded to SFG in 1960s-2000s, and the primary shrinkage period occurred in 2000s. The air freezing index (AFI) and ground freezing index (GFI) decreased dramatically at the rates of 71.00℃·d/decade and 34.33℃·d/decade from 1961 to 2010, respectively. In contrast, the air thawing index (ATI) and ground thawing index (GTI) increased strikingly with the values of 48.13℃·d/decade and 40.37℃·d/decade in the past five decades. Permafrost showed more pronounced changes in freezing/thawing indices since 1990s compared to SFG. The changes of thermal regimes in frozen ground shows closely relations to air warming until the late 1990s, especially in 1998 when the QTP has undergone the most progressively warming. However, a sharp increase in the annual precipitation from 1998 seems to take a stronger control role on thermal degradation in frozen ground than the air warming in 2000s. Meanwhile, the prominent vegetation expansion further promotes thermally unstable of frozen ground in this highly wetting period.

This study addresses the limited coverage of regulatory monitoring for particulate matter 2.5 microns or less in diameter (PM2.5) in Thailand due to the lack of ground station data by developing a model to estimate daily PM2.5 concentrations in small regions of Thailand using satellite data at a 1-kilometer resolution. The study employs multiple linear regression and three machine learning models and finds that the random forest model performs the best for PM2.5 estimation over the period of 2011-2020. The model incorporates several factors such as Aerosol Optical Depth (AOD), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Elevation (EV), Week of the year (WOY), and year and applies them to the entire region of Thailand without relying on monitoring station data. Model performance is evaluated using the coefficient of determination (R2) and root mean square error (RMSE), and the results indicate high accuracy for training (R2: 0.95, RMSE: 5.58 μg/m3), validation (R2: 0.78, RMSE: 11.18 μg/m3), and testing (R2: 0.71, RMSE: 8.79 μg/m3) data. These PM2.5 data can be used to analyze the short- and long-term effects of PM2.5 on population health and inform government policy decisions and effective mitigation strategies.

Based on the ERA5 reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF), the climatology of the planetary boundary layer height (PBLH) in Jiangsu, China, is studied. The PBLH based on ERA5 is verified by using radiosonde data, and the results show that the PBLH based on ERA5 fits very well with the PBLH diagnosed by the radiosonde data. Overall, the daytime average PBLH is between 700-1200 m, which is higher in the north and lower in the south. It is between 100 and 400 m at night, and it is lower in the north and higher in the south. PBLH exhibits complex spatiotemporal variation. In the daytime, the PBLH in inland areas is highest in spring, followed by fall and summer, and lowest in winter. At night, the seasonal variation in the PBLH is less obvious. The seasonal variation in the PBLH in coastal areas is higher in fall and winter and lower in spring and summer. PBLH shows an obvious diurnal cycle, usually reaching its peak at 14:00 (LST) or 15:00 (LST). The diurnal cycle of the PBLH is significantly positively correlated with the near-surface temperature and wind speed and significantly negatively correlated with the relative humidity and lower tropospheric stability. The daytime PBLH increases significantly in most areas. The increase in PBLH can be attributed to the increase in near-surface temperature and the decrease in near-surface relative humidity and lower tropospheric stability.

Using a database of 378 hail days between 20981 and 2020, the climatic characteristics of 23 convective parameters from sounding data and ERA5 data were statistically analyzed. The goal of this work is to evaluate the usefulness and representativeness of convective parameters derived from sounding data and reanalysis data for the operational forecast of the hail phenomenon. As a result, average values from 12:00 UTC were 433J / Kg for CAPE in the case of data from ERA5 and 505 J/kg from rawinsonde respectively. The Spearman correlation coefficients matrix between the values of the parameters indicates high correlations between the parameters calculated based on the parcel theory, the humidity indices, and the complex indices. The probability for large hail maximizes with high low level and boundary layer moisture, high CAPE, and a high lifted condensation level height.

Three facts characterize the hailstorm of 30 August 2022 in the Catalan village of La Bisbal d'Empordà and its surroundings: first, the most dramatic, the death of a child hit by a hailstone. Second, the damage to most of the roofs and cars in the town. Finally, because of the record of hail size (more than 10 cm) in Catalonia in at least the last 30 years. This research focuses on the radar field comparison and the observations provided by an electronic survey of the study area. The results reveal that weather radar sub-estimated the hail size because of different factors. On the opposite, some reporters provided an inaccurate hour. The difference of three months between the hail event and the electronic survey is the probable cause of this mistake in the time estimation. However, the survey delay helped to avoid answers with larger hail sizes than those provided by the official spotters.

In this study we investigated the association between weather types (WTs) and the Urban Heat Island Intensity (UHII) in the region of Attica (Greece). The application of the methodology results in ten WTs over Attica region. The UHII was calculated for every hour of the day from 2008 to 2017, using a new air temperature dataset produced by Copernicus Climate Change Service. To have more clear results concerning the association between WTs and UHII, we have used also the upper 5% of UHII (Urban Overheating-UO). The UO have been estimated for two-time intervals (daytime and nighttime) and for the warm period (June-September). The UHII frequency distribution as well as the spatial characteristics of the UO were also investigated. It was found that UO was amplified under WT2 during the night while, WT10 was mainly responsible for exacerbated UO magnitude at daytime, in all months. Furthermore, analysis results revealed that the UO effect is more pronounced in Athens during the night, especially at Athens center. The daytime hot-spots identified mainly in sub-urban and rural areas. Therefore, this methodology may help for heat mitigation strategies and climate adaptation measures, in urban environments.

In the framework of the FireStorm project, four portable weather stations were installed in the Lousã/Estrela mountain range. Given that the Portuguese Institute for Sea and Atmosphere’s sur-face network has two weather stations installed in this region, the new data allows an improved monitoring of the vertical variability of near-surface variables in this mountainous region. As most of the wildfires in mainland Portugal affect areas with complex terrain with elevations below 1200 m and major fires continue to burn overnight, it is also relevant to monitor the vertical changes of meteorological variables in the nighttime period, as these may exhibit large variability. This study provides the first assessment of the available data, with focus on the summer seasons of 2021 and 2022. The results highlight the large variability that was observed in the region and suggest that the risk of extreme fire behaviour in the nighttime period may be underestimated.

The horizontal storm structure surrounding lightning ignited wildfires is examined using Vaisala’s National Lightning Detection Network (NLDN), NCEP’s Stage IV gauge corrected radar precipitation mosaic, and the US Forest Service’s Fire Occurrence Database. Though lightning flash density peaks strongly around fire ignitions on the instantaneous 1 km scale, on the hourly 10 km scale both the lightning and precipitation peaks are typically offset from fire ignitions. Lightning density is higher and precipitation lower around ignition points compared to non-ignition points. Both regression and horizontal distributions are consistent with the claim that positive flashes have a stronger association with ignition than negative flashes, but the statistical significance remains ambiguous

A better understanding of background tropospheric ozone delivers multiple benefits. Robust estimates of regional background ozone assist policy makers to understand the limits to reducing ozone by controlling anthropogenic emissions alone and assist in characterising changes in the atmospheric environment and its composition. Long time series of background ozone measurements can also help quantify the influence of human activity on the global and regional atmosphere. Background tropospheric ozone measurements representative of continental air masses are however scarce in Australia, limiting our knowledge of background atmospheric composition. Here we use k-means clustering to identify a cluster of measurements from the long-term monitoring station at Oakdale, NSW which are likely to be representative of background air. The cluster is associated with NOx-limited air masses of continental origin. From this analysis we estimate background ozone representative of Eastern Australia. We find recent (2017-2022) mean ozone mixing ratios of 28.5 ppb and identify a statistically significant (α=0.05) trend in the mean of +1.8 (1.0 – 2.8) ppb/decade. Our methods demonstrate that some long-term monitoring stations within or near urban areas can provide suitable conditions and datasets for regional Global Atmosphere Watch monitoring.

By using various skill scores and spatial characteristics of spatial verification methods and tradi-tional techniques of the model evaluation tool (MET, V10.0.0), the gridded precipitation obser-vation CMPAV (V2.0) and three datasets that derived from local (LOC), Shanghai (SHA), and Grapes (GRA) model respectively are conducted to assess the 3 lead day rainfall forecast with 0.5-day intervals during summer of 2020 over central east China. Results have shown that LOC generally outperforms the other two for most skill scores but usually with relatively larger un-certainties than SHA, and it has the least displacement errors for moderate rainfall among the three datasets. However, the rainfall of GRA has been heavily underestimated and accompanied with large displacement error. Both LOC and SHA have shown almost equitable abilities in forecasting convection and rainstorms of the large area but with a slightly over-forecast of local convection, while LOC likely over-forecasts the local rainstorms. In addition, SHA slightly favors over-forecast on a broad scale range and a broad threshold range, and LOC slightly misses the rainfall exceeding 100 mm. Generally, for a broadly comparative evaluation on rainfall, the popular dichotomous methods should be recommended under considering reasonable classifi-cation of thresholds if the accuracy is highly demanded. And most spatial methods should be suggested to conduct with proper pre-handling of non-rainfall event cases. Especially, the veri-fications including spatial characteristic difference information could be recommended in a computationally sufficient environment.

Climate change is an issue that is the order of the day, and among the many consequences it has on the environment one is its influence on the amount and frequency of rainfall on our planet, since our water supply depends on them. Therefore, in order to quantify these effects, a rainfall analysis has been carried out using data collected in Bilbao over the last 150 years. Thanks to this, it has been possible to observe that, although the total annual amount seems to suffer a slight decrease, hardly quantifiable with certainty, it is true that the intra-annual patterns are more concentrated at certain times of the year, with greater imbalances between one month and another than in previous years. However, taking into account the capacity of the reservoirs that supply the Greater Bilbao area, it can be concluded that this does not pose any problem for Bilbao's supply, at least at present.

Atmospheric turbulence, which produces chaotic motions in the planetary boundary layer, can inhibit mixing between fast-reacting species produced or released at different locations. This segregation process modifies the effective rate at which reactions occur between these species, and are not appropriately accounted for in coarse resolution models since these models assume complete mixing of tracers within each gridbox. Here we present a few examples of LES-based simulations applied to chemically reactive species in a forested area with high emissions of biogenic hydrocarbons, an urban area rich in anthropogenic emissions; and a maritime area with high emissions of reduced sulfur species.

The representation of aerosols in climate-chemistry models is important for air quality and climate change research, but it can require significant computational resources. To overcome this, simpler modules such as modal aerosol modules with three lognormal modes (MAM3) can be used. In this study, the coupling of the Carbon Bond Mechanism, version Z (CBMZ), and MAM3 chemistry modules in WRF-CAM5 was improved by adding biomass-burning emissions to both gas- and particle-phase chemistry and incorporating a conversion mechanism between volatile organic compounds (VOCs) and secondary organic carbons (SOCs). The study conducted six simulations over the western U.S. and northeastern Pacific region and compared the model’s performance with observational benchmarks such as reanalysis, ground-based, and satellite data. The results showed that the model with enhanced chemistry capabilities had a 31% and 58% reduction in root-mean-square errors (RMSE) for black carbon (BC) and organic carbon (OC) surface concentrations, respectively. The earlier release of the WRF-CAM5 version had two deficiencies that were addressed in this study. This research highlights the importance of accurate aerosol representation in climate-chemistry models for improving accuracy and reducing errors in simulations.

: This study investigates the use of various thunderstorm indices in predicting severe thunderstorms events during the monsoon season in four different regions in India. The research evaluates the performance of the prediction model using a model skill score and utilizes the Weather Research and Forecasting (WRF) model with the double moment microphysics scheme to simulate model cases. It also compares fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for predicting severe thunderstorms events. The results of this study show that in-corporating thunderstorm indices with model skill scores improves severe thunderstorms fore-casting in the monsoon season in India. The result revealed that determining the optimal threshold for each index is crucial in achieving accurate predictions. The study also highlights the importance of considering multiple indices rather than relying on a single index to predict severe thunderstorms events. The advance indices such as Energy Helicity Index (EHI), Supercell Composite Parameter (SCP), mainly works well with extreme severe thunderstorms. The simplistic indices can predict the weak or severe thunderstorm easily. The use of multiple thunderstorm indices can also help mete-orologists to make more accurate predictions, which can further enhance public safety. In conclu-sion, this study demonstrates the potential of incorporating thunderstorm indices with model skill scores like HSS and TSS and combinations of different skill scores in severe thunderstorms fore-casting during the monsoon season in India. Future research can build upon the findings of this study to develop more accurate and reliable severe weather forecasting models.

A reliable flood early warning system must take into account the mechanisms that cause heavy precipitation events and accurate surface hydrology modeling. In this project, analysis of atmospheric processes and hydrological modelling of selected flood events over Dire Dawa is conducted using various observational/reanalysis data and uncoupled WRF-Hydro model simulations. To comprehend the processes causing such severe precipitation occurrences, large scale atmospheric fields linked to selected extreme precipitation events are examined using ERA5 reanalysis. The land surface was configured at 1 km resolution while 250 m sub-grid resolution was set to perform the routing process. Model forcing for the uncoupled WRF-Hydo model is obtained from ERA5 reanalysis data. Sensitivity of stream-flow simulation to various parameter values such as hydrolic conductivity and surface infiltration coefficient was carried out for August 2006. The result of the sensitivity experiment reveals that infiltration-runoff, hydrolic soil conductivity and saturated volumetric soil moisture with the parameter value of 0.1, 1.5 and 1.0, respectively are found to produce realistic spatial and temporal distribution of stream-flow. The extreme flood events of March 2005 and April 2007 were studied further to assess the performance of WRF-Hydro model and to understand the underlying atmospheric mechanisms causing these heavy precipitation events. The result of hydrological simulation demonstrated that uncoupled WRF-Hydro simulation reproduced both the temporal evolution and the spatial pattern reasonably well. Our analysis indicated that the amount of precipitation during these two events exceeded the long-term average by several factors, furthermore, the anomalies cover larger areas of eastern Ethiopia. Associated to these extreme events, upper level subtropical westerly jet-streams were anomalously stronger and also extended further southward favouring upper level divergence over the region. At lower level, the notable circulation anomalies include anomalous positive pressure anomaly over Sudan/Egypt leading to northerly flow anomaly over Red Sea, strengthening of southerly influx from southern Indian ocean due to stronger Mascarene High. The encouraging results from WRF-Hydro simulation suggest that this modelling framework can be implemented in operational context within national and regional forecasting centers as a key component to establish a flood monitoring and early warning system.

The influence of small-scale processes at the ocean-atmosphere boundary layer such as spray and foam on the surface waves prediction is studied. Estimates of the effect of including the exact number of specific fragmentation "parachute" type in the spray on the resulting drag coefficient is shown. For the estimates, the numerical simulations within WAVEWATCH III wave model are performed. The parameterizations of wind input were tested within WAVEWATCH III wave model: default ST4 and ST6 parameterizations and the ST1 and ST6 parameterizations used together with the implemented drag coefficient parameterization. The proposed parameterization takes into account the presence of foam and spay. The obtained results are compared with the NDBC buoys data. The importance of small-scale processes for waves at hurricane winds prediction and the prospects for their inclusion in modern numerical wave models is shown.

This study analyses the detection efficiency of the Worldwide Lightning Location Network (WWLLN) of sensors over Spain, a comparison is made with the Meteorological Spanish Agency (AEMET) Lightning Detection Network. The WWLLN operates a planetary distributed network of Very Low Frequency (VLF) antennas which detect lightning VLF signals around the Earth. Very high currents from lightning strokes radiate strong VLF signals in the band 6-22 kHz, which are detected up to 10,000 km, therefore WWLLN receiving sensors can be sparsely spaced. However the detection capability of VLF signals depends on the environment, and clean areas with a good ground and no metal parts around must be selected for proper reception. For the area of Spain and Portugal we had two WWLLN sensors in 2012, and three sensors later on for the data of 2020, 2021 and 2022. The sensors in Iberian Peninsula are at a distance of around 760 km; that is very close in comparison with other WWLLN sensors in the Earth which are around 5.000-15.000 km. The WWLLN sensors send the timing of the detection to the central sensor where the location of the lightning is determined with an improved algorithm when a mini-mum of 5 sensors detect the VLF signal. We show that detection efficiency was excellent and consistent with previous studies. Lightning location data from 1st January 2012 to 30st April 2012 from AEMET was used to analyze time and space shared events with the WWLLN network with 0.5s time difference and 20 km distance. The results improved in 2020, 2021 and 2022. More than 40,000 lightning strikes were analyzed in the present work, and we found a detection efficiency > 38%.

Micro-nano-plastics (MNPs) are an important constituent of atmospheric aerosol. However, there is still no standard procedure for their sampling and size fractionation, which is an obstacle to the aggregation and critical analysis of results obtained by different research groups. This review focuses on the sampling and fractionation methodologies used for MNPs. Moreover, a straightforward optimized methodology for the sampling and fractionation is proposed.

X-band dual-polarization phased array radar (XPAR-D) possesses high temporal-spatial resolutions and plays a significant role in detecting meso- and micro-scale convective systems. However, the precipitation attenuation it endures necessitates an effective correction method. This study selected radar data from XPAR-D at the peak of Maofeng Mountain in Guangzhou during May 16-17, 2020 from three precipitation stages after quality control. Attenuation coefficients are calculated for different precipitation types through scattering simulations of raindrop size distribution (RSD) data. Drawing upon this, an attenuation correction algorithm (MZH-KDP method) is proposed for radar reflectivity factor (ZH) according to different raindrop types, and is compared to the ZH-KDP method currently in use. The results indicate that the attenuation amount of XPAR-D echoes depends on the attenuation path and echo intensity. When the attenuation path is shorter and the echo intensity is weaker, the amount of attenuation and correction is smaller. Difficulties arise when there are noticeable deviations in such a situation, which are challenging to solve via attenuation correction methods. Longer attenuation paths and stronger echoes highlight the advantages of the MZH-KDP method, while the ZH-KDP method tends to overcorrect the bias. The MZH-KDP method outperforms the ZH-KDP method for different precipitation types. The superior correction capability of the MZH-KDP method provides a significant advantage in improving the performance of XPAR-D for the detection of extreme weather.

Tropical cyclones have long been known to be powered by turbulent enthalpy fluxes from the ocean surface and retarded by turbulent momentum fluxes into the surface. Here were review evidence that the development and structure of these storms are also partially controlled by turbulence in the outflow near the storm top. Finally, we present new research that shows that tropical cyclone-like, low-aspect-ratio vortices are most likely in systems in which the bottom heat flux is controlled by mechanical turbulence and the top boundary is insulating.

Aerosols play an important role in Earth’s climate system, thus long time ground based measurements of aerosol optical properties are useful in understanding this role. 10 years of quality-assured measurements between 2010 and 2020 are used to investigate the aerosol climatology in the Cluj-Napoca area, in North-Western Romania. In this study, we analyze the Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA) and Ångström exponent obtained by CIMEL sun photometer, part of the Aerosol Robotic Network (AERONET) to extract the seasonality of aerosols in the region and investigate the aerosol climatology of the area. Higher aerosol loads are found during July and August. Ångström exponent has the lowest values in April and May and the highest in August. The classification of aerosols using AERONET data is performed to separate dust, biomass burning, polluted urban, marine and continental dominant aerosol mixtures. In addition, the study presents the validation efforts of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) dataset against AERONET AOD over 10 years period.

Clarifying the response of plant phenology to drought duration is helpful for accurately interpreting and predicting carbon sinks in ecosystems. Based on the response of different phenological periods of the dominant species Stipa krylovii to monthly, seasonal and semiannual time scale drought in the typical steppe of Inner Mongolia from 1983 to 2018, the results revealed that (1) the start of the growing season (SOS) was characterized by an advance-delay-advance pattern, and the heading stage (HOS), flowering stage (FOS) and end of the growing season (EOS) all showed consistent advanced trends, which provided additional insight into the conclusions of previous studies that found the SOS was advanced in arid and semiarid regions. (2) The response mechanism of the SOS to the timing of drought was not consistent. Among the response mechanism, the SOS was delayed because of January-February drought at different time scales, but advanced because of April drought at different time scales. The HOS/FOS was delayed by June-July drought at different time scales, and the EOS was advanced by August-September drought at different time scales. (3) More importantly, the SOS, HOS, FOS and EOS were affected by predrought and its persistence, and the effects were greater the closer to the phenological periods that drought occurred. (4) At the monthly scale, droughts in January, June and August were the critical drought periods affecting the SOS, HOS/FOS and EOS, while seasonal and semiannual scale droughts in February, June-July and September were the critical periods affecting the SOS, HOS/FOS and EOS. The results of this study enrich our understanding of how drought at different time scales affects different phenological periods, providing a basis for improving plant phenological models.

This study provides, to our knowledge, the first detailed analysis of how surface oil modifies air-sea interaction in a two-way coupled model, i.e., the coupled-ocean-atmosphere-wave-sediment-transport (COAWST) model modified to account for oil-related changes to air-sea fluxes. This study investigates the effects of oil on surface roughness, surface wind, surface and near surface temperature differences, and boundary-layer stability, and how those conditions ultimately affect surface stress. We first conducted twin coupled modeling simulations with and without the influence of oil over the Deepwater Horizon (DWH) oil spill period (April 21 to May 5, 2010) in the Gulf of Mexico. Then we compared the results by using a modularized flux model with parameterizations selected to match those selected in the coupled model adapted to either ignore or account for different atmospheric/oceanic processes in calculating surface stress. When non-oil inputs to the bulk formula treated as unchanged by oil, the surface stress changes are always negative due to oil-related damping of surface roughness alone. However, the oil-related changes to 10-m wind speed and boundary-layer stability are found to play a dominant role in surface stress changes relative to those due to the oil-related surface roughness changes, highlighting that most of the changes in surface stress are due to the oil-related changes in wind speed and boundary-layer stability. Finally, the oil-related changes to surface stress due to the combined oil-related changes in surface roughness, surface wind, and boundary-layer stability are not large enough to have a major impact on the surface current and surface oil transport, indicating that the feedbacks from the surface oil to the surface oil movement itself are minor for forecasting the surface oil transport unless the fractional oil coverage is much larger than found in this study.

The study presents an assessment of climate suitability for outdoor leisure activities in Romania using the Holliday Climate Index (HCI) for the near future (2021-2040), focusing on unfavorable and good climate conditions. The analysis employs data from an ensemble model in the context of RCP45 and RCP85 climate change scenarios. The results indicate that the number of days with low weather suitability is decreasing in almost the entire country, especially during the warm season, while during the winter and spring extended regions may be characterized by a higher number of days favorable for outdoor activities than during the current climate. An estimation of the impact of climate changes on tourism flux in Romania is further carried out, suggesting that the increasing attractivity of climate conditions may lead to an increased number of tourist overnights in the near future, more pronounced in rural destinations.

The formation and localization of sporadic E(Es) layers predicted by the ions vertical drift velocity and its vertical change in the lower thermosphere during nighttime are shown analytically and by numerical simulations. The consideration of existence of a minimum negative value of the vertical change of the ions vertical drift velocity as a necessary condition of formation of Es layer and determining ions convergence rate into this layer, is extended in case of the presence of vertical wind. Upward vertical wind can shift the convergence regions upward, while downward vertical wind shift them downward, unlike the cases of presence of only meridional and zonal winds. It also changes the ion convergence rate compared to the one with just a horizontal wind. It is shown for the first time that the upward constant wind also causes the convergence of ions with maximum rate in the region where the ion-neutral collision frequency is equal to their cyclotron frequency. While demonstrating the presented theory by numerical simulations, HWM14 data is used for the meridional and zonal wind velocity profiles and the presence of its vertical component is assumed. In this case, in addition to the estimated ion drift velocity and its vertical changes, their initial distribution and ambipolar diffusion also determine the development of ion convergence/divergence processes. For small magnitude of vertical wind velocity, its significant influence on the ions/electrons behavior is demonstrated, which shows the importance of both tidal wind and wind changes caused by the propagation of atmospheric gravity waves on the formation of the Es layer. In this theoretical research it is shown that the realistic profile of the wind velocity, which takes into account the vertical component along with its zonal and meridional ones, is important for prediction of the Es layers formation, as well as regions of ion depletion.

Location-specific weather and climate information together with related advisory services are one of the crucial drivers of development in the 21^st century particularly in Sub-Saharan Africa. However, there still exist significant gaps in provision of weather and climate information at scales that effectively address the needs of local people for instance; local farmers and pastoralists. This therefore force local people to rely on indigenous knowledge (IK), to observe and forecast weather conditions. This situation does not exclude Uganda, and thus the current study assessed the efficacy of integrating indigenous knowledge systems into modern weather observational instruments in order to boost and act as a backup mechanism for modern weather observational instruments to increase accuracy and wide coverage of weather observations within Uganda. Results indicated that, respondents across the two pilot sites use a combination of plants, animals, insects, and human behaviours, meteorological and astrological indicators to observe and predict local prevailing weather conditions. Majority of the respondents particularly in Masaka district, believed that the use of indigenous knowledge to observe weather and climatic events is very reliable compared to their counterparts in Entebbe Municipality. Therefore, the integration of indigenous knowledge system in scientific weather observations is very vital.

We propose that, at equilibrium, statistically equal temperatures as mechanical torques are exerted on each kind of gas phase molecules as rates of translational action [@t=ʃmvds/dt=ʃmr2ωdϕ/dt=mv2, J]. These torques result from the impulsive density of resonant quantum fields with molecules, configuring the trajectories of gas molecules while balancing molecular pressure (p=NkT) against the density of field energy (J/m3). Gibbs energy fields contain no resonant quanta at zero Kelvin, with this measure of chemical potential diminishing in magnitude as translational action of the vapor molecules and quantum field energy increases with temperature. In illustration, we show how impulsive torques from quantum fields drive the reversible thermodynamics of Carnot’s heat-work engine cycle, sustain the decreasing atmospheric temperature gradient of increasing molecular entropy with altitude, support the translational action and field energy of vortical wind flow in anticyclones, frictionally warming the Earth’s surface while recycling greenhouse infrared absorption of surface radiation, generating electrical power from air flow in wind farms and destructive power in tropical cyclones. These cases all distinguish symmetrically between a causal field of impulsive quanta (Σhν) that energizes the action of matter and the resultant vis viva of molecular mechanics (mv2). The quanta of these different fields display mean wavelengths from 10-4 m to 1012 m, with mechanical advantages many orders of magnitude greater than the corresponding translational actions, though with mean quantum frequencies (v) similar to those of radial Brownian movement for independent particles (ω). These energy fields are also thermodynamically reversible reservoirs for heat, optimizing work processes on Earth and delaying the achievement of maximum entropy production from short wave solar radiation in conversion to outgoing long wave radiation to space.

High PM10 concentrations are still a significant problem in many parts of the world. In many countries, including Poland, 50μg/m3 is the permissible threshold for a daily averaged PM10 concentration. The number of people affected by this threshold’s exceedance is challenging to estimate and requires high-resolution concentration maps. This paper presents an application of random forests for downscaling regional model air quality results. As policymakers and other end users are eager to receive a detailed resolution PM10 concentration maps, we propose a technique which utilizes the results of regional CTM (GEM-AQ, with 2.5km resolution) and local Gaussian plume model. As a result, we receive a detailed, 250-meter resolution PM10 distribution, which resembles the complex emission pattern in a foothill area in southern Poland. The random forest results are highly consistent with the GEM-AQ and observed concentration. We also discuss different strategies of data training random forest - using additional features and selecting target variables.

Biogenic emission can have significant impact on atmospheric chemistry. Isoprene (C5H8) is known as the most predominant volatile organic compound and its emission is highly dependent on temperature and light (solar radiation). This study aims to investigate future changes of isoprene emission under climate change scenario. The Southeast Asia (SEA) region is home to 15% of the world’s tropical forest, and the biogenic emission from this region can have significant effect on the global climate chemistry. Three climate change scenarios (RCP4.5, RCP6.0 and RCP8.5) were used to drive climate change simulation in 2013 (baseline), 2030 (near-future), 2050 (mid-century), 2070 (post-mid) and 2100 (end of century) using the Weather Research Forecasting (WRF v3.9.1) model. The Model Emission of Gases and Aerosol from Nature (MEGAN v2.1) was then used to simulate isoprene fluxes using climate output datasets from the WRF model. This study highlights that the projected mean surface temperature and PAR (photosynthetic active radiation) were higher in July than in January. Towards the end of the century, the overall surface temperature and PAR over the SEA region is expected to increase by 0.9℃ - 2.1℃, and 7.6 W m-2 – 12 W m-2 under all RCPs. The all-time high isoprene emitters are Borneo and Papua Island. Meanwhile, the projected increment of isoprene emission over this region is between 10 – 14%, 15-30% and 29 – 53% for RCP4.5, RCP6.0 and RCP8.5, respectively. Our result concludes that the total isoprene emission was higher during July, consistent with the high surface temperature and PAR. The results also agreed with the hypothesis of high isoprene emission over the region that has oil palm plantation. Thus, challenges in dealing with future emissions of isoprene in SEA are closely tied to future climate policies to limit the warming of the atmosphere over this region and the land-use conversion of palm oil plantations.

We investigated rainfall patterns in India over during the 60-year period from 1951 to 2010 and predicted changes for the next century (2051-2100) with an assumed 4K warming from large ensemble experiments (190 members). We focused on rainfall patterns during two periods of present-day climate (1951-1980 and 1981-2010) and their projected changes for the near and far future (2051-2080 and 2081-2110). Our analysis revealed that the northeastern region of India and some southern regions received higher rainfall during the period of 1951-2010. This finding is consistent with daily observations from the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE). In a warming climate, rainfall events in India are predicted to carry more precipitation, with the northeast and southern regions experiencing stronger rainfall events. The frequency and intensity of these events (with more than 20mm of rainfall per day, on average) are also expected to increase. Overall, our study suggests that water-related disasters such as flooding and landslides could be much worse in India in the future due to climate warming.

The purpose of this study is to characterize atmospheric pollution and its sources in two West African urban sites during the dry season (January 2009 in Bamako, Mali, and December 2009 in Dakar, Senegal). Particulate Matter (PM) source apportionment was performed using Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF). Carbonaceous components, water-soluble inorganic compounds, and trace elements were analyzed from PM (TSP, PM10, and PM2.5) samples. The measured PM2.5 and PM10 concentrations were 5 to 10 times and 3 to 8 times higher than the 2005 WHO 24-hour standards, respectively. PCA and PMF analyses identify five sources in both cities. The sources contributing to aerosol concentrations (PM2.5 - PM10) in Bamako are motor vehicles (20-13%), solid fuel combustion (16-13%), crustal dust (24-30%), resuspended road dust (26-30%) and secondary aerosols (10-16%). During the sampling period in Bamako, dust was the most prevalent source, accounting for 63% of the total PM10 mass. In Dakar, the main identified sources were motor vehicles (28-19%), mineral dust (16-25%), industries and oil burn-ing (11-10%), sea salts (15-20%), and resuspended road particles (21-29%). The findings also indicate that anthropogenic emissions contribute significantly to the PM2.5 fraction, implying that populations are highly exposed to fine particles.

This paper presents an analysis of future precipitation patterns over the Lake Victoria Basin using bias-corrected CMIP6 model projections. A mean increase of about 5% in mean annual (ANN) and seasonal [March-May (MAM), June-August (JJA), and October-December (OND)] precipitation climatology is expected over the domain by mid-century (2040-2069). The changes intensify towards the end of the century (2070-2099) with an increase in mean precipitation of about 16% (ANN), 10% (MAM), and 18% (OND) expected, relative to the 1985-2014 baseline period. Additionally, the mean daily precipitation intensity (SDII), the maximum 5-day precipitation values (RX5Day), and the heavy precipitation events, represented by the width of the right tail distribution of precipitation (99p-90p) show an increase of 16%, 29%, and 47%, respectively, by the end of the century. The projected changes have a substantial implication for the region - which is already experiencing conflicts over water and water-related resources.

This document serves as supplemental information to Shapero et al. (2023), which is itself a comment on Nowell et al. (2022). Environmental Health Perspective’s publishing standards for article comments have strict word counts and do not allow the addition of new primary data analyses; therefore, this document provides an additional level of detail and supporting analyses that serve as an important backup to and expansion of Shapero et al. (2023) and that also respond to some comments within the author’s reply to our comments (Holmes and Nowell, 2023). Nowell et al. (2022) evaluated potential PM_2.5 community impacts from sugarcane harvesting. However, their analysis is flawed by erroneous assumptions and misapplied technical approaches, as discussed in Shapero et al. (2023) and as detailed in this supplemental information document. Additionally, the authors ended their analysis with 2018 data, but later data shows no marginal increase in PM_2.5 during sugarcane harvest following 2019 even using the authors’ approach (Holmes and Nowell, 2023). Therefore, the authors’ evaluations have no relevance to current conditions. Nonetheless, even their evaluation and conclusions regarding PM_2.5 concentrations purportedly attributable to sugarcane harvesting prior to 2019 are unsupported and technically unsound as detailed below.

Responding to an urgent call for effective and cost-efficient enforcement of emission regulations from ocean-going vessels (OGVs) at sea, the Royal Belgian Institute of Natural Sciences, one of the 17 official partners of the Belgian coastguard structure, launched its airborne sniffer program in 2015. A custom-built sniffer sensor was installed on board of the Belgian coastguard aircraft and has been used for the in-situ measurement of SO2 and NOx in OGV exhausts, providing non-compliance alerts to port inspection authorities in Belgium and other EU member states. To guarantee the consistency in quality and reliability of the airborne non-compliance alerts, the standard operational procedures and measurement methodology have been described and recorded in a sniffer quality management system (SQMS). As part of the SQMS, alerting thresholds for non-compliance with SO2 and NOx standards were defined based on the emission limits and the measurement uncertainty. In addition, compliant measurements for the fuel sulfur content (FSC) were shared through Thetis-EU, which is the sulfur inspection database hosted by the European Maritime Safety Agency (EMSA). By providing both non-compliance alerts and compliant measurements to port inspection authorities, the coastguard aircraft fulfills a first-step in the enforcement chain. This article demonstrates that the reported alerts have not only led to several sanctions, but the analysis also showed that the provision of airborne alerts improved the efficiency of the port inspection authorities drastically, resulting in a cost-efficient improvement of their enforcement strategy. Port inspection authorities were able to follow-up on 46% of the generated FSC alerts. In addition, 43% of the alerted OGVs, which were followed up, were confirmed as non-compliant after a port inspection. Accordingly, conditions were in place to effectively sanction 20% of the non-compliant observations. For NOx alerts, a limited follow-up was done by port inspection authorities and none of the alerts were confirmed by those inspections, which is mainly due to the lack of suitable inspection mechanisms and the absence of additional EU implementation regulations on NOx emission inspections. Aiming at paving the way for a stricter enforcement of airborne non-compliance alerts, a validation analysis was done between the airborne FSC measurements and reference FSC measurements. In addition to onboard measurements from exhaust gas cleaning systems (EGCS), analyzes of fuel samples were also carried out by port inspection authorities in order to acquire reference measurements. The validation analysis presented in this study showed that the empiric deviation of the airborne FSC measurements with the reference measurements was significantly lower than the uncertainty used in the reporting thresholds. It has also been shown that aerial measurements provide evidence-based data that, given an appropriate confidence interval, has the potential to be considered reliable legal evidence.

This study investigates the possible mechanisms related to the wide range of relative humidity in cirrus clouds (RHi). Under the closed adiabatic assumption, the impacts of vertical motion and ice crystal deposition/sublimation on RHi are investigated through in situ observations and parcel model simulations. Vertical motion is an active external force that changes the RHi, and ice crystal deposition/sublimation plays a role in mitigating the change in RHi. They are the two most important mechanisms involved in controlling the RHi fluctuation during cirrus evolution and could well explain the wide range of RHi in wave-related cirrus clouds. Furthermore, a comparison of cloud statistical characteristics from both observations and simulations shows that a very low value (e.g., 0.001) for the water vapor ice deposition coefficient is highly unlikely.

The relation between CO2 emissions and atmospheric CO2 concentration has traditionally been treated with more or less complex models with several boxes. Our approach is motivated by the question of how much CO2 must necessarily be absorbed by sinks. Observations lead to the model assumption, that carbon sinks like oceans or biosphere are linearly dependent on CO2 concentration on a decadal scale. In particular this implies the falsifiable hypothesis that oceanic and biological CO2 buffers have not significantly changed in the past 50 years and are not saturated in the forseeable future. The simple model with 2 parameters explains very well the CO2 emission and historical CO2 concentration data. The model gives estimates of the natural emissions, the pre-industrial CO2 equilibrium concentration levels, the half-life time of an emission pulse, and the future CO2 concentration level from a given emission scenario. This is validated by an ex-post forecast of the last 20 years. The important result is that with the stated polices scenario of the IEA future CO2 concentrations will not rise above 475 ppm. The model is compared with the Carbon modul of the Bern model, mapping their complex IRFs to a single time variant parameter.

FY-3E WindRAD (Fengyun-3E Wind Radar) is a dual-frequency rotating fan-beam scatterometer. Its data characteristics, NOC (NWP Ocean Calibration), and wind retrieval performance are investigated in this paper. The radar view geometry diversity varies across the swath, with maximum diversity in the sweet swaths and limited diversity in the outer and nadir swaths. When NOC backscatter calibration coefficients are computed as a function of incidence angle only (NOCint), a smooth correction is found. However, when relative antenna azimuth angle is included (NOCant), it appears that the corrections as a function of relative azimuth angle vary harmonically and substantially for a specific incidence angle. NOCant corrections yield a better fit of the measurements to the GMF (Geophysical Model Function). Hence NOCant is applied for the wind retrieval analysis from Ku-band, C-band. An extra engineering correction of 0.15dB and 0.2dB is applied on Ku-band and C-band backscatter values, respectively, to reduce the wind speed bias without increasing the standard deviation. Overall, NOCant is the best option for both channels. Also, the instrument backscatter data stability over time is good and the retrieved winds can fulfill operational requirements.

This work compared the performance of three methods for constructing a regional ensemble prediction system (EPS) for wind speed forecasts: dynamical downscaling, breeding of growth modes (BGM), and blending method. The Weather Research and Forecasting (WRF) model was used to downscale the European Centre for Medium-range Weather Forecast (ECMWF) EPS. In addition, as the BGM method needs observation data for generating scaling factors, an alternative method for generating scaling factors was proposed to eliminate dependence on observation data. One-month tests between October 1st and October 30th, 2020, were implemented to evaluate the performance of three methods in the Gansu province of China. The results demonstrate that the blending method outperforms the other two methods. Furthermore, the difference in performance is evident mainly in early forecast lead time and becomes negligible as forecast time increases.

Newton’s law of universal gravitation applies between two point-masses. True gravitation of solid Earth is volume integration of gravitation of all point-masses inside the solid Earth on a point-mass in atmosphere. However, in meteorology the Earth “shrinks” into a point-mass located at Earth center with entire Earth mass to identify the Earth gravitation (untrue). Combination of untrue gravitational and centrifugal accelerations gives effective gravity (geff). Combination of true gravitational and centrifugal accelerations leads to true gravity (g). The true gravity g minus the effective gravity geff is the gravity disturbance vector, δg = g – geff. With the true gravity g used in the basic equations, seven non-dimensional numbers are proposed to identify the importance of δg versus traditional forcing terms such as horizontal pressure gradient force and Coriolis force. These non-dimensional numbers are calculated from two publicly available datasets with the geoid undulation (N) from the static gravity field model EIGEN-6C4 and long-term mean geopotential height (Z), wind velocity (u, v), and temperature (Ta) at 12 pressure levels in troposphere from the NCEP/NCAR reanalyzed climatology. The results demonstrate δg nonnegligible in hydrostatic equilibrium, geostrophic wind, geostrophic vorticity, Ekman pumping, Q vector, and Omega equation, but negligible in thermal wind relation.

Although total nett CO₂ atmospheric flow can be estimated with reasonable accuracy, the contributing gross fluxes between the atmosphere and the earth's surface are poorly understood. This paper presents a method, driven by the objective of simplicity, by which the global outflow and inflow of CO₂ between atmosphere and a globally equivalent "mixing reservoir" can be estimated, using the isotopes ¹⁴C and ¹³C as tracers. It has been asserted that the isotopic carbon in CO₂ cannot be directly used as a tracer in flow studies because it is not subject to the Revelle factor. Evidence is provided showing that this view is mistaken. The model contains 7 key parameters which are used to create synthetic records of Δ¹⁴C and d¹³C spanning 200 years or more, including during the period of atmospheric weapons testing and its decay known as the "bomb pulse". By optimising the fit between these computed values and the historical records of d¹³C and Δ¹⁴C, all seven key parameters are determined. The effective "mixing reservoir" is thereby determined to have a size around six times that of the atmosphere, with global outflux rising from 39.7 GTC yr-1 in 1750 to 58.9 GTC/yr in 2020, this figure probably not including annually cycled carbon.

The paper presents the open source tool climdex-kit which includes utilities to compute, analyze and visualize climate indices based on the input data, target domain and temporal extent defined by the user. It is intended to support researchers as well as practitioners and decision makers to derive, handle and interpret meaningful information for climate change studies and sectoral applications. It currently includes the computation of 30 indices based on temperature and precipitation, describing both mean and extreme climate conditions, and it is designed to work with climate model projections. The tool is written in Python and integrates utilities from the well-established Climate Data Operators (CDO) and NetCDF Operators (NCO) libraries. The specific utilities for selecting, aggregating and visualizing data are thought to help users to produce tailored results improving understanding and communication of future climate change. In order to show the functionalities of the package as well as its potential integration in regional climate services, climdex-kit was applied to an ensemble of downscaled climate model projections up to 2100 for the region Trentino – South Tyrol (north-eastern Italian Alps). The projections for a selection of indices accounting for extreme temperature and precipitation conditions were derived and different visualization choices discussed. The package climdex-kit is developed in a way that allows users to implement additional routines for calculating other indices as well as for easily adapting the routines to handle with different data types and spatio-temporal targets defined by the specific application. Effective climate services can in fact be developed only if flexible tools and customizable climate information are integrated with a clear understanding of data features and limitations.

This note makes available five years of data gathered in a measurement site equipped with a micrometeorological station and two monitoring wells. Series of data of hydrological and atmospheric variables allow to estimate the flux of water across the atmosphere-land interface and to calculate the water budget, that are crucial topics in climate and environmental sciences. The measures of water-table began during 2017, one of the driest year of the whole instrumental period of climate history for the Central Mediterranean. Data from the micrometeorological station have been used to construct two more datasets of daily and monthly cumulative values of different terms of the surface water budget, from which the groundwater infiltration has been estimated. A significant decreasing trend characterizes both the data time series of piezometric heights and estimated infiltration in the considered period. All the data are collected in 2017-2022 datasets that are available in the supplementary material.

Downward short-wave (SW) solar radiation is the only essential energy source powering the atmospheric dynamics, ocean dynamics, biochemical processes, etc. on our planet. Clouds are the main factor limiting the SW flux over the land and the Ocean. For the accurate meteorological measurements of the SW flux one needs expensive equipment - pyranometers. For some cases where one does not need golden-standard quality of measurements, we propose estimating incoming SW radiation flux using all-sky optical RGB imagery which is assumed to incapsulate the whole information about the SW flux. We used DASIO all-sky imagery dataset with corresponding SW downward radiation flux measurements registered by an accurate pyranometer. The dataset has been collected in various regions of the World Ocean during several marine campaigns from 2014 to 2022, and it will be updated. We demonstrate the capabilities of several machine learning models in this problem, which are multilinear regression, Random Forests, Gradient Boosting and convolutional neural networks (CNN). We also applied the inverse target frequency (ITF) re-weighting of the training subset and the approach of pre-classification in order to raise the SW flux approximation quality. We found that the CNN is capable of approximating downward SW solar radiation with higher accuracy compared to existing empiric parameterizations. We also found that the ITF reweighting of the training subset increases the approximation quality substantially. The estimates of the SW radiation flux using all-sky imagery may be of particular use in case of the need for the fast radiative budgets assessment of a site.

Rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 seconds over intervals varying from 15 up to 41minutes in two different locations and in two different years using two different Micro-Rain Radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor ‘frozen’ turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that, MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations, but not the need of those radars for surveillance.

Sea surface wind forecasts in the Adriatic Sea often suffer for unadequate modelling, especially for the wind speed. This has detrimental effects on the accuracy of sea level and storm surge predictions. We present a numerical method to reduce the bias between the sea surface wind observed by the scatterometers and that supplied by the European Centre for Medium-Range Weather Forecasts (ECMWF) global atmospheric model, for storm surge forecasting applications. The method, called “wind bias mitigation”, relies on scatterometer observations to determine a multiplicative factor ∆ws which modulates the standard model wind in order to decrease the bias between scatterometer and model. We compare four different mathematical approaches to this method, for a total of eight different formulations of the multiplicative factor ∆ws. Four datasets are used for the assessment of the eight different bias mitigation methods: a collection of 29 Storm Surge Events (SEVs) cases in the years 2004-2014, a collection of 48 SEVs in the years 2013-2016, a collection of 364 cases of random sea level conditions in the same period, and a collection of the seven SEVs in 2012-2016 that were worst predicted by the Centro Previsioni e Segnalazioni Maree, Comune di Venezia (Tide Forecast and Early Warning Centre of the Venice Municipality - CPSM). The statistical analysis shows that the bias mitigation procedures supplies a mean wind speed more accurate than the standard forecast, when compared with scatterometer observations, in more than 70% of the analyzed cases.

Satellite measurements are one of the main sources of data on the state of the marine environment. To obtain information about the sea brightness coefficient , it is needed to correctly carry out atmospheric correction. In the presence of dust aerosol over the Black Sea, physically incorrect values of the spectral brightness coefficient often occur, and specifically negative values in the IR region of the spectrum. The main objective of the study is to evaluate the influence of dust aerosol on the spectral dependence of sea brightness, based on analytical calculations from the transfer theory using the principle of plane-parallel layers and results of validation of AERONET-OC field and remote sensing data. The work analyzes spectral dependence of the first error eigenvector of the standard atmospheric correction in the presence of dust aerosol. As result it is given that with an absorbing aerosol, the atmospheric correction error is described by the spectral course of molecular scattering, i.e. close to

Nanoparticles (NP), Nanoaerosols (NA), ultrafine particles (UFP), and PM0.1 (particles with a diameter ≤ 0.1 µm or 100 nm) are interchangeably used in the field of atmospheric studies. PM0.1 are emitted from both natural and anthropogenic sources. The main emission sources of PM0.1 are combustion processes, including biomass and fossil fuel. Moreover, secondary formation via atmospheric photochemical reactions can also occur depending on meteorological conditions and the suspended pollutant species. Identifying the physical and chemical characteristics and spatial and temporal variations is vital in terms of understanding the effect of NPs on the environment, the global climate, and human health risks. This review article summarizes recent research on PM0.1 in Thailand. The review involves peer-reviewed papers from Scopus and the Web of Science databases, and includes the most recently published articles in the past ten years (2013-2022). UFPs mainly come from the combustion processes such as motor vehicles. The high mass concentration of PM0.1 that occurs during the dry season in which open fires depend on the specific region of Thailand. Particulate pollution from local and cross-border countries also needs to be considered in terms of the concentrations of ambient nanoparticles. The overall conclusions reached will likely have a beneficial long-term impact on achieving a blue sky over Thailand through the development of coherent policies and managing new air pollution challenges and sharing knowledge with a broader audience.

In this study, we present more than 8 years of observations of the quasi-16-day wave (Q16DW) in the mesosphere and lower thermosphere (MLT) wind at middle latitudes observed by the Mengcheng (33.4°N, 116.5°E) meteor radar. The long-term variation in amplitudes calculated from the data between April 2014 and December 2022 shows enhanced wave activity during winter and early spring (near equinox) and suppressed wave activity during the summer. The Q16DWs are relatively weak in the meridional wind. During the winter months, the Q16DWs in the zonal component exhibit a burst below 85 km, and their amplitudes reach up to 10 m/s. In the early spring, the Q16DWs strengthen above 90 km with amplitudes in excess of 12 m/s. The phase differences between the zonal and meridional components of the Q16DW are, on average, slightly smaller than 90°, suggesting the existence of orthogonal relationships between them. During strong bursts, the periods of the Q16DW in winter range between 15 and 18 d, whereas in winter, the periods tend to be more diffuse. The wintertime Q16DW is amplified, on average, when the zonal wind shear peaks, suggesting that barotropic instability may be one source of Q16DW. Q16DW amplitudes exhibit considerable interannual variability; however, a relationship between the 11-year solar cycle and the Q16DW is not found.

The multiple regression method is still an important tool for establishing precipitation forecast models with a lead time of one season. This study developed a flexible statistical forecast model for July precipitation over the middle-lower reaches of the Yangtze River (MLYR) based on the prophase winter sea surface temperature (SST). According to the characteristics of observed samples and related theoretical knowledge, some special treatments (i.e., more flexible and better–targeted methods) were introduced in the forecast model. These special treatments include a flexible MLYR domain definition, the extraction of indicative signals from the SST field, artificial samples, and the amplification of abnormal precipitation. Rolling forecast experiments show that the linear correlation between prediction and observation is around 0.5, more than half of the abnormal precipitation years can be successfully predicted, and there is no contradictory prediction of the abnormal years. These results indicate that the flexible statistical forecast model is valuable in real-life applications. Furthermore, sensitivity experiments show that forecast skills without these special treatments are obviously decreased. This suggests that forecast models can benefit from using statistical methods in a more flexible and better-targeted way.

The Hunga Tonga-Hunga Ha’apai underwater volcano (20.57°S, 175.38°W) violently erupted on 15 January 2022. The volcanic plume evolution during its initial stages is delineated by using Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)-2 radio occultation (RO) measurements. The bending angle (BA) anomaly over the Tonga volcanic plume (within 200 km from the eruption center) at 5:17 UTC on 15 January shows a prominent peak at higher stratospheric heights. The top of the BA anomaly revealed that negative to positive change occurred at ~38 km indicating the first height where the RO line-of-sight encounters the volcanic plume. The BA anomaly further revealed an increase of ~50% at ~36 km and confirms the volcanic plume reached above ~36 km. Further, the evolution of BA perturbations within 24 hours after the initial explosion is also discussed. From collocated RO profiles with the volcanic plume, we find a clear descending of the peak altitude of the BA perturbation from ~36 km to ~30 km within 24 hours after the initial eruption. The results from the study will provide some insights into advancing our understanding of volcanic cloud dynamics and their implementation in volcanic plume modeling.

In the context of the Atmospheric Observing System (AOS) international program, a new generation spaceborne lidar is expected to be in polar orbit for deriving new observations of aerosol and clouds. In this work, we analyze the added values of these new observations for characterizing aerosol vertical distribution. For this, synthetic observations are simulated using the BLISS lidar simulator in terms of backscatter coefficient at 532 nm. We consider two types of lidar instruments, an elastic backscatter lidar instrument and a high spectral resolution lidar (HSRL). These simulations are performed with atmospheric profiles from a Nature Run (NR) modeled by the MOCAGE Chemical Transport Model. In three case studies involving large events of different aerosol species, the added value of the HSRL channel for measuring aerosol backscatter profiles with respect to simple backscatter measurements is shown. Observations independent from an a-priori lidar Ratio assumption, as done typically for simple backscattering instruments, allows probing the vertical structure of aerosol layers without divergence, even in case of intense episodes. Relative error in the backscatter coefficient profiles are observed to lay between +40% and -40% for low abudancies, with mean biases between +5% and -5%. A 5-day study in the case of desert dust completes the study of the added value of the HSRL channel with relative mean bias from the NR of the order of 1.5%.

Mid-latitude sporadic-E (Es) is an intermittent phenomenon of the lower E region of the ionosphere. Es clouds are thin, transient, and patchy layers of intense ionization, with ionization densities which can be much higher than in the background ionosphere. Oblique reflection of radio signals in the very high frequency (VHF) range is regularly supported, but the mechanism for it has never been clearly established - specular reflection, scattering, and magnetoionic double refraction have all been suggested. This article proposes using the polarization behaviour of signals reflected from intense midlatitude sporadic-E clouds as an indicator of the true reflection mechanism. Results are presented from a measurement campaign in the summer of 2018, which gathered a large amount of data at a receiving station in the UK using 50 MHz amateur radio beacons as signal sources. In all cases the signals received were elliptically polarized, despite being transmitted with linear polarization; there were also indications that polarization behaviour varied systematically with the orientation of the path to the geomagnetic field. This represents, for all the examples recorded, clear evidence that signals were reflected from midlatitude Es by magnetoionic double refraction.

Emissions from wood burning for heating in secondary homes or cabins is an important aspect for the development of high-resolution emission inventories in specific areas. Norway is used as case study as the national wood consumption for heating in cabins is around 20% of the total. Our study shows first a method to estimate emissions from cabins based on traffic data to derive cabin occupancy, which combined with heating need allows for the spatial and temporal distribution of emissions. The combination of residential (RWC) and cabin wood combustion (CWC) emissions shows large spatial and temporal differences, and a temporally “cabin population” can in areas be orders of magnitude larger than the registered population. While RWC emissions have been steadily reduced, CWC have kept relatively constant or even increased, which results on an increase in the cabin share to total heating emissions up to 25-35%. When comparing with regional emissions inventories, our study shows that the gradient between rural and urban areas is not well represented in regional emissions inventories, which resembles a population-based distribution and does not allocate emissions in cabin municipalities. At last, our study shows that CWC emissions may become an increasing environmental concern as higher densification trends in mountain areas are observed.

Accurate knowledge of the rain amount is an crucial driver in several hydro-meteorological applications. This is especially true in complex orography territories, which are typically impervious, thus leaving ungauged most of the mountain areas. Thanks to their spatial and temporal coverage, weather radars can potentially overcome such an issue. However, weather radar, if not accurately processed, can suffer from several limitations (e.g., beam blocking, altitude of the observation, path attenuation, indirectness of the measurement) that can hamper the reliability of the rain estimates performed. In this study, a comparison between rain gauge and weather radar retrievals is performed in the target area of the Abruzzo region in Italy, which is characterized by a heterogeneous orography ranging from the sea side to Apennine ridge. Consequently, the Abruzzo region has an inhomogeneous distribution of the rain gauges, with station density decreasing with the altitude reaching up to approximately 1500 m a.s.l. Notwithstanding, pluviometric inflow spatial distribution shows a sub-regional dependency as a function of four climatic and altimetric factors: coastal, hilly, mountain, and inner plain areas (i.e., Marsica). Such areas are used in this analysis to characterize the radar retrieval vs. rain gauge amounts in each of those zones. Compared to previous studies on the topic, the analysis presented an attention to the importance of an accurate selection of the climatic and altimetric sub-regional areas where undertake the radar vs. rain gauge comparison. This aspect is not only of great importance to correct biases in radar retrievals in a more selective way, but it also paves the way for more accurate hydro-meteorological applications (e.g., hydrological model initialization, quantify the aquifers recharge etc.) which, in general, require the accurate knowledge of rain amounts upstream of a basin. To fill the gap caused by the uneven rain gauge distribution, Ordinary Kriging has been applied on a regional scale to obtain 2D maps of rainfall data, which are cumulated on a monthly and yearly base. Weather radar data from the Italian mosaic are considered as well, in terms of rain rate retrievals and cumulations performed on the same time frame used for rain gauges. The period considered for the analysis is two continuous years: 2017 and 2018. The output of the elaborations are raster maps for both radar and interpolated rain gauges, where every pixel contains a rainfall quantity. Although the results show a general underestimation in the weather radar data especially in mountain and Marsica areas, even though within the 95% confidence interval of the OK estimation. Our analysis highlights that the average bias between radar and rain gauges, in terms of precipitation amounts, is a function of altitude and is almost constant in each of the selected areas. This achievement suggests that after a proper selection of homogeneous target areas, the radar retrievals can be corrected using the denser network of rain gauges typically distributed at lower altitudes and extend such correction at higher altitudes without loss of generality.

In the context of global warming and reduction in fresh water availability is presented a study of the evolution of dew, rain and evapotranspiration in the NW of Africa. The time periods are concerned with the years 2005-2020, using existing data, and years 2020-2100, using the low and high emissions representative concentration pathway scenarios RCP 2.6/8.5 from the Cordex database. A continuous decrease in rain precipitation is observed, on order of -14 mm/decade for the more credible scenario RCP 8.5. The amplitude is maximum on the coast and on the foothills of Atlas. A clear decrease in dew yields is also observed along a NW/SE axis, strongly correlated with a corresponding decrease in relative humidity (up to 7%). Chemical dew and rain data in the representative site of Mirleft correspond to the major cations Na+ > Ca2+ > Mg2+ > K+, similar to a local spring water. Concentrations in rain are about two times less than in dew water. Ionic concentrations are compatible with WHO standards. The seasonal variations of the ionic concentrations in dew and rain follow a volume dilution dependence. The expected diminution in dew and rain volumes according to the RCPs 2.6 and 8.5 should increase the dew and rain ionic concentrations in the future.

The objective of this paper is to estimate the trend of Total Ozone Column (TOC) over Togo. Multi Sensor Reanalysis-2 (MSR-2) TOC over the entire territory of Togo were used. Multiple Linear Regression (MLR) method has been applied to retrieve the interannual contributions of different forcings and the long term variability. It was found that the Annual Oscillation (AnO), the Quasi Biennial Oscillation at 30 mb (QBO30), the Solar Flux (SF), and the El Niño–Southern Oscillation (ENSO) have statistically significant influence on the interannual variability of the TOC. The strongest contribution (22 ± 1.4 DU) is allocated to the AnO while the weakest (< 1 DU) is attributed to the Semi-Annual Oscillations (SAnO). Before the peak year of Equivalent Effective Stratospheric Chlorine (EESC) in tropics in 1997 the trend is negative (- 0.3% ± 0.9% per decade) and is not statistically significant. After the peak year, a statistically significant positive trend is observed. The trend of TOC is 0.6% ± 0.2% per decade. The monthly TOC trend over Togo is positive and statistically significant during the rainy season (particularly during the monsoon period) except in April, unlike during the harmatan period (DJF) where the trend is not significant.

We identified six natural "control knobs" for global weather and climate, which clearly regulate, increase and decrease global temperatures, thus performing a true temperature "control" on Earth. Identified control knobs act as Earth's orbital forcing. We present a detailed Earth orbital model, from where the analysis proceeds. Based on our orbital model, we compare meteorological data from all over the globe, in order to detect orbital forcing fingerprints. Ninety temperature graphs and bar charts demonstrate the results. Empirical meteorological data covers the Pacific ocean, the Atlantic ocean, Arctic and Antarctic sea ice extent, and presents land based meteorological measurements from all continents for the year 2020, as well as multi-decadal data from global daily temperature datasets. A supplementary file provides another 100 additional graphs of weather data and temperature charts. The global orbital "temperature control" is clearly observable in all graphs and charts. Until today, orbital forcing has not been integrated in weather models and CMIP6 climate models.It is concluded that the proposed new orbital forcing should be part of weather and climate forecasting models.

This study presents the algorithm ACTION, defined as Automatic Center detection of Tropical cyclone (TC) using Image processing based on the Operational radar Network. Based on the high visibility of weather radar imagery, the TC’s motion vector is calculated from the continuous image change using optical flow, producing its rotation center as the TC’s center. The algorithm’s performance was verified for typhoons (TCs in the Northwestern Pacific) affecting the Korean Peninsula from 2018–2021 and showed a high detection rate of 80.8% within an error distance of 40 km compared to the best track of the Korea Meteorological Administration (KMA). The detection rate was 100% for typhoons with temporally consistent morphological characteristics. ACTION automatically generates TC center information upon the TC’s initial entry inside the observation radius even in the absence of uniform radar data. ACTION easily calculates using Open Source Computer Vision, performs in real time, and can be directly applied to rapidly generated weather radar images; hence, it is currently being utilized by the KMA. The high-temporal-resolution TC center information calculated through ACTION is expected to improve the efficiency of TC forecasting.

The climate change impacts on some regions of the planet faster and stronger. These areas are known as the hot spots for climate change and Cyprus (Nicosia) in the Mediterranean is one of these spots. This paper aims to analyze the significant changes of atmospheric aerosol characteristics in 2019 and during the extreme event of 25 April 2019. We study the aerosol optical thickness (AOT), Ångström exponent, single scattering albedo, refractive index (imaginary and real parts), size, and vertical distribution of aerosol particles during the event of a high atmospheric aerosol contamination over Nicosia in details. For this purpose, we used the ground-based lidar, observations of the sun-photometer AERONET Nicosia station, satellite products from the Moderate Resolution Imaging Spectroradiometer (MODIS), and back trajectories of air movements calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT). On 23–25 April, according to lidar and sun-photometer observations, strong aerosol pollution over Nicosia was detected. On April 25, 2019, the AOT value exceeds 1.0 at λ = 440 nm. Analysis of the optical and microphysical characteristics supported that the pollution consists of mainly Saharan dust and partly urban aerosols. This assumption was confirmed by HYSPLIT backward trajectories and MODIS images where air masses containing dust particles came from North Africa and from the Eastern part of Europe.

Abstract: Increasing population and urbanization are affecting human health and comfort. In order to get rid of these affects, mankind is changing its enviroment and looking for new life areas. This study investigates the influence of Land Cover Change (LCC) and Normalisied Densly Vegetation Index (NDVI) on Land Surface Temperature (LST) of Fethiye-Göcek Specially Protected Area (SEPA) in easterm mediterranean basin. In the study LCC, NDVI and LST were drived from landsat 5, 7 and 8 satellite image of resolation at 30x30 m acquired between 1995-2020. LST were computed based on Land Use/Land Cover (LULC) types. The Corine Index were used for determination of land uses. The results indicated that water, forest and maquia lands decreasing while urban fabric and bare lands are increasing depend upon the urbanization and forest fires in the basin. These changes in LULC widened the temperature differences between the urban and rural areas. The change in LST is associated with changes in constructional materials in urban land and in vegetation abundance both in the urban and rural areas. Vegetation has an important factor in the temperature of different land covers. That produces warming trend in temperetaure in built-up areas it causes to keep other lands warmer in cold weather. Another important result is affective Urban Heat Island (UHI) on climate change based on the impact of urbanization and land cover changes. Significantly possitive correlation were found between the urbanization rate, population and built-up area and warming rate of average air temperature and so the LST.

Seasonal variations in atmospheric ultrafine particulate matter (PM0.1) were monitored in Bangkok, Thailand, from 2016 to 2017. PM0.1 bound organic carbon (OC), and elemental carbon (EC) were collected by a cascade air sampler that can collect PM0.1 and estimated by a thermal-optical carbon analyzer following the IMPROVE-TOR protocol. The annual average PM0.1 in Bangkok was 14.5 ± 4.7 µg/m3, higher than in large Asian cities like Shanghai and Hanoi. Biomass burning from neighboring areas was shown to increase the particle concentration. Apparent risings in carbon species as OC, EC, and OC/EC ratios in the wet and dry seasons were observed, and Char-EC/Soot-EC displayed that the PM0.1 in the Bangkok atmosphere was influenced mainly by the vehicle exhaust, even though the influence of biomass burning was more sensitive during the dry season. The effective carbon ratio (ECR) displays that carbonaceous aerosol is light-absorbing; the dry season increase contributed to climate and air quality. The finding of this work is of great importance to air pollutants control policies in urban areas.

Nature-based solutions (NbS) - working with and enhancing nature to address societal challenges - are increasingly being featured in climate change adaptation policy and plans. While there is growing evidence that NbS can reduce vulnerability to climate change impacts in general, there is a lack of understanding on the mechanisms through which this can be achieved, particularly in the Global South. To address this, we analyse 85 nature-based interventions in rural areas across the Global South, and factors mediating their effectiveness, based on a systematic map of peer-reviewed studies encompassing a wide diversity of ecosystems, climate impacts, and intervention types. We develop and apply an analytical framework of people’s social-ecological vulnerability to climate change, in terms of six pathways of vulnerability reduction: social and ecological exposure, sensitivity, and adaptive capacity. Most cases (95%) report a reduction in vulnerability, primarily by lowering ecosystem sensitivity to climate impacts (73% of interventions), followed by reducing social sensitivity (52%), reducing ecological exposure (36%), increasing social adaptive capacity (31%), increasing ecological adaptive capacity (19%) and/or reducing social exposure (14%). An analysis of mediating factors shows that social dimensions are equally important as technical factors in NbS to achieving equitable and effective outcomes. Attention to the distinct social and ecological pathways through which vulnerability is reduced helps to harness the multiple benefits of working with nature in a warming world.

We develop a machine learning (ML) approach for improving the accuracy of the horizontal dis-tribution of the aerosol optical depth (AOD) simulated by the CHIMERE chemistry-transport model over Northern Africa using Moderate-Resolution Imaging Spectroradiometer (MODIS) AOD satellite observations. These observations are used during the training phase of the ML method for deriving a regional bias correction of AODs simulated by CHIMERE. The results are daily maps of regional bias corrected AODs with full horizontal coverage over Northern Africa. We test four types of ML models: multiple linear regression (MLR), random forests (RF), gradi-ent boosting (XGB), and multiple layer perceptron networks (NN). We perform comparisons with satellite and independent ground-based observations of AOD that are not used in the training phase. They suggest that all models have overall comparable performances with a slight advantage of the RF model which expresses less spatial artifacts. While the method slightly underestimates the very high AODs, it significantly reduces biases and absolute errors, and clearly enhances linear correlations with respect to independent observations. This im-provement for deriving the AOD is particularly relevant for high dust pollution regions like the Sahara Desert, which dramatically lack ground-based measurements for validations of chemis-try-transport modeling which currently remains challenging and imprecise.

Seasonal oscillations in the partial pressure of carbon dioxide (pCO2) in the Earth’s atmosphere stronger in northern latitudes are assumed to show that terrestrial photosynthesis exceeds respiration in summer reducing the pCO2, but increasing in winter when respiration exceeds photosynthesis. We disagree, proposing that variations in the temperature of the surface mixing zone of seawater also regulate the atmospheric pCO2, thermodynamically. We show that carbonate (CO32-) concentrations will therefore increase in summer with CaCO3 (calcite or aragonite) becoming less soluble, so calcium and carbonate ions are predicted to aggregate more while CO2 concentration falls in warmer seawater, thermodynamically favoring lower atmospheric pCO2. In winter, these physical processes are reversed, redissolving suspended calcite thus increasing carbonate alkalinity; carbonate concentration lessens as bicarbonate and soluble CO2 increase, raising the pCO2 in air. Our numerical computation shows that thermal fluctuations in equilibria favor absorption from air of more than one mole of CO2 per square meter in summer, coinciding with calcite formation maximizing in warmer water, potentially augmenting limestone reefs if there is a trend for increasing temperature . Another assumption we challenge is that upwelling from deeper water is the sole cause of increases in dissolved inorganic carbon (DIC) and alkalinity in surface waters, particularly in the southern hemisphere. Instead, calcite dissolution is favored as water temperature falls near the surface. It is well established that the seasonal summer decline in atmospheric CO2 coincides in fertile seawater with higher rates of biotic calcification and acidity, allowing increased CO2 capture by photosynthesis. However, its reversal in winter is proposed to be also a result of the cyclic dissolution of calcite as temperature falls, facilitated by biogenic respiration now exceeding photosynthesis; this can mutually provide the CO2 needed to convert carbonate ion alkalinity from calcite dissolution with bicarbonate increasing. Physical reasons why this oscillation is more obvious in the northern hemisphere include greater seasonal variations in water temperature (ca. 7.1 oC) being almost twice that in the cooler southern hemisphere (ca. 4.7 oC) and the greater depth of the surface mixing zone of seawater in the southern oceans. Evidence from 13CO2 fluxes between surface seawater and air is also assessed to test this hypothesis but questions remain regarding the regional rates of inorganic precipitation and dissolution of CaCO3 in the mixing zone. In summary, rapid biogenic calcification is favored by summer photosynthesis, but slower abiogenic calcification is more likely in warmer water.