Weather-related risks in aviation are one of the main causes of accidents worldwide. This article aims to analyze aviation risks resulting from adverse weather conditions, using the case study of the Pucallpa accident that occurred on August 23, 2005, in eastern Peru. In this analysis, we have determined the relationship between meteorological information and aviation risks, presented a matrix of risks associated with the most predominant meteorological variables at Pucallpa airport, and drawn up three maps of meteorological risks indicating the month of the year in which their incidence is most relevant. Finally, we conclude that it is essential to emphasize the importance of considering the prevailing weather conditions at Pucallpa airport to help mitigate the potential risks that may occur during different phases of the flight.

From the evening of 1st May 2023 to 3rd May 2023, severe hydro-meteorological events occurred in the Italian Emilia-Romagna region, particularly in the provinces of Forlì-Cesena, Ravenna, Bologna, Modena, and Reggio Emilia. Additionally, on 16th-17th May, another intense meteorological event occurred, impacting the Romagna provinces. Such events caused extensive flooding, landslides, isolation of many areas, evacuation of numerous families, and significant damages to infrastructure, agriculture, buildings, and essential services. Many hilly municipalities were cut off, and thousands of civilians had to be evacuated, losses of life occurred. The consequences beyond the recorded immediate impacts on infrastructure and life were impressive, and extended to the regional economy, specifically in the Fruit Valley, where, in addition to immediate yield losses, long-term damage to orchard production is expected due to persistent flooding. The civil and cultural building heritage has also been heavily affected, both in the countryside and in inhabited centers. Some of the damage, direct and indirect, caused by flooding on buildings will also see an evolution in the medium to long term that will have to be addressed. This paper analyzes the reasons that led to the atmospheric phenomenon to understand the increasing potential occurrence of similar events from the perspective of climate change. This paper document analyzes the causes and effects of these events, additionally considering the future impacts of similar hazards potentially occurring in the context of climate change. It is crucial indeed to remark how such events, when occurring in high density agricultural areas, have the potential to threat the entire regional production system for extended periods. For example, the alteration of direct physiological processes due to root drowning may compromise production of entire productive agricultural extensions and many years will be required to their replacement.

With the rising trends in elderly populations around the world, there is a growing interest in understanding how climate sensitivity is related to their thermal perception. Therefore, we analyzed the associations between mortality in the elderly due to cardiovascular (CVD) and respiratory diseases (RD) and meteorological variables, for three cities in the State of São Paulo, Brazil: Campos do Jordão, Ribeirão Preto and Santos, from 1996 to 2017. We applied the Autoregressive Model Integrated with Moving Average (ARIMA) and the Principal Component Analysis (PCA) in order to evaluate statistical associations. Results showed CVD as a major cause of mortality, particularly in the cold period, when a high mortality rate is also observed due to RD. The mortality rate was higher in Campos do Jordão and lower in Santos (and intermediate values in Ribeirão Preto). Campos do Jordão results indicate an increased probability of mortality from CVD and RD due to lower temperatures. In Ribeirão Preto, the lower relative humidity may be related to the increase in CVD and RD deaths. This study emphasizes that, even among subtropical climates, there are significant differences. Therefore, this can assist decision makers in the implementation of mitigating and adaptive measures.

The accurate detection of clouds is an important first-step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology have exploded, creating a new era in space exploration. Additionally, initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, more recently cloud screening in satellite acquired data is playing an increasingly critical role in the investigation of cloud free data for the retrieval of soil moisture, vegetation cover, ocean colour concentration and sea surface temperatures, as well as environmental monitoring of a host of products, e.g. atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to both accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this paper, the evolution of cloud detection technology is highlighted with advancement in sensor hardware technology and possible AI algorithmic advances. Additionally, a discussion is presented on methods to obtain the cloud truth data needed to determine the accuracy of these cloud detection approaches.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known globally as COVID-19, originated in December 2019 in Wuhan, Hubei province in China and has rapidly spread across the globe ever since. The first recorded case in sub-Saharan Africa was in Nigeria, on the 25th February, 2020. The virus continues to spread, and new variants of the disease have emerged, the number of deaths and new infections in the countries of sub-Saharan Africa has been relatively low compared to predictive models. This could be due to several factors, such as slower transmission dynamics of the virus, a lower-case fatality rate, or a lack of testing or reliable data. Whilst this may also, in part, be due to the robustness of the nations' public health responses, there is scarce reporting on the specifics of this. However, emerging research has demonstrated that various environmental factors could influence virus transmission. The study adopted collected meteorological data that was critically analysed and discussed. The impact of three factors in the context of sub-Saharan African nations: temperature, ultraviolet (UV) exposure and pre-existing infection with Plasmodium (malaria) were discussed. These factors were discussed critically in light of the reduced rates of transmission and mortality observed.

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.

Particulate matter is a severe source of atmospheric pollution in urban cities, and it has adverse effects on human health. This study was conducted during the whole year of 2016 to monitor the concentrations of PM₁₀ and PM_2.5 on the Beijing Hanshiqiao wetland and bare land in Beijing to analyze their correlations with meteorological factors and compare the removal efficiency between two land surface types. The results indicated that (1) the PM₁₀ and PM_2.5 concentrations on the bare land were higher than those on wetland as a whole, reaching the highest value both at night and dusk and the lowest value near noon. The average concentration of PM₁₀ was higher in winter (wetland: 137.48 μg·m^-3; bare land: 164.75 μg·m^-3) and spring (wetland: 205.18 μg·m^-3; bare land: 244.85 μg·m^-3) and the concentration of PM_2.5 on the wetland also reached the higher value in winter and spring with the average of 84.52 μg·m^-3 and 98.98 μg·m^-3, whereas, it was higher in spring and summer on the bare land; (2) concentrations of PM₁₀ and PM_2.5 were significantly positively affected by the relative humidity (P < 0.01) and negatively influenced by wind speed (P < 0.05). The relationship between PM₁₀ and PM_2.5 concentrations and temperature was found complicated: it showed a significantly negative correlation (P < 0.01) in winter and spring and was insignificant in autumn, but in summer, only the correlation between the PM₁₀ concentration and temperature on wetland was significant (P < 0.01); (3) the removal efficiencies of PM₁₀ and PM_2.5 followed the order of spring > winter > autumn > summer on the wetland, and the removal efficiency of PM₁₀ was greater than that of PM_2.5. This study is aim to provide practical measures to improve the air quality and facilitate sustainable development in Beijing.

This study proposes a short- and medium-term electricity consumption prediction algorithm by combining the GRU model suitable for long-term forecasting and the Prophet model suitable for seasonality and event handling. (1) Manufacturing Company B's Electricity consumption data and meteorological data in Naju, Jeollanam-do, South Korea are collected and preprocessed. (2) The preprocessed data proposes the Prophet model in the first step for seasonality and event handling prediction. (3) In the second step, seven multivariate data are experimented with GRU. Specifically, the seven multivariate data consist of six meteorological data and the residuals between the predicted data from the proposed Prophet model in Step 1 and the observed data. These are utilized to predict electricity consumption at 15-minute intervals. (4) Electricity consumption is predicted for short-term (2 days and 7 days) and medium-term (15 days and 30 days) scenarios. The experimental results demonstrate that the proposed method outperforms the conventional Prophet model by more than 23 times and the modified GRU model by more than 2 times in terms of MAPE.

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.

The vulnerability of vegetation in the Middle East to meteorological conditions and climate change, especially those leading to drought, is high. Despite the importance of the Amu Darya and Kabul River Basins (ADB and KRB) as a region in which more than 15 million people live, and its vulnerability to global warming, only several studies addressed the issue of the linkage of meteorological parameters on vegetation for the eastern basins of Afghanistan. In this study, data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Global Precipitation Measurement Mission (GPM), and Land Data Assimilation System (GLDAS) to examine the impact of meteorological parameters on vegetation for the eastern basins of Afghanistan for the period from 2000 to 2021. The study utilized several indices, such as Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Microwave Integrated Drought Index (MIDI). The relationships between meteorological quantities, drought conditions, and vegetation variations were examined by analyzing the anomalies and using regression methods. The results showed that the years 2000, 2001, and 2008 had the lowest vegetation coverage (VC) (56, 56, and 55% of the study area, respectively). On the other hand, the years 2010, 2013, 2016, and 2020 had the highest VC (71, 71, 72, and 72% of the study area, respectively). The trend of the VC for the eastern basins of Afghanistan for the period from 2000 to 2021 was upward. High correlations between VC and soil moisture (R = 0.70, p = 0.0004), and precipitation (R = 0.5, p = 0.008) were found, whereas no significant correlation was found between VC and drought index MIDI. It was revealed that soil moisture, precipitation, land surface temperature, and area under meteorological drought conditions explained 45% of annual VC variability.

Drought presents significant challenges in arid regions, adversely impacting agriculture, water resources, and ecosystems. In Xinjiang, Northwest China, although large-scale climatic phenomena are well studied, finer-scale climatic variability in subregions such as the Ili River Valley (IRV) remains understudied. This knowledge gap impairs effective agricultural planning and environmental management in this ecologically fragile but agriculturally important region. In this study, we analyze the spatiotemporal evolution of drought in the IRV from 1961 to 2023, using data from ten meteorological stations. We applied the SPEI drought Index alongside Sen's trend analysis, the Mann-Kendall test, the cumulative departure method, and wavelet analysis to examine drought patterns. Results indicate significant drying trends in the IRV beginning in 2005, with frequent drought events from 2015 onwards, and 2019 marking a key transition from wet to dry conditions. The overall drought rate was -0.09/10a, suggesting a milder drought severity in the IRV compared to broader Xinjiang. Seasonally, the IRV experiences drier summers and wetter winters relative to the region's averages, with negligible changes in autumn and milder drought conditions in spring. Abrupt changes in drying seasons occurred later than in Xinjiang, with delays of 5, 21, and over 37 years for spring, summer, and autumn, respectively, indicating a lagging response. Spatially, the western plains are more prone to aridification than the central and eastern mountainous regions. significant differences in drought cycles, longer than those in Xinjiang, with distinct wet-dry phases observed over multiple time scales and across seasons highlighting the complexity of drought variability in IRV. In conclusion, the Vally exhibits unique drought characteristics, with milder intensities, pronounced seasonal variation, spatial heterogeneity, and notable resilience to climate change. These findings emphasize the necessity of region-specific drought management strategies, as those effective on a larger regional scale may not be suitable for subregions.

Accurate solar power forecasting is pivotal for the effective planning, management, and operation of power systems, ensuring a sustainable energy supply for consumers while optimizing the integration of renewable energy sources and the functioning of electricity markets. Recent advancements have focused on developing predictive models that offer precise daily forecasts for solar power generation, which are critical for power usage planning and production efficiency. These models typically leverage a diverse range of data inputs, including solar power metrics and meteorological variables such as temperature, humidity, precipitation, solar radiation, and wind speed—factors that significantly influence solar power generation due to their weather-dependent nature. This study introduces a novel hybrid forecasting model that synergizes the capabilities of the Prophet model and Gated Recurrent Units (GRU), leveraging their respective strengths to enhance predictive performance. The proposed model is rigorously evaluated across short-term (2 days, 7 days) and medium-term (15 days, 30 days) forecasting horizons. Experimental results demonstrate that the hybrid model significantly outperforms the existing models, delivering superior accuracy in solar power generation forecasts.

As previous study in many countries shown that stroke patient onsets are not distributed randomly, but associate with meteorological factors. However, the relationship between stroke occurence and meteorological factors remain unknown in mountain area in Guizhou China. This paper firstly introduces multi-source fusion data which is integrated by the mountain meteorological factor, geographic mapping elevation information data and stroke population monitoring of Guizhou province. Mainly elaborates from a few aspects, which concludes data collection, data sorting, data extraction, data cleaning and data production in detail. A completed and detailed meteorological stroke data set was successfully established and multi-source data fusion was realized by the use of Gbase and unified data service interface of Guizhou meteorological big data cloud platform. And we report the impact of meteorological variables in Guizhou mountain area climate on stroke. In this study, we performed a statistical fusion data between January 2017 and December 2020 about stroke incidence rate and meteorological factors respectively, for instance, concerning the mean (Mean), maximum (Max), minimum (Min), median (Median), standard deviation (Std) . We calculated stroke rates (date stroke occurence)/(year stroke occurence) and we investigated the distribution of days included in the label. Results: the distribution of the incidence rate basically conforms to the normal distribution with the label Max 0.007399, label Min 0.000204, label Mean 0.002738, label Median 0.002643, label Std 0.00076. We observed a positive significant correlation of stroke incidence rate directly proportional to the daily temperature difference (between 0 and 20 degrees Celsius), as the value of this factor exhibits variation larger, the incidence rate is gradually increasing. And we investigated from the Pearson correlation coefficients that stroke incidence increasing when meteorological factors at low humidity (less than 40% (RH 80%), temperature higher than 31℃); the 24-hour daily temperature difference varies between 6 and 10℃ (6

With the rapid development of artificial intelligence, machine learning is gradually becoming popular in predictions in all walks of life. In meteorology, It is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modelling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field.

Reference evapotranspiration (ET0) is the first step in calculating crop irrigation demand, and numerous methods have been proposed to estimate this parameter. FAO-56 Penman-Monteith (PM) is the only standard method for defining and calculating ET0. However, it requires radiation, air temperature, atmospheric humidity, and wind speed data, limiting its application in regions where these data are unavailable; therefore, new alternatives are required. This study compared the accuracy of ET0 calculated with the Blaney-Criddle (BC) and Hargreaves-Samani (HS) methods versus PM using information from an automated weather station (AWS) and the NASA-POWER platform (NP) for different periods of time. The information collected corresponding at Module XII of the Lagunera Region Irrigation District 017, a semi-arid region in the North of Mexico. The HS method underestimated by 5.5 % the reference evapotranspiration (ET0) compared to the PM method during the period from March to August, and yielded the best fit in the different evaluation periods: daily, average, and 5-day cumulative; the latter showed the best values of inferential parameters. The information about maximum and minimum temperatures from the NP platform was suitable for estimating ET0 using the HS equation. This data source is a timely alternative, particularly in semi-arid regions where no data from weather stations are available.

In this study, we have first studied the trend in meteorological data from the Harmaleh, Vanai and Farsesh stations in the 50-year period in the Dez catchment area. The meteorological data will be then forecasted using SWAT and Mann-Kendall. Forecasting the results in the Mann-Kendall and SWAT model has been done using the code written in MATLAB software and RCP (4.5, 8.5) scenarios, respectively. Studying the results of the trend in the data of meteorological stations in this catchment area indicated that these two parametric and non-parametric methods have been used to determine trends in meteorological data. The results of the parametric method are positive in all meteorological parameters. Non-parametric method over a period of 50 years shows the presence of trends in the data. The comparison on the forecasting results at maximum temperature suggested that during summer, we will see an increase in temperature compared to the ground state in all three forecasts. The results of the minimum temperature forecast show a decrease in the minimum increase during the winter and the precipitation forecast indicates that at the end of autumn (Nov) precipitation decreased by 20 mm in the Mann-Kendall and 4.5 RCP while RCP8.5 suggests the increase in precipitation compared to the ground state. Studying the runoff forecast results using SWOT show that at the end of winter (Feb) and almost all spring (Mar, Apr) a decrease of about 40%, 15% and 14% will be seen, respectively

This paper examined the multifractal properties of six acknowledged agro-meteorological parameters, such as reference evapotranspiration (ET0), wind speed (U), incoming solar radiation (SR), air temperature (T), air pressure (P), and relative air humidity (RH) of five stations in California, USA. The investigation of multifractality of datasets from stations with differing terrain conditions: Dagget, Bakersfield, Santa Maria, Los Angeles and San Diego using the Multifractal Detrended Fluctuation Analysis showed the existence of a long term persistence and multifractality irrespective of the location. The scaling exponents of SR and ET0 time series are found to be higher for stations with higher altitudes. Subsequently, this study proposed using the novel multifractal cross correlation (MFCCA) method to examine the multiscale-multifractal correlations properties between ET0 and other investigated variables. MFCCA could successfully capture the scale dependent association of different variables and the dynamics in the nature of their associations from seasonal to multi-annual time scale. The multifractal exponents of pressure and relative air humidity are consistently lower than the exponents of ET0, irrespective of station location. This study found that joint scaling exponent was nearly the average of scaling exponents of individual series in different pairs of variables. Additionally, the α-values of joint multifractal spectrum were lower than the α values of both of the individual spectra, validating two universal properties in the mutifractal cross correlation studies for agro-meteorological time series. The temporal evolution of cross-correlation showed similar pattern for all pair-wise associations involving ET0, except for the RH-ET0 link.

This study aims to assess the spatiotemporal characteristics of meteorological droughts in Bangladesh during 1981–2015 using the Effective Drought Index (EDI). Monthly precipitation data for 36 years (1980-2015) obtained from 27 metrological stations, were used in this study. The EDI performance was evaluated for four sub-regions over the country through comparisons with historical drought records identified at the regional scale. Analysis at a regional level showed that EDI could reasonably detect the drought years/events during the study period. The study also revealed that the overall drought severity had increased during the past 35 y; the most significant increasing trend was observed in the central region. The characteristics (severity and duration) of drought were also analysed in terms of spatiotemporal evolution of the frequency of drought events. It was found that the western and central regions of the country are comparatively more vulnerable to drought. Moreover, the southwestern region is more prone to extreme drought, whereas the central region is more prone to severe droughts. In addition, the central region was more prone to extra-long-term droughts, while the coastal areas in the southwestern as well as in the central and north-western region were more prone to long-term droughts. The frequency of droughts in all categories significantly increased during the last quinquennial period (2011 to 2015). The seasonal analysis showed that the north-western areas were prone to extreme droughts during the Kharif (wet) and Rabi (dry) seasons. The central and northern regions were affected by recurring severe droughts in all cropping seasons. Further, the most significant increasing trend of the drought-affected area was observed within the central region, especially during the pre-monsoon (March-May) season. The results of this study can aid policymakers in the development of drought mitigation strategies in the future.

Background: Long-range transport of dust aerosol has intense impacts on the atmospheric environment over wide areas. Methods: The annual and seasonal changes in meteorological parameters associated with the occurrence of dust storms were studied. The features of an intense dust storm and its transport characteristics were studied during June 7th to June 9th 2010 in Ahvaz city. Temporal and spatial distribution of Middle Eastern dust storm event was analyzed by models of HYSPLIT and WRF/Chem, and in- situ observations. Results: A disagreement between the occurrences of dust storms, temperature, relative humidity and rainfall, show the major source of dust storms over Ahvaz city are neighboring countries. Using HYSPLIT results, the dust particles are mainly transported from north western region of Iraq and eastern Syria to downward areas including Ahvaz city. The arrived Dust aerosols mixed with local anthropogenic emissions, led to the highest PM10 concentration of 4200 ppm. The model results were found to well reproduce temporal and spatial distribution of mineral dust concentrations according to in-situ measurements. Conclusion: The performance of WRF/Chem was acceptable for simulation of temporal and spatial distributions of dust storm events. Therefore, it can be taken as a reference in daily air quality forecasting.

Accurate and robust methods are needed to monitor crop growth and predict grain yield and quality in breeding programs under variable agrometeorological conditions. Field experiments were conducted during two successive cropping seasons (2021, 2022) at four trial locations (Estonia, Latvia, Lithuania, Norway). The focus was on assessment of grain yield (GY), grain protein content (GPC), and UAV-derived NDVI measured at different plant growth stages. Performance and stability of 16 selected spring wheat genotypes were assessed under two N application rates (75, 150 kg N ha−1) and over contrasted agrometeorological conditions were assessed, and the quantitative relationships between agronomic traits and UAV-derived variables were figured out. None of the traits were subject to a significant (p&lt;0.05) genotype by nitrogen interaction. High-yielding and high-protein genotypes were detected with high WAASB stability, specifically under high and low N rates. This study highlights the significant effect of NDVI analysis on growth stages GS55 and GS75 as key linear predictors, especially in the context of spring wheat GY. However, the effect of these indices depends on the specific growing conditions in the respective locations, thus limiting their universal utility.

Rice production in the Tonle Sap basin is one of the main drivers for economic and social development in Cambodia. The Tonle Sap basin has experienced many different forms of disasters while more attention has been drawn to drought disaster. The objective of this study is to assess the impacts of drought on agriculture and food security through a case study of the Baribo basin, a sub-basin of Tonle Sap basin, Cambodia. Ground observations and satellite-based products were used for drought assessment from 1985 to 2008 which was the period with relatively good data quality. The Standardized Precipitation Index (SPI) and Standard Vegetation Index (SVI) were selected for meteorological and agricultural droughts assessment, correspondingly. Both SPI and SVI consistently suggested that drought is a major natural hazard causing food insecurity in the target basin. The highest drought intensity (DI) and severity (DS) occurred between 1993-1994 and the longest drought duration (DD) occurred between 2002 and 2006. The most severe damage to rice production was in 2004, affecting about 46% of the total cultivated area. The analysis showed that drought duration had a strong relationship with the affected area growing rice as well as food insecurity in the Tonle Sap basin.

This study aimed at systematically exploring seasonalities of a 16 years series of bacterial identifications in hospitalized patients, considering the infectious site and the community-acquired or hospital-associated origin. Deduplicated bacterial identifications from February 2004 to February 2020 were extracted from the data warehouse of the Institut Hospitalo-Universitaire Mediterranée Infection surveillance system, along with their epidemiological characteristics. Each species’ series was processed using a scientific workflow based on TBATS time series model. Possible co-seasonalities were researched using seasonal peak clustering and series cross-correlations. From 575 bacterial species, only 86 had a sufficient number of cases for the study purpose and only the 15 most frequent species were described in detail. About 60% of species exhibited a seasonality for at least one series, and winter is the season with the most of seasonal peaks. Co-occurrence studies confirm the presence of seasonal complexes associating several species in a same infection location. Possible weather drivers were found for about a third of seasonal series. Seasonality is a frequent characteristic of bacterial infections. Our results showed significant associations of periodicity between pathogens, origin of infection and type of sampling, as well as significant associations of some species with one or more weather drivers.

The core of eLoran ground-based timing navigation system is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on Back-Propagation neural network (BPNN) for complex meteorological environment, which realizes the function of directly mapping propaga-tion delay fluctuation through meteorological factors. Firstly, the theoretical influence of meteoro-logical factors on each component of propagation delay is analyzed based on calculation parame-ters. Then, through the correlation analysis of the measured data, the complex relationship be-tween the seven main meteorological factors and the propagation delay, as well as their regional differences are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with the existing linear model and simple neural network model. eLoran; meteorological factor; propagation delay prediction model; Back-Propagation neural network;

Psychiatric patients are particularly vulnerable to strong weather stimuli, such as foehn, a hot wind that occurs in the alps. However, there is a dearth of research regarding its impact on mental health. This study investigated the impact of foehn wind among patients of a psychiatric hospital located in a foehn area in the Swiss Alps. Analysis was based on anonymized datasets obtained from routine records on admission and discharge, including the Brief Symptom Checklist (BSCL) questionnaire, as well as sociodemographic parameters (age, sex, and diagnosis). Between 2013 and 2020 a total of 10,456 admission days and 10,575 discharge days were recorded. All meteorological data were extracted from the database of the Federal Office of Meteorology and Climatology of Switzerland. We estimated the effect of foehn on the BSCL items using a distributed lag model. Significant differences were found between foehn and non-foehn admissions in obsession-compulsion, Interpersonal Sensitivity, depression, Anxiety, Phobic Anxiety, Paranoid Ideation, and General Severity Index (GSI) (p &lt;.05). Our findings suggest that foehn wind events may negatively affect specific mental health parameters in patients. More research is needed to fully understand the impact of foehn’s events on mental health.

The research presents the inter-comparison of atmospheric variables measured by 9 automatic meteorological stations. This set of data was compared with the measurements of other meteorological stations in order to standardize the values that must be adjusted when taken to different areas. The data of a set of a total of 9 GMX500, which measures conventional meteorological variables, and 10 WS100 sensors, which measures precipitation parameters. The automatic stations were set up at the Huancayo Observatory (Geophysical Institute of Peru) for a period of 5 months. The data set of GMX500 were evaluated comparing with the average of the 9 sensors and the WS100 was compared with a optical disdrometer Parsivel2. The temperature, pressure, relative humidity, wind speed, rainfall rate, and drop size distribution was evaluated. A pair of GMX500 sensors presented high data dispersion, it was found found that the errors came from a bad configuration; once this problem was solved, good agreement was archived, with low RMSE and high correlation. I was found that the WS100 sensors overestimate the precipitation with a percent bias close to 100% and the differences increase with the greater intensity of rain. The DSD retrieved by WS100 have unrealistic behavior with higher concentrations in diameters of 1 mm and 5 mm, in addition to a flattened curve.

There is crescent demand for knowledge improvement of the integrated water vapor (IWV) distribution in regions affected by heat islands that are associated with extreme rainfall events such as in the metropolitan area of Rio de Janeiro (MARJ). This work assessed the suitability and distribution of IWV in the MARJ using products from the Global Navigation Satellite Systems (GNSS), MODerate Resolution Imaging Spectroradiometer (MODIS), and radiosonde. GNSS data were collected by the tracking station named RDJN, from the cooperation of the International GNSS Monitoring and Assessment System (iGMAS) and the National Observatory of Brazil (Observatório Nacional - ON), and the tracking stations ONRJ, RIOD, and RJCG belonging to the Brazilian Network for Continuous Monitoring (RBMC) in the period of January 2015–August 2018. High variability of the near surface air temperature (T) and relative humidity (RH) were observed among eight meteorological sites considered. The mean T differences between sites, up to 4.4 °C, led to mean differences as high as 3.1 K for weighted mean temperature (Tm) and hence 0.83 mm for IWV differences. The performance of the MODIS MOD07 and MYD07 products provided a reasonably good representation of the mean spatial distribution of IWV, especially during the daylight passages of the satellites TERRA and AQUA. Local grid points of MODIS IWV estimates had relatively good agreement with the GNSS-derived IWV, with mean differences from -2.4–1.1 mm considering only daytime passages of the satellites TERRA and AQUA. During nighttime, MODIS underestimated IWV (from -9–-3 mm) with respect to GNSS, due to attenuation of IR radiation by clouds. A contrasting behavior was found in the radiosonde IWV estimates compared with the estimates from GNSS. There were dry biases of 1.4 mm (3.7% lower than expected) by radiosonde IWV during the daytime considering that all other estimates were unbiased and the differences between IWV GNSS and IWV RADS were consistent. Based on the IWV comparisons between radiosonde and GNSS at nighttime, the atmosphere over the radiosonde site is about 1.2 (2.3) mm wetter than over RIOD (RDJN) station. The long time series of the comparisons between IWVRDJN and IWVRIOD showed that the highest values of IWV occurred from the afternoon to nocturnal hours. Further, the atmosphere over the site RIOD was consistently about 1 mm wetter than over RDJN. These results showed the feasibility of the iGMAS RDJN station data compared with the RBMC, MODIS, and radiosonde data to investigate IWV in a region with occurrence of heat islands, and the peculiar physiographic and meteorological characteristics as in the MARJ. This work recommended the usage of complete meteorological station data collocated near every GNSS receiver aiming improvements of local GNSS IWV estimates and serving as additional support for operational numerical assimilation, weather forecast, and nowcast of extreme rainfall events.

QPEs (Quantitative Precipitation Estimates) obtained from remote sensing or ground-based radars could complement or even be an alternative to rain gauge readings. However, to be used in operational applications, a validation process has to be carried out, usually by comparing their estimates with those of a rain gauges network. In this paper, the accuracy of two QPEs are evaluated for three extreme precipitation events in the last decade in the southeast of the Iberian Peninsula. The first QPE is PERSIANN-CCS, a satellite-based QPE. The second is a meteorological radar with Doppler capabilities that works in the C band. Pixel-to-point comparisons are made between the values offered by the QPEs and those obtained by two networks of rain gauges. The results obtained indicate that both QPEs were well below the rain gauge values, especially in extreme rainfall time slots. There seems to be a weak linear association between the value of the discrepancies and the precipitation value of the QPEs. It does not seem that radar is more accurate than PERSIANN-CCS, despite its larger spatial resolution and its commonly higher effectiveness. The main conclusion is that neither PERSIANN-CCS nor radar, without empirical calibration, are acceptable QPEs for the real-time monitoring of meteorological extremes in the southeast of the Iberian Peninsula.

The COVID-19 pandemic has significantly impacted human health and daily life. Meteorological factors play a crucial role in virus transmission. Understanding these factors is critical for pandemic control, yet debates continue due to variations in research methods and data. This study uses a global dataset and focuses on administrative units smaller than 10,000 km². The relationship between daily COVID-19 incidence rates and meteorological factors—including temperature, relative humidity (RH), ultraviolet radiation (UVR), and diurnal temperature range (DTR)—was analyzed across 173 units in 62 countries. The results revealed significant nonlinear responses and lag effects between incidence rates and the meteorological factors. Key inflection points were observed at 0°C and 24°C for temperature, 63% for RH, 102 KJ/m² for UVR, and 15°C for DTR. Below 0°C, infection risk increased with rising temperatures; from 0°C to 24°C, risk decreased; and above 24°C, risk escalated rapidly. RH below 63% correlated inversely with infection risk, while above 63%, the correlation was positive. Infection risk decreased with UVR below 102 KJ/m² but increased beyond this threshold. These nonlinear responses are primarily driven by the differential effects of these factors on virus survival, transmission, and human immunity. Conditions around 24°C, 63% RH, 102 KJ/m² UVR, and DTR below 15°C appear optimal for human immunity, correlating with lower infection risk. High temperature and low UVR prompted immediate infection responses, while low temperatures, high UVR, and larger DTR showed delayed effects. These findings provide new, more reliable evidence for understanding the mechanisms driving COVID-19 pandemic dynamics and for preventing the future normalization of the epidemic.

In this study, based on frost formation data provided by Harbin Meteorological Bureau, combined with geographical factors, temperature and population density, linear fitting, Mann-Kendall mutation test, pettitt method and sliding T test were used to analyze the spatio-temporal variation characteristics of frost date in Harbin Municipality and the effects of geographical factors, temperature and population density on frost regularity. The results showed that (1) The earliest year of FSD appeared was 1966 and 1967, on the 255th, i.e., August 18th, and the latest was 283 days, i.e., October 10th, 2006. The earliest year in which the FED appeared was April 24th of 2015, the 114th day of that year, and the latest was April 21st of 1974, the 141st day of that year. The year with the most frost was 2012, with 161 days, and the year with the shortest was 1966, with 123 days. (2) Throughout the study period, FSD increased by 7.8 days at a rate of -1.27d/10a, FED increased by 10.9 days at a rate of 1.77d/10a, and FFS increased by 18.9 days at a rate of 3.05d/10a. The tendency rate of FSD and FFS at each site in Harbin showed an increasing trend. For FED, the tendency rate of some sites showed an increasing trend. In general, FSD showed a delayed trend, FED showed an advanced trend, and FFS showed a prolonged trend. Using the method of unary linear regression, FSD of each site showed an increasing trend, FFS showed an increasing trend, and FED showed a decreasing trend in general. (3) Throughout the study period, FSD was mutated in 2000, and the arrival time of it in the study area averaged the 265th, i.e., September 22nd, and after that, the arrival time of it changed to the 272nd, i.e., September 29th of that year. FED was mutated in 2006, and the arrival time of it in the study area averaged 128th, i.e., April 8th. After the mutation, the arrival time of FED changed to 121st, i.e., April 1st of that year. FFS was mutated in 2004. Before the mutation, with an average arrival time of 137days in the study area .But after that, the time changed to 150 days.(4) FSD and FFS in Harbin Municipality were negatively correlated with latitude and positively correlated with temperature, while FED was positively correlated with latitude and negatively correlated with temperature. The FSD, FED and FFS in the central part of Harbin Municipality were the earliest, the latest and the longest, respectively, so the Pearson correlation coefficient method and the influence of longitude could not be reflected in the multiple regression. Several factors were judged by grey correlation analysis method. The longitude is significantly related to FSD, FED and FFS.

The Baltic Sea coast is one of the geographical areas of Europe most threatened by the current climate change. This issue concerns erosional processes, groundwater flows affecting sea-shore settings. Long term hydrological investigations within the western sector of the Łeba Barrier, northern Poland, based on a network of monitoring stations, coupled with the meteorology records of the past 12 years, revealed significant fluctuations in ground water fluxes from the Baltic Sea on the adjoining mainland. Groundwater resources are negatively affected by sea-level drops in the coastal areas along the southern Baltic coast. The low sea levels strengthen the effects of low precipitation and high temperatures, causing altogether the occurrence of hydrogeological drought in the coastal sandbar areas. Drought periods are characterized by lowering of water-tables in the Slowinski National Park. The obtained results have direct relevance to a long-term assessment of water resources, environmental protection of the climatically most susceptible ecosystems, as well as the tourism management of this popular visitors’ destination.

Multisource meteorological re-analyses are the most reliable forcing data for driving hydrological models to simulate streamflow. We aimed to assess the different hydrological responses through hydrological modeling in the upper Lancang-Mekong River Basin (LMRB) using the two gridded meteorological datasets, climate forecast system reanalysis (CFSR) and the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS). We selected the Pearson’s correlation coefficient (R), percent bias (PBIAS), and root mean square error (RMSE) indices to compare the six meteorological variables of the two datasets. The spatial distributions of the statistical indicators in the CFSR and CMADS, namely, the R, PBIAS, and RMSE values, were different. Furthermore, the soil and water assessment tool plus (SWAT+) model was used to do hydrological modeling based on CFSR and CMADS meteorological re-analyses in the upper LMRB. Different meteorological datasets resulted in significant differences of hydrological responses, which reflected by different sensitive parameters and their optimal value. These different calibrated optimal values of sensitive parameters further lead to the different simulated water balance components between CFSR- and CMADS-based SWAT+ model. These findings can help in a better understanding of the strengths and weaknesses of different meteorological re-analysis datasets and the roles on the hydrological modeling.

A COVID-19 disease threatens the population and the economies of the countries significantly. Till now, this pandemic has affected 215 counties or territories. Unavailability of vaccine is the primary concern for the society. To avoid the spread of this disease, social isolation must be preserved and the inter and intra-population movement must be minimized. To reduce the possibility of transmission, the categorization of regions based on susceptibility to COVID-19 infection is a must.Due to the unavailability of a large amount of paper collection for this novel COVID-19 diseases, we used current literature available on a COVID-19 susceptibility of the diabetic patient, human reproductivity, hemodialysis patient’s, pregnant women and meteorological factors and geographical location. Countries in the cold region are more susceptible to the risk of COVID-19 transmission. There was no evidence of the spread of this disease from non-respiratory bodies. Diabetic patients and pregnant women were found to be more susceptible to COVID-19 infection. Anosmia was observed in the majority of the COVID-19 infected cases in European countries. No evidence indicates COVID-19's impact on the human reproductive system explicitly. No cases of vertical transmission of this disease have been observed until now. All the studies available till now is the small scale study. Correlation with something always does not mean causation. There are certain factor like pollution level, temperature Diurnal temperature range, geographical factor, humidity, pollution level, wind speed, population density, medical healthcare facilities social and political factor plays a critical role in transmitting the SARS-COV-2 virus. Besides the adverse effects, it has taught us to shed our selfish goals and to promote the welfare of all.

COVID-19, caused by SARS-CoV-2, is responsible for widespread mortality and economic loss across almost 212 countries. The distribution of disease prevalence is, however, uneven and likely dependent on the range of human behavior and/or the pathogen and environmental variables. Here in this research we examined the correlation between daily and total COVID-19 case reports from Bangladesh and meteorological variables (average temperature, average humidity, and average wind speed) against one another using Spearman rank and Kendall correlation tests to highlight significant meteorological variables in relation to COVID-19. The tests revealed that both temperature and humidity had a non-significant correlation with COVID-19 transmission amid epicenter (EC) and non-epicenter (NEC) areas. However, a weak (KCC: 0.168, p < 0.05; SCC: 0.271, p < 0.05) correlation was found for temperature in ECs. Wind speed showed moderate to strong correlation in ECs with a very high statistical significance to both daily new cases [(KCC: 0.494, p < 0.01); (SCC: 0.689, p < 0.01)] and total cases [(KCC: 0.426, p < 0.01); (SCC: 0.617, p < 0.01)]. The COVID-19 transmission was found to have a weak (KCC: 0.354, p < 0.01) to moderate (SCC: 0.465, p < 0.01) correlation with daily new cases; however, a moderate (KCC: 0.497, p < 0.01) to strong (SCC: 0.712, p < 0.01) correlation with total cases was found in NECs. The results suggest that temperature and humidity have little influence on COVID-19 transmission, but wind speed may have some influence. Countries should prioritize social engineering to modify human behavior in order to combat the spread of COVID-19 and future pandemics.

The growth of photosynthetic organisms requires specific ranges of temperature and photosynthetically active radiation that are technically difficult to achieve and control, especially in outdoors cultures. In such cases, a typical meteorological sequence can be a useful tool to estimate the growth of photosynthetic organisms. This work proposes a new methodology based on long-term meteorological sequences to simulate the growth of photosynthetic organisms. A case of study that addresses the simulation of the growth of microalgae (Chlorella vulgaris) in Riosequillo, north of the Madrid region (Spain), is shown for the four seasons of the year, and these estimates are then compared with the observed results of an experimental microalgae culture in domestic wastewater. The results also show great agreement with the probability distribution function of the biomass concentration per day, obtaining the best results for typical meteorological sequences of summer and spring. The methodology seems to confirm the representativeness of typical meteorological sequences and also allows the identification of the most likely production scenarios for the feasibility analysis of projects, which has potential application in decision-making processes.

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported an extremely high number of positive cases and deaths, while some reported too few COVID-19 related cases and mortality. In this paper, the factors that could affect the transmission of COVID-19 and its risk level in different counties have been determined and analyzed. Using Pearson Correlation, K-means clustering, and several classification models, the most critical ones were determined. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, percentage of rural areas, and percent of uninsured people in each county were the most significant and effective attributes.

Solar radiation is an element and a meteorological factor that is present in several processes, such as evapotranspiration, photosynthesis, and energy generation, among others. However, in some regions, there is a limitation in surface data measurements. In this study, 87 empirical models were evaluated to estimate global radiation (Hg) in the Brazilian Amazon Biome; these models were divided into five groups according to the input variables, such as insolation (group I), air temperature (group II), relative humidity (group III), astronomical variables (group IV), and hybrid models (group V) as input variables. The estimates were evaluated by their significance (t-test) and then according to the statistical metrics of the models' performance (R2, MBE, RMSE, and d). The group V model [Hg/Ho = a + b lnΔT + c (S/So)d)] presented the best statistical performance in all evaluated indicators, followed by the group I model [Hg/Ho = a + b (S/So)c ] and then the group II model [Hg/Ho = a + bΔT + c ΔT0.25 + d ΔT0.5 + e (Tmed/Ho)]. The group III models presented low statistical performance, and the group IV models were not significant (NS) in all evaluated meteorological stations. In general, it was found that the simple or hybrid models based on insolation and air temperature were efficient in estimating Hg in the Brazilian Amazon Biome.

The objective of this paper was to verify the applicability of statistical learning (SL) compared to human reasoning with respect to the Universal Thermal Climate Index (UTCI), a complex tool for the assessment of outdoor thermal stress. UTCI is an equivalent temperature index based on the 48-dimensional output of an advanced model of human thermoregulation formed by 12 variables at four consecutive 30-minute intervals, which were calculated for 105642 thermal conditions from extreme cold to extreme heat. Comparing the performance of SL algorithms to the results accomplished by an international endeavor involving more than 40 experts from 23 countries, we found that random forests and k-nearest neighbors closely predicted UTCI values, but that clustering applied after dimension reduction algorithms (principal component analysis and t-distributed stochastic neighbor embedding) were inadequate for risk assessment in relation to the UTCI stress categories. This indicates a potential supportive role for SL, as it will not (yet) fully replace the bio-meteorological expert knowledge.

The assessment and prediction of drought risk under future climate change and land use land cover (LULC) scenarios is critically important for drought prevention and mitigation, as it enables a clearer understanding of potential shifts in drought patterns. The primary aim of this study is to evaluate sub-seasonal and seasonal meteorological and hydrological drought hazards across the Yellow River Basin (YRB) under projected future climate conditions and LULC patterns. The BCC-CSM1-1 climate model projections from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset are utilized to represent future climate for 2025-2060 under RCP 4.5 and 8.5 scenarios. The CA-Markov model is employed to predict future LULC distributions. Meteorological and hydrological drought risks across different YRB zones are evaluated through a copula-based risk assessment approach, based on the joint probability distribution of drought duration and severity. The results indicate that sub-seasonal meteorological and hydrological droughts will likely be the primary concern moving forward. Specifically, the upper YRB (zones A, B, C) exhibits greater vulnerability to sub-seasonal meteorological drought, while the Loess Plateau (zones C, E) shows higher susceptibility to sub-seasonal hydrological drought. Moreover, zone F in the downstream region may experience increased seasonal hydrological drought risk due to projected urban expansion in the middle and lower portions of the YRB.

A distinction is made between data rescue (i.e., copying, digitizing and archiving) and data recovery that implies deciphering, interpreting and transforming early instrumental readings and their metadata to obtain high-quality datasets in modern units. This requires a multidisciplinary approach that includes: palaeography and knowledge of Latin and other languages to read the handwritten logs and additional documents; history of science to interpret the original text, data e metadata within the cultural frame of the 17th, 18th and early 19th century; physics and technology to recognize bias of early instruments or calibrations, or to correct for observational bias; astronomy to calculate and transform the original time in canonical hours that started from twilight. The liquid-in-glass thermometer was invented in 1641 and the earliest temperature records started in 1654. Since then, different types of thermometers were invented, based on the thermal expansion of air or selected thermometric liquids with deviation from linearity. Reference points, thermometric scales, calibration methodologies were not comparable, and not always adequately described. Thermometers had various locations and exposures, e.g., indoor, outdoor, on windows, gardens or roofs, facing different directions. Readings were made only one or a few times a day, not necessarily respecting a precise time schedule: this bias is analysed for the most popular combinations of reading times. The time was based on sundials and local Sun, but the hours were counted starting from twilight. In 1789-90 Italy changed system and all cities counted hours from their lower culmination (i.e., local midnight), so that every city had its local time; in 1866, all the Italian cities followed the local time of Rome; in 1893, the whole Italy adopted the present-day system, based on the Coordinated Universal Time and the time zones. In 1873, when the International Meteorological Committee (IMO) was founded, later transformed in World Meteorological Organization (WMO), a standardization of instruments and observational protocols was established, and all data became fully comparable. In the early instrumental period, from 1654 to 1873, the comparison, correction and homogenization of records is quite difficult, mainly because of the scarcity or even absence of metadata. This paper deals about this confused situation, discussing the main problems, but also the methodologies to recognize missing metadata, distinguish indoor from outdoor readings; correct and transform early datasets in unknown or arbitrary units into modern units; finally, in which cases it is possible to reach the quality level required by WMO. The focus is to explain the methodology needed to recover early instrumental records, i.e., the operations that should be performed to interpret, correct, and transform the original raw data into a high-quality dataset of temperature, usable for climate studies.

It is common knowledge that estimating the height component of GNSS stations in general is much more problematic than estimating the horizontal position. Many different effects, such as tectonic signals, non-tectonic signals, atmospheric delay, noise, etc., are known to affect the height component of GNSS stations more than the horizontal component. However, the height component of GNSS stations is still poorly estimated. In this study, the height time series of 37 continuous GNSS stations covering the 2014–2019 date range is used from the Turkish National Permanent GNSS Network-Active (TUSAGA-Active). Since it is easier to interpret the effects of the height component due to its topographic features and seasonal changes being more effective than in the rest of the country, stations were chosen in the Eastern Anatolia region of Turkey. The daily coordinates of the GNSS stations were obtained as a result of the GAMIT/GLOBK software solution. By applying time series analysis to the daily coordinate values of the stations, statistically significant trends, periodic and stochastic components of the stations were determined. As a result of the analysis, the vertical velocities of the GNSS stations and the standard deviations of the vertical velocities were determined. Furthermore, when the height components of continuous GNSS stations were examined, it was seen that there were seasonal effects, and it was investigated whether the height components were related to meteorological parameters. For that, simple linear regression analysis was performed to determine how dependent the height components of the continuous GNSS stations were on meteorological parameters. As a result of the analysis, the height components of the continuous GNSS stations are dependent on meteorological parameters such as temperature, pressure, relative humidity, wind speed, and precipitation. In addition, height component time series analysis of continuous GNSS stations was performed by using Autoregressive Moving Average (ARMA) models from linear time series methods. As a result of the study, the performance of the ARMA modeling results again indicated the dependence of the height component of the continuous GNSS stations on the meteorological parameters.

The impacts of climate change on precipitation and drought characteristics over Bangladesh were examined by using the daily precipitation outputs from 29 bias-corrected general circulation models (GCMs) under the representative concentration pathway (RCP) 4.5 and 8.5 scenarios. A precipitation-based drought estimator, namely, the Effective Drought Index (EDI), was applied to quantify the characteristics of drought events in terms of the severity and duration. The changes in drought characteristics were assessed for the beginning (2010–2039), middle (2040–2069), and end of this century (2070–2099) relative to the 1976–2005 baseline. The GCMs were limited in regard to forecasting the occurrence of future extreme droughts. Overall, the findings showed that the annual precipitation will increase in the 21st century over Bangladesh; the increasing rate was comparatively higher under the RCP8.5 scenario. The highest increase of rainfall is expected to happen over the drought-prone northern region. The general trends of drought frequency, duration, and intensity are likely to decrease in the 21st century over Bangladesh under both RCP scenarios, except for the maximum drought intensity during the beginning of the century, which is projected to increase over the country. The extreme and medium-term drought events did not show any significant changes in the future under both scenarios except for the medium-term droughts, which decreased by 55% compared to the base period during the 2070s under RCP8.5. However, extreme drought days will likely increase in most of the cropping seasons for the different future periods under both scenarios. The spatial distribution of changes in drought characteristics indicates that the drought-vulnerable areas are expected to shift from the northwestern region to the central and the southern region in the future under both scenarios due to the effects of climate change.

The rescue, digitisation, quality control, preservation and utilization of long and high quality meteorological and climate records, particularly related to historical marine data, are crucial for advancing our understanding of the Earth's climate system. In combination with land and air measurements, the historical marine records serve as foundational pillars in linking present and past weather and climate information, offering essential insights into natural climate variability, extreme events in marine areas, baseline data for assessing current changes, and inputs for en-hancing predictive climate models and reanalyses. This paper provides an overview of marine data rescue activities covering the past centuries, presents and highlights several ongoing projects across the world and how the data are used in an integrative and international framework. Current and future continuous efforts in data rescue, digitisation, quality control and the de-velopment of temporally high resolution meteorological and climatological observations from the Sea, will greatly help to further complete our understanding and knowledge of the Earth system including extremes, as well as improving the quality of reanalysis.

In the southeastern coast of Brazil, the bays of Ilha Grande and Sepetiba are linked by the Ilha Grande Channel, where remarkably vigorous currents have been consistently observed. The sheer intensity of these currents defies a simple explanation through tidal forcing alone, prompting researchers to delve into potential influences such as basin resonance and the baroclinic effect. This study aims to elucidate the role of remote meteorological effects within this complex hydrodynamic system. A numerical approach with a coastal model nested within an ocean model was employed, enabling an in-depth examination of the intricate interplay between meteorological and tidal forcings. The study revealed a significant finding: the lag in signal propagation plays a pivotal role in determining how these signals impact the dynamics of the bays. The astronomical signal exhibits a minimal lag along the coast (1 minute), and leads to water level differences between the sea and the coastline, resulting in the generation of tidal currents at the bay entrances. On the other hand, the remote meteorological signal, with a stronger signal lag along the coast (4.92 hours), leads to the creation of water level difference between the bay’s entrances, inducing significant fluxes along the narrow Ilha Grande Channel.

Intelligent ship technology is currently an international research hotspot, and model predictive control is widely used in the path-tracking control of intelligent vehicles. To construct an intelligent ship anti-disturbance path-tracking control method, firstly, an environmental disturbance model was constructed with the actual meteorological data of the target sea area. Secondly, the Fossen ship equation of motion is linearized and discretized as the ship motion model. Thirdly, the expression of the prediction equation is derived from the ship motion model. Fourthly, the cost function is constructed by using the polar diameter and polar angle values of the ship. Fifth, the power function in the cost function is replaced with an exponential function to obtain an improved cost function. Sixthly, according to the Lyapunov theory and the MPC terminal constraint theory, the stability of the improved cost function is verified. Seventh, different test paths are set up, the environmental disturbance model is taken as the external disturbance, the ship motion model, the prediction equation, and the improved cost function are used to design the anti-disturbance path-tracking control algorithm according to the model prediction control idea for simulation experiments. Finally, different MATLAB simulation results show that the improved cost function can resist disturbance of the external wave, current, and wind, and effectively track the target path. Therefore, this study provides a reference for improving the navigation safety of ship path-tracking.

Oil spills may have devastating effects on marine ecosystems, public health, the economy, and coastal communities. Therefore, scientific literature contains various up-to-date, advanced oil spill predictive models, capable to simulate the trajectory and evolution of an oil slick generated by the accidental release from ships, hydrocarbons production, or other activities. To predict in near real time oil spill transport and fate with increased reliability these models are usually coupled operationally to synoptic meteorological, hydrodynamic, and wave models. The present study reviews the available different met-ocean forcings that have been used in oil spill modeling, simulating hypothetical or real oil spill scenarios, worldwide. Nine state-of-the-art oil spill models are critically examined in terms of the met-ocean data used as forcing inputs in the simulation of twenty-four case studies. Results illustrate that most oil spill models are coupled to different resolution, forecasting meteorological and hydrodynamic models, posing, however, limited consideration in the forecasted wave field (expressed as the significant wave height, the wave period and the Stokes drift) that may affect oil transport, especially at the coastal areas. Moreover, the majority of oil spill models lacks any linkage to the background biogeochemical conditions, hence, limited consideration is given in processes like oil biodegradation, photo-oxidation and sedimentation. Future advancements in oil spill modeling should be directed towards the full operational coupling with high-resolution atmospheric, hydrodynamic, wave, and biogeochemical models, improving our understanding in the relative impact of each physical and oil weathering process.

Evaporation is one of the main processes in the hydrological cycle, and it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, the evaporation is a complex and nonlinear phenomenon; therefore, the data-based methods can be used to have precise estimations of it. In this regard, in the present study, Gaussian Process Regression (GPR), Nearest-Neighbor (IBK), Random Forest (RF) and Support Vector Regression (SVR) were used to estimate the pan evaporation (PE) in the meteorological stations of Golestan Province, Iran. For this purpose, meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W) and sunny hours (S) collected from the Gonbad-e Kavus, Gorgan and Bandar Torkman stations from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The outcome indicates that the optimum state of Gonbad-e Kavus, Gorgan and Bandar Torkman stations, Gaussian Process Regression (GPR) with the error values of 1.521, 1.244, and 1.254, the Nearest-Neighbor (IBK) with error values of 1.991, 1.775, and 1.577, Random Forest (RF) with error values of 1.614, 1.337, and 1.316, and Support Vector Regression (SVR) with error values of 1.55, 1.262, and 1.275, respectively, have more appropriate performances in estimating PE. It found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W, and S had the most accurate performances and proposed for precise estimation of PE. Due to the high rate of evaporation in Iran and the lack of measurement instruments, the findings of the current study indicated that the PE values might be estimated with few easily measured meteorological parameters accurately.

Renewable energy especially insights into solar energy are crucial in the energy business cycle. In this paper, an impact study of meteorological variables on solar energy production is conducted. The paper discusses the pipeline of data analysis specific to the case study and exploits open-source machine-learning algorithms to build predictive models. RandomForestRegressor and XGBoosting regressors are used to build the predictive model. The application code is integrated into an analytic software service (Power BI) and tested on Solar energy Open Data of Saudi Arabia. The solar energy production is found to be high between 800-1000 barometric pressure locations. Global Horizontal Irradiation is found to increase when the atmospheric temperature is more than 34∘ C. The impact of relative humidity is that the produced energy is high at lower humidity points. The wind speed effect is that the produced energy is significant between 2 to 4 m/s and reaches its maximum at 2.4 m/s. The predictive models produce nearly real-time predictions with Random Forest with R²: 0.909. The paper provides Global Horizontal Irradiation estimation equations using meteorological coefficients generated by the trained regressor. The paper presents implementation methodologies and records various instances of test results. It highlights the efficacy of machine learning in predictive modeling for renewable energy applications.

The three-axis stabilization geostationary remote sensing satellite features high time resolution and plays a significant role in weather forecasting and environmental monitoring. With the future development of satellite technology, we propose a rapid geolocation algorithm for three-axis stabilization geostationary remote sensing satellites from the perspective of software algorithms. The method assesses conventional remote sensing data geolocation methods and the coordination group for meteorological satellites (CGMS, Coordination Group for Meteorological Satellites) nominal grid publishing format. First, the initial incident viewing vector of each detector on the sensor was constructed. Then, by reflecting the satellite platform's east-west and north-south mirrors, the outgoing vector of the payload coordinate system was obtained. This vector was converted to the satellite body coordinate system and, subsequently, to the satellite orbit coordinate system. The conversion of the earth-fixed coordinate system and the mirror rotation angle under ideal CGMS grid conditions were obtained, and the mirror rotation angle was converted to the corresponding CGMS nominal grid position to complete data positioning. The algorithm omits the complex process of calculating the intersection point between the viewing vector and the geodetic ellipsoid sphere from the earth-fixed coordinate system, resulting in an 83.3% improvement in computational efficiency without compromising positioning accuracy compared with conventional geolocation methods. This algorithm greatly improves the processing efficiency of geostationary meteorological satellite positioning, but its limitation is that the data processing results must be released in the CGMS nominal grid format.

Currently, many researchers have an interest in the investigation of the electric field in the fair-weather electric environment, along with its diurnal and seasonal variations across all regions of the world. However, a similar study in the southern part of Siberia has not yet been carried out. In this regard, the study aims to estimate the mean values of the electric field and their variations in the mountain and steppe landscapes using measurement data from the Khakass-Tyva expedition in 2022. The maximum values of positive ion density were noted at the site in the Iyussko-Shirinsky steppe between Belyo and Tus salt lakes in the Khakass-Minusinsk ba-sin. The maximum values of negative ion density were observed at site in the Shol tract in the center part of the Tyva depression. The potential gradient tends to increase with altitude and reaches a maximum in the highlands. The maximum values of the potential gradient were noted in the highlands plateau near the Mongun-Taiga mountain massif and Khindiktig-Khol lake. The diurnal cycles of potential gradient at different observation sites were divided into two groups: 1) a diurnal cycle in the form of a double wave; 2) a daily cycle with a more complex course due to the strong influence of local factors.

COVID-19 has created anxiety not only in individuals but also in health organizations, and countries worldwide. Not a single industry is left un-influenced and loss is being estimated in billions of dollars. The widespread of this pandemic disease has challenged researchers all over the world. Some of the researchers are working to invent its cure while, others are applying computing technologies to stop its spread, by analyzing and identifying patterns for prediction and forecasting. This is by no doubt the hottest area of research for the last 100 years. This survey has targeted the research published in computing sub-domains to combat the pandemic. The survey has clustered the scientific efforts into logical groups: surveillance, metrological effects, social media analytics, image processing and business and economy, analysis and modeling. It will serve as a leading source for the followings: researchers who want to identify what has been achieved in different computing sub-domains, those who need fresh authenticated datasets openly accessible for different research contexts and what are future directions in this area of research. The findings of analysis and modeling can be also useful for government agencies who want to set priorities and formulate policies.

Studying hydro-meteorological factors variations and its links to large-scale atmospheric circulation systems can facilitate the understanding of the hydrological processes and sustainable water resources management in the source region of the Yangtze River (SRYR). Currently, researches mostly focused on the temporal and spatial variation characteristics in hydro-meteorological factors; however, researches on the hydro-meteorological variations and its links to large-scale atmospheric circulation systems in the SRYR are scarce. Based on long-term hydro-meteorological and reanalysis data, this research investigated multiscale variations of hydro-meteorological factors and its links to large-scale atmospheric circulation characteristic indices during 1957~2012 in the SRYR. The results showed that the amounts of streamflows and precipitation in the SRYR declined during the 1990s. Since the 2000s, the amounts of streamflows and precipitation had increased significantly climate in the SRYR. The change trends of precipitation and streamflows in the SRYR are synergetic at annual and seasonal scales, and have three significant periods, namely 3~5 years, 15–20 years and 30–40 years. The South Asia monsoon (SAM) plays a relatively more important role in the hydro-meteorological factors changes in the SRYR. The relative contributions of SAM to streamflows and precipitation changes were 83.6% and 78%, respectively. During the driest (wettest) year, the SAM is relatively weak (strong), and brings less (more) southwest airflow into the SRYR, less (more) precipitation and streamflows will be generated in the SRYR.

Improving air quality and reducing human exposure to unhealthy levels of airborne chemicals are important global missions, particularly in China. Satellite remote sensing offers a powerful tool to examine regional trends in NO2, thus providing a direct measure of key parameters that strongly affect surface air quality. To accurately resolve spatial gradients in NO2 concentration using satellite observations and thus understand local and regional aspects of air quality, a priori input data at sufficiently high spatial and temporal resolution to account for pixel-to-pixel variability in the characteristics of the land and atmosphere are required. In this paper, we adapt the Berkeley High Resolution product (BEHR v3.0A, v3.0B and v3.0C) and meteorological outputs from the Weather Research and Forecasting (WRF) model to describe column NO2 in southern China. The BEHR approach is particularly useful for places with large spatial variabilities and terrain height differences such as China. We retrieved tropospheric NO2 vertical column density (TVCD) within part of southern China, for four seasons of 2015, based upon satellite datasets from Ozone Monitoring Instrument (OMI). Retrieval results are validated by comparing with MAX-DOAS tropospheric column measurements conducted in Guangzhou. BEHR retrieval algorithms are more consistent with MAX-DOAS measurements than OMI-NASA retrieval, opening new windows into research questions that require high spatial resolution, for example retrieving NO2 vertical column and ground pollutant concentration in China and other countries.

Due to the advantages of wide coverage and continuity, remotely sensed data are widely used for large-scale drought monitoring to compensate the deficiency and discontinuity of meteorological data. However, few researches have focused on the capability of various remotely sensed drought indices (RSDIs) for representing the spatio-temporal variations of the meteorological droughts. In this study, five RSDIs, namely Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Modified Temperature Vegetation Dryness Index (MTVDI) and Normalized Vegetation Supply Water Index (NVSWI) were calculated using Moderate Resolution Imaging Spectroradiometer (MODIS) monthly NDVI and LST. The monthly NDVI and LST data were filtered by Savitzky-Golay (S-G) filtering method. Meteorological station-based drought index represented by Standardized Precipitation Evapotranspiration Index (SPEI) was compared with RSDIs. And the dimensionless Skill Score (SS) method was adopted to identify the spatiotemporally optimal RSDIs for presenting the meteorological droughts in the Yellow River basin (YRB) from 2000 to 2015. The results indicated that (1) RSDIs revealed a decreasing trend to the overall YRB consistent with SPEI except for in winter, and different variations of seasonal trends spatially; (2) the optimal RSDIs in spring, summer, autumn and winter were VHI, TCI, MTVDI and VCI, respectively, and the average correlation coefficient between the RSDIs and SPEI was 0.577 (=0.05); (3) different RSDIs have a 0–3 months’ time-lags compared with meteorological drought index.

A three-year survey was conducted to estimate the incidence of grapevine trunk diseases (GTDs) in Greece and identify fungi associated with the disease complex. In total, 310 vineyards in different geographical regions in northern, central, and southern Greece were surveyed, and 533 fungal strains were isolated from diseased vines. Morphological, physiological and molecular (5.8S rRNA gene-ITS sequencing) analyses revealed that isolates belonged to 35 distinct fungal genera, including well-known (e.g. Botryosphaeria sp, Diaporthe spp., Eutypa sp., Diplodia sp., Fomitiporia sp., Phaeoacremonium spp., Phaeomoniella sp.) and lesser-known (e.g. Neosetophoma sp., Seimatosporium sp., Didymosphaeria sp., Kalmusia sp.) grapevine wood inhabitants. GTDs-inducing population structure differed significantly among the discrete geographical zones. Phaeomoniella chlamydospora (26.62%, n=70), Diaporthe spp. (18.25%, n=48) and F. mediterranea (10.27%, n=27) were the most prevalent in Heraklion, whereas D. seriata, Alternaria spp., P. chlamydospora and Fusarium spp. were predominant in Nemea (central Greece). In Amyntaio and Kavala (northern Greece), D. seriata was the most frequently isolated species (&gt;50% frequency). Multi-genes (rDNA-ITS, LSU, tef1-α, tub2, act) sequencing of selected isolates, followed by pathogenicity tests revealed that Neosetophoma italica, Seimatosporium vitis, Didymosphaeria variabile and Kalmusia variispora caused wood infection with the former being the most virulent. To the best of our knowledge, this is the first report of N. italica associated with GTDs worldwide. This is also the first record of K. variispora, S. vitis and D. variabile associated with wood infection of grapevine in Greece. The potential associations of disease indices with vine age, cultivar, GTD-associated population structure and the prevailed meteorological conditions in different viticultural zones in Greece are presented and discussed.

In the context of global changes, Nature-Based Solutions (NBSs) increasingly draw attention as a possible way to reduce disaster risk associated with extreme hydro-meteorological events while providing human well-being and biodiversity benefits at the same time. The PHUSICOS platform is dedicated to gather and analyse relevant NBSs used to reduce disaster risk associated with extreme hydro-meteorological events in mountainous and hilly lands. To design the platform, an in-depth review of 11 existing platforms has been performed. The platform currently references 152 literature NBS cases and is continuously enriched with demonstrator sites through the contribution of NBS community. The platform also proposes a qualitative assessment of the NBSs collected according to 15 criteria related with five ambits: disaster risk reduction, technical and economical feasibility, environment, society and local economy. This paper presents the structure of the platform and a first analysis of its content.

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The early part of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns have been quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology-air pollution relationships have been visualised/interpretated on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs elevated PM10 pollution in the valley. Two local meteorological patterns, the hot-dry-northwesterly wind conditions and the hot-dry-calm conditions, were found most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of eastward migrating continental high pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.

Fourteen Russian potato cultivars (cvs.) grown for three years in field plots of VIR (Northwestern region of the Russian Federation) were evaluated for productivity and a number of biochemical parameters of tubers. The 12 parameters that have been evaluated in tubers annually one month after harvesting and after cold storage (at 2 - 4°C) include the content of dry matter, starch, protein, ascorbic acid, carotenoids and β-carotene, anthocyanins, total sugars and monosaccharides (reducing sugars), phenolic compounds, as well as total acidity, and antioxidant activity. The cultivars differed significantly in productivity and biochemical composition of tubers. Dry matter content ranged from 16.40% in cv. ʻKumachʼ to 27.28% in ʻNayadaʼ, starch content ranged from 8.96 % in cvs. ʻFioletovyjʼ, ʻKumachʼ to 20.08% in ‘Sokur’, the total sugar content reached 2.46% in cv. ʻSiverskij’ whereas ‘Sokur’ has the smallest content of reducing sugars in post-harvest tubers. Protein content ranged from 1.02% to 2.77% with the highest accumulation in cv. ʻSokurʼ. Content of ascorbic acid (vitamin C) ranged from 6.3 mg/100 g FW in ʻKrasa Meŝeryʼ to 26.6 mg/100 g FW in cv. ʻKrasavchikʼ. Carotenoids content ranged from 0.157 mg/100 g FW in cv. ‘Nayada’ to 0.645 mg/100 g FW in cv. ʻGrandʼ. Content of anthocyanins in potato ranged from 1.04 mg/100g FW (not colored tubers of cv. ʻKrasa Meŝeryʼ) to 283.04 mg/100g FW (colored tubers of cv. ʻFioletovyiʼ). The nutritional value of tubers (content of dry matter, sugars, starch and protein) was determined to a greater extent (38-49%) by varietal characteristics, rather than by meteorological conditions in vegetation period. The content of vitamin C, carotenoids, protein and anthocyanins varied depending on the year. The highest content of dry matter, starch and carotenoids was noted in tubers harvested in 2019, marked by low temperatures and precipitation deficit in growing season. The conditions of 2020 with low temperatures and excess precipitation were the most favorable for anthocyanin’s accumulation in potato tubers while not for other biochemical parameters. Of the cultivars, ʻSokurʼ had the highest content of dry matter, protein and starch. Cv. ʻFioletovyiʼ had small starch content and high level of anthocyanins. Cv. ʻPlamyaʼ can be recommended for potato products manufacturing due to dry matter content above 20% and reducing sugars content 0.24%..