Version 1
: Received: 20 September 2024 / Approved: 23 September 2024 / Online: 23 September 2024 (14:07:46 CEST)
How to cite:
Ekpetere, K. O.; Mehta, A. V.; Coll, J. M.; Liang, C.; Onochie, S. O.; Ekpetere, M. C. Extreme Rainfall Anomalies Based on IMERG Remote Sensing Data in CONUS: A Multi-Decade Case Study via the IPE Web Application. Preprints2024, 2024091774. https://doi.org/10.20944/preprints202409.1774.v1
Ekpetere, K. O.; Mehta, A. V.; Coll, J. M.; Liang, C.; Onochie, S. O.; Ekpetere, M. C. Extreme Rainfall Anomalies Based on IMERG Remote Sensing Data in CONUS: A Multi-Decade Case Study via the IPE Web Application. Preprints 2024, 2024091774. https://doi.org/10.20944/preprints202409.1774.v1
Ekpetere, K. O.; Mehta, A. V.; Coll, J. M.; Liang, C.; Onochie, S. O.; Ekpetere, M. C. Extreme Rainfall Anomalies Based on IMERG Remote Sensing Data in CONUS: A Multi-Decade Case Study via the IPE Web Application. Preprints2024, 2024091774. https://doi.org/10.20944/preprints202409.1774.v1
APA Style
Ekpetere, K. O., Mehta, A. V., Coll, J. M., Liang, C., Onochie, S. O., & Ekpetere, M. C. (2024). Extreme Rainfall Anomalies Based on IMERG Remote Sensing Data in CONUS: A Multi-Decade Case Study via the IPE Web Application. Preprints. https://doi.org/10.20944/preprints202409.1774.v1
Chicago/Turabian Style
Ekpetere, K. O., Sandra Ogugua Onochie and Michael Chinedu Ekpetere. 2024 "Extreme Rainfall Anomalies Based on IMERG Remote Sensing Data in CONUS: A Multi-Decade Case Study via the IPE Web Application" Preprints. https://doi.org/10.20944/preprints202409.1774.v1
Abstract
A web application – IMERG Precipitation Extractor (IPE) was developed that relies on the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) data available at a global coverage. The IPE allows users to query, visualize, and download time series satellite precipitation data for various locations, including points, watersheds, country extents, and digitized areas of interest. It supports different temporal resolutions ranging from 30 minutes to 1 week. Additionally, the IPE facilitates advanced analyses such as storm tracking and anomaly detection, which can be used to monitor climate change through variations in precipitation frequency and intensity. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2,360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region (e.g., Nevada), the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of -22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash-Sutcliffe Efficiency (NSE) of 0.80, and a Kling-Gupta Efficiency (KGE) of 0.52. In the wet region (e.g., Louisiana), the average CC was 0.93, the PRB was 24.82%, the RMSE was 0.96, the MBR was 0.79, the NSE was 0.80, and the KGE was 0.18. Median RAI indices from both IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS, advocating for further global climate change studies.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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