Extreme Rainfall Anomalies Based on IMERG Remote Sensing Data in CONUS: A Multi-Decade Case Study via the IPE Web Application

dc.contributor.authorEkpetere, Kenneth Okechukwu
dc.contributor.authorMehta, Amita V.
dc.contributor.authorColl, James Matthew
dc.contributor.authorLiang, Chen
dc.contributor.authorOnochie, Sandra Ogugua
dc.contributor.authorEkpetere, Michael Chinedu
dc.date.accessioned2024-10-28T14:30:19Z
dc.date.available2024-10-28T14:30:19Z
dc.date.issued2024-09-23
dc.description.abstractA 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.
dc.description.sponsorshipThis work was supported by the National Science Foundation under the Kansas NSFEPSCoR, award number OIA-2148878
dc.description.urihttps://www.preprints.org/manuscript/202409.1774/v1
dc.format.extent25 pages
dc.genrejournal articles
dc.genrepreprints
dc.identifierdoi:10.13016/m2aa6i-aatd
dc.identifier.urihttps://doi.org/10.20944/preprints202409.1774.v1
dc.identifier.urihttp://hdl.handle.net/11603/36727
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.relation.ispartofUMBC GESTAR II
dc.relation.ispartofUMBC Faculty Collection
dc.rightsAttribution 4.0 International CC BY 4.0 Deed
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectIMERG
dc.subjectrainfall frequencies
dc.subjectIPE
dc.subjectRainfall anomaly index
dc.subjectrainfall storm
dc.subjectclimate change
dc.subjectrainfall intensity
dc.subjectCONUS
dc.subjectweb application
dc.subjectNOAA
dc.titleExtreme Rainfall Anomalies Based on IMERG Remote Sensing Data in CONUS: A Multi-Decade Case Study via the IPE Web Application
dc.typeText

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