Assessment of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) Precipitation Products in Northwest China

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(8), P. 1364 - 1364

Published: April 11, 2025

This study evaluates the applicability of IMERG satellite precipitation product in Northwest China using data from more than 6000 ground-level meteorological stations during warm season (April–September) 2016 to 2023. The evaluation spans climatological, annual, monthly, and daily time scales with different intensities. can well capture spatial temporal climatology, decreasing southeast China, peaking August. correlation coefficient (CC) between ground-observed is 0.69. However, systematically overestimates at monthly scales, especially areas relatively low climatology. At scale, represent events very well, southeastern part China. light rainfall while underestimating other While performs detecting rain events, its accuracy diminishes for heavier rainfall, highlighting limitations monitoring extreme precipitation. Probability Detection (POD) consistently above 0.9, Torrential Rainfall POD below 0.7. These findings provide insights into effective application forecasting

Language: Английский

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Weakening trends of glacier and snowmelt-induced floods in the Upper Yarkant River Basin, Karakoram during 1961‒2022

Advances in Climate Change Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

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Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco

Atmosphere, Journal Year: 2023, Volume and Issue: 14(8), P. 1239 - 1239

Published: Aug. 1, 2023

Accurate and spatially distributed precipitation data are fundamental to effective water resource management. In Morocco, as in other arid semi-arid regions, exhibits significant spatial temporal variability. Indeed, there is an intra- inter-annual variability the northwest rainier than rest of country. Bouregreg watershed, this irregularity, along with a sparse gauge network, poses major challenge for context, remote sensing could provide viable alternative. This study aims precisely evaluate performance four gridded daily products: three IMERG-V06 datasets (GPM-F, GPM-L, GPM-E) reanalysis product (ERA5). The evaluation conducted using 11 rain stations over 20-year period (2000–2020) on various scales (daily, monthly, seasonal, annual) pixel-to-point approach, employing different classification regression metrics machine learning. According findings, GPM products showed high accuracy low margin error terms bias, RMSE, MAE. However, it was observed that ERA5 outperformed identifying patterns demonstrated stronger correlation. results also performed better during summer months seasonal assessment, relatively lower higher biases rainy months. Furthermore, these excellent capturing intensities, highest light rain. particularly important regions where most falls under low-intensity category. Although estimates global coverage at spatiotemporal resolutions, their currently insufficient would require improvement. To address this, we employed artificial neural network (ANN) model bias correction enhancing raw from GPM-F product. indicated slight increase correlation coefficient reduction biases, Consequently, research supports applicability North Western Morocco.

Language: Английский

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PP-Loss: An imbalanced regression loss based on plotting position for improved precipitation nowcasting

Theoretical and Applied Climatology, Journal Year: 2024, Volume and Issue: 155(7), P. 5909 - 5923

Published: May 2, 2024

Language: Английский

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Continental evaluation of GPM IMERG V07B precipitation on a sub-daily scale

Remote Sensing of Environment, Journal Year: 2025, Volume and Issue: 321, P. 114690 - 114690

Published: March 6, 2025

Language: Английский

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