Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

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

View through CrossRef
This study evaluates the applicability of the IMERG satellite precipitation product in Northwest China using data from more than 6000 ground-level meteorological stations during the warm season (April–September) from 2016 to 2023. The evaluation spans climatological, annual, monthly, and daily time scales with different precipitation intensities. IMERG precipitation can well capture the spatial and temporal precipitation climatology, with precipitation decreasing from southeast to Northwest China, and peaking in August. The correlation coefficient (CC) between IMERG precipitation and ground-observed precipitation is 0.69. However, IMERG precipitation systematically overestimates precipitation at climatological, annual, and monthly scales, especially in areas with relatively low precipitation climatology. At the daily time scale, IMERG precipitation data can represent precipitation events very well, especially in the southeastern part of Northwest China. IMERG precipitation overestimates light rainfall while underestimating precipitation of other intensities. While IMERG precipitation performs well in detecting light rain events, its accuracy diminishes for heavier rainfall, highlighting limitations for monitoring extreme precipitation. The Probability of Detection (POD) for light rainfall events is consistently above 0.9, while for Torrential Rainfall events, the POD is below 0.7. These findings provide insights into the effective application of IMERG data in precipitation monitoring and forecasting in Northwest China.
Title: Assessment of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) Precipitation Products in Northwest China
Description:
This study evaluates the applicability of the IMERG satellite precipitation product in Northwest China using data from more than 6000 ground-level meteorological stations during the warm season (April–September) from 2016 to 2023.
The evaluation spans climatological, annual, monthly, and daily time scales with different precipitation intensities.
IMERG precipitation can well capture the spatial and temporal precipitation climatology, with precipitation decreasing from southeast to Northwest China, and peaking in August.
The correlation coefficient (CC) between IMERG precipitation and ground-observed precipitation is 0.
69.
However, IMERG precipitation systematically overestimates precipitation at climatological, annual, and monthly scales, especially in areas with relatively low precipitation climatology.
At the daily time scale, IMERG precipitation data can represent precipitation events very well, especially in the southeastern part of Northwest China.
IMERG precipitation overestimates light rainfall while underestimating precipitation of other intensities.
While IMERG precipitation performs well in detecting light rain events, its accuracy diminishes for heavier rainfall, highlighting limitations for monitoring extreme precipitation.
The Probability of Detection (POD) for light rainfall events is consistently above 0.
9, while for Torrential Rainfall events, the POD is below 0.
7.
These findings provide insights into the effective application of IMERG data in precipitation monitoring and forecasting in Northwest China.

Related Results

Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China
Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China
Accurate estimation of high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) and Tropical Rainfall Measuring Mission (TRMM) is critical for h...
Comprehensive Evaluation of Global Precipitation Measurement Mission (GPM) IMERG Precipitation Products over Mainland China
Comprehensive Evaluation of Global Precipitation Measurement Mission (GPM) IMERG Precipitation Products over Mainland China
Due to the difficulty involved in obtaining and processing a large amount of data, the spatial distribution of the quality and error structure of satellite precipitation products a...
Spatiotemporal Assessments on the Satellite‐Based Precipitation Products From Fengyun and GPM Over the Yunnan‐Kweichow Plateau, China
Spatiotemporal Assessments on the Satellite‐Based Precipitation Products From Fengyun and GPM Over the Yunnan‐Kweichow Plateau, China
AbstractHigh‐quality precipitation data are vital for hydrological applications and climate change research. In this study, we evaluated two satellite‐based precipitation products ...
Spatio-temporal Distribution Characteristics of Summer Precipitation Duration in Northwest China
Spatio-temporal Distribution Characteristics of Summer Precipitation Duration in Northwest China
Based on the daily precipitation observation data of 208 rain-gauge stations in Northwest China from 1961 to 2020, we use the statistical analysis method, the Mann-Kendall test met...
Probabilistic near real-time retrievals of Rain over Africa using deep learning
Probabilistic near real-time retrievals of Rain over Africa using deep learning
We introduce Rain over Africa (RoA), a public retrieval algorithm providing near real-time precipitation estimates over the African continent. The retrievals are based on Meteosat ...
IMERG Multi-Satellite Products Across Two Decades
IMERG Multi-Satellite Products Across Two Decades
<p>The Version 06 Global Precipitation Measurement (GPM) mission products were completed over the last year, capping five years of development since the launch of the...
Probabilistic near real-time retrievals of Rain over Africa using deep learning
Probabilistic near real-time retrievals of Rain over Africa using deep learning
We introduce Rain over Africa (RoA), a public retrieval algorithm providing near real-time precipitation estimates over the entire African continent. The retrievals are based on Me...

Back to Top