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Hydrometeorological Assessment of Satellite and Model Precipitation Products Over Taiwan


Satellite and model precipitation such as the Global Precipitation Measurement (GPM) data are valuable in hydrometeorological applications. This study investigates the performance of various satellite and model precipitation products in Taiwan from 2015 to 2017, including data derived from the Integrated Multisatellite Retrievals for GPM Early and Final Runs (IMERG_E and IMERG_F), Global Satellite Mapping of Precipitation in near–real time (GSMaP_NRT), and the Weather Research and Forecasting (WRF) Model. We assess these products by comparing them against data collected from 304 surface stations and gauge-based gridded data. Our assessment emphasizes factors influential in precipitation estimation, such as season, temperature, elevation, and extreme event. Further, we assess the hydrological response to each precipitation product via continuous flow simulation in two selected watersheds. The results indicate that the performance of these precipitation products is subject to seasonal and regional variations. The satellite products (i.e., IMERG and GSMaP) perform better than the model (i.e., WRF) in the warm season and vice versa in the cold season, most apparently in northern Taiwan. For selected extreme events, WRF can simulate better rainfall amount and distribution. The seasonal and regional variations in precipitation estimation are also reflected in flow simulations: IMERG in general produces the most rational flow simulation, GSMaP tends to overestimate and be least useful for hydrological applications, while WRF simulates high flows that show accurate time to the peak flows and are better in the southern watershed.

Article / Publication Data
Available Metadata
Early Online Release
October 21, 2021
Fiscal Year
Peer Reviewed
Publication Name
Journal of Hydrometeorology
Published On
November 01, 2021
Publisher Name
American Meteorological Society
Print Volume
Submitted On
June 30, 2021


Not available


Authors who have authored or contributed to this publication.

  • Pin-Lun Li - lead None
  • Liao-Fan Lin - second Gsl
    Cooperative Institute for Research in the Atmosphere, Colorado State University
    NOAA/Global Systems Laboratory