Abstract
The number of precipitation products at the global scale has increased rapidly, and the accuracy of these products directly affects the accuracy of hydro-meteorological simulation and forecast. Therefore, the applicability of these precipitation products should be comprehensively evaluated to improve their application in hydrometeorology. This paper evaluated the performances of six widely used precipitation products in southwest China by quantitative assessment and contingency assessment. The precipitation products were Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42 version 7 (TRMM 3B42 V7), Global Satellite Mapping of Precipitation (GSMaP MVK), Integrated Multi-satellitE Retrievals for GPM final run (GPM IMERG Final), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network—Climate Data Record (PERSIANN-CDR), Climate Hazards Infrared Precipitation with Stations version 2.0 (CHIRPS V2.0), and the Global Land Data Assimilation System version 2.0 (GLDAS V2.0). From the above six products, the daily-scale precipitation data from 2001 to 2019 were chosen to compare with the measured data of the rain gauge, and the data from the gauges were classified by river basin and elevation. All precipitation products and measured data were evaluated by statistical indicators. Results showed that (1) GPM IMERG Final and CHIRPS V2.0 performed well in the Yarlung Zangbo River (YZ) basin, while GPM IMERG Final and GLDAS V2.0 performed well in the Lantsang River (LS), Nujiang River (NJ), Yangtze River (YT), and Yellow River (YL) basins; (2) in the upper and middle reaches of the YZ basin, GPM IMERG Final and CHIRPS V2.0 were outstanding in all evaluated products; downstream of the YZ basin, all six products performed well; and upstream of the LS and NJ, GPM IMERG Final, TRMM 3B42 V7, CHIRPS V2.0, and GLDAS V2.0 can be recommended as a substitute for measured data; and (3) GPM IMERG Final and GLDAS V2.0 can be seen as substitutes for measured data when elevation is below 4000 m. GPM IMERG Final and CHIRPS V2.0 were recommended when elevation is above 4000 m. This study provides a reference for data selection of hydro-meteorological simulation and forecast in southwest China and also provides a basis for multi-source data assimilation and fusion.
Highlights
Precipitation is one of the most important components of the water cycle [1,2,3,4,5,6,7]
Our study focused on all basins (YZ, Lantsang River (LS), Nujiang River (NJ), Yangtze River (YT), and Yellow River (YL)) in southwest China to evaluate the precipitation datasets (TRMM 3B42 V7, GSMAP-MVK, GPM IMERG Final, PERSIANN-CDR, GLDAS V2.0, and Climate Hazards Infrared Precipitation with Stations (CHIRPS) V2.0) for 20 years (2001–2019)
We focused on southwest China, including Xizang, Gansu, Sichuan, and Yunnan Provinces; we mainly focused on the area of 21–37◦ N and 80–104◦ E, which is rich in water resources
Summary
Precipitation is one of the most important components of the water cycle [1,2,3,4,5,6,7]. A large number precipitation products have emerged (Table 1) [1,2,3,4,16,17,18,19], and they can be divided into four groups: (1) precipitation products corrected by gauge data, such as Integrated Multi-satellitE Retrievals for GPM (GPM IMERG) final, Tropical Rainfall Measuring Mission (TRMM) 3B42 V7, Rainfall Estimate (RFE), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network—Climate Data Record (PERSIANN CDR), the Global Satellite Mapping of Precipitation (GSMaP) Gauge, Climate Hazards Infrared Precipitation with Stations (CHIRPS), and the Climate Prediction Center morphing technique (CMORPH); (2) multi-source precipitation datasets, which are prepared by merging infrared (IR) estimates (high sampling frequency, indirectness, less accurate) and passive microwave (PWM) estimates (direct, accurate, roughly temporal sampling), such as GSMaP MVK, GSMaP RNL, PERSIANN, TRMM 3B42 RT, GPM IMERG Early, and GPM IMERG Late; (3) single-source precipitation datasets, which can provide precipitation products from a single satellite sensor type, such as PERSIANN CCS; and (4) reanalysis precipitation products, which are prepared by various hydrological or atmospheric models, such as Asian Precipitation—Highly-Resolved Observational Data Integration Evaluation (APHRODITE), CPC Unified Gauged Analysis of Global Daily Precipitation, and GLDAS These precipitation products can be seen as a supplement for rain gauge data in sparse areas, the retrieval algorithms have pros and cons. Vidhi et al [15] evaluated TRMM 3B42 V7 in the Himalayan region and concluded that the satellite shows good performance at an altitude of 1000–2000 m, but there was still a large error between the precipitation monitored by the satellite and the precipitation monitored by the gauge in the region with complex topography
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