Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales

Abstract

This study provides a comprehensive evaluation of eight high spatial resolution gridded precipitation products in Adige Basin located in Italy within 45-47.1°N. The Adige Basin is characterized by a complex topography, and independent ground data are available from a network of 101 rain gauges during 2000-2010. The eight products include the Version 7 TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Analysis 3B42 product, three products from CMORPH (the Climate Prediction Center MORPHing technique), i.e., CMORPH_RAW, CMORPH_CRT and CMORPH_BLD, PCDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record), PGF (Global Meteorological Forcing Dataset for land surface modelling developed by Princeton University), CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and GSMaP_MVK (Global Satellite Mapping of Precipitation project Moving Vector with Kalman-filter product). All eight products are evaluated against interpolated rain gauge data at the common 0.25° spatial resolution, and additional evaluations at native finer spatial resolution are conducted for CHIRPS (0.05°) and GSMaP_MVK (0.10°). Evaluation is performed at multiple temporal (daily, monthly and annual) and spatial scales (grid and watershed). Evaluation results show that in terms of overall statistical metrics the CHIRPS, TRMM and CMORPH_BLD comparably rank as the top three best performing products, while the PGF performs worst. All eight products underestimate and overestimate the occurrence frequency of daily precipitation for some intensity ranges. All products tend to show higher error in the winter months (December-February) when precipitation is low. Very slight difference can be observed in the evaluation metrics and aspects between at the aggregated 0.25° spatial resolution and at the native finer resolutions (0.05°) for CHIRPS and (0.10°) for GSMaP_MVK products. This study has implications for precipitation product development and the global view of the performance of various precipitation products, and provides valuable guidance when choosing alternative precipitation data for local community.

Multiscale Comparative Evaluation of the GPM IMERG v5 and TRMM 3B42 v7 Precipitation Products from 2015 to 2017 over a Climate Transition Area of China

“…When the time scale increased, the r values were significantly improved, and the RRMSE values were significantly reduced. Similar findings have been reported in previous evaluation results [15,22]. A reason for the higher accuracy of satellite precipitation products at monthly and annual scales was that the GPCC monthly product was used for bias-correction of the 3B42 v7 and IMERG v5 products [11,40].…”

“…Moreover, the nearest pixel to a rain gauge was extracted to conduct the comparison between the point-based rain gauge and pixel-based GPM or TRMM in the study. However, there is a typical scale mismatch issue between point-based rain gauge data and the gridded precipitation products [22]. Many interpolation methods (inverse distance weighting, kriging, spline, and Thiessen polygon for example) [22,33,53] and grid-to-point techniques (such as nearest neighbor, bilinear weighted interpolation) [41] have been proposed to compare the point-based rain gauge data and pixel-based satellite precipitation data.…”

“…However, there is a typical scale mismatch issue between point-based rain gauge data and the gridded precipitation products [22]. Many interpolation methods (inverse distance weighting, kriging, spline, and Thiessen polygon for example) [22,33,53] and grid-to-point techniques (such as nearest neighbor, bilinear weighted interpolation) [41] have been proposed to compare the point-based rain gauge data and pixel-based satellite precipitation data. Each method has its advantages and disadvantages, and its performance depends on various factors and varies from region to region [22].…”

“…Many interpolation methods (inverse distance weighting, kriging, spline, and Thiessen polygon for example) [22,33,53] and grid-to-point techniques (such as nearest neighbor, bilinear weighted interpolation) [41] have been proposed to compare the point-based rain gauge data and pixel-based satellite precipitation data. Each method has its advantages and disadvantages, and its performance depends on various factors and varies from region to region [22]. It is an interesting topic to develop a common method in all study areas for comparing the point-based rain gauge and pixel-based satellite precipitation.…”

Evaluation of TRMM Product for Monitoring Drought in the Kelantan River Basin, Malaysia

“…The bias-corrected TMPA-3B43 product is generated using multiple satellite images and algorithms, as well as the GPCC product. However, the performance of the product is affected by the errors in the satellite sampling technique, precipitation retrieval methods and bias correction algorithms used in the GPCC product development [49]. Therefore, further improvements of the mentioned errors should be conducted in this region, to improve the accuracy of the TMPA-3B43 in monthly and seasonal precipitation estimation.…”