Abstract
Abstract Reliable precipitation measurement is a crucial component in hydrologic studies. Although satellite-based observation is able to provide spatial and temporal distribution of precipitation, the measurements tend to show systematic bias. This paper introduces a grid-based precipitation merging procedure in which satellite estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN–CCS) are adjusted based on the Climate Prediction Center (CPC) daily rain gauge analysis. To remove the bias, the hourly CCS estimates were spatially and temporally accumulated to the daily 1° × 1° scale, the resolution of CPC rain gauge analysis. The daily CCS bias was then downscaled to the hourly temporal scale to correct hourly CCS estimates. The bias corrected CCS estimates are called the adjusted CCS (CCSA) product. With the adjustment from the gauge measurement, CCSA data have been generated to provide more reliable high temporal/spatial-resolution precipitation estimates. In the case study, the CCSA precipitation estimates from the proposed approach are compared against ground-based measurements in high-density gauge networks located in the southwestern United States.
Highlights
Accurate estimation of precipitation is crucial to a range of hydrologic and climatic applications
Given the challenge of improving the reliability of high-resolution, large-extent rainfall maps based on satellite observation over land, we introduce a grid-based merging procedure in which satellite estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks– Cloud Classification System (PERSIANN–CCS) are integrated with a grid-based ground measurement source known as the Climate Prediction Center (CPC) daily rain gauge analysis to produce a satellite–gauge bias-adjusted precipitation product called PERSIANN–CCSA (CCSA)
The CCSA compared to the CCS shows a bias reduction of 92%, 85%, 86%, 84%, and 84% for the 1, 3, 6, and 12-hr and daily resolutions, respectively
Summary
Accurate estimation of precipitation is crucial to a range of hydrologic and climatic applications. Because blending approaches using LEO and GEO satellite information may provide potential improvement than using one single source, without referencing to the ground measurement, those precipitation estimates may be biased from surface rainfall, either regionally or temporally. Given the challenge of improving the reliability of high-resolution, large-extent rainfall maps based on satellite observation over land, we introduce a grid-based merging procedure in which satellite estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks– Cloud Classification System (PERSIANN–CCS) are integrated with a grid-based ground measurement source known as the Climate Prediction Center (CPC) daily rain gauge analysis to produce a satellite–gauge bias-adjusted precipitation product called PERSIANN–CCSA (CCSA).
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