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  • Author or Editor: Wei Shi x
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Farid Ishak Boushaki, Kuo-Lin Hsu, Soroosh Sorooshian, Gi-Hyeon Park, Shayesteh Mahani, and Wei Shi

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.

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Viviane B. S. Silva, Vernon E. Kousky, Wei Shi, and R. Wayne Higgins

Abstract

A gauge-only precipitation data quality control and analysis system has been developed for monitoring precipitation at NOAA’s Climate Prediction Center (CPC). Over the past 10 yr the system has been used to develop and deliver many different precipitation products over the United States, Mexico, and Central and South America. Here the authors describe how the system has been applied to develop improved gridded daily precipitation analyses over Brazil. Consistent with previous studies, comparisons between the the gridded analyses and station observations reveal fewer dry days, a greater number of low precipitation days, and fewer extreme precipitation events in the gridded analyses. Even though the gridded analysis system reduces the number of dry days and increases the number of wet days, there is still a good correlation between time series of the gridpoint precipitation values and observations.

Retrospective analyses are important for computing basic statistics such as mean daily/monthly rainfall, extremes, and probabilities of wet and dry days. The CPC gridded precipitation analyses can be used in hydrologic and climate variability studies dealing with large spatial-scale anomaly patterns, such as those related to ENSO. The analyses can also be used as a benchmark for evaluating model simulations, serve as a basis for real-time monitoring, and provide statistics on the occurrence of large-scale heavy rainfall events and dry periods.

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