Abstract
Abstract. Hourly Satellite Precipitation Estimates (SPEs) may be the only available source of information for operational hydrologic and flash flood prediction due to spatial limitations of radar and gauge products. SPEs are prone to larger systematic errors and more uncertainty sources in comparison with ground based radar and gauge precipitation products. The present work develops an approach to seamlessly blend satellite, radar and gauge products to fill gaps in ground-based data. To mix different rainfall products, the bias of any of the products relative to each other should be removed. The study presents and tests a proposed ensemble-based method which aims to estimate spatially varying multiplicative biases in hourly SPEs using a radar-gauge rainfall product and compare it with previously used bias correction methods. Bias factors were calculated for a randomly selected sample of rainy pixels in the study area. Spatial fields of estimated bias were generated taking into account spatial variation and random errors in the sampled values. Bias field parameters were determined on a daily basis using the shuffled complex evolution optimization algorithm. To include more error sources, ensembles of bias factors were generated and applied before bias field generation. We demonstrate this method using two satellite-based products, CPC Morphing (CMORPH) and Hydro-Estimator (HE), and a radar-gauge rainfall Stage-IV (ST-IV) dataset for several rain events in 2006 over Oklahoma. The method was compared with 3 simpler methods for bias correction: mean ratio, maximum ratio and spatial interpolation without ensembles. Bias ratio, correlation coefficient, root mean square error and mean absolute difference are used to evaluate the performance of the different methods. Results show that: (a) the methods of maximum ratio and mean ratio performed variably and did not improve the overall correlation with the ST-IV in any of the rainy events; (b) the method of interpolation was consistently able to improve all the performance criteria; (c) the method of ensembles outperformed the other 3 methods.
Highlights
This study proposes and evaluates a method for improving hourly Satellite Precipitation Estimates (SPEs) by correcting biases with respect to a radar-gauge product
Rainfall estimates based on IR imagery from Geostationary Operational Environmental Satellites (GOES) would be useful to weather forecasters and water managers because their high spatial (4 km) and time resolution facilitates hydrological prediction in comparison with the ones based on Passive MicroWave (PMW) from polar-orbiting satellites
A simple and widespread approach to reduce the error in one rainfall product relative to another, reference product is to multiply the rainfall from the first product by a “bias factor” chosen to optimize the correspondence of the two products where they overlap
Summary
This study proposes and evaluates a method for improving hourly Satellite Precipitation Estimates (SPEs) by correcting biases with respect to a radar-gauge product. A simple and widespread approach to reduce the error in one rainfall product relative to another, reference product is to multiply the rainfall from the first product by a “bias factor” chosen to optimize the correspondence of the two products where they overlap Authors such as Anagnostou et al (1998), Smith and Krajewski (1990), Ahernet et al (1986), and Seo et al (1999) estimated constant bias factors that were applied to the entire estimated rainfall field to correct biases in radar precipitation products, as compared to point-based observations from rain gauges. The area is one with good rain gauge and radar coverage and is intended to provide a preliminary test of our method for correcting bias in satellite-based rainfall estimates. CMORPH has been operational and data are available since 2002 from the Climate Prediction Center (CPC) of the National Centers for Environmental Prediction (NCEP) Oklahoma region make it well suited for investigating the capability of different approaches to correct bias errors from 2.3 Radar-gauge Stage-IV (ST-IV).
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