Bias correction of satellite rainfall estimates using a radar-gauge product – a case study in Oklahoma (USA)

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 ensemblebased 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 Correspondence to: K. Tesfagiorgis (ktesfagiorgis@gc.cuny.edu) 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.

Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal

“…This suggests that there is still much work to be done in appropriately leveraging the high spatial resolution and temporal coverage of geostationary satellites to improve precipitation estimates at fine scales and short latency, particularly for mountainous areas, such as Nepal, where limited ground-based data is available for calibration and validation in near real time. Cokriging or related approaches could be tested for optimally merging station observations with remote sensing products having different resolutions to form an accurate high-resolution precipitation map [13,37].…”

Precipitation estimation over radar gap areas based on satellite and adjacent radar observations

“…The initial satellite estimates were then adjusted by an ensemble bias correction method (Tesfagiorgis et al, 2011b) using the radar data. The merging of the bias-corrected satellite precipitation and interpolated radar precipitation fields over the radar gap areas was implemented by optimal weight merging.…”

“…Furthermore, since the P (R) of the prior information, constructed during the period close to the designated retrieval time, was a good description of the true probability distribution of the precipitation fields observed by the radar, the P (R) was simply replaced by the frequency of each element in the prior information. After the retrieval of the satellite precipitation, a bias correction based on an ensemble bias factor field (Tesfagiorgis et al, 2011b) computed from the radar precipitation was employed in order to improve the retrieval accuracy. The bias factor, which was defined as the ratio of a radar rain rate and the retrieved satellite precipitation at a specific location and time, was randomly selected over the study area.…”

“…Ensembles of the perturbed bias factor and bias field were then generated. The details of the ensemble bias correction method were described by Tesfagiorgis et al (2011b). By using the bias field, the bias-corrected precipitation estimation from the satellite was calculated.…”

Retrieval of Rainfall by Combining Rain Gauge, Ground-Based Radar and Satellite Measurements over Phimai, Thailand

“…Multiplicative correction was applied to adjust for the bias of the regression line slope via spatially variable bias factors (B) (Tesfagiorgis et al 2011). B is closely linked to temporal and spatial variation in the rainfall field.…”