 |
| Titel |
Bias correction of satellite rainfall estimates using a radar-gauge product – a case study in Oklahoma (USA) |
| VerfasserIn |
K. Tesfagiorgis, S. E. Mahani, N. Y. Krakauer, R. Khanbilvardi |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 15, no. 8 ; Nr. 15, no. 8 (2011-08-25), S.2631-2647 |
| Datensatznummer |
250012931
|
| Publikation (Nr.) |
 copernicus.org/hess-15-2631-2011.pdf |
|
|
|
|
|
| Zusammenfassung |
| 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. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|