Evaluation of Bias Correction Method for Satellite-Based Rainfall Data
With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-06-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/6/884 |
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| author | Haris Akram Bhatti Tom Rientjes Alemseged Tamiru Haile Emad Habib Wouter Verhoef |
| author_facet | Haris Akram Bhatti Tom Rientjes Alemseged Tamiru Haile Emad Habib Wouter Verhoef |
| author_sort | Haris Akram Bhatti |
| collection | DOAJ |
| description | With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW’s) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach. |
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| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T00:37:25Z |
| publishDate | 2016-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-1790643291a04403be027eafc2b11b822022-12-22T03:10:18ZengMDPI AGSensors1424-82202016-06-0116688410.3390/s16060884s16060884Evaluation of Bias Correction Method for Satellite-Based Rainfall DataHaris Akram Bhatti0Tom Rientjes1Alemseged Tamiru Haile2Emad Habib3Wouter Verhoef4Department of Water Resources, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, Enschede 7514 AE, The NetherlandsDepartment of Water Resources, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, Enschede 7514 AE, The NetherlandsInternational Water Management Institute, P.O. Box 5689, Addis Ababa, EthiopiaDepartment of Civil Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USADepartment of Water Resources, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, Enschede 7514 AE, The NetherlandsWith the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW’s) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.http://www.mdpi.com/1424-8220/16/6/884CMORPHbias factorGilgel Abbeysatellite rainfall correctionoptimum window size |
| spellingShingle | Haris Akram Bhatti Tom Rientjes Alemseged Tamiru Haile Emad Habib Wouter Verhoef Evaluation of Bias Correction Method for Satellite-Based Rainfall Data Sensors CMORPH bias factor Gilgel Abbey satellite rainfall correction optimum window size |
| title | Evaluation of Bias Correction Method for Satellite-Based Rainfall Data |
| title_full | Evaluation of Bias Correction Method for Satellite-Based Rainfall Data |
| title_fullStr | Evaluation of Bias Correction Method for Satellite-Based Rainfall Data |
| title_full_unstemmed | Evaluation of Bias Correction Method for Satellite-Based Rainfall Data |
| title_short | Evaluation of Bias Correction Method for Satellite-Based Rainfall Data |
| title_sort | evaluation of bias correction method for satellite based rainfall data |
| topic | CMORPH bias factor Gilgel Abbey satellite rainfall correction optimum window size |
| url | http://www.mdpi.com/1424-8220/16/6/884 |
| work_keys_str_mv | AT harisakrambhatti evaluationofbiascorrectionmethodforsatellitebasedrainfalldata AT tomrientjes evaluationofbiascorrectionmethodforsatellitebasedrainfalldata AT alemsegedtamiruhaile evaluationofbiascorrectionmethodforsatellitebasedrainfalldata AT emadhabib evaluationofbiascorrectionmethodforsatellitebasedrainfalldata AT wouterverhoef evaluationofbiascorrectionmethodforsatellitebasedrainfalldata |