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 eld 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 an...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2016
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/81164 |
| _version_ | 1855524994213216256 |
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| author | Bhatti, H.A. Rientjes, T.H.M. Haile, Alemseged Tamiru Habib, E. Verhoef, W. |
| author_browse | Bhatti, H.A. Habib, E. Haile, Alemseged Tamiru Rientjes, T.H.M. Verhoef, W. |
| author_facet | Bhatti, H.A. Rientjes, T.H.M. Haile, Alemseged Tamiru Habib, E. Verhoef, W. |
| author_sort | Bhatti, H.A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the eld 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 coef cient (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 ef ciency of our bias correction approach. |
| format | Journal Article |
| id | CGSpace81164 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace811642025-03-11T09:50:20Z Evaluation of bias correction method for satellite-based rainfall data Bhatti, H.A. Rientjes, T.H.M. Haile, Alemseged Tamiru Habib, E. Verhoef, W. satellite observation rain remote sensing catchment areas runoff water hydrology precipitation meteorology spatial distribution With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the eld 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 coef cient (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 ef ciency of our bias correction approach. 2016 2017-05-23T04:32:19Z 2017-05-23T04:32:19Z Journal Article https://hdl.handle.net/10568/81164 en Open Access MDPI Bhatti, H. A.; Rientjes, T.; Haile, Alemseged Tamiru; Habib, E.; Verhoef, W. 2016. Evaluation of bias correction method for satellite-based rainfall data. Sensors, 16(6):1-16. doi: 10.3390/s16060884 |
| spellingShingle | satellite observation rain remote sensing catchment areas runoff water hydrology precipitation meteorology spatial distribution Bhatti, H.A. Rientjes, T.H.M. Haile, Alemseged Tamiru Habib, E. Verhoef, W. Evaluation of bias correction method for satellite-based rainfall data |
| 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 | satellite observation rain remote sensing catchment areas runoff water hydrology precipitation meteorology spatial distribution |
| url | https://hdl.handle.net/10568/81164 |
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