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Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods
Quantifying the effects of future changes in the frequency of precipitation extremes is a key challenge in assessing the vulnerability of hydrological systems to climate change but is difficult as climate models do not always accurately simulate daily precipitation. This article compares the perform...
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| Format: | Default Article |
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2013
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| Online Access: | https://hdl.handle.net/2134/22131 |
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| _version_ | 1818172146348720128 |
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| author | Thomas Lafon Simon J. Dadson Gwen Buys Christel Prudhomme |
| author_facet | Thomas Lafon Simon J. Dadson Gwen Buys Christel Prudhomme |
| author_sort | Thomas Lafon (6730019) |
| collection | Figshare |
| description | Quantifying the effects of future changes in the frequency of precipitation extremes is a key challenge in assessing the vulnerability of hydrological systems to climate change but is difficult as climate models do not always accurately simulate daily precipitation. This article compares the performance of four published techniques used to reduce the bias in a regional climate model precipitation output: (1) linear, (2) nonlinear, (3) γ-based quantile mapping and (4) empirical quantile mapping. Overall performance and sensitivity to the choice of calibration period were tested by calculating the errors in the first four statistical moments of generated daily precipitation time series and using a cross-validation technique. The study compared the 1961-2005 precipitation time series from the regional climate model HadRM3.0-PPE-UK (unperturbed version) with gridded daily precipitation time series derived from rain gauges for seven catchments spread throughout Great Britain. We found that while the first and second moments of the precipitation frequency distribution can be corrected robustly, correction of the third and fourth moments of the distribution is much more sensitive to the choice of bias correction procedure and to the selection of a particular calibration period. Overall, our results demonstrate that, if both precipitation data sets can be approximated by a γ-distribution, the γ-based quantile-mapping technique offers the best combination of accuracy and robustness. In circumstances where precipitation data sets cannot adequately be approximated using a γ-distribution, the nonlinear method is more effective at reducing the bias, but the linear method is least sensitive to the choice of calibration period. The empirical quantile mapping method can be highly accurate, but results were very sensitive to the choice of calibration time period. However, it should be borne in mind that bias correction introduces additional uncertainties, which are greater for higher order moments. |
| format | Default Article |
| id | rr-article-9485972 |
| institution | Loughborough University |
| publishDate | 2013 |
| record_format | Figshare |
| spelling | rr-article-94859722013-01-01T00:00:00Z Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods Thomas Lafon (6730019) Simon J. Dadson (3777904) Gwen Buys (7190900) Christel Prudhomme (7190294) Atmospheric sciences not elsewhere classified Other earth sciences not elsewhere classified Regional climate model Bias correction Daily precipitation Downscaling Cross-validation UK Earth Sciences not elsewhere classified Atmospheric Sciences Quantifying the effects of future changes in the frequency of precipitation extremes is a key challenge in assessing the vulnerability of hydrological systems to climate change but is difficult as climate models do not always accurately simulate daily precipitation. This article compares the performance of four published techniques used to reduce the bias in a regional climate model precipitation output: (1) linear, (2) nonlinear, (3) γ-based quantile mapping and (4) empirical quantile mapping. Overall performance and sensitivity to the choice of calibration period were tested by calculating the errors in the first four statistical moments of generated daily precipitation time series and using a cross-validation technique. The study compared the 1961-2005 precipitation time series from the regional climate model HadRM3.0-PPE-UK (unperturbed version) with gridded daily precipitation time series derived from rain gauges for seven catchments spread throughout Great Britain. We found that while the first and second moments of the precipitation frequency distribution can be corrected robustly, correction of the third and fourth moments of the distribution is much more sensitive to the choice of bias correction procedure and to the selection of a particular calibration period. Overall, our results demonstrate that, if both precipitation data sets can be approximated by a γ-distribution, the γ-based quantile-mapping technique offers the best combination of accuracy and robustness. In circumstances where precipitation data sets cannot adequately be approximated using a γ-distribution, the nonlinear method is more effective at reducing the bias, but the linear method is least sensitive to the choice of calibration period. The empirical quantile mapping method can be highly accurate, but results were very sensitive to the choice of calibration time period. However, it should be borne in mind that bias correction introduces additional uncertainties, which are greater for higher order moments. 2013-01-01T00:00:00Z Text Journal contribution 2134/22131 https://figshare.com/articles/journal_contribution/Bias_correction_of_daily_precipitation_simulated_by_a_regional_climate_model_a_comparison_of_methods/9485972 CC BY-NC-ND 4.0 |
| spellingShingle | Atmospheric sciences not elsewhere classified Other earth sciences not elsewhere classified Regional climate model Bias correction Daily precipitation Downscaling Cross-validation UK Earth Sciences not elsewhere classified Atmospheric Sciences Thomas Lafon Simon J. Dadson Gwen Buys Christel Prudhomme Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods |
| title | Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods |
| title_full | Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods |
| title_fullStr | Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods |
| title_full_unstemmed | Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods |
| title_short | Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods |
| title_sort | bias correction of daily precipitation simulated by a regional climate model: a comparison of methods |
| topic | Atmospheric sciences not elsewhere classified Other earth sciences not elsewhere classified Regional climate model Bias correction Daily precipitation Downscaling Cross-validation UK Earth Sciences not elsewhere classified Atmospheric Sciences |
| url | https://hdl.handle.net/2134/22131 |