Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of Extremes
Highly resolved and accurate daily precipitation data are required for impact models to perform adequately and correctly measure the impacts of high-risk events. In order to produce such data, bias correction is often needed. Most of those statistical methods correct the probability distributions of...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/16/4/480 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850155951419555840 |
|---|---|
| author | Philippe Ear Elena Di Bernardino Thomas Laloë Adrien Lambert Magali Troin |
| author_facet | Philippe Ear Elena Di Bernardino Thomas Laloë Adrien Lambert Magali Troin |
| author_sort | Philippe Ear |
| collection | DOAJ |
| description | Highly resolved and accurate daily precipitation data are required for impact models to perform adequately and correctly measure the impacts of high-risk events. In order to produce such data, bias correction is often needed. Most of those statistical methods correct the probability distributions of daily precipitation by modeling them with either empirical or parametric distributions. A recent semi-parametric model based on a penalized Berk–Jones (BJ) statistical test, which allows for automatic and personalized splicing of parametric and non-parametric distributions, has been developed. This method, called the Stitch-BJ model, was found to be able to model daily precipitation correctly and showed interesting potential in a bias correction setting. In the present study, we will consolidate these results by taking into account the seasonal properties of daily precipitation in an out-of-sample context and by considering dry days probabilities in our methodology. We evaluate the performance of the Stitch-BJ method in this seasonal bias correction setting against more classical models such as the Gamma, Exponentiated Weibull (ExpW), Extended Generalized Pareto (EGP) or empirical distributions. Results show that a seasonal separation of data is necessary in order to account for intra-annual non-stationarity. Moreover, the Stitch-BJ distribution was able to consistently perform as well as or better than all the other considered models over the validation set, including the empirical distribution, which is often used due to its robustness. Finally, while methods for correcting dry day probabilities can be easily applied, their relevance can be discussed as temporal and spatial correlations are often neglected. |
| format | Article |
| id | doaj-art-667b9cf005634df4bbe4e31ec81cda69 |
| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-667b9cf005634df4bbe4e31ec81cda692025-08-20T02:24:43ZengMDPI AGAtmosphere2073-44332025-04-0116448010.3390/atmos16040480Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of ExtremesPhilippe Ear0Elena Di Bernardino1Thomas Laloë2Adrien Lambert3Magali Troin4Hydroclimat, 13400 Aubagne, FranceLaboratoire J.A. Dieudonné, UMR CNRS 7351, Université Côte d’Azur, 06108 Nice, FranceLaboratoire J.A. Dieudonné, UMR CNRS 7351, Université Côte d’Azur, 06108 Nice, FranceHydroclimat, 13400 Aubagne, FranceHydroclimat, 13400 Aubagne, FranceHighly resolved and accurate daily precipitation data are required for impact models to perform adequately and correctly measure the impacts of high-risk events. In order to produce such data, bias correction is often needed. Most of those statistical methods correct the probability distributions of daily precipitation by modeling them with either empirical or parametric distributions. A recent semi-parametric model based on a penalized Berk–Jones (BJ) statistical test, which allows for automatic and personalized splicing of parametric and non-parametric distributions, has been developed. This method, called the Stitch-BJ model, was found to be able to model daily precipitation correctly and showed interesting potential in a bias correction setting. In the present study, we will consolidate these results by taking into account the seasonal properties of daily precipitation in an out-of-sample context and by considering dry days probabilities in our methodology. We evaluate the performance of the Stitch-BJ method in this seasonal bias correction setting against more classical models such as the Gamma, Exponentiated Weibull (ExpW), Extended Generalized Pareto (EGP) or empirical distributions. Results show that a seasonal separation of data is necessary in order to account for intra-annual non-stationarity. Moreover, the Stitch-BJ distribution was able to consistently perform as well as or better than all the other considered models over the validation set, including the empirical distribution, which is often used due to its robustness. Finally, while methods for correcting dry day probabilities can be easily applied, their relevance can be discussed as temporal and spatial correlations are often neglected.https://www.mdpi.com/2073-4433/16/4/480bias correctionextreme value theorygoodness-of-fitparametric distributionprecipitation modeling |
| spellingShingle | Philippe Ear Elena Di Bernardino Thomas Laloë Adrien Lambert Magali Troin Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of Extremes Atmosphere bias correction extreme value theory goodness-of-fit parametric distribution precipitation modeling |
| title | Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of Extremes |
| title_full | Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of Extremes |
| title_fullStr | Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of Extremes |
| title_full_unstemmed | Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of Extremes |
| title_short | Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of Extremes |
| title_sort | seasonal bias correction of daily precipitation over france using a stitch model designed for robust representation of extremes |
| topic | bias correction extreme value theory goodness-of-fit parametric distribution precipitation modeling |
| url | https://www.mdpi.com/2073-4433/16/4/480 |
| work_keys_str_mv | AT philippeear seasonalbiascorrectionofdailyprecipitationoverfranceusingastitchmodeldesignedforrobustrepresentationofextremes AT elenadibernardino seasonalbiascorrectionofdailyprecipitationoverfranceusingastitchmodeldesignedforrobustrepresentationofextremes AT thomaslaloe seasonalbiascorrectionofdailyprecipitationoverfranceusingastitchmodeldesignedforrobustrepresentationofextremes AT adrienlambert seasonalbiascorrectionofdailyprecipitationoverfranceusingastitchmodeldesignedforrobustrepresentationofextremes AT magalitroin seasonalbiascorrectionofdailyprecipitationoverfranceusingastitchmodeldesignedforrobustrepresentationofextremes |