Machine Learning for Model Error Inference and Correction
Abstract Model error is one of the main obstacles to improved accuracy and reliability in numerical weather prediction (NWP) and climate prediction conducted with state‐of‐the‐art, comprehensive high‐resolution general circulation models. In a data assimilation framework, recent advances in the cont...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2020-12-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2020MS002232 |
| _version_ | 1819118979781754880 |
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| author | Massimo Bonavita Patrick Laloyaux |
| author_facet | Massimo Bonavita Patrick Laloyaux |
| author_sort | Massimo Bonavita |
| collection | DOAJ |
| description | Abstract Model error is one of the main obstacles to improved accuracy and reliability in numerical weather prediction (NWP) and climate prediction conducted with state‐of‐the‐art, comprehensive high‐resolution general circulation models. In a data assimilation framework, recent advances in the context of weak‐constraint 4D‐Var have shown that it is possible to estimate and correct for a large fraction of systematic model error which develops in the stratosphere over short forecast ranges. The recent explosion of interest in machine learning/deep learning technologies has been driven by their remarkable success in disparate application areas. This raises the question of whether model error estimation and correction in operational NWP and climate prediction can also benefit from these techniques. In this work, we aim to start to give an answer to this question. Specifically, we show that artificial neural networks (ANNs) can reproduce the main results obtained with weak‐constraint 4D‐Var in the operational configuration of the IFS model of the European Centre for Medium‐Range Weather Forecasts (ECMWF). We show that the use of ANN models inside the weak‐constraint 4D‐Var framework has the potential to extend the applicability of the weak‐constraint methodology for model error correction to the whole atmospheric column. Finally, we discuss the potential and limitations of the machine learning/deep learning technologies in the core NWP tasks. In particular, we reconsider the fundamental constraints of a purely data‐driven approach to forecasting and provide a view on how to best integrate machine learning technologies within current data assimilation and forecasting methods. |
| first_indexed | 2024-12-22T05:57:29Z |
| format | Article |
| id | doaj.art-d548c094e36647cdbbffb6f198a2f7b3 |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2024-12-22T05:57:29Z |
| publishDate | 2020-12-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-d548c094e36647cdbbffb6f198a2f7b32022-12-21T18:36:40ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662020-12-011212n/an/a10.1029/2020MS002232Machine Learning for Model Error Inference and CorrectionMassimo Bonavita0Patrick Laloyaux1European Centre for Medium‐Range Weather Forecasts Reading UKEuropean Centre for Medium‐Range Weather Forecasts Reading UKAbstract Model error is one of the main obstacles to improved accuracy and reliability in numerical weather prediction (NWP) and climate prediction conducted with state‐of‐the‐art, comprehensive high‐resolution general circulation models. In a data assimilation framework, recent advances in the context of weak‐constraint 4D‐Var have shown that it is possible to estimate and correct for a large fraction of systematic model error which develops in the stratosphere over short forecast ranges. The recent explosion of interest in machine learning/deep learning technologies has been driven by their remarkable success in disparate application areas. This raises the question of whether model error estimation and correction in operational NWP and climate prediction can also benefit from these techniques. In this work, we aim to start to give an answer to this question. Specifically, we show that artificial neural networks (ANNs) can reproduce the main results obtained with weak‐constraint 4D‐Var in the operational configuration of the IFS model of the European Centre for Medium‐Range Weather Forecasts (ECMWF). We show that the use of ANN models inside the weak‐constraint 4D‐Var framework has the potential to extend the applicability of the weak‐constraint methodology for model error correction to the whole atmospheric column. Finally, we discuss the potential and limitations of the machine learning/deep learning technologies in the core NWP tasks. In particular, we reconsider the fundamental constraints of a purely data‐driven approach to forecasting and provide a view on how to best integrate machine learning technologies within current data assimilation and forecasting methods.https://doi.org/10.1029/2020MS002232model errorneural networksweak‐constraint 4D‐Vardata assimilationartificial intelligenceECMWF |
| spellingShingle | Massimo Bonavita Patrick Laloyaux Machine Learning for Model Error Inference and Correction Journal of Advances in Modeling Earth Systems model error neural networks weak‐constraint 4D‐Var data assimilation artificial intelligence ECMWF |
| title | Machine Learning for Model Error Inference and Correction |
| title_full | Machine Learning for Model Error Inference and Correction |
| title_fullStr | Machine Learning for Model Error Inference and Correction |
| title_full_unstemmed | Machine Learning for Model Error Inference and Correction |
| title_short | Machine Learning for Model Error Inference and Correction |
| title_sort | machine learning for model error inference and correction |
| topic | model error neural networks weak‐constraint 4D‐Var data assimilation artificial intelligence ECMWF |
| url | https://doi.org/10.1029/2020MS002232 |
| work_keys_str_mv | AT massimobonavita machinelearningformodelerrorinferenceandcorrection AT patricklaloyaux machinelearningformodelerrorinferenceandcorrection |