Machine Learning for Model Error Inference and Correction. (30th November 2020)

• Record Type: Journal Article
• Authors: Bonavita, Massimo; Laloyaux, Patrick
• Is Part Of: Journal of advances in modeling earth systems. Volume 12:Number 12(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-30
• Subjects: model error -- neural networks -- weak‐constraint 4D‐Var -- data assimilation -- artificial intelligence -- ECMWF; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2020MS002232
• Languages: English
• ISSNs: 1942-2466

Abstract

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. Plain Language Summary: Model error is one of the main obstacles to improved accuracy and reliability in current numerical weather prediction and in climate prediction. Recent advances in data assimilation at the European Centre for Medium‐Range Weather Forecasts (ECMWF) indicate that it is possible to estimate and correct for a large fraction of systematic model error in the stratosphere. The question we address here is whether machine learning techniques can be used alone and in conjunction with standard data assimilation methods to improve on those results. We show that it is indeed possible to extend current data assimilation capabilities in operational state‐of‐the‐art forecast systems using machine learning tools, and we discuss the potential and limitations of future applications of these ideas to other core NWP tasks. Key Points: Model error is currently the biggest barrier to improve forecast accuracy in weather and climate prediction An artificial neural network (ANN) is trained to simulate the error in the ECMWF operational model Using the model error from the ANN inside weak‐constraint 4D‐Var extends the model error correction to the whole atmospheric column