Exploring the potential and limitations of weak‐constraint 4D‐Var

2020

J Adv Model Earth Syst

Self Cite

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.

Section: Training the Annsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 75%

Section: Discussion and Research Perspectivesmentioning

confidence: 99%

Section: Setup Of the Regression Problemmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning for Model Error Inference and Correction

Bonavita

2020

J Adv Model Earth Syst

Self Cite

“…10.1029/2020MS002232 this revised WC-4DVar implementation has been to impose scale separation between the error covariance matrices describing the spatial structures of the background error B and of the model systematic errors Q (see Laloyaux, Bonavita, Chrust, et al, 2020;Laloyaux, Bonavita, Dahoui, et al, 2020, for a detailed explanation). The scale separation allows to successfully de-alias initial state and model error corrections during the 4D-Var minimization and is consistent with the view that model biases represent a type of errors that take place on larger spatial and longer temporal scales than background errors.…”

Section: Journal Of Advances In Modeling Earth Systemsmentioning

confidence: 99%

Machine Learning for Model Error Inference and Correction

Bonavita

2020

Preprint

Self Cite

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 SummaryModel 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.

Deep learning to estimate model biases in an operational NWP assimilation system

Kurth

Dueben

et al. 2022

Preprint

Model error is one of the main obstacles to improved accuracy and reliability in numerical weather prediction (NWP) conducted with state-of-the-art atmospheric models. To deal with model biases, a modification of the standard 4D-Var algorithm, called weak-constraint 4D-Var, has been developed where a forcing term is introduced into the model to correct for the bias that accumulates along the model trajectory. This approach reduced the temperature bias in the stratosphere by up to 50% and is implemented in the ECMWF operational forecasting system. Despite different origins and applications, Data Assimilation and Deep Learning are both able to learn about the Earth system from observations. In this paper, a deep learning approach for model bias correction is developed using temperature retrievals from Radio Occultation (RO) measurements. Neural Networks require a large number of samples to properly capture the relationship between the temperature first-guess trajectory and the model bias. As running the IFS data assimilation system for extended periods of time with a fixed model version and at realistic resolutions is computationally very expensive, we have chosen to train, the initial Neural Networks are trained using the ERA5 reanalysis before using transfer learning on one year of the current IFS model. Preliminary results show that convolutional neural networks are adequate to estimate model bias from RO temperature retrievals. The different strengths and weaknesses of both deep learning and weak constraint 4D-Var are discussed, highlighting the potential for each method to learn model biases effectively and adaptively.