Polar Vortex Multi-Day Intensity Prediction Relying on New Deep Learning Model: A Combined Convolution Neural Network with Long Short-Term Memory Based on Gaussian Smoothing Method

The initial field has a crucial influence on numerical weather prediction (NWP). Data assimilation (DA) is a reliable method to obtain the initial field of the forecast model. At the same time, data are the carriers of information. Observational data are a concrete representation of information. DA is also the process of sorting observation data, during which entropy gradually decreases. Four-dimensional variational assimilation (4D-Var) is the most popular approach. However, due to the complexity of the physical model, the tangent linear and adjoint models, and other processes, the realization of a 4D-Var system is complicated, and the computational efficiency is expensive. Machine learning (ML) is a method of gaining simulation results by training a large amount of data. It achieves remarkable success in various applications, and operational NWP and DA are no exception. In this work, we synthesize insights and techniques from previous studies to design a pure data-driven 4D-Var implementation framework named ML-4DVAR based on the bilinear neural [d=Dong.R]network networks (BNN). The framework replaces the traditional physical model with the BNN model for prediction. Moreover, it directly makes use of [d=Dong.R]the ML model obtained from the simulation data the simulation data obtained from the learning model to implement the primary process of 4D-Var, including the realization of [d=Dong.R]the short-term forecast process the model short-term forecast and the tangent linear and adjoint models. We test a strong-constraint 4D-Var system with the Lorenz-96 model, and we compared the traditional 4D-Var system with ML-4DVAR. The experimental results demonstrate that the ML-4DVAR framework can achieve better assimilation results and significantly improve computational efficiency.

show abstract

“…The definition of RMSE is shown in Equation ( 10 ).

Determinable coefficient (

) is a statistic that measures the goodness of fit [ 43 ].

is the ratio of the covariance of the two datasets to the standard deviation of the two datasets.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Framework for Four-Dimensional Variational Data Assimilation Based on Machine Learning

Dong

Leng

Zhao

et al. 2022

Entropy

View full text Add to dashboard Cite

show abstract

“…Previous studies used different statistical models to predict stratospheric variability and identify SSW events at timescales longer than 1 week, such as multiple linear regression (MLR) and fully connected neural networks (NNs), and found that a well-trained NN can exhibit promising skill in the prediction of SPV variations (e.g., Blume and Matthes, 2012; Peng et al, 2021). However, given the multiple variables with different lead times used in these studies and the nonlinear structure of NNs, the key factors and features that the NNs use to make the prediction are not clear.…”

Section: Motivationmentioning

confidence: 99%

Modeling stratospheric polar vortex variation and identifying vortex extremes using explainable machine learning

Beucler

Székely

et al. 2022

Environ. Data Science

View full text Add to dashboard Cite

The winter stratospheric polar vortex (SPV) exhibits considerable variability in magnitude and structure, which can result in extreme SPV events. These extremes can subsequently influence weather in the troposphere from weeks to months and thus are important sources of surface predictability. However, the predictability of the SPV extreme events is limited to 1–2 weeks in state-of-the-art prediction systems. Longer predictability timescales of SPV would strongly benefit long-range surface prediction. One potential option for extending predictability timescales is the use of machine learning (ML). However, it is often unclear which predictors and patterns are important for ML models to make a successful prediction. Here we use explainable multiple linear regressions (MLRs) and an explainable artificial neural network (ANN) framework to model SPV variations and identify one type of extreme SPV events called sudden stratospheric warmings. We employ a NN attribution method to propagate the ANN’s decision-making process backward and uncover feature importance in the predictors. The feature importance of the input is consistent with the known precursors for extreme SPV events. This consistency provides confidence that ANNs can extract reliable and physically meaningful indicators for the prediction of the SPV. In addition, our study shows a simple MLR model can predict the SPV daily variations using sequential feature selection, which provides hints for the connections between the input features and the SPV variations. Our results indicate the potential of explainable ML techniques in predicting stratospheric variability and extreme events, and in searching for potential precursors for these events on extended-range timescales.

show abstract

Exploiting a variational auto-encoder to represent the evolution of sudden stratospheric warmings

Chen,

Liang,

et al. 2024

Environ. Res.: Climate

View full text Add to dashboard Cite

Sudden stratospheric warmings (SSWs) are the most dramatic events in the wintertime stratosphere. Such extreme events are characterized by substantial disruption to the stratospheric polar vortex, which can be categorized into displacement and splitting types depending on the morphology of the disrupted vortex. Moreover, SSWs are usually followed by anomalous tropospheric circulation regimes that are important for subseasonal-to-seasonal prediction. Thus, monitoring the genesis and evolution of SSWs is crucial and deserves further advancement. Despite several analysis methods that have been used to study the evolution of SSWs, the ability of deep learning methods has not yet been explored, mainly due to the relative scarcity of observed events. To overcome the limited observational sample size, we use data from historical simulations of the Whole Atmosphere Community Climate Model version 6 to identify thousands of simulated SSWs, and use their spatial patterns to train the deep learning model. We utilize a convolutional neural network combined with a variational auto-encoder – a generative deep learning model – to construct a phase diagram that characterizes the SSW evolution. This approach not only allows us to create a latent space that encapsulates the essential features of the vortex structure during SSWs, but also offers new insights into its spatiotemporal evolution mapping onto the phase diagram. The constructed phase diagram depicts a continuous transition of the vortex pattern during SSWs. Notably, it provides a new perspective for discussing the evolutionary paths of SSWs: the variational auto-encoder gives a better-reconstructed vortex morphology and more clearly organized vortex regimes for both displacement-type and split-type events than those obtained from principal component analysis. Our results provide an innovative phase diagram to portray the evolution of SSWs, in which particularly the splitting SSWs are better characterized. Our findings support the future use of deep learning techniques to study the underlying dynamics of extreme stratospheric vortex phenomena, and to establish a benchmark to evaluate model performance in simulating SSWs.

show abstract

Polar Vortex Multi-Day Intensity Prediction Relying on New Deep Learning Model: A Combined Convolution Neural Network with Long Short-Term Memory Based on Gaussian Smoothing Method

Cited by 3 publications

References 89 publications

A Framework for Four-Dimensional Variational Data Assimilation Based on Machine Learning

A Framework for Four-Dimensional Variational Data Assimilation Based on Machine Learning

Modeling stratospheric polar vortex variation and identifying vortex extremes using explainable machine learning

Exploiting a variational auto-encoder to represent the evolution of sudden stratospheric warmings

Contact Info

Product

Resources

About