A hybrid data assimilation system based on machine learning
In one sentence. Hybrid DA system uses a bilinear network as tangent-linear/adjoint and a CNN to adaptively blend 4DVar and EnKF analyses, tested on Lorenz-96 with lower cost.
Abstract
In the earth sciences, numerical weather prediction (NWP) is the primary method of predicting future weather conditions, and its accuracy is affected by the initial conditions. Data assimilation (DA) can provide high-precision initial conditions for NWP. The hybrid 4DVar-EnKF is currently an advanced DA method used by many operational NWP centres. However, it has two major shortcomings: The complex development and maintenance of the tangent linear and adjoint models and the empirical combination of the results of 4DVar and EnKF. In this paper, a new hybrid DA method based on machine learning (HDA-ML) is presented to overcome these drawbacks. In the new method, the tangent linear and adjoint models in the 4DVar part of the hybrid algorithm can be easily obtained by using a bilinear neural network to replace the forecast model, and a CNN model is adopted to fuse the analysis of 4DVar and EnKF to adaptively obtain the optimal coefficient of combination rather than the empirical coefficient as in the traditional hybrid DA method. The hybrid DA methods are compared with the Lorenz-96 model using the true values as labels. The experimental results show that HDA-ML improves the assimilation performance and significantly reduces the time cost. Furthermore, using observations instead of the true values as labels in the training system is more realistic. The results show comparable assimilation performance to that in the experiments with the true values used as the labels. The experimental results show that the new method has great potential for application to operational NWP systems.
Labels
- Topic
- Data assimilation
- Type
- New model or method
- Method
- CNN / U-Net
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.3389/feart.2022.1012165 OpenAlex:W4313559365
- Added
- 2026-10-22
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