RIKEN’s activities to integrate data assimilation and AI/ML

At RIKEN, the Japan’s national flagship research institute for all sciences, we have been exploring several attempts to integrate data assimilation (DA) and AI/ML. DA integrates the (usually process-driven) model and data, while AI/ML is purely data driven and is proven to be very powerful in many applications. An example is to integrate data-driven AI/ML-based precipitation nowcasting with process-driven numerical weather prediction (NWP). We developed a nowcasting system based on a convolutional long short term memory (LSTM) which takes several time steps of 2-D precipitation image data to predict future images. NWP with radar DA produces future precipitation images, which can be input to the data-driven LSTM to further improve the predicted images. Another example is to develop ML’ed observation operators for satellite radiances. We obtained an improvement by purely ML’ed observation operators without any information from a physically based radiative transfer model. The third example is to use DA with an ML’ed surrogate model for producing more accurate analyses for further training the ML’ed surrogate model. We found that DA with flow-independent background error covariance could produce more accurate ML’ed surrogate model, but ensemble-based DA resulted in a mixed situation probably because the ensemble forecasts by the ML’ed surrogate model may not produce proper error covariance. We also explored developing a limited-area ML’ed surrogate NWP model in collaboration with IMT-Atlantique. In this presentation, we will share the most recent activities of integrating DA and AI/ML at RIKEN.