[AAS01-10] Toward an integrated NWP-DA-AI system for precipitation prediction
Keywords:nowcast, machine learning, NWP
Our 30-second-update 3D nowcasting system is running in real time since 2017; the system adopts an optical-flow-based algorithm in the 3D space. Because convective clouds evolve rapidly within a 10-minute forecast, sometimes the assumption of Lagrangian persistence is violated, and the prediction skill of the optical-flow-based system drops quickly with the forecast lead time. The SCALE-LETKF system, on the other hand, provides physically based predictions; therefore, we would expect that the NWP outperforms the nowcast for longer forecasts. Therefore, merging NWP and nowcast will provide better predictions compared to each of them.
Recent advances in the machine-learning algorithms will provide an efficient algorithm for that purpose. In this study, a three-dimensional extension of the Convolutional Long Short-Term Memory (Conv-LSTM; Shi et al., 2015), a kind of deep-learning algorithm, is applied to PAWR nowcasting. In addition to the Conv-LSTM with past observations, we also develop a Conv-LSTM that accepts forecast data from numerical weather prediction (NWP) or optical-flow-based nowcast. NWP uses HPC resources with full physics equations of the atmosphere, so that Conv-LSTM with NWP would be a new direction toward fusing Big Data and HPC, in which training with the big data from high-resolution NWP and Data Assimilation (DA), as well as PAWR observation would be a challenge.
The 3D Conv-LSTM successfully made predictions of convective storms; in some cases, Conv-LSTM had additional skill in capturing intensification and weakening of precipitation that were not predicted by the optical-flow. On average, the Conv-LSTM-based system outperformed the optical-flow-based system statistically. Furthermore, Conv-LSTM with forecast data outperformed that without forecast data.