Combining NWP and Observations with AI

The SINFONY Project (seamless integrated forecasting system) aims to enable seamless forecast between the Nowcasting (NWC) on the one hand and numerical weather prediction (NWP) on the other. The advantages of both systems are that NWP is reliable for longer lead times and NWC is a fast product. Whereas the disadvantages are that NWP is computational expensive and NWC is unreliable for longer lead times.

For example, in convective processes, NWP may not capture convective features on small scales and NWC may not capture the evolution of convective dynamics. A solution to this problem would be to combine the information provided by NWP with the recent data from observational systems and NWC. This is where the new methods of AI in weather forecasting and data assimilation can help us.

In our work we examine the application of the AI-Var algorithm (proposed by J. Keller and R. Potthast, arXiv:2406.00390) to convective scale weather forecasting. This algorithm allows for a fast calculation of the analysis state given a forecast and (nowcasted) observations. For this reason, we investigate how the temporal evolution of uncertainties can be included in the AI-Var algorithm.

More precisely, we show first conceptual results of a reformulation of the AI-Var algorithm. In this approach we are able to include time dependent background error correlations (“error of the day”). For example, we apply the algorithm to 2m-temperature and precipitation. In the future we plan to further include observations such as radiation, wind gusts, visibility, ceiling (clouds) and others.