AI-Driven Correction of Precipitation Forecasts in Dense Urban Environments

Urban extreme precipitation presents a considerable challenge owing to its escalating societal repercussions and the constraints associated with opportune forecasting. This study presents an artificial intelligence (AI) model aimed at improving daily precipitation forecasts for complex urban environments, driven by a fully urbanized Weather Research and Forecasting (uWRF) model. Our use case is New York City, which has historically experienced significant social, infrastructural, and economic consequences from such events. Using New York state mesonet station observations, it was found that there were around 63 extreme precipitation (>39 mm/day) cases during the 2018–2023 summer season. For all these extreme precipitation cases, the uWRF model adequately predicts precipitation; however, biases exist in both the spatial and temporal occurrence of maximum precipitation. To address these issues, a well-tested AI model, Attention U-Net, has been applied to improve precipitation forecasts. In this case, uWRF hourly precipitation data at 1 km spatial resolution serves as the input, while the Multi-Radar/Multi-Sensor System (MRMS) daily accumulated precipitation data is used as the target variable. Future research will focus on evaluating the performance of the Attention U-Net model for several out-of-sample extreme precipitation events and further addressing spatial biases using MRMS data, with the goal of improving both the accuracy and reliability of forecasts for urban extreme precipitation. We will also explore transferability to other locations.