A Qualitative and Quantitative Evaluation of the WRF Model Simulations for High Resolution Urban Rainfall Forecasting 

Abstract

In the past decade, there has been an increment in the magnitude and frequency of severe flood events across Bangalore city due to rise in rainfall intensities, increase in urban population, and drastic changes in urban landscape. The effect of urban development in a rapidly growing city has a substantial impact on the urban environment, leading to frequent flooding during monsoon and post-monsoon. Hence, a reliable forecasting system for rainfall at an urban scale is of priority to enhance the preparedness for disaster management. In this regard, a framework is developed for Bangalore City to dynamically downscale the daily rainfall prediction from National Centers for Environmental Prediction-Global Forecast System (NCEP-GFS) to high-resolution rainfall predictions, 3 km and 1 km spatial resolutions at 15-minute interval, employing the Weather Research and Forecasting (WRF) model. The model utilizes the initial and the boundary conditions forced at 06 UTC, resulting in a 24-hour lead-time forecast. The primary objective of this study is to test the performance of high-resolution WRF forecast with respect to the observed rainfall, using qualitative and quantitative statistical skill scores for the monsoon of 2023. Rank probability score at the municipal administrative level and performance indices such as critical success index, bias score, heidke skill score, false alarm ratio, and probability of detection at grid level are used for qualitative analysis. Whereas, quantitative measures are coefficient of determination, correlation, root mean square error, mean bias, and mean absolute error at grid as well as station levels. These metrics are estimated for various rainfall events and for different lead times. The study found that, the grid level correlation coefficient values for heavy rainfall events in 2023 fall in the range of 0.6 – 0.8 for the northern part of Bangalore city for both the spatial resolutions. Overall, our findings suggest that the forecasting framework can efficiently issue rainfall prediction with a lead time of 24 hours. This forecast can be further coupled with 1D and 2D hydrological models to predict flood inundation.