Theoretical and practical assessment of X-band weather radar rainfall estimation

One common issue of high-resolution X-band weather radars is the attenuation phenomenon caused by extreme rainfall and cloud burst, typical in southern Sweden. The granted project aims to investigate how the wealth of data from several scans of a network of X-band weather radars at different elevation angles can be integrated to address a single radar’s attenuation and overshooting issues. The generated dataset fully covers the overlapping area of the X-band weather radars spatially and temporally. In the next step, we investigate how using a spatiotemporally complete high resolution precipitation dataset can improve the in-house hydraulic models (MIKE) of the municipality of south Sweden (i.e., Malmö, Eslöv, etc.). For this, we replace the traditional forcing data, i.e., rain gauge data, with the developed spatio-temporal dataset and recalibrate the models. The proposed project aims to develop an AI-based flow model capable of simulating and predicting flow to the sewer and stormwater flow systems. We investigate whether or not the AI-based approach can represent the urban rainfall-runoff system better than the conventional numerical
approach (MIKE model).