Temporal Super-Resolution, Ground Adjustment, and Advection Correction of Radar Rainfall Using 3-D-Convolutional Neural Networks
Julius Polz,
Luca Glawion,
Hiob Gebisso
et al.
Abstract:Weather radars are highly sophisticated tools for quantitative precipitation estimation and provide observations with unmatched spatial representativeness. However, their indirect measurement of precipitation high above ground leads to strong systematic errors compared to direct rain gauge measurements. Additionally, the temporal undersampling from 5-minute instantaneous radar measurements requires advection correction. We present ResRadNet, a 3D-convolutional residual neural network approach, to reduce these … Show more
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