RAINFALL NOWCASTING BY COMBINING RADAR, RAIN GAUGE AND MICROWAVE LINK MEASUREMENTS

The objective of this work is to provide high-resolution rain rate estimates and nowcasts for hydro-meteorological application, by combing radar, rain gauge and microwave link data. The goal is to retrieve with greater accuracy the rain rate at the ground level, that is consistent with all the different measurements, incorporating the uncertainty associated with the different sources of information. A variational approach (3DVar) has been used to find the best estimate of the rain rate, and its error covariance. Nowcasts are then produced by assuming Lagrangian persistence. The velocity field is obtained from the radar-derived rain fields, and the rain rate field is advected using the Total Variance Diminishing (TVD) scheme. Since the forecast windows are smaller than 6h, we can assume that the advection, so the propagation model, is linear. This allows us to also propagate the error covariance of the rain rate, and we can use these two in the 3DVar at the next observation time. This approach can be seen as a Variational Kalman Filter (VKF), in which the covariance of the prior is not constant but dependent on time. Idealised experiments show that the VKF is precise and gives good results due to the dynamic error covariance, it is stable and the bias can be kept under control. It is a convenient form for online real-time processing, it is easy to formulate and implement. The proposed approach is tested using real data from 14 rain gauges, 14 microwave links and the operational radar rain product from MeteoSwiss located in Zürich (Switzerland).