We propose the use of a stochastic variational frame prediction deep neural network with a learned prior distribution trained on two-dimensional rain radar reflectivity maps for precipitation nowcasting with lead times of up to 2 1 2 hours. We present a comparison to a standard convolutional LSTM network and assess the evolution of the structural similarity index for both methods. Case studies are presented that illustrate that the novel methodology can yield meaningful forecasts without excessive blur for the time horizons of interest.
[1] Short term precipitation forecasts based on Lagrangian advection of radar echoes are robust and have more skill than numerical weather prediction models over time scales of several hours. This is because the models do not generally capture well the initial precipitation distribution. We will refer to the advection-based methods as radar nowcasts. Over longer time scales, we expect the models to perform better than nowcast methods as they resolve dynamically the large scale flow. We verify this conceptual picture of the relative accuracy of radar nowcasts and model forecasts using conventional skill scores. We identify the cross-over point in time where model forecasts start to have more skill than nowcast methods. This occurs at about 6 hours after the forecast is initiated. Abstract Short-term high-resolution precipitation forecasting has important implications for navigation, flood forecasting, and other hydrological and meteorological concerns. This article introduces a pixel-based algorithm for Short-term Quantitative Precipitation Forecasting (SQPF) using radar-based rainfall data. The proposed algorithm called Pixel- Based Nowcasting (PBN) tracks severe storms with a hierarchical mesh-tracking algorithm to capture storm advection in space and time at high resolution from radar imagers. The extracted advection field is then extended to nowcast the rainfall field in the next 3 hr based on a pixel-based Lagrangian dynamic model. The proposed algorithm is compared with two other nowcasting algorithms (WCN: Watershed-Clustering Nowcasting and PER: PERsistency) for ten thunderstorm events over the conterminous United States. Object-based verification metric and traditional statistics have been used to evaluate the performance of the proposed algorithm. It is shown that the proposed algorithm is superior over comparison algorithms and is effective in tracking and predicting severe storm events for the next few hours. A new advection-based nowcasting scheme for precipitation has been developed for the Gandolf system. The method employed removes the need to split the radar analysis into contiguous rain areas (CRA's) and uses a smoothness constraint. Thus, it is similar to the Variational Echo Tracking approach. Optical flow ideas are used to diagnose the advection velocity of blocks within rain analyses, which involve the direct solution of the Lagrangian persistence equation. In block-based approaches, the radar analyses are partitioned a-priori and a CRA may be split over a number of blocks. This scheme is compared with the old Gandolf advection scheme, which is based on CRA's, and the new scheme performs better-both in cases associated with severe flooding and over a continuous verification period of 3 months. The benefit is conjectured to be due mainly to the difficulty in unambiguously identifying CRA's, which is particularly troublesome on small domains, such as the UK. Thus, it is concluded that block-based methods are likely to be superior to object-based methods in the majority of cases.