# Precipitation nowcasting using a stochastic variational frame predictor with learned prior distribution

arXiv: https://arxiv.org/abs/1905.05037 · HTML: https://mlsanity.com/paper/1905.05037/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2019.

## Built on by (most cited first, 4 of 4)

- [Physics-informed neural networks for the shallow-water equations on the sphere](https://mlsanity.com/paper/2104.00615/) (2021)
- [Nowcasting-Nets: Deep Neural Network Structures for Precipitation Nowcasting Using IMERG](https://mlsanity.com/paper/2108.06868/) (2021)
- [Precipitaion Nowcasting using Deep Neural Network](https://mlsanity.com/paper/2203.13263/) (2022)
- [Improving trajectory calculations using deep learning inspired single image superresolution](https://mlsanity.com/paper/2206.04015/) (2022)

## Abstract (arXiv)

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.