High-Resolution Radar Echo Prediction with Machine Learning

The standard for weather radar nowcasting in the Central Europe region is the COTREC extrapolation method. We propose a recurrent neural network based on the PredRNN architecture, which outperforms the COTREC 60 minutes predictions by a significant margin.

Nowcasting, as a complement to numerical weather predictions, is a well-known concept. However, the increasing speed of information flow in our society today creates an opportunity for its effective implementation. Methods currently used for these predictions are primarily based on the optical flow and are struggling in the prediction of the development of the echo shape and intensity.

In this work, we are benefiting from a data-driven approach and building on the advances in the capabilities of neural networks for computer vision. We define the prediction task as an extrapolation of sequences of the latest weather radar echo measurements. To capture the spatiotemporal behaviour of rainfall and storms correctly, we propose the use of a recurrent neural network using a combination of long short term memory (LSTM) techniques with convolutional neural networks (CNN). Our approach is applicable to any geographical area, radar network resolution and refresh rate.

We conducted the experiments comparing predictions for 10 to 60 minutes into the future with the Critical Success Index, which evaluates the spatial accuracy of the predicted echo and Mean Squared Error. Our neural network model has been trained with three years of rainfall data captured by weather radars over the Czech Republic. Results for our bordered testing domain show that our method achieves comparable or better scores than both COTREC and optical flow extrapolation methods available in the open-source pySTEPS and rainymotion libraries.

With our work, we aim to contribute to the nowcasting research in general and create another source of short-time predictions for both experts and the general public.