Short-term Precipitation Forecasting in The Netherlands: An Application of Convolutional LSTM neural networks to weather radar data

TL;DR

This paper demonstrates that ConvLSTM neural networks, combining CNNs and LSTMs, can effectively predict short-term precipitation patterns from weather radar data in the Netherlands, improving accuracy in complex weather scenarios.

Contribution

The study introduces a novel ConvLSTM autoencoder architecture tailored for short-term precipitation forecasting using radar data, showcasing its superior performance over traditional methods.

Findings

High accuracy in predicting precipitation movement and intensity

Effective modeling of spatial and temporal weather patterns

Potential for improved meteorological forecasting in complex regions

Abstract

This work addresses the challenge of short-term precipitation forecasting by applying Convolutional Long Short-Term Memory (ConvLSTM) neural networks to weather radar data from the Royal Netherlands Meteorological Institute (KNMI). The research exploits the combination of Convolutional Neural Networks (CNNs) layers for spatial pattern recognition and LSTM network layers for modelling temporal sequences, integrating these strengths into a ConvLSTM architecture. The model was trained and validated on weather radar data from the Netherlands. The model is an autoencoder consisting of nine layers, uniquely combining convolutional operations with LSTMs temporal processing, enabling it to capture the movement and intensity of precipitation systems. The training set comprised of sequences of radar images, with the model being tasked to predict precipitation patterns 1.5 hours ahead using the preceding data. Results indicate high accuracy in predicting the direction and intensity of precipitation movements. The findings of this study underscore the significant potential of ConvLSTM networks in meteorological forecasting, particularly in regions with complex weather patterns. It contributes to the field by offering a more accurate, data-driven approach to weather prediction, highlighting the broader applicability of ConvLSTM networks in meteorological tasks.