  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.
