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

• URL: http://arxiv.org/abs/2312.01197v1
• Date: Sat, 2 Dec 2023 18:13:45 GMT
• Title: Short-term Precipitation Forecasting in The Netherlands: An Application of Convolutional LSTM neural networks to weather radar data
• Authors: Petros Demetrakopoulos
• Abstract summary: The research exploits the combination of Convolutional Neural Networks (CNNs) layers for spatial pattern recognition and LSTM network layers for modelling temporal sequences. The model was trained and validated on weather radar data from the Netherlands. Results indicate high accuracy in predicting the direction and intensity of precipitation movements.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Neural Networks with LSTM and GRU in Modeling Active Fires in the Amazon [0.0]
  This study presents a comprehensive methodology for modeling and forecasting the historical time series of active fire spots detected by the AQUA_M-T satellite in the Amazon, Brazil. The approach employs a mixed Recurrent Neural Network (RNN) model, combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to predict the monthly accumulations of daily detected active fire spots.
  arXiv  Detail & Related papers  (2024-09-04T13:11:59Z)
• Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators [4.852378895360775]
  We evaluate the efficiency of three surrogate models in speeding up experimental research by simulating land surface processes. Our findings indicate that while all models on average demonstrate high accuracy over the forecast period, the LSTM network excels in continental long-range predictions when carefully tuned.
  arXiv  Detail & Related papers  (2024-07-23T13:26:05Z)
• SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
  Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. Traditional methods often struggle to capture the complex dynamics of meteorological systems. We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
  arXiv  Detail & Related papers  (2024-05-29T08:00:15Z)
• VN-Net: Vision-Numerical Fusion Graph Convolutional Network for Sparse Spatio-Temporal Meteorological Forecasting [12.737085738169164]
  VN-Net is the first attempt to introduce GCN method to utilize multi-modal data for better handling sparse-temporal meteorological forecasting. VN-Net outperforms state-of-the-art by a significant margin on mean absolute error (MAE) and root mean square error (RMSE) for temperature, relative humidity, and forecasting.
  arXiv  Detail & Related papers  (2024-01-26T12:41:57Z)
• Learning Robust Precipitation Forecaster by Temporal Frame Interpolation [65.5045412005064]
  We develop a robust precipitation forecasting model that demonstrates resilience against spatial-temporal discrepancies. Our approach has led to significant improvements in forecasting precision, culminating in our model securing textit1st place in the transfer learning leaderboard of the textitWeather4cast'23 competition.
  arXiv  Detail & Related papers  (2023-11-30T08:22:08Z)
• Deep Learning for Day Forecasts from Sparse Observations [60.041805328514876]
  Deep neural networks offer an alternative paradigm for modeling weather conditions. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 has a high temporal and spatial resolution, respectively, up to 2 minutes and 1 km as well as a low operational latency.
  arXiv  Detail & Related papers  (2023-06-06T07:07:54Z)
• Time-to-Green predictions for fully-actuated signal control systems with supervised learning [56.66331540599836]
  This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods.
  arXiv  Detail & Related papers  (2022-08-24T07:50:43Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
  We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
  arXiv  Detail & Related papers  (2022-02-10T11:37:00Z)
• Numerical Weather Forecasting using Convolutional-LSTM with Attention and Context Matcher Mechanisms [10.759556555869798]
  We introduce a novel deep learning architecture for forecasting high-resolution weather data. Our Weather Model achieves significant performance improvements compared to baseline deep learning models.
  arXiv  Detail & Related papers  (2021-02-01T08:30:42Z)
• SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture [5.28539620288341]
  We show that it is possible to produce an accurate precipitation nowcast using a data-driven neural network approach. We evaluate our approaches on a real-life datasets using precipitation maps from the region of the Netherlands and binary images of cloud coverage of France.
  arXiv  Detail & Related papers  (2020-07-08T20:33:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.