Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data

• URL: http://arxiv.org/abs/2202.04964v1
• Date: Thu, 10 Feb 2022 11:37:00 GMT
• Title: Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data
• Authors: Andreas Holm Nielsen, Alexandros Iosifidis, Henrik Karstoft
• Abstract summary: 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.
• Score: 86.1450118623908
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change. Here, we investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) and transfer learning to forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. We apply state-of-the-art interpretation techniques from the machine learning literature to attribute particular regions of interest or potential teleconnections relevant for any given weather cluster prediction or regime transition. We demonstrate superior forecasting performance relative to several classical meteorological benchmarks, as well as logistic regression and random forests. 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. Finally, we find transfer learning to be of paramount importance, similar to previous data-driven atmospheric forecasting studies.

Related papers

• Data driven weather forecasts trained and initialised directly from observations [1.44556167750856]
  Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction. Data-driven systems have been trained to forecast future weather by learning from long historical records of past weather. We propose a new approach, training a neural network to predict future weather purely from historical observations.
  arXiv  Detail & Related papers  (2024-07-22T12:23:26Z)
• 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)
• Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [55.13352174687475]
  This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which Generalizes weather forecasts to Finer-grained Temporal scales. Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale. We introduce a lead time-aware training framework to promote the generalization of the model at different lead times.
  arXiv  Detail & Related papers  (2024-05-22T16:21:02Z)
• Forecasting the Future with Future Technologies: Advancements in Large Meteorological Models [3.332582598089642]
  The field of meteorological forecasting has undergone a significant transformation with the integration of large models. Models like FourCastNet, Pangu-Weather, GraphCast, ClimaX, and FengWu have made notable contributions by providing accurate, high-resolution forecasts.
  arXiv  Detail & Related papers  (2024-04-10T00:52:54Z)
• Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
  We present an AI-based data assimilation model, i.e., Adas, for global weather variables. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
  arXiv  Detail & Related papers  (2023-12-18T09:05:28Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Convolutional GRU Network for Seasonal Prediction of the El Ni\~no-Southern Oscillation [24.35408676030181]
  We present a modified Convolutional Gated Recurrent Unit (ConvGRU) network for the El Nino-Southern Oscillation (ENSO) region-temporal sequence prediction problem. The proposed ConvGRU network, with an encoder-decoder sequence-to-sequence structure, takes historical SST maps of the Pacific region as input and generates future SST maps for subsequent months within the ENSO region. The results demonstrate that the ConvGRU network significantly improves the predictability of the Nino 3.4 index compared to LIM, AF, and RNN.
  arXiv  Detail & Related papers  (2023-06-18T00:15:45Z)
• Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks [8.086653045816151]
  Current operational forecasting models are based on physics and use supercomputers to simulate the atmosphere. An emerging class of weather models based on neural networks represents a paradigm shift in weather forecasting. We present a neural network that is capable of large-scale precipitation forecasting up to twelve hours ahead.
  arXiv  Detail & Related papers  (2021-11-14T22:53:04Z)
• Short-term precipitation prediction using deep learning [5.1589108738893215]
  We show that a 3D convolutional neural network using a single frame of meteorology fields is capable of predicting the precipitation spatial distribution. The network is developed based on 39-years (1980-2018) data of meteorology and daily precipitation over the contiguous United States.
  arXiv  Detail & Related papers  (2021-10-05T06:37:24Z)
• A generative adversarial network approach to (ensemble) weather prediction [91.3755431537592]
  We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe. The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
  arXiv  Detail & Related papers  (2020-06-13T20:53:17Z)

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.