Generative assimilation and prediction for weather and climate

• URL: http://arxiv.org/abs/2503.03038v1
• Date: Tue, 04 Mar 2025 22:36:29 GMT
• Title: Generative assimilation and prediction for weather and climate
• Authors: Shangshang Yang, Congyi Nai, Xinyan Liu, Weidong Li, Jie Chao, Jingnan Wang, Leyi Wang, Xichen Li, Xi Chen, Bo Lu, Ziniu Xiao, Niklas Boers, Huiling Yuan, Baoxiang Pan,
• Abstract summary: We introduce Generative Assimilation and Prediction (GAP)<n>GAP is a unified framework for assimilation and prediction of both weather and climate.<n>It excels in a broad range of weather-climate related tasks, including data assimilation, seamless prediction, and climate simulation.
• Score: 9.319028023682494
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks. However, existing approaches mostly focus on the prediction task, and do not incorporate the necessary data assimilation. Moreover, these models suffer from error accumulation in long roll-outs, limiting their applicability to seasonal predictions or climate projections. Here, we introduce Generative Assimilation and Prediction (GAP), a unified deep generative framework for assimilation and prediction of both weather and climate. By learning to quantify the probabilistic distribution of atmospheric states under observational, predictive, and external forcing constraints, GAP excels in a broad range of weather-climate related tasks, including data assimilation, seamless prediction, and climate simulation. In particular, GAP is competitive with state-of-the-art ensemble assimilation, probabilistic weather forecast and seasonal prediction, yields stable millennial simulations, and reproduces climate variability from daily to decadal time scales.

Related papers

• Data-driven Seasonal Climate Predictions via Variational Inference and Transformers [31.98107454758077]
  We train generative models on climate model output for seasonal predictions. We analyse the method's performance in predicting interannual anomalies beyond the climate change-induced trend.
  arXiv  Detail & Related papers  (2025-03-26T11:51:23Z)
• FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere [53.22497376154084]
  We propose FengWu-Weather to Subseasonal (FengWu-W2S), which builds on the FengWu global weather forecast model and incorporates an ocean-atmosphere-land coupling structure along with a diverse perturbation strategy. Our hindcast results demonstrate that FengWu-W2S reliably predicts atmospheric conditions out to 3-6 weeks ahead, enhancing predictive capabilities for global surface air temperature, precipitation, geopotential height and intraseasonal signals such as the Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO) Our ablation experiments on forecast error growth from daily to seasonal timescales reveal potential
  arXiv  Detail & Related papers  (2024-11-15T13:44:37Z)
• Enhancing multivariate post-processed visibility predictions utilizing CAMS forecasts [0.0]
  Weather forecasts increasingly incorporate ensemble predictions of visibility. Post-processing is recommended to enhance the reliability and accuracy of predictions. Our study confirms that post-processed forecasts are substantially superior to raw and climatological predictions.
  arXiv  Detail & Related papers  (2024-06-20T09:57:49Z)
• Uncertainty quantification for data-driven weather models [0.0]
  We study and compare uncertainty quantification methods to generate probabilistic weather forecasts from a state-of-the-art deterministic data-driven weather model, Pangu-Weather. Specifically, we compare approaches for quantifying forecast uncertainty based on generating ensemble forecasts via perturbations to the initial conditions. In a case study on medium-range forecasts of selected weather variables over Europe, the probabilistic forecasts obtained by using the Pangu-Weather model in concert with uncertainty quantification methods show promising results.
  arXiv  Detail & Related papers  (2024-03-20T10:07:51Z)
• Weather Prediction with Diffusion Guided by Realistic Forecast Processes [49.07556359513563]
  We introduce a novel method that applies diffusion models (DM) for weather forecasting. Our method can achieve both direct and iterative forecasting with the same modeling framework. The flexibility and controllability of our model empowers a more trustworthy DL system for the general weather community.
  arXiv  Detail & Related papers  (2024-02-06T21:28:42Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• 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)
• SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models [13.331224394143117]
  Uncertainty quantification is crucial to decision-making. dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts. We propose to amortize the computational cost by emulating these forecasts with deep generative diffusion models learned from historical data.
  arXiv  Detail & Related papers  (2023-06-24T22:00:06Z)
• Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting [10.083361616081874]
  This study explores an application of machine learning (ML) models as post-processing tools for subseasonal forecasting. Lagged numerical ensemble forecasts and observational data, including relative humidity, pressure at sea level, and geopotential height, are incorporated into various ML methods. For regression, quantile regression, and tercile classification tasks, we consider using linear models, random forests, convolutional neural networks, and stacked models.
  arXiv  Detail & Related papers  (2022-11-29T01:11:04Z)
• Improving seasonal forecast using probabilistic deep learning [1.1988695717766686]
  We develop a probabilistic deep neural network model to enhance seasonal forecast capability and forecast diagnosis. By leveraging complex physical relationships encoded in climate simulations, our model demonstrates favorable deterministic and probabilistic skill. We give a more definitive answer to how the El Nino/Southern Oscillation, the dominant mode of seasonal variability, modulates global seasonal predictability.
  arXiv  Detail & Related papers  (2020-10-27T21:02:26Z)
• 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.