WSSM: Geographic-enhanced hierarchical state-space model for global station weather forecast

• URL: http://arxiv.org/abs/2501.11238v1
• Date: Mon, 20 Jan 2025 02:57:02 GMT
• Title: WSSM: Geographic-enhanced hierarchical state-space model for global station weather forecast
• Authors: Songru Yang, Zili Liu, Zhenwei Shi, Zhengxia Zou,
• Abstract summary: We introduce a novel Mamba-based approach tailored for Global Station Weather Forecasting (GSWF) Geographical knowledge is integrated in addition to the widely-used positional encoding to represent the absolute special-temporal position. Our method effectively improves the overall prediction accuracy and addresses the challenge of forecasting extreme weather events.
• Score: 24.738518411545485
• License:
• Abstract: Global Station Weather Forecasting (GSWF), a prominent meteorological research area, is pivotal in providing timely localized weather predictions. Despite the progress existing models have made in the overall accuracy of the GSWF, executing high-precision extreme event prediction still presents a substantial challenge. The recent emergence of state-space models, with their ability to efficiently capture continuous-time dynamics and latent states, offer potential solutions. However, early investigations indicated that Mamba underperforms in the context of GSWF, suggesting further adaptation and optimization. To tackle this problem, in this paper, we introduce Weather State-space Model (WSSM), a novel Mamba-based approach tailored for GSWF. Geographical knowledge is integrated in addition to the widely-used positional encoding to represent the absolute special-temporal position. The multi-scale time-frequency features are synthesized from coarse to fine to model the seasonal to extreme weather dynamic. Our method effectively improves the overall prediction accuracy and addresses the challenge of forecasting extreme weather events. The state-of-the-art results obtained on the Weather-5K subset underscore the efficacy of the WSSM

Related papers

• OneForecast: A Universal Framework for Global and Regional Weather Forecasting [44.203835175341]
  This paper proposes a global-regional nested weather forecasting framework based on graph neural networks (GNNs) By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region to capture local high-frequency features. For high-resolution regional forecasts, we propose a neural nested grid method to mitigate boundary information loss.
  arXiv  Detail & Related papers  (2025-02-01T06:49:16Z)
• Advancing Marine Heatwave Forecasts: An Integrated Deep Learning Approach [3.8713566366330325]
  Extreme climate phenomenon heatwaves (MHWs) pose significant challenges to marine ecosystems and industries. This study introduces an integrated deep learning approach to forecast short-to-long-term MHWs on a global scale.
  arXiv  Detail & Related papers  (2024-11-19T06:11:52Z)
• 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)
• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• Leveraging data-driven weather models for improving numerical weather prediction skill through large-scale spectral nudging [1.747339718564314]
  This study illustrates the relative strengths and weaknesses of physics-based and AI-based approaches to weather prediction. A hybrid NWP-AI system is proposed, wherein GEM-predicted large-scale state variables are spectrally nudged toward GraphCast predictions. Results indicate that this hybrid approach is capable of leveraging the strengths of GraphCast to enhance the prediction skill of the GEM model.
  arXiv  Detail & Related papers  (2024-07-08T16:39:25Z)
• How far are today's time-series models from real-world weather forecasting applications? [22.68937280154092]
  WEATHER-5K is a comprehensive collection of observational weather data that better reflects real-world scenarios. It enables a better training of models and a more accurate assessment of the real-world forecasting capabilities of TSF models. We provide researchers with a clear assessment of the gap between academic TSF models and real-world weather forecasting applications.
  arXiv  Detail & Related papers  (2024-06-20T15:18:52Z)
• 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)
• FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [56.73502043159699]
  This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution. It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model. The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
  arXiv  Detail & Related papers  (2024-01-28T13:23:25Z)
• 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)
• Generative Modeling of High-resolution Global Precipitation Forecasts [2.1485350418225244]
  We present improvements to the architecture and training process of a current state-of-the art deep learning precipitation model (FourCastNet) using a novel generative adversarial network (GAN) Our improvements achieve superior performance in capturing the extreme percentiles of global precipitation, while comparable to state-of-the-art NWP models in terms of forecast skill at 1--2 day lead times.
  arXiv  Detail & Related papers  (2022-10-22T17:21:16Z)

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.