Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings

• URL: http://arxiv.org/abs/2504.09340v1
• Date: Sat, 12 Apr 2025 20:58:02 GMT
• Title: Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings
• Authors: Simon Adamov, Joel Oskarsson, Leif Denby, Tomas Landelius, Kasper Hintz, Simon Christiansen, Irene Schicker, Carlos Osuna, Fredrik Lindsten, Oliver Fuhrer, Sebastian Schemm,
• Abstract summary: We present a framework for building kilometer-scale machine learning limited area models with boundary conditions imposed through a flexible boundary forcing method.<n>We perform systematic evaluation of different design choices, including the boundary width, graph construction and boundary forcing integration.<n>Our findings demonstrate great potential for machine learning limited area models in the future of regional weather forecasting.
• Score: 8.597348667137577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning is revolutionizing global weather forecasting, with models that efficiently produce highly accurate forecasts. Apart from global forecasting there is also a large value in high-resolution regional weather forecasts, focusing on accurate simulations of the atmosphere for a limited area. Initial attempts have been made to use machine learning for such limited area scenarios, but these experiments do not consider realistic forecasting settings and do not investigate the many design choices involved. We present a framework for building kilometer-scale machine learning limited area models with boundary conditions imposed through a flexible boundary forcing method. This enables boundary conditions defined either from reanalysis or operational forecast data. Our approach employs specialized graph constructions with rectangular and triangular meshes, along with multi-step rollout training strategies to improve temporal consistency. We perform systematic evaluation of different design choices, including the boundary width, graph construction and boundary forcing integration. Models are evaluated across both a Danish and a Swiss domain, two regions that exhibit different orographical characteristics. Verification is performed against both gridded analysis data and in-situ observations, including a case study for the storm Ciara in February 2020. Both models achieve skillful predictions across a wide range of variables, with our Swiss model outperforming the numerical weather prediction baseline for key surface variables. With their substantially lower computational cost, our findings demonstrate great potential for machine learning limited area models in the future of regional weather forecasting.

Related papers

• 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)
• CaFA: Global Weather Forecasting with Factorized Attention on Sphere [7.687215328455751]
  We propose a factorized-attention-based model tailored for spherical geometries to mitigate this issue. The deterministic forecasting accuracy of the proposed model on $1.5circ$ and 0-7 days' lead time is on par with state-of-the-art purely data-driven machine learning weather prediction models.
  arXiv  Detail & Related papers  (2024-05-12T23:18:14Z)
• 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)
• Graph-based Neural Weather Prediction for Limited Area Modeling [12.576113481317527]
  We adapt the graph-based NeurWP approach to the limited area setting and propose a multi-scale hierarchical model extension. Our approach is validated by experiments with a local model for the Nordic region.
  arXiv  Detail & Related papers  (2023-09-29T16:20:34Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• A case study of spatiotemporal forecasting techniques for weather forecasting [4.347494885647007]
  The correlations of real-world processes aretemporal, and the data generated by them exhibits both spatial and temporal evolution. Time series-based models are a viable alternative to numerical forecasts. We show that decompositiontemporal prediction models reduced computational costs while improving accuracy.
  arXiv  Detail & Related papers  (2022-09-29T13:47:02Z)
• Increasing the accuracy and resolution of precipitation forecasts using deep generative models [3.8073142980733]
  We train a conditional Generative Adversarial Network -- coined CorrectorGAN -- to produce ensembles of high-resolution, bias-corrected forecasts. CorrectorGAN, once trained, produces predictions in seconds on a single machine. Results raise exciting questions about the necessity of regional models, and whether data-driven downscaling and correction methods can be transferred to data-poor regions.
  arXiv  Detail & Related papers  (2022-03-23T09:45:12Z)
• Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere [7.918783985810551]
  We present a significantly-improved data-driven global weather forecasting framework using a deep convolutional neural network (CNN) New developments in this framework include an offline volume-conservative mapping to a cubed-sphere grid. Our model is able to learn to forecast complex surface temperature patterns from few input atmospheric state variables.
  arXiv  Detail & Related papers  (2020-03-15T19:57:34Z)

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.