Post-processing improves accuracy of Artificial Intelligence weather forecasts

• URL: http://arxiv.org/abs/2504.12672v1
• Date: Thu, 17 Apr 2025 06:05:10 GMT
• Title: Post-processing improves accuracy of Artificial Intelligence weather forecasts
• Authors: Belinda Trotta, Robert Johnson, Catherine de Burgh-Day, Debra Hudson, Esteban Abellan, James Canvin, Andrew Kelly, Daniel Mentiplay, Benjamin Owen, Jennifer Whelan,
• Abstract summary: We test the application of Bureau of Meteorology's statistical post-processing system, IMPROVER, to ECMWF's deterministic AIFS.<n>We show that blending AIFS with NWP models improves overall forecast skill, even when AIFS alone is not the most accurate component.
• Score: 0.14043931310479374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial Intelligence (AI) weather models are now reaching operational-grade performance for some variables, but like traditional Numerical Weather Prediction (NWP) models, they exhibit systematic biases and reliability issues. We test the application of the Bureau of Meteorology's existing statistical post-processing system, IMPROVER, to ECMWF's deterministic Artificial Intelligence Forecasting System (AIFS), and compare results against post-processed outputs from the ECMWF HRES and ENS models. Without any modification to configuration or processing workflows, post-processing yields comparable accuracy improvements for AIFS as for traditional NWP forecasts, in both expected value and probabilistic outputs. We show that blending AIFS with NWP models improves overall forecast skill, even when AIFS alone is not the most accurate component. These findings show that statistical post-processing methods developed for NWP are directly applicable to AI models, enabling national meteorological centres to incorporate AI forecasts into existing workflows in a low-risk, incremental fashion.

Related papers

• OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from Observations [11.729902584481767]
  OMG-HD is an AI-based high-resolution weather forecasting model designed to make predictions directly from observational data sources.<n>We achieve up to a 13% improvement on RMSE for 2-meter temperature, 17% on 10-meter wind speed, 48% on 2-meter specific humidity, and 32% on surface pressure.
  arXiv  Detail & Related papers  (2024-12-24T07:46:50Z)
• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models [0.08271752505511926]
  Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. Recently released suite of AI-based weather models produces medium-range forecasts within seconds. We assess the forecast skill of three top-performing AI-models for convective parameters against reanalysis and ECMWF's operational numerical weather prediction model IFS.
  arXiv  Detail & Related papers  (2024-06-13T07:46:03Z)
• 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)
• 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)
• FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation [67.20588721130623]
  We develop an AI-based cyclic weather forecasting system, FengWu-4DVar. FengWu-4DVar can incorporate observational data into the data-driven weather forecasting model. Experiments on the simulated observational dataset demonstrate that FengWu-4DVar is capable of generating reasonable analysis fields.
  arXiv  Detail & Related papers  (2023-12-16T02:07:56Z)
• Comparative Evaluation of Metaheuristic Algorithms for Hyperparameter Selection in Short-Term Weather Forecasting [0.0]
  This paper explores the application of metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) We evaluate their performance in weather forecasting based on metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE)
  arXiv  Detail & Related papers  (2023-09-05T22:13:35Z)
• 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 S-curves: Recurrent Neural Networks for Technology Forecasting [60.82125150951035]
  We develop an autencoder approach that employs recent advances in machine learning and time series forecasting. S-curves forecasts largely exhibit a mean average percentage error (MAPE) comparable to a simple ARIMA baseline. Our autoencoder approach improves the MAPE by 13.5% on average over the second-best result.
  arXiv  Detail & Related papers  (2022-11-28T14:16:22Z)
• Deep Learning for Post-Processing Ensemble Weather Forecasts [14.622977874836298]
  We propose a mixed model that uses only a subset of the original weather trajectories combined with a post-processing step using deep neural networks. We show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble.
  arXiv  Detail & Related papers  (2020-05-18T14:23:26Z)

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.