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Uncertainty quantification for data-driven weather models (2403.13458v2)

Published 20 Mar 2024 in physics.ao-ph, stat.AP, and stat.ML

Abstract: AI-based data-driven weather forecasting models have experienced rapid progress over the last years. Recent studies, with models trained on reanalysis data, achieve impressive results and demonstrate substantial improvements over state-of-the-art physics-based numerical weather prediction models across a range of variables and evaluation metrics. Beyond improved predictions, the main advantages of data-driven weather models are their substantially lower computational costs and the faster generation of forecasts, once a model has been trained. However, most efforts in data-driven weather forecasting have been limited to deterministic, point-valued predictions, making it impossible to quantify forecast uncertainties, which is crucial in research and for optimal decision making in applications. Our overarching aim is to systematically 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, with the use of statistical and machine learning methods for post-hoc uncertainty quantification. 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 and provide improvements over ensemble forecasts from the physics-based ensemble weather model of the European Centre for Medium-Range Weather Forecasts for lead times of up to 5 days.

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Citations (4)
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Summary

  • The paper demonstrates that integrating UQ methods in Pangu-Weather improves forecast accuracy for lead times up to 5 days.
  • It compares initial condition perturbations and post-hoc approaches to generate probabilistic forecasts from deterministic data.
  • The study shows that post-hoc methods like DRN excel at short lead times, while IC-based methods perform well for longer forecasts.

Uncertainty Quantification for Data-Driven Weather Models: A Case Study on Pangu-Weather

Introduction to Uncertainty Quantification in Weather Forecasting

The recent rise of AI-based, data-driven approaches for weather forecasting has shown significant potential in outperforming traditional numerical weather prediction (NWP) models. These data-driven models, devoid of explicit physical equations, infer atmospheric behavior directly from historical data. Despite their lower computational demands and rapid forecast generation post-training, a considerable focus has primarily been on deterministic outcomes. This approach overlooks the inherent uncertainty in weather forecasting, which is critical for research advancements and decision-making processes in practical applications. Consequently, this paper's central theme is exploring and benchmarking methods for integrating uncertainty quantification (UQ) within a deterministic data-driven weather model — specifically, the Pangu-Weather system.

Study Approach and Methodologies

The paper explores a comparative analysis of UQ methodologies to transmute deterministic forecasts from Pangu-Weather into probabilistic ones. This comparison spans two primary classes of UQ methods:

  1. Initial Condition (IC)-Based Approaches: These methods exploit the concept of generating ensemble forecasts by introducing perturbations to the model's initial conditions. Variants tested include Gaussian noise perturbations, random field perturbations, and initializing with perturbed conditions from a physics-based ensemble weather model.
  2. Post-Hoc (PH) Approaches: Contrasting the IC-based methods, PH methods post-process deterministically generated forecasts to append uncertainty post-estimations. This procedure involves statistical or machine learning techniques, leveraging historical forecast-observation pairs. Two significant methods evaluated are EasyUQ, based on isotonic distributional regression, and Distributional Regression Networks (DRNs), which are NN-based ensemble post-processing methods.

Key Findings and Observations

The paper presents a comprehensive comparison of the aforementioned UQ methods, executed through medium-range forecasts of selected weather variables over Europe. The benchmarking against ensemble outputs from a state-of-the-art physics-based model (ECMWF’s ensemble) reveals insightful findings:

  • Probabilistic forecasts generated through the application of UQ methods on Pangu-Weather demonstrate notable improvements over the ECMWF ensemble for lead times up to 5 days.
  • Among the evaluated methods, the PH approaches, especially DRN, markedly excel at shorter lead times, highlighting their potential in capturing forecast uncertainty beneficially.
  • The IC-based methods, particularly those leveraging random field perturbations, perform commendably at extended lead times, suggesting their utility in reflecting the intrinsic spread of atmospheric conditions.

Theoretical and Practical Implications

The paper’s exploration into UQ methods for data-driven weather forecasting extends significant theoretical and practical implications:

  • Theoretical: The demonstrated methodologies for integrating UQ present a foundational step towards advancing the predictability and reliability of data-driven weather models. This advancement is critical for enriching the theoretical understanding of atmospheric dynamics without relying on traditional physics-based approaches.
  • Practical: From an application standpoint, transitioning deterministic forecasts into probabilistic ones offers enhanced decision-making capabilities, particularly in sectors heavily reliant on accurate and reliable weather predictions.

Future Directions in AI and Weather Forecasting

Looking ahead, this paper paves the way for future endeavors in the field of AI-driven weather forecasting. Investigating inherently probabilistic data-driven approaches, extending the comparison to other state-of-the-art data-driven models, and exploring multivariate UQ strategies represent promising avenues for research. Moreover, the scalability of these methodologies to global, high-resolution forecasts remains an essential area for further exploration, potentially leveraging advancements in machine learning architectures and computational resources.

Concluding Remarks

The research undertaken in this paper contributes significantly to the growing body of knowledge surrounding data-driven weather forecasting. By systematically comparing and analyzing UQ methods within the context of Pangu-Weather, the paper not only benchmarks current capabilities but also outlines a roadmap for future advancements in this exciting field of paper.

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