arXiv:2511.14218v1 Announce Type: new 
Abstract: Weather forecasting is fundamentally challenged by the chaotic nature of the atmosphere, necessitating probabilistic approaches to quantify uncertainty. While traditional ensemble prediction (EPS) addresses this through computationally intensive simulations, recent advances in Bayesian Deep Learning (BDL) offer a promising but often disconnected alternative. We bridge these paradigms through a unified hybrid Bayesian Deep Learning framework for ensemble weather forecasting that explicitly decomposes predictive uncertainty into epistemic and aleatoric components, learned via variational inference and a physics-informed stochastic perturbation scheme modeling flow-dependent atmospheric dynamics, respectively. We further establish a unified theoretical framework that rigorously connects BDL and EPS, providing formal theorems that decompose total predictive uncertainty into epistemic and aleatoric components under the hybrid BDL framework. We validate our framework on the large-scale 40-year ERA5 reanalysis dataset (1979-2019) with 0.25{\deg} spatial resolution. Experimental results show that our method not only improves forecast accuracy and yields better-calibrated uncertainty quantification but also achieves superior computational efficiency compared to state-of-the-art probabilistic diffusion models. We commit to making our code open-source upon acceptance of this paper.

يتناول المقال تحديات التنبؤ بالطقس بسبب الطبيعة الفوضوية للغلاف الجوي، مما يتطلب أساليب احتمالية لإدارة عدم اليقين. تعتمد أنظمة التنبؤ التقليدية (EPS) على محاكاة مكثفة، بينما يقدم التعلم العميق البايزي (BDL) نهجًا جديدًا ولكنه غالبًا ما يكون معزولًا. يقدم هذا العمل إطارًا هجينًا من BDL للتنبؤ بالطقس من خلال الفرق، والذي يفصل عدم اليقين التنبؤي إلى مكونات معرفية وعشوائية. كما يؤسس إطارًا نظريًا يربط بين BDL وEPS، مما يعزز فهم عدم اليقين التنبؤي.

El artículo aborda los desafíos de la predicción meteorológica debido a la naturaleza caótica de la atmósfera, lo que requiere métodos probabilísticos para gestionar la incertidumbre. Los sistemas de predicción por conjuntos tradicionales (EPS) dependen de simulaciones extensas, mientras que el Aprendizaje Profundo Bayesiano (BDL) presenta un enfoque novedoso pero a menudo aislado. Este trabajo introduce un marco híbrido de BDL para la predicción meteorológica por conjuntos que separa la incertidumbre predictiva en componentes epistémicos y aleatorios. También establece una base teórica que vi…

Cet article aborde les défis de la prévision météorologique en raison de la nature chaotique de l'atmosphère, nécessitant des méthodes probabilistes pour gérer l'incertitude. Les systèmes de prévision par ensembles traditionnels (EPS) reposent sur des simulations étendues, tandis que l'apprentissage profond bayésien (BDL) propose une approche nouvelle mais souvent isolée. Ce travail introduit un cadre hybride BDL pour la prévision météorologique par ensembles qui sépare l'incertitude prédictive en composants épistémiques et aléatoires. Il établit également une base théorique reliant BDL et EPS…

The article discusses the challenges of weather forecasting due to the chaotic nature of the atmosphere, which requires probabilistic methods to manage uncertainty. Traditional ensemble prediction systems (EPS) rely on extensive simulations, while Bayesian Deep Learning (BDL) presents a novel yet often isolated approach. This work introduces a hybrid BDL framework for ensemble weather forecasting that separates predictive uncertainty into epistemic and aleatoric components. It also establishes a theoretical foundation linking BDL and EPS, enhancing the understanding of predictive uncertainty.

Bridging the Gap Between Bayesian Deep Learning and Ensemble Weather Forecasts

arXiv:2511.17176v1 Announce Type: cross 
Abstract: Accurate prediction of extreme weather events remains a major challenge for artificial intelligence based weather prediction systems. While deterministic models such as FuXi, GraphCast, and SFNO have achieved competitive forecast skill relative to numerical weather prediction, their ability to represent uncertainty and capture extremes is still limited. This study investigates how state of the art deterministic artificial intelligence based models respond to initial-condition perturbations and evaluates the resulting ensembles in forecasting extremes. Using three perturbation strategies (Gaussian noise, Hemispheric Centered Bred Vectors, and Huge Ensembles), we generate 50 member ensembles for two major events in August 2022: the Pakistan floods and the China heatwave. Ensemble skill is assessed against ERA5 and compared with IFS ENS and the probabilistic AIFSENS model using deterministic and probabilistic metrics. Results show that flow dependent perturbations produce the most realistic ensemble spread and highest probabilistic skill, narrowing but not closing the performance gap with numerical weather prediction ensembles. Across variables, artificial intelligence based weather models capture temperature extremes more effectively than precipitation. These findings demonstrate that input perturbations can extend deterministic models toward probabilistic forecasting, paving the way for approaches that combine flow dependent perturbations with generative or latent-space uncertainty modeling for reliable artificial intelligence-driven early warning systems.

تبرز دراسة حديثة قيود نماذج التنبؤ بالطقس المعتمدة على الذكاء الاصطناعي في التنبؤ بدقة بالأحداث الجوية المتطرفة. على الرغم من التقدم في النماذج الحتمية مثل FuXi وGraphCast وSFNO، إلا أن قدرتها على تمثيل عدم اليقين والتقاط الأحداث المتطرفة لا تزال محدودة. تقيم الدراسة أداء هذه النماذج باستخدام تقنيات التنبؤ بالفرق خلال أحداث مهمة في أغسطس 2022، بما في ذلك الفيضانات في باكستان وموجة الحر في الصين.

Un estudio reciente destaca las limitaciones de los modelos de predicción meteorológica basados en inteligencia artificial para predecir con precisión eventos climáticos extremos. A pesar de los avances en modelos deterministas como FuXi, GraphCast y SFNO, su capacidad para representar la incertidumbre y capturar extremos sigue siendo limitada. La investigación evalúa el rendimiento de estos modelos utilizando técnicas de pronóstico por conjuntos durante eventos significativos en agosto de 2022, incluidos las inundaciones en Pakistán y la ola de calor en China.

Une étude récente met en lumière les limites des modèles de prévision météorologique basés sur l'intelligence artificielle dans la prévision précise des événements météorologiques extrêmes. Malgré les avancées des modèles déterministes tels que FuXi, GraphCast et SFNO, leur capacité à représenter l'incertitude et à prédire les extrêmes reste contrainte. La recherche évalue la performance de ces modèles en utilisant des techniques de prévision par ensemble lors d'événements significatifs en août 2022, y compris les inondations au Pakistan et la vague de chaleur en Chine.

A recent study highlights the limitations of artificial intelligence-based weather prediction models in accurately forecasting extreme weather events. Despite advancements in deterministic models like FuXi, GraphCast, and SFNO, their capacity to represent uncertainty and predict extremes remains constrained. The research evaluates the performance of these models using ensemble forecasting techniques during significant events in August 2022, including the Pakistan floods and the China heatwave.

Bridging the Gap Between Bayesian Deep Learning and Ensemble Weather Forecasts

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