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ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Deep Learning; Spatiotemporal Analysis; Weather Forecasting
Abstract:

Weather forecasting is a fundamental task in spatiotemporal data analysis, with broad applications across a wide range of domains. Existing data-driven forecasting methods typically model atmospheric dynamics over a fixed short time interval, e.g., 6 hours, and rely on naive autoregression-based rollout for long-term forecastsing, e.g., 5 days. However, this paradigm suffers from two key limitations: (1) it often inadequately models the spatial and multi-scale temporal dependencies inherent in global weather systems, and (2) the rollout strategy struggles to balance error accumulation with the capture of fine-grained atmospheric variations. In this study, we propose ARROW, an Adaptive-Rollout Multi-scale temporal Routing method for Global Weather Forecasting. To contend with the first limitation, we construct a multi-interval forecasting model that forecasts weather across different time intervals. Within the model, the Shared-Private Mixture-of-Experts captures both shared patterns and specific characteristics of atmospheric dynamics across different time scales, while Ring Positional Encoding accurately encodes the circular latitude structure of the Earth when representing spatial information. For the second limitation, we develop an adaptive rollout scheduler based on reinforcement learning, which selects the most suitable time interval to forecast according to the current weather state. Experimental results demonstrate that ARROW achieves state-of-the-art performance in global weather forecasting, establishing a promising paradigm in this field.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes ARROW, a transformer-based weather forecasting system combining multi-interval forecasting with adaptive rollout scheduling. It resides in the 'Multi-Scale Temporal Routing and Adaptive Rollout' leaf, which contains only two papers including this one. This represents a relatively sparse research direction within the broader transformer-based weather forecasting landscape, suggesting the specific combination of adaptive rollout mechanisms and multi-scale temporal routing remains underexplored compared to more populated branches like general transformer models or neural operator architectures.

The taxonomy reveals that ARROW sits within the transformer-based branch, adjacent to efficient latent rollout methods and general transformer forecasters. Neighboring branches include neural operator architectures emphasizing spectral methods and generative probabilistic approaches for uncertainty quantification. The scope note explicitly distinguishes this leaf by requiring 'explicit mechanisms for adaptive rollout scheduling or multi-interval temporal routing strategies,' separating it from standard transformers without such adaptivity. This positioning suggests the work bridges temporal adaptivity concerns with transformer computational frameworks, diverging from purely architectural innovations in spherical geometry or Fourier-based operators.

Among the three contributions analyzed, the literature search examined 23 candidates total with no clearly refuting prior work identified. The multi-interval forecasting model examined 10 candidates with none refuting, the adaptive rollout scheduler examined 4 candidates with none refuting, and the integrated ARROW framework examined 9 candidates with none refuting. Given the limited search scope of 23 papers from top-K semantic matching, these statistics suggest that within the examined subset, no direct overlaps were detected, though the analysis does not claim exhaustive coverage of all potentially relevant prior work in adaptive rollout or multi-scale temporal modeling.

Based on the limited literature search, the work appears to occupy a distinct position combining adaptive rollout with multi-scale temporal routing in transformer architectures. The sparse population of its taxonomy leaf and absence of refuting candidates among 23 examined papers suggest novelty within the analyzed scope, though the search scale leaves open the possibility of relevant work outside the top-K semantic matches or in adjacent research communities not fully captured by this taxonomy.

Taxonomy

Core-task Taxonomy Papers
20
3
Claimed Contributions
23
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: global weather forecasting with adaptive rollout strategies. The field of data-driven weather prediction has evolved into a rich ecosystem of approaches, organized around several major branches. Neural operator architectures leverage Fourier-based and spectral methods to handle spherical geometry efficiently, as seen in works like FourCastNet[2] and Spherical Fourier[3]. Transformer-based systems, including models such as Scaling Transformer[5] and OneForecast[9], apply attention mechanisms to capture spatial and temporal dependencies across atmospheric variables. Generative and probabilistic methods, exemplified by Spherical Dyffusion[15] and Climate Normalizing Flows[17], focus on uncertainty quantification and ensemble generation. Foundation models like Prithvi[10] aim for multi-use case flexibility, while hybrid approaches such as Hybrid Decomposition Weather[4] blend statistical and neural components. Evaluation methodologies, represented by ChaosBench[14], provide standardized benchmarks, and operational systems address real-time deployment challenges. These branches collectively span the spectrum from architectural innovation to practical forecasting deployment. A particularly active line of work centers on improving long-range forecast stability and computational efficiency through adaptive strategies. Within the transformer-based branch, ARROW[0] introduces multi-scale temporal routing to dynamically adjust rollout steps, addressing error accumulation in extended forecasts. This approach contrasts with fixed-step methods like Scaling Transformers Skillful[13], which rely on uniform temporal resolution, and complements recent efforts such as STORM[19], which also explores adaptive mechanisms for temporal progression. Meanwhile, works like WeatherMesh[11] and Gridpoint Relaxation[7] tackle related challenges of spatial adaptivity and iterative refinement. ARROW[0] sits at the intersection of temporal adaptivity and transformer architectures, emphasizing how intelligent rollout scheduling can enhance forecast skill without sacrificing computational tractability, a theme that distinguishes it from purely architectural innovations in neighboring studies.

Claimed Contributions

Multi-Interval Forecasting Model with Shared-Private Mixture-of-Experts and Ring Positional Encoding

The authors introduce a unified forecasting model that handles multiple time intervals simultaneously. It uses a Shared-Private Mixture-of-Experts to capture both shared and interval-specific atmospheric dynamics, and Ring Positional Encoding to represent the Earth's circular latitude structure.

10 retrieved papers
Adaptive Rollout Scheduler based on reinforcement learning

The authors design a scheduler that dynamically chooses rollout time intervals conditioned on current weather states. This scheduler is trained via Q-learning to balance error accumulation with fine-grained atmospheric evolution, alternating optimization with multi-step fine-tuning.

4 retrieved papers
ARROW framework integrating adaptive rollout and multi-scale routing

The authors present ARROW, a complete framework that combines the multi-interval forecasting model with the adaptive rollout scheduler. This integration formulates adaptive rollout as a decision-making problem and achieves state-of-the-art performance in global weather forecasting.

9 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Multi-Interval Forecasting Model with Shared-Private Mixture-of-Experts and Ring Positional Encoding

The authors introduce a unified forecasting model that handles multiple time intervals simultaneously. It uses a Shared-Private Mixture-of-Experts to capture both shared and interval-specific atmospheric dynamics, and Ring Positional Encoding to represent the Earth's circular latitude structure.

Contribution

Adaptive Rollout Scheduler based on reinforcement learning

The authors design a scheduler that dynamically chooses rollout time intervals conditioned on current weather states. This scheduler is trained via Q-learning to balance error accumulation with fine-grained atmospheric evolution, alternating optimization with multi-step fine-tuning.

Contribution

ARROW framework integrating adaptive rollout and multi-scale routing

The authors present ARROW, a complete framework that combines the multi-interval forecasting model with the adaptive rollout scheduler. This integration formulates adaptive rollout as a decision-making problem and achieves state-of-the-art performance in global weather forecasting.

Table of Contents