[2510.09734] ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting

Computer Science > Machine Learning arXiv:2510.09734 (cs) [Submitted on 10 Oct 2025 (v1), last revised 31 Mar 2026 (this version, v2)] Title:ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting Authors:Jindong Tian, Yifei Ding, Ronghui Xu, Hao Miao, Chenjuan Guo, Bin Yang View a PDF of the paper titled ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting, by Jindong Tian and 5 other authors View PDF HTML (experimental) 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 forecasting, 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 charact...