WeatherSyn: An Instruction Tuning MLLM For Weather Forecasting Report Generation

• URL: http://arxiv.org/abs/2605.07522v1
• Date: Fri, 08 May 2026 09:53:33 GMT
• Title: WeatherSyn: An Instruction Tuning MLLM For Weather Forecasting Report Generation
• Abstract summary: We propose the Weather Forecasting Report (WFR) task and construct the first instruction-tuning dataset for this task, namedDatasetNameL.<n>Based on this corpus, we develop the first model, ModelNameL, specialized in generating weather forecast reports.
• Score: 24.52950725546607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate weather forecast reporting enables individuals and communities to better plan daily activities and agricultural operations. However, the current reporting process primarily relies on manual analysis of multi-source data, which leads to information overload and reduced efficiency. With the development of multimodal large language models (MLLMs), leveraging data-driven models to analyze and generate reports in the weather forecasting domain remains largely underexplored. In this work, we propose the Weather Forecasting Report (WFR) task and construct the first instruction-tuning dataset for this task, named~\DatasetNameL, which covers 31 cities in America and 8 weather aspects. Based on this corpus, we develop the first model, \ModelNameL, specialized in generating weather forecast reports. Evaluation across multiple metrics on our dataset shows that \ModelNameL~ consistently outperforms leading closed-source MLLMs, particularly on structurally complex weather aspects. We further analyze its performance across diverse geographic regions and weather aspects. \ModelNameL~ demonstrates strong transferability across different regions, highlighting its zero-shot generalization capability. \ModelNameL~offers valuable insight for developing MLLMs specialized in weather report generation. .

Related papers

• Nexus : An Agentic Framework for Time Series Forecasting [72.73790673303154]
  Time series forecasting requires reasoning with unstructured contextual data such as news or events.<n>We introduce Nexus, a multi-agent forecasting framework that decomposes prediction into specialized stages.<n>We show that current-generation LLMs possess substantially stronger intrinsic forecasting ability than previously recognized.
  arXiv  Detail & Related papers  (2026-05-14T05:12:13Z)
• CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting [74.29761960394848]
  We propose CastFlow, a dynamic agentic forecasting framework for large language models (LLMs)<n>CastFlow organizes the forecasting process into planning, action, forecasting, and reflection, establishing an agentic workflow.<n>Third, CastFlow adopts a role-specialized design that combines general-purpose reasoning with specialized numerical forecasting.
  arXiv  Detail & Related papers  (2026-04-30T13:24:42Z)
• Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting [52.53742727060265]
  Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets.<n>Here, we introduce Earth System Foundation Model (ESFM), a fully open model building on the 3Dwin UNet backbone of the pioneering model.<n>We present findings using dense gridded data (ERA5, CMIP6), regionally masked dense data, sparse gridded MODIS satellite data, and station data.<n>Results demonstrate competitive or superior performance relative to state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2026-04-20T11:40:39Z)
• Hierarchical AI-Meteorologist: LLM-Agent System for Multi-Scale and Explainable Weather Forecast Reporting [3.0354231393746685]
  Hierarchical AI-Meteorologist generates explainable weather reports using a hierarchical forecast reasoning and weather keyword generation.<n>Our framework performs multi-scale reasoning across hourly, 6-hour, and daily aggregations to capture both short-term dynamics and long-term trends.
  arXiv  Detail & Related papers  (2025-11-28T17:27:06Z)
• ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics [27.060978828050352]
  Large Language Models (LLMs) can process vast amounts of unstructured text data, extract meaningful insights, and generate detailed assessments.<n>LLMs can seamlessly transfer their general language understanding to smaller models, enabling these models to retain key knowledge while being fine-tuned for specific tasks.<n>We propose Extreme Weather Reasoning-Aware Alignment (EWRA), a method that enhances small language models (SLMs) by incorporating structured reasoning paths.
  arXiv  Detail & Related papers  (2025-04-27T01:15:14Z)
• Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data [22.54998659323974]
  This paper presents a novel machine learning (ML) model, called MiMa (short for Micro-Macro), that integrates both near-surface observational data from Kentucky Mesonet stations.<n>MiMa significantly outperforms current models, with Re-MiMa offering precise short-term forecasts for ungauged locations.
  arXiv  Detail & Related papers  (2024-12-11T23:55:38Z)
• WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning [69.82211470647349]
  We introduce the first generalist weather foundation model (WeatherGFM)<n>It addresses a wide spectrum of weather understanding tasks in a unified manner.<n>Our model can effectively handle up to ten weather understanding tasks, including weather forecasting, super-resolution, weather image translation, and post-processing.
  arXiv  Detail & Related papers  (2024-11-08T09:14:19Z)
• CLLMate: A Multimodal Benchmark for Weather and Climate Events Forecasting [28.560095276214543]
  We propose Weather and Climate Event Forecasting (WCEF) to predict weather and climate events.<n>CLLMate is the first dataset for WCEF using 26,156 environmental news articles with ERA5 reanalysis data.<n>We systematically benchmark 23 existing MLLMs on CLLMate, including closed-source, open-source, and our fine-tuned models.
  arXiv  Detail & Related papers  (2024-09-27T18:00:13Z)
• MM-Forecast: A Multimodal Approach to Temporal Event Forecasting with Large Language Models [55.5765505287505]
  We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. We propose to identify two essential functions that images play in the scenario of temporal event forecasting, i.e., highlighting and complementary. We develop a novel framework, named MM-Forecast, which incorporates these function descriptions into large language models.
  arXiv  Detail & Related papers  (2024-08-08T11:44:57Z)
• A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting [45.0261082985087]
  We conduct a comprehensive evaluation of Large Language Models (LLMs) for temporal event forecasting.<n>We find that fine-tuning LLMs with raw texts can significantly improve performance.<n>However, issues such as popularity bias and the long-tail problem persist in LLMs.
  arXiv  Detail & Related papers  (2024-07-16T11:58:54Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)

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.