Knowledge-Guided Adaptive Mixture of Experts for Precipitation Prediction

2025-09-14

AI Summary

Precipitation forecasting is critical for agriculture, disaster prevention, and sustainable development; however, the spatiotemporal resolution mismatches and modality-specific characteristics of heterogeneous observational data (e.g., radar, satellite, and ground stations) severely hinder the effectiveness of conventional deep learning models in multimodal fusion. To address this, we propose Knowledge-guided Adaptive Mixture-of-Experts (KA-MoE), which employs a dynamic routing mechanism to assign dedicated experts based on input modality and spatiotemporal patterns, integrated with cross-modal alignment and interpretable modeling techniques. KA-MoE achieves state-of-the-art nowcasting accuracy for complex weather events—such as Hurricane Ian—outperforming all baseline models. It further provides an interactive visualization toolkit for meteorological pattern interpretation and decision support. Our key contributions are: (i) the first knowledge-enhanced MoE architecture specifically designed for meteorological multimodal heterogeneous data; and (ii) an end-to-end precipitation forecasting framework that jointly ensures high accuracy and strong interpretability.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Accurate precipitation forecasting is indispensable in agriculture, disaster management, and sustainable strategies. However, predicting rainfall has been challenging due to the complexity of climate systems and the heterogeneous nature of multi-source observational data, including radar, satellite imagery, and surface-level measurements. The multi-source data vary in spatial and temporal resolution, and they carry domain-specific features, making it challenging for effective integration in conventional deep learning models. Previous research has explored various machine learning techniques for weather prediction; however, most struggle with the integration of data with heterogeneous modalities. To address these limitations, we propose an Adaptive Mixture of Experts (MoE) model tailored for precipitation rate prediction. Each expert within the model specializes in a specific modality or spatio-temporal pattern. We also incorporated a dynamic router that learns to assign inputs to the most relevant experts. Our results show that this modular design enhances predictive accuracy and interpretability. In addition to the modeling framework, we introduced an interactive web-based visualization tool that enables users to intuitively explore historical weather patterns over time and space. The tool was designed to support decision-making for stakeholders in climate-sensitive sectors. We evaluated our approach using a curated multimodal climate dataset capturing real-world conditions during Hurricane Ian in 2022. The benchmark results show that the Adaptive MoE significantly outperformed all the baselines.

Problem

• Integrating multi-source heterogeneous data for precipitation prediction
• Addressing challenges in spatial-temporal resolution and modality fusion
• Improving accuracy and interpretability in rainfall forecasting models

Innovation

• Adaptive Mixture of Experts model
• Dynamic router for expert assignment
• Interactive web-based visualization tool