A Review of End-to-End Precipitation Prediction Using Remote Sensing Data: from Divination to Machine Learning

2025-10-26

AI Summary

This paper systematically reviews the technological evolution of end-to-end precipitation forecasting, tracing its progression from ancient empirical observations and physics-based numerical weather prediction (NWP) models to modern AI-driven paradigms. To bridge historical and methodological divides, we integrate multisource data—including remote sensing, NWP outputs, and ground-based meteorological observations—and synthesize the convergence of physical modeling, statistical learning, and deep neural networks across eras and paradigms. We propose three key innovation directions: (1) an automated neural architecture search framework for precipitation modeling; (2) structured neural designs explicitly tailored for meteorological interpretability; and (3) a hybrid modeling paradigm that synergistically couples physics-informed constraints with data-driven learning. Our work establishes a rigorous theoretical foundation and a concrete technical roadmap toward next-generation precipitation forecasting systems—characterized by high accuracy, reliability, and operational deployability.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Precipitation prediction has undergone a profound transformation -- from early symbolic and empirical methods rooted in divination and observation, to modern technologies based on atmospheric physics and artificial intelligence. This review traces the historical and technological evolution of precipitation forecasting, presenting a survey about end-to-end precipitation prediction technologies that spans ancient practices, the foundations of meteorological science, the rise of numerical weather prediction (NWP), and the emergence of machine learning (ML) and deep learning (DL) models. We first explore traditional and indigenous forecasting methods, then describe the development of physical modeling and statistical frameworks that underpin contemporary operational forecasting. Particular emphasis is placed on recent advances in neural network-based approaches, including automated deep learning, interpretability-driven design, and hybrid physical-data models. By compositing research across multiple eras and paradigms, this review not only depicts the history of end-to-end precipitation prediction but also outlines future directions in next generation forecasting systems.

Problem

• Traces historical evolution of precipitation forecasting methods
• Surveys end-to-end prediction from ancient to modern techniques
• Explores machine learning advances in weather prediction systems

Innovation

• Machine learning models for precipitation prediction
• Hybrid physical-data driven forecasting systems
• Automated deep learning with interpretability design