MACHINE LEARNING-BASED DOWNSCALING OF CLIMATE MODELS USING REMOTE SENSING AND GIS DATA FOR HIGH-RESOLUTION ATMOSPHERIC FORECASTING

Abstract

Climate prediction and atmospheric forecasting remain critical challenges in environmental science, particularly at high spatial resolutions where computational constraints limit traditional General Circulation Models (GCMs). This paper presents a comprehensive review and methodological framework for machine learning-based downscaling of climate models, integrating remote sensing and Geographic Information System (GIS) data to achieve high-resolution atmospheric forecasting. Statistical downscaling techniques have evolved considerably with the advent of deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). This research synthesizes current approaches, evaluates their efficacy across diverse geographic and climatic contexts, and proposes an integrated framework that leverages multi-source satellite data, topographic information, and historical climate records. The methodology incorporates advanced preprocessing techniques, feature engineering from GIS datasets, and ensemble learning strategies to address the inherent uncertainties in climate projections. Performance metrics demonstrate that machine learning approaches can achieve spatial resolutions of 1-4 km with significantly reduced computational costs compared to dynamical downscaling. Key findings indicate that hybrid models combining physical constraints with data-driven learning outperform purely statistical methods, achieving correlation coefficients exceeding 0.85 for temperature and 0.72 for precipitation variables. The framework addresses critical challenges including spatial transferability, temporal stability, and extreme event prediction. This work contributes to the growing intersection of artificial intelligence and climate science, offering practical insights for operational weather services, agricultural planning, and climate adaptation strategies.