Identifying high resolution benchmark data needs and Novel data-driven methodologies for Climate Downscaling

TL;DR

This paper explores the integration of information retrieval and deep learning to improve high-resolution climate downscaling, addressing data needs and methodological advancements for more precise local climate predictions.

Contribution

It introduces novel data-driven methodologies and identifies high-resolution data requirements to enhance climate downscaling accuracy using deep learning techniques.

Findings

Deep learning models significantly improve downscaling precision.

Efficient data retrieval methods support better climate modeling.

Integrated approach enhances local climate forecast accuracy.

Abstract

We address the essential role of information retrieval in enhancing climate downscaling, focusing on the need for high-resolution datasets and the application of deep learning models. We explore the requirements for acquiring detailed spatial and temporal climate data, crucial for accurate local forecasts, and discuss how deep learning (DL) techniques can significantly improve downscaling precision by modelling the complex relationships between climate variables. Additionally, we examine the specific challenges related to the retrieval of relevant climatic data, emphasizing methods for efficient data extraction and utilization to support advanced model training. This research underscores an integrated approach, combining information retrieval, deep learning, and climate science to refine the process of climate downscaling, aiming to produce more accurate and actionable local climate projections.