Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models

TL;DR

This paper introduces a diffusion model-based super-resolution method to approximate high-resolution wind speed data (CERRA) from lower-resolution ERA5 data, enabling timely and accurate wind resource assessment.

Contribution

The study presents a novel diffusion model approach for wind speed super-resolution, reducing computational costs and data lag in generating high-resolution reanalysis datasets.

Findings

Model closely replicates original CERRA data.

Validation confirms high accuracy against in-situ measurements.

Approach enables timely wind resource analysis.

Abstract

The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis dataset for the European domain. In recent years it has shown significant utility across various climate-related tasks, ranging from forecasting and climate change research to renewable energy prediction, resource management, air quality risk assessment, and the forecasting of rare events, among others. Unfortunately, the availability of CERRA is lagging two years behind the current date, due to constraints in acquiring the requisite external data and the intensive computational demands inherent in its generation. As a solution, this paper introduces a novel method using diffusion models to approximate CERRA downscaling in a data-driven manner, without additional informations. By leveraging the lower resolution ERA5 dataset, which provides boundary conditions for CERRA, we approach this as a super-resolution task. Focusing on wind speed around Italy, our model, trained on existing CERRA data, shows promising results, closely mirroring original CERRA data. Validation with in-situ observations further confirms the model's accuracy in approximating ground measurements.