Online stochastic generators using Slepian bases for regional bivariate wind speed ensembles from ERA5

TL;DR

This paper introduces an online stochastic generator for regional bivariate wind speed ensembles from ERA5, utilizing Slepian bases for efficient, real-time data approximation and reduced storage, applicable across global regions.

Contribution

It presents a novel online updating method for wind speed ensemble generation using Slepian bases, enabling real-time emulation with minimal storage requirements.

Findings

The OSG performs well in statistical metrics across case studies.

It is comparable to full-data trained models in accuracy.

Supports near real-time wind speed ensemble generation.

Abstract

Reanalysis data, such as ERA5, provide a comprehensive and detailed representation of the Earth's system by assimilating observations into climate models. While crucial for climate research, they pose significant challenges in terms of generation, storage, and management. For 3-hourly bivariate wind speed ensembles from ERA5, which face these challenges, this paper proposes an online stochastic generator (OSG) applicable to any global region, offering fast stochastic approximations while storing only model parameters. A key innovation is the incorporation of the online updating, which allows data to sequentially enter the model in blocks of time and contribute to parameter updates. This approach reduces storage demands during modeling by eliminating the need to store and analyze the entire dataset, and enables near real-time emulations that complement the generation of reanalysis data. The Slepian concentration technique supports the efficiency of the proposed OSG by representing the data in a lower-dimensional space spanned by data-independent Slepian bases optimally concentrated within the specified region. We demonstrate the flexibility and efficiency of the OSG through two case studies requiring long and short blocks, specified for the Arabian-Peninsula region (ARP). For both cases, the OSG performs well across several statistical metrics and is comparable to the SG trained on the full dataset.