Warped multifidelity Gaussian processes for data fusion of skewed environmental data

TL;DR

This paper introduces a warped multifidelity Gaussian process method for accurate data fusion of skewed environmental data, improving wind speed prediction and air quality management.

Contribution

The paper presents a novel WMFGP approach that effectively handles skewed data and integrates multiple data sources for environmental variable prediction.

Findings

Enhanced wind speed data gap filling demonstrated in simulations.

Improved air quality prediction accuracy through better wind speed estimates.

Method effectively manages skewness and varying data reliability.

Abstract

Understanding the dynamics of climate variables is paramount for numerous sectors, like energy and environmental monitoring. This study focuses on the critical need for a precise mapping of environmental variables for national or regional monitoring networks, a task notably challenging when dealing with skewed data. To address this issue, we propose a novel data fusion approach, the \textit{warped multifidelity Gaussian process} (WMFGP). The method performs prediction using multiple time-series, accommodating varying reliability and resolutions and effectively handling skewness. In an extended simulation experiment the benefits and the limitations of the methods are explored, while as a case study, we focused on the wind speed monitored by the network of ARPA Lombardia, one of the regional environmental agencies operting in Italy. ARPA grapples with data gaps, and due to the connection between wind speed and air quality, it struggles with an effective air quality management. We illustrate the efficacy of our approach in filling the wind speed data gaps through two extensive simulation experiments. The case study provides more informative wind speed predictions crucial for predicting air pollutant concentrations, enhancing network maintenance, and advancing understanding of relevant meteorological and climatic phenomena.