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Machine learning based parameter sensitivity of regional climate models—a case study of the WRF model for heat extremes over Southeast Australia
Journal article   Open access   Peer reviewed

Machine learning based parameter sensitivity of regional climate models—a case study of the WRF model for heat extremes over Southeast Australia

P Reddy, Sandeep Chinta, Richard Matear, John Taylor, Harish Baki, Marcus Thatcher, Jatin Kala and Jason Sharples
Environmental research letters, Vol.19(1), 014010
2024
DOI: https://doi.org/10.1088/1748-9326/ad0eb0
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Published2.32 MBDownloadView
Published (Version of Record)CC BY V4.0,  Open Access

Abstract

Climate models Complex variables Extreme heat Extreme weather Heat Learning algorithms Machine learning Moisture content Parameter identification Parameter sensitivity Physics Regional development Relative humidity Saturated soils Sensitivity analysis Soil water Water content Weather Weather forecasting Wildfires Wind speed
Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionable mitigation and adaptation strategies. Regional climate models are commonly used to better understand the dynamics of these events. These models have very large input parameter sets, and the parameters within the physics schemes substantially influence the model's performance. However, parameter sensitivity analysis (SA) of regional models for heat extremes is largely unexplored. Here, we focus on the southeast Australian region, one of the global hotspots of heat extremes. In southeast Australia Weather Research and Forecasting (WRF) model is the widely used regional model to simulate extreme weather events across the region. Hence in this study, we focus on the sensitivity of WRF model parameters to surface meteorological variables such as temperature, relative humidity, and wind speed during two extreme heat events over southeast Australia. Due to the presence of multiple parameters and their complex relationship with output variables, a machine learning (ML) surrogate-based global SA method is considered for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity of 24 adjustable parameters in seven different physics schemes of the WRF model. Results show that out of these 24, only three parameters, namely the scattering tuning parameter, multiplier of saturated soil water content, and profile shape exponent in the momentum diffusivity coefficient, are important for the considered meteorological variables. These SA results are consistent for the two different extreme heat events. Further, we investigated the physical significance of sensitive parameters. This study's results will help in further optimising WRF parameters to improve model simulation.

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Domestic collaboration
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Citation topics
8 Earth Sciences
8.19 Oceanography, Meteorology & Atmospheric Sciences
8.19.113 Weather Forecasting
Web Of Science research areas
Environmental Sciences
Meteorology & Atmospheric Sciences
ESI research areas
Environment/Ecology
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