Evaluating Different WRF Model Configurations to Dynamically Downscale Climate Projections of Renewable Resources for CONUS

In recent years, it has become more important to assess renewable energy resources under future climate scenarios in order to explore and comprehend the possible effects of a changing climate on grid resilience under a high-renewable future. The renewable energy sector has acknowledged that climate model projections are a valuable source of data for analyzing the impacts of future climate change on renewable energy resources. Dynamical downscaling is a powerful approach which can create physically consistent, high-resolution climate datasets for regional studies. The first part of this study is to perform numerical experiments using the Weather Research and Forecasting (WRF) model and provide insights on optimizing WRF configurations for representing the key atmospheric variables related to renewable energy applications (e.g., solar irradiance, precipitation, and wind speed, etc.). The ERA5 reanalysis data is used for initial and boundary conditions of the WRF model to implement 15-year simulation for a 8 km grid, which covers the contiguous United States (CONUS). Different physics options within the WRF model are explored to find the best configuration to accurately generate downscaled reanalysis data. The dynamically downscaled outputs are evaluated against high-quality gridded observations, e.g., the National Solar Radiation Database (NSRDB) and the Stage IV multi-radar/multi-sensor precipitation product across 17 climate zones for CONUS domain. In the second part, we focus on testing the best WRF configuration obtained from the first step to downscale actual general circulation model (GCM) projections. Two GCM datasets, including the MPI-ESM1-2-HR and the bias-corrected GCM are selected as inputs to the WRF dynamical downscaling. This presentation will summarize the comprehensive analysis to assess the results from various numerical experiments. We will also present our efforts on developing the National Climate Database (NCDB) that is the platform for meteorological climate data for renewable energy applications.