Evaluating WRF-Solar EPS cloud mask forecast using the NSRDB. (1st September 2022) - Yang, Jaemo; Sengupta, Manajit; Jiménez, Pedro A.; Kim, Ju-Hye; Xie, Yu
Highlights: Two methods are applied to consider all clouds and partially removed clouds from 2-km NSRDB in assessing 9-km WRF-Solar EPS cloud mask forecast. Cloud detection metrics are used to quantify and evaluate ensemble cloud mask forecasts from the WRF-Solar EPS. Current capability of WRF-Solar EPS in predicting different types of clouds is evaluated using the NSRDB. Abstract: Improving the accuracy of day-ahead solar forecasts using numerical weather prediction models requires improving the forecasting of cloud occurrence and properties. Validating cloud forecasts is challenging because this requires evaluating different types of clouds over a wide variety of regions. This study analyzes the cloud occurrence (or cloud mask) over the contiguous United States (CONUS), predicted by the Weather Research and Forecasting-Solar Ensemble Prediction System (WRF-Solar EPS), to identify the strengths and limitations of the model in reproducing cloud fields. To enable the in-depth analysis of cloud mask forecasts covering CONUS, we use satellite observations from the National Solar Radiation Database (NSRDB). Two evaluation methods are implemented to consider all clouds and partially removed clouds from the 2-km NSRDB in evaluating the 9-km WRF-Solar EPS cloud mask. Cloud detection metrics as well as the frequency of cloud occurrence are used to quantify the monthly performance of WRF-Solar EPS. Mismatched cloud frequency (MCF) is used to assess the model's capability to predict different types of clouds, which are classified using three levels of cloud optical depth (COD) and cloud top height (CTH). The day-ahead forecasts covering the full year of 2018 demonstrate that WRF-Solar EPS produces MCFs ranging from 27%–46%, 13%–34%, and 8%–19% for thin, mid-thickness, and thick clouds, respectively. For three CTH levels, the model shows MCFs ranging from 19%–46%, 16%–33%, and 8%–27% for low-level, middle-level, and high-level clouds, respectively. This comprehensive characterization of model performance helps identify model weakness and will eventually lead to improvements in cloud and solar radiation forecasting.