Improving the prediction of DNI with physics-based representation of all-sky circumsolar radiation. (1st January 2022)

• Record Type: Journal Article
• Authors: Xie, Yu; Yang, Jaemo; Sengupta, Manajit; Liu, Yangang; Zhou, Xin
• Is Part Of: Solar energy. Volume 231(2022)
• Page Start: 758
• Page End: 766
• Publication Date: 2022-01-01
• Subjects: Direct normal irradiance -- Solar radiation -- Solar forecasting -- Circumsolar radiation -- NWP; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2021.12.016
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: Direct normal irradiance (DNI) is computed by a physics-based solution of the gross solar radiation in the circumsolar region. The new DNI model is coupled with a numerical weather prediction model to forecast day-ahead DNI over the contiguous United States. The significant bias in the conventional forecasting models has been remarkably reduced. Abstract: Direct normal irradiance (DNI) is often interpreted differently in ground measurements and forecasts using numerical weather prediction (NWP) models, leading to substantial bias in DNI forecasting especially under cloudy-sky conditions. To mitigate the bias, we use the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) to conduct physics-based simulations of solar radiation in the circumsolar region. The DNI is quantified using the sum of the radiation along the sun direction and the scattered radiation within the circumsolar region. FARMS-DNI is coupled with the Weather Research and Forecasting model with solar extensions (WRF-Solar) to predict day-ahead DNI in 9-km pixels over an extended domain covering the contiguous United States. The DNI forecasts for the entire year of 2018 are validated using surface-based observations at the Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) sites and a satellite-based solar radiation product developed by the procedure of the National Solar Radiation Data Base (NSRDB). The results show that the WRF-Solar coupled with the conventional Beer-Bouguer-Lambert law underestimes the overall DNI forecasts by 30%-60%, which is a joint effect of the uncertainties in computing the circumsolar radiation and forecasting cloud properties. This bias is significantly reduced by using FARMS-DNI.