Integration of a physics-based direct normal irradiance ($\mathrm{DNI}$) model to enhance the National Solar Radiation Database ($\mathrm{NSRDB}$)

Xie, Yu; Sengupta, Manajit; Yang, Jaemo; Buster, Grant; Benton, Brandon; Habte, Aron; Liu, Yangang

doi:10.1016/j.solener.2023.112195

Integration of a physics-based direct normal irradiance ($$\mathrm{DNI}$$) model to enhance the National Solar Radiation Database ($$\mathrm{NSRDB}$$)

Journal Article · Tue Nov 21 23:00:00 EST 2023 · Solar Energy

DOI:https://doi.org/10.1016/j.solener.2023.112195· OSTI ID:2228474

^[1]; ^[1]; Yang, Jaemo ^[1]; ^[1]; Benton, Brandon ^[1]; Habte, Aron ^[1]; ^[2]

National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Brookhaven National Laboratory (BNL), Upton, NY (United States)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

View Accepted Manuscript (DOE)

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: AC36-08GO28308; SC0012704

OSTI ID:: 2228474

Report Number(s):: NREL/JA--5D00-86353; MainId:87126; UUID:64e93017-ec9d-41ef-93fa-99671d5104fe; MainAdminID:71189

Journal Information:: Solar Energy, Journal Name: Solar Energy Vol. 266; ISSN 0038-092X

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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