Smoke from 2020 United States wildfires responsible for substantial solar energy forecast errors. (1st March 2022)

• Record Type: Journal Article
• Authors: Juliano, Timothy W; Jiménez, Pedro A; Kosović, Branko; Eidhammer, Trude; Thompson, Gregory; Berg, Larry K; Fast, Jerome; Motley, Amber; Polidori, Andrea
• Is Part Of: Environmental research letters. Volume 17:Number 3(2022)
• Publication Date: 2022-03-01
• Subjects: numerical weather prediction -- solar energy -- wildfire smoke emissions; Environmental sciences -- Periodicals; Human ecology -- Research -- Periodicals; Environmental health -- Periodicals; 333.7
• DOI: 10.1088/1748-9326/ac5143
• Languages: English
• ISSNs: 1748-9326

Abstract

Abstract: The 2020 wildfire season (May through December) in the United States was exceptionally active, with the National Interagency Fire Center reporting over 10 million acres ( > 40 000 km 2 ) burned. During the September 2020 wildfire events, large concentrations of smoke particulates were emitted into the atmosphere. As a result, smoke was responsible for ∼10%–30% reduction in solar power production during peak hours as recorded by the California Independent System Operator (CAISO) sites. In this study, we focus on a 9 d period in September when wildfire smoke had a profound impact on solar energy production. During the smoke episodes, hour-ahead forecasts utilized by CAISO did not include the effects of smoke and therefore overestimated the expected power production by ∼10%–50%. Here we use multiple observational networks and a numerical weather prediction (NWP) model to show that the wildfire events of 2020 had a significantly detrimental influence on solar energy production due to high aerosol loading. We find that including the contribution of biomass burning particles greatly improves the day-ahead solar energy bias forecast of both global horizontal irradiance and direct normal irradiance by nearly ∼50%. Our results suggest that a more comprehensive treatment of aerosols, including biomass burning aerosols, in NWP models may be an important consideration for energy grid balancing, in addition to solar resource assessment, as solar power reliance increases.