The Efficient Integration of Dust and Numerical Weather Prediction for Renewable Energy Applications - Xi Chen; Mei Chong; Shian‐Jiann Lin; Zhi Liang; Paul Ginoux; Yuan Liang; Bihui Zhang; Qian Song; Shengkai Wang; Jiawei Li; Yimin Liu (Journal of Advances in Modeling Earth Systems)
Abstract The growing demand for renewable energy underscores the importance of accurate dust forecasting in regions with abundant wind and solar resources. However, leading real‐time global numerical weather prediction (NWP) models often lack dust modules due to computational constraints. Current “Near‐Real‐Time” dust forecasting services can only run after the completion of NWP, failing to meet the timeliness requirements for reporting power generation plans to the grids. This work proposes a global dust‐weather integrated (iDust) model development paradigm, efficiently incorporating dust modules into the dynamical core. Using about one‐eighth additional computing power, iDust extends global 12.5 km resolution NWP with dust prediction capabilities. iDust's forecasting abilities are evaluated against ECMWF CAMS forecast and NASA MERRA2 reanalysis, including verifications over China from March to May 2023 and three extreme dust events. Results show that iDust outperforms its counterparts in dust storm forecasting intensity and timing. Using iDust, global 12.5‐km 10‐day hourly dust storm forecast simulations initiated at 00UTC can produce results by 06UTC, enabling timely forecasting of severe dust storms with concentrations exceeding 1,000 μg/m3. This novel capability of iDust can meet the urgent forecasting needs of the renewable energy industry for extreme dust conditions, supporting the green energy transition.