My research focuses on the study of Stratocumulus (Sc) clouds for climate and solar resource prediction. Sc clouds are the most common cloud type on Earth, and they strongly reflect incoming solar radiation. Sc clouds have a strong negative radiative effect on the Earth’s radiative balance. However, the spatial and temporal coverage of Sc clouds are not well-presented in current climate or weather models. The inaccuracy of modeling Sc clouds results in large uncertainty in cloud feedback effect in climate models and presents challenges for utilities and system operators to maintain reliable service for the electric grid. To study short-term Sc cloud lifetime, I combine physical insights with statistical methods to forecast Sc cloud dissipation time. In this project, Geostationary Operational Environmental Satellite (GOES) images are used to track the most inland edge of Sc clouds in time and extrapolated into the future during the day. To study longer term Sc cloud lifetime, I use observational data, large eddy simulation (LES), and the weather research and forecasting (WRF) model to better understand the interplay of physical processes that control Sc clouds.
Operational WRF solar forecast for SDG&E
Stratocumulus cloud group github
Line forecast github
Wu, E., Clemesha, R. E. S., & Kleissl, J. (2018). Coastal Stratocumulus cloud edge forecasts. Solar Energy, 164, 355–369. https://doi.org/10.1016/j.solener.2018.02.072
Riley, E., Lave, M., Wu, E., Dise, J., Tirumalai, T., Bosch, J. L., & Tammineedi, C. (2016). On the ability of ground based global horizontal irradiance measurements to reduce error in satellite derived plane of array irradiance data for fixed tilt photovoltaic power plants. In Conference Record of the IEEE Photovoltaic Specialists Conference (Vol. 2016–November, pp. 297–300). https://doi.org/10.1109/PVSC.2016.7749597
Atwood, A. R., Wu, E., Frierson, D. M. W., Battisti, D. S., & Sachs, J. P. (2016). Quantifying climate forcings and feedbacks over the last millennium in the CMIP5-PMIP3 models. Journal of Climate, 29(3), 1161–1178. https://doi.org/10.1175/JCLI-D-15-0063.1
Kankiewicz, A., Dise, J., Wu, E., & Perez, R. (2014). Solar 2014: Reducing Solar Project Uncertainty With an Optimized Resource Assessment Tuning Methodology. 2014 American Solar Energy Society Annual Conference, 1–6. Retrieved from https://www.cleanpower.com/wp-content/uploads/Satellite-data-tuning-06202014.pdf