@inproceedings{af1f07bc6f4d4310a937fe51c5843924,
title = "Intra-hour Cloud Index Forecasting with Data Assimilation",
abstract = "We introduce a computational framework to forecast cloud index fields for up to one hour on a spatial domain that covers a city. Our method combines a 2D advection model with cloud motion vectors (CMVs) derived from a mesoscale numerical weather prediction (NWP) model and optical flow acting on successive, geostationary satellite images. We use ensemble data assimilation to combine these sources of cloud motion information based on the uncertainty of each data source. Our technique produces forecasts that have similar or lower root mean square error than reference techniques that use only optical flow, NWP CMV fields, or persistence. Further discussion and results of the forecasting system presented here can be found in [1].",
keywords = "NWP, advection, data assimilation, ensemble forecast, geostationary satellite, optical flow",
author = "Harty, {Travis M.} and Holmgren, {William F.} and Lorenzo, {Antonio T.} and Matthias Morzfeld",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 46th IEEE Photovoltaic Specialists Conference, PVSC 2019 ; Conference date: 16-06-2019 Through 21-06-2019",
year = "2019",
month = jun,
doi = "10.1109/PVSC40753.2019.8980592",
language = "English (US)",
series = "Conference Record of the IEEE Photovoltaic Specialists Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2420--2427",
booktitle = "2019 IEEE 46th Photovoltaic Specialists Conference, PVSC 2019",
}