Intra-hour cloud index forecasting with data assimilation

Travis M. Harty, William F. Holmgren, Antonio T. Lorenzo, Matthias Morzfeld

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

We introduce a computational framework to forecast cloud index (CI)fields for up to one hour on a spatial domain that covers a city. Such intra-hour CI forecasts are important to produce solar power forecasts of utility scale solar power and distributed rooftop solar. Our method combines a 2D advection model with cloud motion vectors (CMVs)derived from a mesoscale numerical weather prediction (NWP)model and sparse 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. We describe how the method operates on three representative case studies and present results from 39 cloudy days.

Original languageEnglish (US)
Pages (from-to)270-282
Number of pages13
JournalSolar energy
Volume185
DOIs
StatePublished - Jun 2019

Keywords

  • Advection
  • Data assimilation
  • Ensemble forecast
  • Geostationary satellite
  • NWP
  • Optical flow

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Materials Science(all)

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