Intra-hour Cloud Index Forecasting with Data Assimilation

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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].

Original languageEnglish (US)
Title of host publication2019 IEEE 46th Photovoltaic Specialists Conference, PVSC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2420-2427
Number of pages8
ISBN (Electronic)9781728104942
DOIs
StatePublished - Jun 2019
Event46th IEEE Photovoltaic Specialists Conference, PVSC 2019 - Chicago, United States
Duration: Jun 16 2019Jun 21 2019

Publication series

NameConference Record of the IEEE Photovoltaic Specialists Conference
ISSN (Print)0160-8371

Conference

Conference46th IEEE Photovoltaic Specialists Conference, PVSC 2019
Country/TerritoryUnited States
CityChicago
Period6/16/196/21/19

Keywords

  • advection
  • data assimilation
  • ensemble forecast
  • geostationary satellite
  • NWP
  • optical flow

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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