Geostrophic Wind Dependent Probabilistic Irradiance Forecasts for Coastal California

Coastal California has enormous potential for rooftop solar photovoltaic (PV) energy production. To reduce grid-integration costs, accurate and certain solar energy forecasts are required. However, frequent marine layer fog and stratus conditions limit the accuracy of numerical weather prediction models, especially during the summer. Thus, irradiance uncertainty is large and probabilistic forecast intervals are generally wide. To produce narrow and meaningful forecast intervals, the correlation of uncertainty to local meteorological conditions describing synoptic-scale atmospheric flow was considered. Specifically, the direction and magnitude of geostrophic flow were used as an indicator of coastal cloud cover probability to produce regime-dependent forecast intervals. The method was tested in summer 2011 for coastal California. The forecast interval was smaller than that provided by previous methods for all except clear conditions and contained 80% of irradiance measurements.

[1]  C. W. Rogers,et al.  The Formation of Marine Fog and the Development of Fog-Stratus Systems along the California Coast , 1979 .

[2]  John Bjørnar Bremnes,et al.  Probabilistic wind power forecasts using local quantile regression , 2004 .

[3]  Z. Janjic.  The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes , 1994 .

[4]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[5]  Zavisa Janjic,et al.  Comments on “Development and Evaluation of a Convection Scheme for Use in Climate Models” , 2000 .

[6]  Hans-Georg Beyer,et al.  Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  Clifford W. Hansen,et al.  Statistical criteria for characterizing irradiance time series. , 2010 .

[8]  J. Hansen,et al.  A parameterization for the absorption of solar radiation in the earth's atmosphere , 1974 .

[9]  Dale F. Leipper.  Fog Forecasting Objectively in the California Coastal Area Using LIBS , 1995 .

[10]  Eugenia Kalnay,et al.  Ensemble Forecasting at NMC: The Generation of Perturbations , 1993 .

[11]  E. Ebert.  Ability of a Poor Man's Ensemble to Predict the Probability and Distribution of Precipitation , 2001 .

[12]  D. Leipper.  Fog on the U.S. West Coast: A Review , 1994 .

[13]  J. Kleissl,et al.  Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States , 2011 .

[14]  P. Ineichen,et al.  A new operational model for satellite-derived irradiances: description and validation , 2002 .

[15]  U. Damrath,et al.  Probabilistic precipitation forecasts from a deterministic model: a pragmatic approach , 2005 .

[16]  Michael R. Chernick,et al.  Bootstrap Methods: A Practitioner's Guide , 1999 .

[17]  C. M. Kishtawal,et al.  Multimodel Ensemble Forecasts for Weather and Seasonal Climate , 2000 .

[18]  T. Hoff,et al.  Validation of short and medium term operational solar radiation forecasts in the US , 2010 .

[19]  Graeme L. Stephens,et al.  Radiation Profiles in Extended Water Clouds. II: Parameterization Schemes , 1978 .

[20]  P. Ineichen,et al.  A new airmass independent formulation for the Linke turbidity coefficient , 2002 .

[21]  Christian Reise,et al.  SoDa: a project for the integration and exploitation of networked solar radiation databases , 2002 .

[22]  Rob J Hyndman,et al.  Sample Quantiles in Statistical Packages , 1996 .

[23]  Graeme L. Stephens,et al.  The Parameterization of Radiation for Numerical Weather Prediction and Climate Models , 1984 .

[24]  Robert Gall,et al.  Scientific Documentation for the NMM Solver , 2010 .