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Toward Improved Solar Irradiance Forecasts: Introduction of Post-Processing to Correct the Direct Normal Irradiance from the Weather Research and Forecasting Model

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Abstract

Solar electricity production is highly dependent on atmospheric conditions. This study focuses on comparing model forecasts with observations for the period of May–December, 2011. The Weather Research and Forecasting model was run for two nested domains centered on Arizona in order to better capture the complex terrain driven dynamics of the region. The modeling performance from the simulation with the Global Forecast System model output as initial and boundary condition was better, with respect to both direct normal irradiance and global horizontal irradiance, than that with the North American Mesoscale model output. The observed aerosol optical depth is correlated with the water vapor, soil moisture and wind-blown dust and therefore, the aerosol optical depth is parameterized by the modeling outputs for these variables. The aerosol correction factor reduces the relative root mean square error from 12 to 6 %. In cases where dust was transported at high altitude, our algorithm did not correct the bias of direct normal irradiance.

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Abbreviations

ACM2:

Asymmetric convective model ver. 2

AERONET:

AErosol RObotic NETwork

AOD:

Aerosol optical depth

CALIPSO:

Cloud-aerosol lidar and infrared pathfinder satellite observation

DNI:

Direct normal irradiance

DNIc :

Corrected DNI

DNIf :

Forecasted DNI

ECMWF:

European centre for medium-range weather forecast

GFS:

Global forecast system

GHI:

Global horizontal irradiance

HYSPLIT:

HYbrid Single Particle Lagrangian Integrated Trajectory Model

MBE:

Mean bias error

MOS:

Model output statistics

NAM:

North American Mesoscale

NCEP:

National Center for Environmental Prediction

NDFD:

National Digital Forecast Database

NWP:

Numerical weather prediction

rMBE:

Relative mean bias error

rRMSE:

Relative root mean square error

RMSE:

Root mean square error

RRTMG:

Rapid radiative transfer model global

WRF:

Weather research and forecasting

WSM6:

WRF Single-Moment 6-classes

C water :

Water vapor mixing ratio correction factor

C soil :

Soil moisture correction factor

F :

Forecast

N :

Number of sample

O :

Observation

r w :

Water vapor mixing ratio at 2 m altitude

U 10 :

Wind speed at 10 m altitude

Φ 10 :

Soil moisture from surface to 10 cm depth

γ :

Pearson correlation coefficient

τ min :

Minimum value of AOD

τ wind :

AOD due to wind-blown dust

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Acknowledgments

This study was funded by Tucson Electric Power, Arizona Research Institute for Solar Energy, University of Arizona Renewable Energy Network.

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Authors and Affiliations

  1. Center for Energy Research, University of California San Diego, 9500 Gilman Drive, Mail code 0417, San Diego, CA, 92093, USA

    Chang Ki Kim & Matthew Clarkson

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  1. Chang Ki Kim
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Correspondence to Chang Ki Kim.

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Kim, C.K., Clarkson, M. Toward Improved Solar Irradiance Forecasts: Introduction of Post-Processing to Correct the Direct Normal Irradiance from the Weather Research and Forecasting Model. Pure Appl. Geophys. 173, 1751–1763 (2016). https://doi.org/10.1007/s00024-015-1203-x

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  • DOI: https://doi.org/10.1007/s00024-015-1203-x

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