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Adapting WRF for Solar Forecasts

Adapting WRF for Solar Forecasts. Clear sky conditions - more often, less error, most easily fixed Cloudy conditions – less frequent, more error All errors worse for DNI than GHI. Clear sky conditions. Error mostly a function of water vapor and aerosols

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Adapting WRF for Solar Forecasts

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  1. Adapting WRF for Solar Forecasts Clear sky conditions - more often, less error, most easily fixed Cloudy conditions – less frequent, more error All errors worse for DNI than GHI

  2. Clear sky conditions • Error mostly a function of water vapor and aerosols • Models do not do a good job handling aerosols • Background aerosols, locally generated dust, and then both are magnified by increased humidity. • Aerosol source for Tucson mostly locally generated by human activity or wind • Wind direction also matters • Current version of algorithm to correct for this reduces clear sky error from a monthly average of about to about (10-3.3%) • Nearly eliminates bias in both clear and cloudy sky conditions • Should improve as study gets more data with time

  3. May clear sky average error by time of day Forecast-Observed Error () Sunrise Noon Sunset

  4. June average error by time of dayclear sky, no smoke Error () Sunrise Noon Sunset

  5. Cloudy Conditions • Significantly more variability • Can break up clouds into different cases with different physics • Dust algorithm nearly eliminates bias in July, suggesting total cloud mass is proper • Error mostly related to time and location of convective clouds • Slight underprediction of winter clouds? We will get more data on this later this year.

  6. Current all sky(cloudy+clear) dust/dewpoint correction statsForecast minus actual, units

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