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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• 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
May clear sky average error by time of day Forecast-Observed
-100
-50
0
50
100
150
200
250
Sunrise Noon Sunset
Erro
r ()
June average error by time of dayclear sky, no smoke
-100
-50
0
50
100
150
200
Sunrise Noon Sunset
Erro
r ()
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.
Current all sky(cloudy+clear) dust/dewpoint correction stats
Forecast minus actual, units
Month Bias(total error) Average error:
uncorrected May 83.1398 138.59
corrected May -4.48947 81.2545
uncorrected June 91.3892 141.603
corrected June -0.0650794 78.5955
uncorrected July 96.5335 272.241
corrected July -18.3734 221.625