This paper investigates the accuracy of weather research and forecasting
by improving coding for solar radiation forecasting for location in Dili Timor
Leste. Weather Research and Forecasting (WRF) model version 3.9.1 is used in
this study for improvement purposes. The shortwave coding of WRF is used to
improve in order to decrease error simulation. The importance of improving WRF
coding at a specific region will reduce the bias and root mean square root when
comparing to the observed data. This study uses high resolution based on the
WRF modeling to stabilize the performance of forecasting. The decrease in error
performance will be expected to enhance the value of renewable energy. The
results show the root mean square error of the WRF default is 233 W/m2 higher compared to 205 W/m2 from the WRF improvement
model. In addition, the Mean Bias Error (MBE) of the WRF default is obtained
value 0.06 higher than 0.03 from the WRF improvement in rainy days. Meanwhile,
on sunny days, the performance Root Mean Square Error (RMSE) of WRF default is
327 W/m2 higher than 223 W/m2 from the WRF improvement. The MBE of WRF improvement obtained 0.13 lower
compared to 0.21 of WRF default coding. Finally, this study concludes that
improving the shortwave code under the WRF model can decrease the error
performance of the WRF simulation for local weather forecasting.
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