Accuracy of the solar irradiance forecasts of the Japan Meteorological Agency mesoscale model for the Kanto region, Japan

In this study, forecast characteristics of a global horizontal irradiance (GHI) forecasts by a meteorological mesoscale model (MSM) developed by the Japan Meteorological Agency (JMA) for the Kanto region, Japan were investigated during the period 2008–2010 for the next day based on forecast on different issue times on the previous day of photovoltaic (PV) power production.The evaluation of the GHI forecasting errors by MSM showed that the mean bias error (MBE) values of the GHI range from −50 to 50 W/m2 in a year. The root mean square error (RMSE) values in winter were about 90–100 W/m2, while the RMSE values in summer approached up to 150 W/m2. The hourly GHI forecasting errors indicated that the GHI values were generally underestimated (overestimated) in summer (winter) compared with the observations. The dependence on different four initial times were found from RMSE values, suggesting that forecasts of initial time of 21UTC were generally better than those of the other initial times.Appearance frequency of cloud types based on the visual monitoring in cases of relatively large GHI forecasting errors, which cases were selected by normalized forecast error by the surface observations (the threshold of >0.4 or <−0.4 are given for the Kanto region), indicated that stratocumulus (cirrus) clouds for the overestimation (underestimation) of the GHI were occurred significantly, while altocumulus and cumulus clouds were observed as a whole. Verifications in GHI forecasts indicated that improvements of the treatment of these clouds in MSM were necessary for the GHI forecasting and the PV power production accurately.

• The mesoscale model (MSM) can be used for power prediction of photovoltaic systems.
• Characteristics of GHI forecasts were investigated in various time scales.
• Dependence on the initialization times of forecast errors are found.
• Under(over)estimations in summer (winter) of the forecast errors are found.
• The frequency of cloud types for cases of large forecast errors are investigated.