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

Available online at www.sciencedirect.com

Solar Energy 98 (2013) 138–152 www.elsevier.com/locate/solener

Accuracy of the solar irradiance forecasts of the Japan Meteorological Agency mesoscale model for the Kanto region, Japan Hideaki Ohtake a,⇑, Ken-ichi Shimose a, Joao Gari da Silva Fonseca Jr. a, Takumi Takashima a, Takashi Oozeki a, Yoshinori Yamada b a

Research Center for Photovoltaic Technologies, National Institute of Advanced Industrial Science and Technology, Japan b Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan Available online 17 November 2012 Communicated by: Associate Editor Jan Kleissl

Abstract 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. Crown Copyright Ó 2012 Published by Elsevier Ltd. All rights reserved. Keywords: GHI; Mesoscale model (MSM); Forecasting errors; Validation; Photovoltaics; Japan

1. Introduction A photovoltaic (PV) power production is dependent on various meteorological elements such as a global horizontal irradiance (GHI), temperature, cloudiness, relative humidity and aerosol. Thus, forecast characteristics of meteorological elements that vary widely in both temporal and ⇑ Corresponding author. Address: National Institute of Advanced Industrial Science and Technology, Research Center for Photovoltaic Technologies, OSL 1-1-1 Umezono, Ibaraki 8568-305, Japan. Tel.: +81 29 849 1526; fax: +81 29 861 5829. E-mail address: [email protected] (H. Ohtake).

spatial scales are important for accurate forecast of the PV power production and stabilization of an electric power system with other power generation systems. Recently, several engineering methods based on gridpoint value (GPV) datasets of a mesoscale model (MSM) at the Japan Meteorological Agency (JMA) (Saito et al., 2006, 2007; JMA, 2007) have been used for the forecast of the PV power production (e.g., Fonseca et al., 2011). MSM is a physically based weather forecast model. In their study, meteorological elements such as cloud cover, relative humidity, and temperature, which are indirectly relevant to the GHI, were used as an input data for forecasting the GHI and the PV power production because GHI were

0038-092X/$ - see front matter Crown Copyright Ó 2012 Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.solener.2012.10.007

H. Ohtake et al. / Solar Energy 98 (2013) 138–152

not included in MSM–GPV datasets at JMA. However, the use of GHI by MSM is expected to help realize more accurate forecasts of the PV power production. Many of MSM or global models can forecast the GHI values up to few or several days in advance. However, these forecasts have various forecasting errors (e.g., Armstrong, 2000; Remund et al., 2008; Lorenz et al., 2009; Perez et al., 2010; Lara-Fanego et al., 2012). For instance, seasonal and inter-annual variations in GHI have been investigated (e.g., Perez et al., 2010; Davy and Troccoli, 2012). Moreover, forecasts characteristics of the GHI in several different physically based models have been compared (Remund et al., 2008; Lorenz et al., 2009; Mathiesen and Kleissl, 2011). The impact of aerosols on radiative transfer processes in models has been discussed (e.g., Zamora et al., 2003; Lara-Fanego et al., 2012). Regional characteristics of GHI have also been studied (e.g., Zamora et al., 2005). Remund et al. (2008) and Lara-Fanego et al. (2012) conducted bias evaluations of GHI produced by the MSM known as the Weather Research and Forecasting (WRF) and reported systematic overestimation of the GHI during clear-sky conditions due to the treatment of aerosol optical depth in the WRF model. Since the extent of cloud fields and cloud optical thickness are the chief meteorological elements for the GHI, previous studies evaluated the accuracy of GHI under various daylight conditions, including clear skies, partial cloud coverage, and overcast skies (e.g., Heinemann et al., 2006; Ruiz-Arias et al., 2008; Lara-Fanego et al., 2012; Mathiesen and Kleissl, 2011). However, the GHI forecasting errors attributed to specific cloud types have not been evaluated thus far. To improve the GHI for the next day based on forecast on different issue times on the previous day for the PV power production using the GHI by MSM, it is necessary to understand the forecast accuracy of the GHI. The JMA investigated the forecast accuracy of GHI for the period of 3 years, from spring 2004 to autumn 2007, for the Japanese Islands and the surrounding regions (Nagasawa, 2006, 2008). They reported that the forecast characteristics of the GHI tended to have a negative bias in summer. For the forecasts of the PV power production, however, detailed characteristics of the GHI forecasting errors should be clarified. The main goal of this study is to examine forecast characteristics of the GHI by MSM over the Kanto region near Tokyo, Japan, at various temporal resolutions such as hourly, daily, monthly, and seasonal time scales. Furthermore, we will investigate an appearance frequency of cloud types observed when forecasting errors are relatively large, which have not definitively revealed by previous studies. This research will be useful for improving the microphysical and radiative transfer processes in MSM in order to realize improvements in both the GHI by MSM and the PV power production. The contents of this paper are organized in the following manner. The descriptions of surface-observed GHI data used in this analysis are presented in Section 2. MSM used

139

in this study is described in Section 3. The estimation of the GHI forecasting errors and its dependence on initialization times are investigated in Section 4. Results of statistical analysis on cloud types in cases of relatively large forecasting errors are also described in this section. Discussions and summary are provided in the Section 5. 2. Data The locations of the ground observational stations and the topography around the Kanto region are shown in Fig. 1. The Kanto region has an area of approximately 32,500 km2. The northern and western areas include mountainous and complex topography, while the eastern and southern areas are surrounded by the Pacific Ocean and are in a plain. Solar irradiances (i.e., GHI) are measured with a pyranometer (Kipp & Zonen CM3) for the JMA stations of Tokyo, Choshi, Utsunomiya, and Maebashi, in the Kanto region, Japan. For Tsukuba station, direct and diffuse irradiances are measured from direct sunlight using a pyrheliometer (Kipp & Zonen CH1) and the pyranometer (Kipp & Zonen CM21). Only the diffuse sensor is shaded by a sun-tracking automatic shadowing disk to avoid direct solar beams. The observed GHI values are calculated by the sum of direct and diffuse irradiances. Hourly averaged GHI datasets are recorded in an archive. In the observations, the GHI values are produced every 10 min, and hourly averaged GHI values are calculated for the time of an hour ahead on the hour. In the JMA stations, the glass domes of both the pyranometers and the pyrheliometer are routinely cleaned using a feather brush and a soft cloth to remove contaminants such as dust, dewdrops, ice, and snow particles, which can significantly affect the measurement. To remove missing values, the data within the period of thunderbolts or equipment maintenance is neglected from the following analysis. Because the observed GHI datasets measured and calibrated by JMA are of the highest quality in Japan, the surface-observed GHI datasets were used in this analysis to validate the GHI. 3. Numerical model 3.1. The model Numerical weather predictions of the GHI are performed with MSM developed by the JMA (e.g., Saito et al., 2006, 2007). The model domain of MSM is a region surrounding the Japan Islands with a domain size of 3600 km  2900 km  21.8 km in the x, y, and z directions, respectively. The horizontal grid spacing is 5 km. Fifty vertical levels with hybrid vertical coordinates approaching the z coordinate near the surface and the z coordinate near the model top are introduced. For an initial condition, meso-analysis using a four-dimensional variational data assimilation technique is considered in MSM. Lateral boundary conditions are supplied by the JMA global

140

H. Ohtake et al. / Solar Energy 98 (2013) 138–152

Fig. 1. (a) Detailed surface topography around the Kanto region, Japan and (b) the location of the Kanto region are shown. The rectangular domain in panel (b) indicates the location of close-up view of panel (a). Five red squares indicate the locations of the JMA operational stations. The blue dots indicate the target region including Tsukuba, Tokyo and Choshi stations to construct time series of the daily GHI energy shown in Figs. 9 and 10.

spectral model with a horizontal resolution of 20 km at 6 h intervals. MSM operated at 3 h intervals, 8 times per day; 15 h (33 h) forecasts are made at four times per day at 00, 06, 12, and 18UTC (03, 09, 15, and 21UTC). Here, the local standard time (LST) in Japan is UTC +9 h. Fig. 2 shows a schematic image of MSM forecast cycle. The longer forecast outputs of MSM (33 h forecasts) are used in this analysis (Fig. 2), since the PV power production are forecasted for the next day based on forecast on different issue times on the previous day. The period 2008–2010 is selected for the analysis. The shortwave radiative transfer scheme of MSM is based on a 22-band model that includes parameterizations of optical absorptions reported by Briegleb (1992) for water vapor and by Freidenreich and Ramaswamy (1999) for other gases. Cloud optical parameters including optical thickness (s), single scatter albedo (x), and asymmetry

factor (g) are estimated from both the liquid water path and the effective radius of cloud particles on the basis of previous research (Slingo, 1989; Ebert and Curry, 1992); for cloud particles, the difference in optical characteristics of cloud water or ice is considered. Vertical profiles of monthly averaged aerosols (s, x and g) for continent and oceans, which based on the satellite observation by a MODIS sensor, are also given in the radiative processes. Although previous studies tested the radiation parameterization of MSM (e.g., Nagasawa, 2006), a sensitivity experiment about the parameterization has not been conducted in our evaluation. A partial condensation scheme proposed by Sommeria and Deardorff (1977) is introduced in the radiative transfer processes of MSM to treat the effect of the sub-grid scale condensation because relatively small-scale clouds within the horizontal resolution cannot be resolved in MSM.

Fig. 2. Schematic diagram of MSM forecast schedules.

H. Ohtake et al. / Solar Energy 98 (2013) 138–152

The horizontal grid size of the radiative transfer processes is 10 km, since radiative transfer processes are calculated by thinning out at every other grid points in the x and y directions to reduce calculation costs. MSM calculates the radiative transfer at 15-min intervals. The hourly averaged GHI values in the forecasts are also calculated for the time of 1 h ahead on the hour. 3.2. Evaluation method To estimate the GHI forecasting errors, the evaluation are conducted by the mean bias error (MBE) and the root mean square error (RMSE) as defined by, MBE ¼

N 1X ei N i¼1

ð1Þ

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 XN 2 e RMSE ¼ i¼1 i N

141

ð2Þ

where ei = Imodel  Iobs are the difference (i.e., forecast error) between the forecasted GHI (Imodel) and the observed GHI (Iobs) for the JMA stations; N is the total number of samples. To compare seasonal differences in forecasting errors, a normalized forecast error is defined by the surface observed irradiance for the JMA stations, which the normalized values were frequently used in previous studies to evaluate values of solar irradiations (e.g., Hoff et al., 2012): I model  I obs I obs

ð3Þ

Forecasting errors are evaluated using hourly GHI datasets. Although 33 h forecasts are made in MSM, all the evaluations (MBE and RMSE) in this study are conducted

Fig. 3. Time series of monthly MBE values of the GHI from MSM in the period from 2008 to 2010. (a–c) Maebashi, (d–f) Utsunomiya, (g–i) Tsukuba, (j– l) Choshi and (m–o) Tokyo, respectively. The columns of blue, red, green and pink colors means the MBE values for initialization times of 03, 09, 15 and 21UTC, respectively.

142

H. Ohtake et al. / Solar Energy 98 (2013) 138–152

in only the daytime. Data in the nighttime (i.e., no solar irradiance) is neglected from the evaluation. Here, when the extraterrestrial solar irradiance incident at the top of the atmosphere (TOA) calculated theoretically is zero, the time zone is defined as night. In addition, statistical evaluations of the GHI forecast errors are compared with those of a persistence model as a trivial reference in Section 4.1. Here persistence model forecasts are computed using the previous 24 h observed values at the same hour. 4. Results 4.1. The statistical evaluation in forecasting errors and the dependence on different initialization times Fig. 3 shows time series of monthly MBE values of GHI with different four initial times of 03, 09, 15 and 21UTC for

five stations in the Kanto region, Japan. Here, the MBE values are calculated in the daytime from 07 LST to 21 LST of a target day, since the results of initial time of 21UTC were product after 07 LST at each day. Overestimations of the GHI in the period from winter to spring tend to be found, while underestimations in the period from summer to autumn are frequently found for all initial times. However, the MBE values for Tokyo station in 2008 tend to be overestimated (Fig. 3m). For Choshi station on July and August from 2009 to 2010 (Fig. 3k and l), minimum MBE values of the GHI shows the significant underestimation and approach to about – 100 W m2. The difference of MBE values in the GHI arising from different initial times is not large and similar seasonal variations of MBE values are found. In the time series of monthly RMSE values of the GHI (Fig. 4), statistical seasonal variations are found. The RMSE values in mid-winter are smaller than those in

Fig. 4. As in Fig. 3, but for monthly RMSE values.

H. Ohtake et al. / Solar Energy 98 (2013) 138–152 Table 1 The statistical evaluation of both the seasonal and annual GHI values in the period from 2008 to 2010 for different initialization tmes and persistence model for Tsukuba station. The unit of both the MBE and RMSE values is W/m2. We defined the four seasons, winter (December– February (DJF)), spring (March–May (MAM)), summer (June–August (JJA)) and autumn (September–November (SON)), respectively. Station Tsukuba

2008 MBE

2009 RMSE

MBE

2010 RMSE

MBE

RMSE

Winter(DJF) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

12.8 7.8 12.7 8.9 1.4

66.7 63.3 63.5 60.9 179.9

23.7 28.2 24.4 22.0 2.0

89.0 86.5 82.4 71.8 176.6

27.4 25.0 22.0 25.6 1.8

85.6 78.9 69.1 68.3 147.9

Spring(MAM) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

26.6 27.0 18.5 24.0 3.2

127.2 126.3 114.7 111.7 257.3

12.7 16.1 13.9 10.6 2.7

124.7 128.3 119.0 115.9 273.9

25.7 19.6 14.1 20.9 6.0

133.1 128.4 127.0 112.6 295.4

Summer(JJA) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

20.2 14.6 10.4 3.4 3.4

160.6 158.5 149.5 148.0 242.5

45.0 50.5 41.7 39.5 0.6

164.7 159.9 152.3 149.7 235.4

49.3 57.4 52.4 39.5 2.1

135.9 139.2 131.7 124.8 221.4

Autumn(SON) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

3.7 6.3 0.9 0.2 0.6

102.4 100.6 92.1 94.6 194.3

4.0 6.8 5.7 3.8 1.0

113.7 109.3 103.4 94.1 214.9

2.9 12.1 8.5 1.2 3.4

108.6 106.7 102.6 92.0 210.4

5.2 6.4 5.1 9.3 0.1

121.9 120.2 112.1 110.9 223.4

4.8 5.1 3.8 4.2 0.1

128.2 126.2 119.2 114.0 230.4

1.5 8.1 7.9 0.5 0.2

119.4 117.6 112.5 103.6 228.4

Annual 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

summer as a whole. In particular, the RMSE values for Choshi station, the RMSE values on August of 2008 and in the period from July to August of 2009 reach about 200 W m2 (Fig. 4j and k) and the GHI forecasting errors are higher than the other stations in each year. The RMSE values by the initial times are generally decreased from 03UTC to 21UTC initial times. Results of initial times of 21UTC are generally better than the other initial times, although there are some exceptions for months and for stations. Tables 1–5 summarize seasonal and annual averaged MBE and RMSE values of the GHI in the period from 2008 to 2010 for all five stations in the Kanto region. As a trivial reference, results of the persistence model are also included in this table. The MBE values for winter and spring tend to be positive (overestimation), while those for summer are significant negative (underestimation). However, there are some exceptions; for instance, overestimation tendencies are found in summer in some initial times for Tsukuba, Tokyo, Utsunomiya and Maebashi

143

Table 2 Same as in Table 1, but for Choshi station. Station Choshi

2008 MBE

2009 RMSE

MBE

2010 RAISE

MBE

RMSE

Winter(DJF) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

25.6 20.7 24.3 20.1 1.2

80.5 74.0 73.6 67.9 197.8

26.3 28.1 26.1 17.6 3.8

98.7 94.5 93.2 75.6 187.0

30.6 29.4 26.9 27.4 0.3

97.3 88.8 81.5 79.5 173.2

Spring(MAM) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

25.4 21.5 19.0 25.3 2.7

124.0 120.4 118.0 108.8 271.6

13.2 13.9 8.3 3.2 3.4

124.9 123.9 115.2 113.5 286.6

9.4 5.1 0.8 7.8 4.0

127.5 127.7 128.1 112.0 298.5

Summer(JJA) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

63.2 52.8 47.0 32.1 1.1

184.3 170.9 163.6 152.3 252.8

68.6 66.5 63.2 57.7 0.7

187.9 188.2 178.2 174.3 255.6

85.5 89.4 85.0 75.3 1.3

164.9 165.0 162.1 147.2 215.0

Autumn(SON) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INTT Persistence

5.2 5.7 5.1 1.0 2.9

106.9 110.0 97.6 91.4 219.6

15.9 17.1 19.2 19.2 2.5

129.0 120.2 121.8 119.2 236.6

5.9 13.3 11.8 5.2 4.1

111.2 112.4 107.5 101.2 228.4

3.6 2.8 1.2 2.6 0.0

132.4 126.2 120.4 111.8 239.2

13.3 12.5 14.0 15.7 0.4

141.4 138.7 133.1 128.2 246.5

15.6 19.9 20.0 13.8 0.1

129.7 128.7 125.7 114.6 235.3

Annual 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

stations in 2008. The RMSE values for summer (winter) are relatively larger (smaller) than those for spring and autumn. The RMSE values in spring for all stations are slightly higher than those in autumn. As expected, the decrease of the forecast lead time of initial times (from 03UTC to 21UTC) tends to decrease the RMSE values for both seasonal and annual periods for each year. For the annual period, the RMSE values for Choshi station (Table 2) where the location is near the coast (see Fig. 1) tend to be larger than the other stations. The RMSE values of the persistence model are about two times higher rather than those of MSM for all stations in the Kanto region, suggesting that MSM performs significantly better than the persistence model for both seasonal and annual periods. 4.2. Seasonal variations in the GHI An example of time series of hourly GHI of both forecasts and observations during 2010 is shown in Fig. 5. Here, we show the results for Tsukuba station, since this station locates in the Kanto Plains and it is considered that localities for weather conditions due to the topography (i.e., mountains) and for the distance from a coast are

144

H. Ohtake et al. / Solar Energy 98 (2013) 138–152

Table 3 Same as in Table 1, but for Tokyo station. Station Tokyo

2008 MBE

Table 4 Same as in Table 1, but for Maebashi station.

2009 RMSE

MBE

2010 RMSE

MBE

Station Maebashi

2000

MBE

RMSE

86.5 83.0 77.3 76.4 149.6

5.3 9.6 7.0 5.9 1.5

75.6 72.5 66.8 65.3 152.7

1.8 2.3 0.7 2.6 0.3

91.0 91.3 86.1 84.6 157.7

3.4 0.3 0.9 3.9 0.4

90.6 92.1 83.0 78.6 159.7

31.9 26.8 23.2 27.3 4.8

132.0 124.7 117.4 108.8 299.5

Spring(MAM) 03UTC _INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

28.6 29.9 26.1 27.5 3.0

132.6 124.3 118.3 114.0 284.1

19.1 17.5 15.2 19.2 2.7

103.8 106.6 105.6 102.8 280.2

14.5 11.5 6.7 14.4 3.9

125.9 125.2 118.9 118.7 312.8

165.0 161.1 159.1 161.1 257.0

22.0 31.5 27.3 14.5 2.5

134.9 135.7 129.0 120.8 230.3

Summer(JJA) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

2.0 6.0 4.5 6.5 4.0

165.4 153.1 138.8 139.1 243.7

22.3 24.3 26.8 16.1 0.7

159.1 148.0 150.3 150.2 249.5

43.6 45.3 44.5 29.8 1.7

146.8 145.4 142.6 131.0 214.3

11.7 8.0 7.6 8.0 2.2

117.3 111.3 107.9 105.0 230.3

13.0 4.3 8.0 17.1 2.3

104.4 96.7 94.9 90.6 212.2

Autumn(SON) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

4.4 1.9 7.4 1.7 0.0

108.1 98.6 93.7 86.9 201.8

8.9 5.4 4.8 9.2 2.4

99.4 102.6 96.8 94.1 195.2

17.1 20.6 19.6 6.8 2.5

117.7 111.1 109.9 100.2 218.2

13.3 11.6 11.3 12.5 0.1

130.3 126.6 124.3 123.2 242.5

12.0 5.5 6.1 13.4 0.1

117.9 113.9 108.3 102.1 232.6

Annual 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

5.9 4.6 2.4 7.4 0.0

I27.3 118.3 109.9 107.2 229.3

1.4 0.2 2.4 3.4 0.1

118.5 115.8 114.3 112.7 229.3

11.6 14.5 15.4 5.2 0.1

123.6 121.7 110.4 110.6 236.0

19.8 14.1 19.5 19.3 1.2

80.3 74.6 76.4 74.1 181.3

26.5 31.4 28.9 24.6 2.2

90.8 90.1 88.2 83.0 184.8

30.1 28.3 25.8 28.2 0.5

Spring(MAM) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

48.9 44.6 40.5 45.8 3.2

134.9 133.0 113.3 116.6 268.4

34.9 34.6 31.6 31.4 1.4

127.6 124.2 121.9 120.2 276.8

Summer(JJA) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

10.6 14.5 18.4 31.4 3.0

163.8 158.4 153.3 153.9 246.4

16.3 22.4 18.6 11.3 1.8

Autumn(SON) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

16.5 24.1 15.7 15.9 1.3

115.5 107.5 103.9 95.5 204.6

Annual 03UTC_INIT 09UTC_INIT 15UTC INIT 21UTC_INIT Persistence

24.2 24.7 23.8 28.8 0.0

129.7 124.8 117.2 116.2 230.3

smaller compared to the other four stations. The hourly GHI values for the initial time of 03UTC are shown. The peak GHI values in mid-winter (i.e., in January and December) in both forecasts and observations are about 500 W m2, while the peak GHI in the early summer (i.e. in May and June) approaches to about 950 W m2. The GHI values in the forecasts tend to be similar to the observations. The temporal decrease of the observed GHI is caused by the increase in cloud coverage during a rainy season in the Kanto region (the period (from June to early July) by the arrow indicated in Fig. 5b). The decreased peak GHI could be also reproduced well in forecasts (Fig. 5a). Similar tendency are found for the other stations (not shown). Fig. 6 shows scatter plots of the GHI for each month in 2010. Hourly GHI values tend to be in line with the one-toone line. The tilt of fitting color lines indicates that overestimations in winter (December–February) and underestimations in summer (June–August) of the GHI are frequently found. Correlation coefficients on March (r = 0.86) and October (r = 0.89) in 2010 are relatively lower than the other months. Similar tendency of the GHI forecasting errors are found in the other period from 2008 to 2009 for the other stations (not shown).

MBE

2010

Winter(DJF) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

Winter(DJF) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

RMSE

2008

RMSE

MBE

RMSE

4.3. Daily and hourly variations in the GHI Fig. 7 shows an example of time series of hourly GHI values in the late rainy season for 1 month (on July of 2010). The rainy season ended in the beginning of July 2010. Cloudy conditions caused the decrease in the GHI and its temporal variations on July 05, 06, and 07, which it seems to be difficult to reproduce these variations by MSM. However, the model can often reproduce the observed GHI even under other cloudy conditions (i.e., on July 09 and 13). We found through forecast results that different initial times frequently tend to produce different GHI forecasting errors, such as those observed on July 03, 14, 16, and 17. After the end of the rainy season, some clear-sky cases (i.e., July 21, 24, 27 and 28) which judged from satellite visible images with no cloud fields in the Kanto region (not shown) are found. Hourly GHI values by MSM under clear-sky conditions show clear diurnal variations, which are in line with the observations. To compare seasonal differences in the GHI forecasting errors, the frequency of the GHI forecasting errors normalized by the surface observed values using the definition in Eq. (3) in Section 3.2 are shown in Fig. 8. Here, periods during 3 months in each winter (December–February)

H. Ohtake et al. / Solar Energy 98 (2013) 138–152

145

Table 5 Same as in Table 1, but for Utsunomiya station. Station Utsunomiya

2008

2009

2010

MBE

RMSE

MBE

RMSE

Winter(DJF) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

MBE

RMSE

0.7 3.8 0.5 0.6 1.5

73.0 75.0 62.6 65.3 175.3

8.9 12.3 6.5 7.5 1.0

88.0 84.9 79.7 74.4 166.6

16.0 11.3 10.1 13.8 1.3

86.2 84.9 75.4 69.8 155.6

Spring(MAM) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

35.4 S7.7 28.2 34.5 -2.6

134.6 126.0 117.3 118.5 275.0

29.4 28.2 25.0 26.9 1.2

126.3 130.6 123.8 118.3 278.3

35.8 32.3 24.2 29.6 -5.0

141.8 135.2 124.2 122.8 296.9

Summer(JJA) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

15.0 6.8 16.1 28.0 4.2

179.2 163.6 156.5 156.7 245.2

9.3 9.7 9.7 3.5 0.2

144.4 134.7 138.9 143.5 227.1

19.8 23.5 20.3 4.4 2.2

154.9 152.2 145.3 142.5 225.5

Autumn(SON) 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

4.7 6.9 2.6 4.0 0.7

102.8 99.4 97.0 92.5 199.1

4.4 5.6 6.2 9.5 1.3

107.5 106.0 98.9 94.7 189.4

3.8 8.3 6.5 2.5 1.8

107.4 104.1 102.5 88.4 219.1

Annual 03UTC_INIT 09UTC_INIT 15UTC_INIT 21UTC_INIT Persistence

14.5 12.7 11.2 17.6 0.1

131.5 123.1 116.1 115.9 229.8

8.3 8.9 6.9 10.1 0.3

120.2 117.5 114.5 112.9 222.3

6.7 2.6 1.5 10.3 0.0

127.9 124.2 117.0 112.0 232.9

Fig. 5. Time series of both (a) the hourly GHI values from MSM with the initial time of 03UTC and (b) the observations for Tsukuba station during 2010. The arrows in the period from June to mid-July indicate the rainy season over the Kanto region.

and summer (June–August) are investigated. For all initial times, positive (negative) monthly evaluations of both the mean values and the skewness values in winter (summer) mean overestimations (underestimations) of the GHI by MSM. Standard deviations of the GHI forecasting errors for all initial times range from 0.36 to 0.55 in winter and from 0.35 to 0.45 in summer. Note that the kurtosis values for all initial times tend to be large in winter from 6.14 to 7.37 on February 2010 than other months. The kurtosis values of the persistence model tend to be smaller than those of MSM for all initial times, except for February in 2010 for all initial times and August in 2010 for 03, 09 and 15UTC, respectively. These results suggested that the GHI values by MSM are better than the persistence model in seasonal periods. 4.4. Daily accumulated GHI energy To investigate the daily accumulated GHI energy (DGHIE) at a regional scale in the Kanto region, Figs. 9 and 10 show examples of time series of DGHIE and the GHI forecasting errors for winter (on January, 2010) and summer (on July, 2010), respectively. The observed DGHIE are averaged for the three stations (Tsukuba,

Fig. 6. Hourly forecasted GHI by MSM versus hourly observed GHI for Tsukuba station during 2010. Horizontal and vertical axes indicate observed and forecasted hourly GHI values, respectively. Cold and warm coloring of the plots indicates data in winter and summer, respectively. Lines and letters “r” denote least squares linear fits and correlation coefficients for each month, respectively.

146

H. Ohtake et al. / Solar Energy 98 (2013) 138–152

Fig. 7. Time series of hourly GHI values in both the forecasts and the observations on July 2010 for Tsukuba station. The black solid line indicates the surface-observed GHI values. The blue, red, green and pink circles indicate the GHI by MSM at the initial times of 03, 09, 15, 21UTC, respectively. For the GHI by MSM, nine model grids nearest to the station are averaged.

Tokyo, and Choshi), because these stations are in the Kanto Plains (see Fig 1). The model grids in MSM surrounding the three JMA stations are averaged in this analysis (the blue dots region shown in Fig. 1). The DGHIE for the initial time of 03UTC are used. Most of forecasted DGHIE in winter are consistent with the observations as a whole (Fig. 9a). Forecasting errors exceeding approximately 5 MJ m2 day1 (1.4 kW h m2 day1) of the DGHIE in summer (mainly underestimations) are often found (Fig. 10a). However, the normalized forecasting errors described in Section 3.2 between winter and summer (Figs. 9b and 10b) show that many of normalized forecasting errors range from 0.4 to 0.4, suggesting that seasonal differences in the GHI forecasting errors are not large. Similar tendencies are found in the normalized forecasting errors for the other 2 years (not shown). Compared with the persistence model based on the MBE and RMSE values, in cases of the sharp decrease of the observed GHI; for instance, in periods from 11 to 13 January, from 21, 22, 28 and 30 January and from 08

to 11 July (see Figs. 9d and 10d), the RMSE values of the persistence model are larger about 2–4 times than MSM. In cases of temporal decrease of the GHI values, as expected, MSM performs better than the persistence model. In cases of continuous slight underestimations in the period from 14 to 28 July, the RMSE values of the persistence model are almost same or less compared with MSM (see Fig. 10a and d). In this period, the cloudiness distribution or optical thickness of clouds in MSM seems to be produced overall larger or thicker than the observations and the persistence forecasts are better than MSM ones. 4.5. Large forecast error cases 4.5.1. Detection of large forecast error case In this section, the relationship between cloud types and large forecasting errors of the GHI from MSM will be examined. To detect cases of relatively large forecasting errors, we set >0.4 or <0.4 as a threshold of the normalized forecasting errors at the regional scale in the Kanto

H. Ohtake et al. / Solar Energy 98 (2013) 138–152

147

Fig. 8. Appearance frequency for forecasting errors normalized by the observed solar irradiance at the surface of hourly GHI by MSM with the initial time of 03, 09, 15, 21UTC (colors) with the persistence model (black) in (a–c) winter (from December 2009 to February 2010) and (d–f) summer (from June to August 2010). The statistical evaluation (mean values (mean), standard deviations (sd), skewness and kurtosis) of both GHI forecasts with four initialization times and the persistence model are shown on the bottom in each figure.

region described in Section 4.4 (see blue dots region shown in Fig. 1a), which is following the Eq. (3). The selection of these cases is made on daily basis. According to this threshold, the total 205 cases (i.e., days) during the period of 3 years are selected. Cloud types, classified visually at the JMA stations, have been recorded by trained technicians based on the guidelines of the JMA. In the following

subsection, an appearance frequency of observed cloud types in cases of relatively large forecasting errors will be discussed. 4.5.2. Cloud types To investigate the appearance frequency of observed cloud types for a full year (i.e., all sky conditions), we first

148

H. Ohtake et al. / Solar Energy 98 (2013) 138–152

Fig. 9. Example of time series of (a) the daily GHI energy for the observed GHI (black circles;Iobs), the forecasted GHI (blue squares; Imodel) with the initial times of 03UTC and the extraterrestrial solar irradiance at the TOA (red stars; Itop) on January of 2010, respectively. (b) Daily forecasting errors normalized by the observations at the surface (Iobs). Time series of daily (c) MBE and (d) RMSE values by MSM (black bar) with those for persistence model (gray bar) are also shown. The additional axis of the unit (kW h/m2/day) corresponding to the left unit (MJ/m2/day) in panel (a) is shown on the right side of the panel.

show the results for Tsukuba, Choshi, and Tokyo stations (see Fig. 1a) in 2008, 2009, and 2010, respectively (Table 6). The frequency is calculated by the number of the selected cases for each year shown in the top of the each table. For all sky conditions, Ci clouds in the high-level (from 5 km to 10 km above mean sea level (ASL)), Ac clouds in the middle-level (from 2 km to 5 km ASL) are frequently found. In the low-level troposphere (below 2 km ASL), Sc clouds are relatively found rather than St clouds. Cu clouds as convective clouds are often observed for all sky conditions. Appearance frequency of Cb clouds is low as a whole. The frequency of “No clouds” for Tsukuba and Tokyo stations decrease from 09 LST to 15 LST in each year, while those for Choshi station, which is a coastal station, increase slightly. It should be noted that clouds in the higher- and/or middle-level troposphere cloud not be observed by the visual monitoring of cloud types at the surface when Sc and St clouds extended to the low-level troposphere.

In cases of relatively large forecasting errors as described in Section 4.5.1, the frequency of cloud types are summarized in Table 7. The results show that Ci, Ac and As clouds are often present in higher-level and middle level layers. Sc and St clouds (i.e., nonprecipitating clouds), which these clouds occur under relatively stable stratification in the low-level troposphere, are present in the low-level layers. Cu clouds also appear, although Cu clouds often also include convective and nonprecipitating clouds. Conversely, the frequency for Cb and Ns (i.e., precipitating) clouds are low as a whole. The difference in morning (09 LST) and daytime (15 LST) results is not large. “No clouds” (i.e., clear sky conditions) are not found in these cases. We also divide the cases of the relative large forecast error into two gropes of both the overestimation and the underestimation, respectively. In cases of the overestimation (Table 8), Ac, Sc and Cu clouds are often observed. Particularly, the frequency of Sc clouds is higher than those for the total cases of large forecast error cases (compared

H. Ohtake et al. / Solar Energy 98 (2013) 138–152

149

Fig. 10. Same as Fig. 9, but for July of 2010.

to Table 7). Cb is not observed at all. On the other hands, in cases of the underestimation (Table 9), the frequency of Ci and Cu clouds are higher than those in the total of large forecast error cases (Table 7). Particularly, the increase of frequency of Ci clouds is characteristic. On the other hand, the frequency of Sc clouds is quite low in cases compared to the total of large forecast error cases (Table 7). The results showed that Sc clouds in the lower-level for the overestimation and Ci clouds in the higher-level for the underestimation are significantly occurred, while Ac and Cu clouds tend to be observed in two gropes. The present classifications of cloud types in this analysis are obtained through the visual monitoring of cloud types at the JMA stations; therefore, observed morphological characteristics of clouds and cloud types could have been altered by human perception. In this paper, however, the selected 205 cases during 3 years are determined to be sufficient for detecting the cloud types in cases of relatively large forecasting errors. 5. Discussions and summary Forecast characteristics of the GHI by the operational meteorological (physical) MSM developed by the JMA in

the Kanto region, Japan, were investigated for 3 years from 2008 to 2010. The analysis was based on ground-observed GHI datasets at the JMA stations. The objective was to evaluate the GHI forecasting errors for the accurate forecast of PV power production for the next day based on forecast on different issue times on the previous day. The evaluation of the GHI show that the MBE values of the GHI range from 50 to 50 W/m2 in a year. The RMSE values in winter are about 90–100 W/m2, while those in summer approaches up to 150 W/m2. Forecasting errors of hourly GHI indicate that the GHI values are generally underestimated (overestimated) in summer (winter) compared with the observations. The dependence on different four initialization times were also investigated based on the RMSE values, suggesting that results of initial time of 21UTC are generally better than those of the other initial times. Previous studies discussed the productivity of low- and mid-level cloud fractions in MSM (e.g., Nagasawa, 2006, 2008). However, the relationship between cloud distributions in the low, middle, and upper levels produced by MSM and relatively large forecasting errors should be examined in detail. In this paper, we investigated the appearance frequency of cloud types based on the visual

150

H. Ohtake et al. / Solar Energy 98 (2013) 138–152

Table 6 Appearance frequency of cloud types for a full year at 09 LST and 15 LST in (a) 2008, (b) 2009 and (c) 2010 for Tsukuba, Choshi and Tokyo stations, respectively. The 10 cloud types: convective clouds such as cumulus (Cu) and cumulonimbus (Cb), stratiform or cumulus clouds in the low-level clouds (stratocumulus, Sc; stratus, St), and middle-level (altocumulus, Ac; Altostratus, As; Nimbostratus, Ns) and higher-level (cirrus, Ci; cirrocumulus, Cc; and cirrostratus, Cs) are shown, respectively. The letter of “No clouds” means clouds are not observed (i.e., in clear-sky conditions). The number of cases analyzed in this frequency is shown in “Cases:” at the top of each Table. Location

OBS Time

(a) 2008 Tsukuba

Case:366 09LST 15LST 09LST 15LST 09LST 15LST

Choshi Tokyo (b) 2009 Tsukuba Choshi Tokyo (c)2010 Tsukuba Choshi Tokyo

Cloud types

High-level clouds

Middle-level clouds

Low-level clouds

Convective clouds

No clouds

Ci

Cc

Cs

Ac

As

Ns

Sc

St

Cu

Cb

24.6 35.5 34.2 37.2 30.9 38.8

1.9 1.6 0.5 1.1 0.8 1.6

2.2 2.7 0.8 1.1 2.5 3.0

39.6 50.5 36.6 36.3 30.3 33.3

3.8 3.0 3.6 4.4 2.7 3.3

1.4 0.5 2.7 1.9 7.1 6.6

28.4 22.4 41.5 33.1 35.8 28.7

14.8 7.1 9.6 7.1 9.3 7.4

58.2 70.2 61.7 65.6 49.5 69.7

0.0 1.9 0.8 1.6 0.0 4.9

22.0(%) 10.0 9.0 12.0 36.0 11.0

Case:365 09LST 15LST 09LST 15LST 09LST 15LST

34.0 46.6 25.5 33.7 35.3 40.5

1.6 1.9 0.8 0.8 2.7 3.0

2.7 1.6 0.3 0.8 1.6 3.6

38.6 49.3 41.6 46.0 36.4 44.9

6.0 6.3 3.8 3.0 2.5 3.3

2.7 1.6 3.0 3.0 4.4 4.1

27.9 19.5 32.3 26.3 37.3 30.1

17.0 7.4 15.1 11.8 7.7 5.2

57.0 75.9 58.9 65.5 57.3 78.1

0.3 1.1 0.5 0.5 0.3 1.4

28.0(%) 5.0 15.0 16.0 29.0 11.0

Case:60 09LST 15LST 09LST 15LST 09LST 15LST

31.5 38.1 38.1 38.6 34.8 45.8

0.5 0.3 0.0 0.3 1.4 1.1

2.5 3.6 0.5 1.4 1.6 1.6

36.7 41.9 46.6 47.4 42.2 47.7

4.1 4.4 3.8 5.8 1.4 1.1

0.8 0.0 2.2 1.9 4.7 5.8

27.7 23.3 27.1 24.9 31.0 21.9

6.8 4.7 14.0 9.6 7.9 8.2

66.0 74.2 69.3 73.2 64.7 79.2

0.3 0.8 0.3 0.8 0.0 2.7

11.0(%) 7.0 4.0 9.0 16.0 5.0

Table 7 Same as in Table 6, but for cases of relative large forecasting errors. Location

(a) 2008 Tsukuba Choshi Tokyo (b) 2009 Tsukuba Choshi Tokyo (c) 2010 Tsukuba Choshi Tokyo

OBS Time

Cloud types

High-level clouds

Middle-level clouds

Low-level clouds

Convective clouds

Ci

Cc

Cs

Ac

As

Ns

Sc

St

Cu

Cb

Case:63 09LST 15LST 09LST 15LST 09LST 15LST

14.3 9.5 19.0 15.9 17.5 9.5

0.0 0.0 0.0 4.8 0.0 0.0

1.6 1.6 1.6 0.0 1.6 0.0

47.6 54.0 42.9 38.1 30.2 34.9

6.3 6.3 7.9 9.5 3.2 11.1

3.2 0.0 6.3 4.8 15.9 17.5

46.0 42.9 71.4 58.7 55.6 63.5

27.0 20.6 15.9 19.0 22.2 20.6

46.0 52.4 57.1 58.7 39.7 49.2

0.0 3.2 0.0 4.8 0.0 6.3

0.0(%) 0.0 0.0 0.0 0.0 0.0

Case:82 09LST 15LST 09LST 15LST 09LST 15LST

18.3 32.9 7.3 24.4 19.5 31.7

0.0 0.0 1.2 1.2 1.2 1.2

2.4 0.0 0.0 0.0 0.0 0.0

35.4 51.2 32.9 4.02 32.9 46.3

9.8 8.5 8.5 3.7 4.9 4.9

6.1 2.4 6.1 6.1 8.5 9.8

52.4 41.5 52.4 47.6 54.9 53.7

31.7 17.1 23.2 23.2 14.6 11.0

51.2 65.9 48.8 61.0 53.7 73.2

0.0 2.4 0.0 1.2 0.0 1.2

0.0(%) 0.0 0.0 0.0 0.0 0.0

Case:60 09LST 15LST 09LST 15LST 09LST 15LST

8.3 8.3 21.7 16.7 13.3 18.3

0.0 0.0 0.0 1.7 0.0 1.7

0.0 0.0 0.0 0.0 0.0 0.0

23.3 26.7 45.0 48.3 41.7 38.3

11.7 13.3 10.0 11.7 1.7 3.3

1.7 0.0 6.7 6.7 8.3 16.7

55.0 46.7 48.3 50.0 63.3 55.0

15.0 15.0 15.0 21.7 18.3 25.0

56.7 60.0 60.0 70.0 53.3 60.0

0.0 0.0 0.0 0.0 0.0 0.0

0.0(%) 0.0 0.0 0.0 0.0 0.0

monitoring of cloud types in cases of relatively large forecasting errors. When relatively large errors were detected in GHI, whereby normalized forecasting errors by the

No clouds

observed irradiance at the surface were >0.4 or <0.4, Ci, Ac, Sc and Cu clouds were frequently observed, respectively. Particularly, Sc clouds in the lower-level for overes-

H. Ohtake et al. / Solar Energy 98 (2013) 138–152

151

Table 8 Same as in Table 6, but for cases of overestimation of the GHI. Location

OBS Time

(a) 2008 Tsukuba

Case:46 09LST 15LST 09LST 15LST 09LST 15LST

Choshi Tokyo (b) 2009 Tsukuba Choshi Tokyo (c) 2010 Tsukuba Choshi Tokyo

Cloud types

High-level clouds

Middle-level clouds

Low-level clouds

Convective clouds

No clouds

Ci

Cc

Cs

Ac

As

Ns

Sc

St

Cu

17.4 4.3 13.0 8.7 15.2 0.0

0.0 0.0 0.0 2.2 0.0 0.0

2.2 2.2 2.2 0.0 2.2 0.0

43.5 47.8 39.1 34.8 23.9 23.9

6.5 6.5 4.3 8.7 2.2 15.2

4.3 0.0 6.5 4.3 17.4 17.4

56.5 56.5 76.1 67.4 65.2 69.6

30.4 23.9 10.9 21.7 21.7 21.7

0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0

0.0(%) 0.0 0.0 0.0 0.0 0.0

Case:57 09LST 15LST 09LST 15LST 09LST 15LST

17.5 17.5 1.8 12.3 12.3 17.5

0.0 0.0 0.0 0.0 0.0 0.0

3.5 0.0 0.0 0.0 0.0 0.0

29.8 36.8 24.6 24.6 21.1 29.8

10.5 10.5 12.3 5.3 5.3 5.3

7.0 3.5 8.8 8.8 10.5 14.0

59.6 49.1 64.9 61.4 64.9 66.7

29.8 22.8 17.5 24.6 19.3 14.0

45.6 54.4 45.6 52.6 49.1 63.2

0.0 1.8 0.0 1.8 0.0 0.0

0.0(%) 0.0 0.0 0.0 0.0 0.0

Case:51 09LST 15LST 09LST 15LST 09LST 15LST

3.9 3.9 15.7 9.8 7.8 11.8

0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0

13.7 15.7 41.2 41.2 35.3 31.4

11.8 13.7 9.8 13.7 2.0 3.9

2.0 0.0 5.9 5.9 7.8 17.6

64.7 54.9 54.9 54.9 72.5 60.8

15.7 15.7 11.8 19.6 19.6 25.5

51.0 54.9 56.9 66.7 47.1 54.9

0.0 0.0 0.0 0.0 0.0 0.0

0.0(%) 0.0 0.0 0.0 0.0 0.0

Cb

Table 9 Same as in Table 6, but for cases of underestimation of the GHI. Location

OBS Time

(a) 2008 Tsukuba

Case:63 09LST 15LST 09LST 15LST 09LST 15LST

Choshi Tokyo (b) 2009 Tsukuba Choshi Tokyo (c) 2010 Tsukuba Choshi Tokyo

Cloud types

High-level clouds

Middle-level clouds

Low-level clouds

Convective clouds

Ci

Cc

Cs

Ac

As

Ns

Sc

St

Cu

Cb

5.9 23.5 35.3 35.3 23.5 35.3

0.0 0.0 0.0 11.8 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0

58.8 70.6 52.9 47.1 47.1 64.7

5.9 5.9 17.6 11.8 5.9 0.0

0.0 0.0 5.9 5.9 11.8 17.6

17.6 5.9 58.8 35.3 29.4 47.1

17.6 11.8 29.4 11.8 23.5 17.6

76.5 88.2 52.9 70.6 64.7 64.7

0.0 11.8 0.0 17.6 0.0 23.5

0.0(%) 0.0 0.0 0.0 0.0 0.0

Case:82 09LST 15LST 09LST 15LST 09LST 15LST

20.0 68.0 20.0 52.0 36.0 64.0

0.0 0.0 4.0 4.0 4.0 4.0

0.0 0.0 0.0 0.0 0.0 0.0

48.0 84.0 52.0 76.0 60.0 84.0

8.0 4.0 0.0 0.0 4.0 8.0

4.0 0.0 0.0 0.0 4.0 0.0

36.0 24.0 24.0 16.0 32.0 24.0

36.0 4.0 36.0 20.0 4.0 4.0

64.0 92.0 56.0 80.0 64.0 96.0

0.0 4.0 0.0 0.0 0.0 4.0

0.0(%) 0.0 0.0 0.0 0.0 0.0

Case:60 09LST 15LST 09LST 15LST 09LST 15LST

33.3 33.3 55.6 55.6 44.4 55.6

0.0 0.0 0.0 11.1 0.0 11.1

0.0 0.0 0.0 0.0 0.0 0.0

77.8 88.9 66.7 88.9 77.8 77.8

11.1 11.1 11.1 0.0 0.0 0.0

0.0 0.0 11.1 11.1 11.1 11.1

0.0 0.0 11.1 22.2 11.1 22.2

11.1 11.1 33.3 33.3 11.1 22.2

88.9 88.9 77.8 88.9 88.9 88.9

0.0 0.0 0.0 0.0 0.0 0.0

0.0(%) 0.0 0.0 0.0 0.0 0.0

timation cases and Ci clouds in the higher-level for underestimation cases are significantly occurred, while Ac and Cu clouds are generally observed. Furthermore, regional characteristics of the GHI forecasting errors have been also investigated in previous research (e.g., Zamora et al., 2005; Yoshida et al., 2011). Yoshida et al. (2011) verified the GHI forecasting errors

No clouds

by the JMA nonhydrostatic model (JMA–NHM), which was similar to experimental design of MSM in this study, on the southern part of the Tohoku area in the eastern Japan and reported overestimations of the GHI in summer. Although several setups for the forecasts in JMA–NHM of Yoshida et al. (2011) slightly differed from those in the present study, the same shortwave radiative transfer

152

H. Ohtake et al. / Solar Energy 98 (2013) 138–152

scheme was used. In this study, we focused in only the Kanto region. Since the Japan Islands extend from the north toward the southwest and are surrounded by the Pacific Ocean (Fig. 1b), these regions exhibit a wide range of meteorological conditions. Therefore, the investigation of regional characteristics of the GHI forecasting errors is necessary; future research will focus on this issue. The results obtained in this study are helpful for improving processes relevant to the GHI in MSM. The verification of the GHI forecasting errors demonstrated the requirement for improving the treatment of specific clouds (i.e., Ci, Ac, Sc and Cu clouds) in the microphysical and/or radiative transfer processes in the model. Particularly, several parameters relevant to the GHI in the partial condensation scheme of MSM at JMA are fixed as a constant value for a whole domain of MSM. In order to improve the accurate GHI relevant to these specific clouds, such parameters should be represented as a function of physical parameters; for instance, temperature and humidity. Such improvements can result in better forecasts of GHI and the accurate forecasts of the PV power production. Acknowledgments We are grateful to the personnel of the Forecast Research Department of the Meteorological Research Institute, Tsukuba Aerological Observatory, and the Numerical Prediction Division of Japan Meteorological Agency for their helpful comments. We would also like to thank Dr. Jan Kleissl of associate editor of Solar Energy and three anonymous reviewers for their helpful comments on the manuscripts. This study was supported by New Energy and Industrial Development Organization (NEDO), Japan. Generic Mapping Tools (GMTs) software was used to plot the data. References Armstrong, M., 2000. Comparison of MM5 forecast shortwave radiation with data obtained from the atmospheric radiation measurement program. In: Master of Science Scholarly Paper. University of Maryland, USA. Briegleb, B.P., 1992. Delta-eddington approximation for solar radiation in the NCAR community climate model. J. Geophys. Res. 97, 7603–7612. Davy, R.J., Troccoli, A., 2012. Interannual variability of solar energy generation in Australia. Sol. Energy 86 (12), 3554–3560. Ebert, E.E., Curry, J.A., 1992. A parameterization of ice cloud optical properties for climate models. J. Geophys. Res. 97, 3831–3836. Freidenreich, S.M., Ramaswamy, V., 1999. A new multiple-band solar radiative parameterization for general circulation models. J. Geophys. Res. 104 (31389–31), 409. Fonseca Jr., J.G.S., Oozeki, T., Takashima, T., Koshimizu, G., Uchida, Y., Ogimoto, K., 2011. Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in Kitakyushu. Jpn. Prog. Photovolt: Res. Appl.. http://dx.doi.org/10.1002/pip. 1152. Heinemann, D., Lorenz, E., Girodo, M., 2006. Forecasting of solar radiation. In: Dunlop, E.D., Wald, L., Sˇu´ri, M. (Eds.), Solar Energy

Resource Management for Electricity Generation from Local Level to Global Scale. Nova Science Publishers, Hauppauge. Hoff, T.E., Perez, R., Kleissl, J., Renne, D., Stein, J., 2012. Reporting of irradiance modeling relative prediction errors. Progr. Photovolt. Res. Appl.. http://dx.doi.org/10.1002/pip. 2225. Japan Meteorological Agency, 2007. Outline of the operational numerical weather prediction at the Japan Meteorological Agency. Appendix to WMO numerical weather prediction progress report. Japan Meteorological Agency, Tokyo, Japan, , pp. 194. Lara-Fanego, V., Ruiz-Arias, J.A., Pozo-Va´zquez, D., Santos-Alamillos, F.J., Tovar-Pescador, J., 2012. Evaluation of the WRF model solar irradiance forecasts in Andalusia (southern Spain). Sol. Energy 86 (8), 2200–2217. Lorenz, E., Remund, J., Mu¨ller, S.C., Traunmu¨ller, W., Steinmaurer, G., Pozo, D., Ruiz-Arias, J.A., Fanego, V.L., Ramirez, L., Romeo, M.G., Kurz, C., Pomares, L.M., Guerrero, C.G., 2009. Benchmarking of different approaches to forecast solar irradiance. In: Proceedings of the 24th European Photovoltaic Solar Energy Conference, Hamburg, Germany. Mathiesen, P., Kleissl, J., 2011. Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States. Sol. Energy 85, 967–977. Nagasawa, R., 2006. Improvement of a radiation process for the nonhydrostatic model. In 12th Conference on Atmospheric Radiation. American Meteorological Society, P2-10, URL . Nagasawa, R., 2008. Radiation process. Suuchiyohoka Houkoku Bessatsu. 54, 149–165 (in Japanese). Perez, R., Kivalov, S., Schlemmer, J., Hemker, K., Renne´, D., Hoff, T., 2010. Validation of short and medium term operational solar radiation forecasts in the US. Sol. Energy 84 (12), 2161–2172. Remund, J., Perez, R., Lorenz, E., 2008. Comparison of solar radiation forecasts for the USA. In: Proceedings of the 23rd European Photovoltaic Solar Energy Conference, Valencia, Spain. Ruiz-Arias, J.A., Pozo-Va´zquez, D., Sa´nchez-Sa´nchez, N., Monta´vez, J.P., Hayas-Barru, A., Tovar-Pescador, J., 2008. Evaluation of two MM5-PBL parameterizations for solar radiation and temperature estimation in the south-eastern area of the Iberian Peninsula. Il, Nuovo Cimento. 31 (5–6), 825–842. Saito, K., Fujita, T., Yamada, Y., Ishida, J., Kumagai, Y., et al., 2006. The operational JMA nonhydrostatic mesoscale model. Mon. Weather. Rev. 134, 1266–1298. Saito, K., Ishida, J., Aranami, K., Hara, T., Segawa, T., et al., 2007. Nonhydrostatic atmospheric models and operational development at JMA. J. Meteor. Soc. Jpn. 85B, 271–304. Slingo, A., 1989. A GCM parameterization for the shortwave radiative properties of water clouds. J. Atmos. Sci. 46, 1419–1427. Sommeria, G., Deardorff, J.W., 1977. Subgrid-scale condensation in models of nonprecipitating clouds. J. Atmos. Sci. 34, 344–355. Yoshida, R., Sawada, M., Yamazaki, T., Iwasaki, T., 2011. Verification of Nonhydrostatic-model simulated radiation using in situ observational data in Miyagi, Yamagata and north part of Fukushima prefectures. Tenki. 58 (7), 599–605 (in Japanese). Zamora, R.J., Solomon, S., Dutton, E.G., Bao, J.W., Trainer, M., Portmann, R.W., White, A.B., Nelson, D.W., McNider, R.T., 2003. Comparing MM5 radiative fluxes with observations gathered during the 1995 and 1999 Nashville southern oxidant studies. J. Geophys. Res. 108 (D2), 4050. Zamora, R.J., Dutton, E.G., Trainer, M., McKeen, S.A., Wilczak, J.M., Hou, Y.T., 2005. The accuracy of solar irradiance calculations used in mesoscale numerical weather prediction. Mon. Weather Rev. 133, 783– 792.