Environ Earth Sci (2013) 70:1681–1687 DOI 10.1007/s12665-013-2255-9
ORIGINAL ARTICLE
Estimation of global radiation in China and comparison with satellite product Wenwu Qing • Rensheng Chen • Weimin Sun
Received: 6 December 2011 / Accepted: 15 January 2013 / Published online: 31 January 2013 Ó Springer-Verlag Berlin Heidelberg 2013
Abstract A modified solar radiation model, incorporating into several satellites remote sensing information such as NCEP/NCAR data, EOS-AURA satellite data, and Chinese FY-2C geo-stationary meteorological satellite data, is presented. The model is an attempt to modify Chen’s radiation model and examine its estimation accuracy at various places in six different climatic zones of China. The verification of model is also carried out by comparing between calculated radiation value using modified model and radiation product of FY-2C satellites. According to the NSE values, the adaptability of model is reasonably high in Mid-Temperate Zone (MTZ), Warm Temperate Zone (WTZ), Tibetan Plateau Zone (TPZ), and Cold Temperature Zone (CTZ) climate regimes and slightly low in Subtropical Zone (SZ) and Tropical Zone (TZ) climate regimes. The comparison between modeled radiation values and FY-2C radiation product values shows that the radiation product of FY-2C satellites is superior to the modified model in SZ and TZ climate regimes. Keywords Solar radiation model Satellite global radiation product Total cloud percent
W. Qing (&) College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China e-mail:
[email protected] R. Chen Qilian Alpine Ecology and Hydrology Research Station, Chinese Academy of Sciences, Lanzhou, China W. Sun Beijing Institute of Petrochemical Technology, Beijing, China
Introduction The global solar radiation is an indispensable parameter for the mathematical simulation in many vegetative, biophysical, and hydrological models (Inci and Emin 1999; Wang et al. 2010). An accurate knowledge of the temporal and spatial distribution of the global radiation at an interested location is of vital importance (EI-Sebaii 2003). Unfortunately, solar radiation is still a scarcely measured variable with respect to other meteorological variables such as temperature and relative humidity, because the solar radiation recording stations are very sparse in most regions. For example, there are 726 long-term meteorological stations, of which only 98 meteorological stations have measured global radiation since 1993 (Chen et al. 2006). Several empirical models toward global radiation estimation are adapted to easily obtained observations of air temperature, sunshine hours, and total cloud amount to construct a model for clear and cloudy cases, because such approaches have the advantages of operational simplicity, low computational cost, and accessible inputs (Yang et al. 2006). However, in a remote or sparsely populated location, these methods may be confronted with a major challenge: the available meteorological data are scarce. As an alternative, the satellite-based solar radiation models can solve these shortcomings and provide an operational approach for spatial prediction (Perez et al. 2002; Martins et al. 2003). The geostationary satellites monitor the state of the atmosphere and its processes, and provide continuous data both in space and time. Moreover, the missing and bad data from satellite are also very rare compared with ground measurements (Schillings et al. 2004). During the past decade, various methods have been proposed to estimate the solar radiation from satellite data (Perez et al. 2002; Martins et al. 2003;
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Schillings et al. 2004; Janjaia et al. 2011), while a few similar attempts to estimate global radiation in China have been found in the existing works. Yang et al. (2006) had estimated solar radiation in seven stations under different climate regimes in China. In their work, except the ozone thickness, which was the satellite product, other input parameters such as sunshine duration, air temperature, and relative humidity were all the routine meteorological observations. Chen et al. (2007) had used a parameterized radiation transfer model, incorporating the NCEP/NCAR reanalysis data, to calculate the global radiation in Heihe river basin in the Northwest of China. As the surface meteorological measurements were unavailable in the inland river basin, Chen’s work was an attempt to estimate the global radiation with remotely sensed data in China. In this research, the adaptability of Chen’s model is discussed all over China because the accuracy of the model is usually lower than claimed by the inventors when it is applied to other areas. Another convenient solution to obtain the information on the geographical distribution of global radiation is to directly use satellite reanalysis data (Vignolaa et al. 2007; Lohmann et al. 2006), such as the National Centers for Environmental Prediction (NCEP) (Kalnay et al. 1996) reanalysis data sets, the European Centre for MediumRange Weather Forecasts (ECMWF) ERA-40 data sets (Uppala et al. 2005), and the Chinese FY2 satellite data sets (Yuan 2012). However, the reanalysis data may lack the accuracy due to the fact that the parameterization of numerical weather prediction module in reanalysis models is different. Therefore, it is necessary to check the accuracy of the satellite product for two reasons. First, if broad agreement between two very different approaches to evaluate the global radiation is found, confidence in the accuracy of the model increases. Second, an examination of differences between the calculated global radiation and the Chinese FY-2C satellite global radiation product can complement each other and provide a comprehensive solar radiation database.
Model and data
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modified by Chen et al. (2007) with a few improvements. According to the model, the horizontal global radiation Ig the sum of beam radiation Ib, and diffuse radiation Is on horizontal surface, is given (Bird and Hulstrom 1979; Chen et al. 2007): Ig ¼ Ib þ Is
ð1Þ
Ib ¼ I0 cos h Tr Ta Tw To Tu =ER
ð2Þ
Is ¼ Ias þ IG
ð3Þ
Ias ¼ I0 cos h Tw To Tu TAA ½0:5ð1 Tr Þ þ Ba ð1 TAS Þ=½1 m þ m1:02 =ER IG ¼ rg rs ðIb þ Ias Þ=ð1 rg rs Þ
ð5Þ 2
where I0 is the solar constant (1,367 W/m ), Tr, Ta, Tw, To, and Tu are the transmittances of Rayleigh scattering, aerosol extinction, water vapor absorption, ozone absorption, and uniformly mixed gases (carbon dioxide and oxygen) absorption, respectively. Ias and IG are the atmospheric scattering of diffuse irradiance and the solar irradiance on a horizontal surface from multiple reflections between ground and sky, respectively. The methods to calculate the correction factor for the earth-sun distance ER, the solar zenith angle h (in degree), the transmittance of aerosol absorptance TAA, the transmittance of aerosol scattering TAS, the relative air mass m, the ratio of the forward-scattered irradiance to the total scattered irradiance due to aerosols Ba, the ground albedo rg, and the sky albedo rs can be found in the initial paper (Chen et al. 2007).
Solar radiation on horizontal surface for cloudy conditions Due to lack of cloud optical thickness, the information on cloud extinction could be defined as the ratio of total cloud percent (TCP), which indirectly indicates the presence of cloud and provides qualitative information about its optical thickness. The global solar radiation for cloudy sky can be calculated by (Chen et al. 2007): Qg ¼ Ib K1 ð1 TCPÞ þ Is K2 ð1 TCPÞ
The model used in the Heihe river basin in China includes two radiative transmittances. The model first estimates the hourly global radiation on horizontal surface for clear sky, and then calculates the radiation flux under actual weather with a cloud extinction function. Clear sky global radiation on horizontal surface The clear sky radiation model on horizontal surface is based on Bird’s model (Bird and Hulstrom 1979) and
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ð4Þ
þ C K3 TCP Ig
ð6Þ
where K1, K2, K3 are empirical coefficient. The parameter C is the function of TCP and the precipitation rate (mm/h), whose detailed information can be found in the initial paper (Chen et al. 2007). In this equation, the first term on the right represents the direct irradiance for cloudy condition, while the sum of the second and third terms represents the diffuse irradiance received from the portion of the ground hemisphere.
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Satellite data In Chen’s model (Chen et al. 2007), the satellite information inputted into the model, such as the ozone amount, the precipitable water, the TCP data and the precipitation rate data, are all from NCEP/NCAR reanalysis database. In this work, the ozone amount, the precipitable water, and the precipitation rate data still use the NCEP/NCAR reanalysis data because the extinction effect caused by these factors is relatively smaller, compared with the cloud and the aerosol optical depth (AOD) (Schillings et al. 2004). Generally, solar irradiance at ground is mostly affected by cloud, which is a strongly random phenomenon and could induce the direct radiation to reduce close down to 0 (W/m2). Hence, the TCP data derived from the NCEP/ NCAR database, of which the spatial resolution is about 1.875 longitude degree 9 1.957 latitude degree and the temporal resolution is 6 h (Kalnay et al. 1996), is probably not sufficient to represent the atmospheric state. Therefore, in this work a higher spatial and temporal resolution TCP data with the spatial resolution 0.1° 9 0.1° and the temporal resolution 1 h, derived from Chinese FY-2C geostationary meteorological satellite, is used to calculate the global irradiance. The Chinese FY-2C geo-stationary meteorological satellite was launched on 19 October 2004 and was becoming fully operational in 2006 (Yuan 2012). The satellite is the Chinese first operational meteorological satellite, which is also the fourth satellite of the FY series and is located above the Equator at longitude 105°E, and some 35,800 km away. The importance that the aerosol attenuates the solar irradiance could be found in many anterior researches (Schillings et al. 2004; Yang et al. 2006). The AOD is characteristic of the high variability in both space and time. However, in most cases the AOD data, especially the values at the wavelength of 380 and 500 nm as input in the Bird’s clear-sky model, are unavailable in many satellite data. Therefore, in Chen’s model, the author assumed that the AOD was a constant throughout the year, and the two parameters of aerosol attenuate were both set as empirical parameters with the value 0.09 and 0.2, respectively (Chen et al. 2007). In this work, the AOD are the OMAERUV Level-2 aerosol data product derived from the observation by the Ozone Monitoring Instrument (OMI) on the EOSAURA satellite. The OMI is a nadir pointing hyper-spectral imaging sensor that provides daily global measurements of earth-atmosphere back scattered radiances in 1,560 wavelength bands from 264 to 504 nm at a spatial resolution of 13 9 24 km. As the satellite does not have the AOD at 380 nm, it is assumed that the AOD at 388 nm is equal to the AOD at 380 nm. The AOD at 500 nm is available in the satellite. More information about the aerosol product can be found in Torres et al. (2007).
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The Chinese FY-2C geo-stationary meteorological satellite also provides daily global radiation data on horizontal land surface with the spatial resolution 0.1° 9 0.1°. It is necessary to make a comparison between the radiation product of FY-2C satellites and the estimated result, which could provide a comprehensive solar radiation database and evaluate the performance of the solar radiation model. Model calibration and validation To examine the performance of the proposed model, the measured global radiation data at 27 stations are obtained from the Chinese Meteorological Administration. These stations have diverse climate regimes (from humid to dry) and widely varying elevations (from the sea level up to thousands of meters). As demonstrated in a previous study (Zang et al. 2012), by using temperature-strip method, the climate regimes of China could be divided into six types, namely Tropical Zone (TZ) ([8,000 °C), Subtropical Zone (SZ) (4,500–8,000 °C), Warm Temperate Zone (WTZ) (3,400–4,500 °C), Mid-Temperate Zone (MTZ) (1,600– 3,400 °C), Cold Temperate Zone (CTZ) (\1,600 °C) and Tibetan Plateau Zone (TPZ). Figure 1 shows a general layout of the six major climates and the distribution of meteorological stations, and Table 1 lists a summary of the geographical information at these stations. The performances of the calculated irradiation values are evaluated by the efficiency criterion NSE, which is given (Nash and Sutcliffea 1970): Pn ðObsi Esti Þ2 NSE ¼ 1 Pni¼1 2 i¼1 ðObsi Obsi Þ
ð7Þ
where Obsi is the measured daily global radiation in time i, Esti is the estimated daily global radiation in time i, Obsi is the mean measured daily series and n is the length of the series. A model is more efficient when NSE is closer to 1. The measured global radiation data in 2006 are used for model calibration with numerical iteration methods, while the data in 2007–2008 are used to validate the model.
Results and discussion Figure 1 presents the spatial distribution of the efficiency criterion NSE computed at the validation sites in 2006. According to the NSE values, there exists a good agreement between the estimated and the measured global radiation at most stations, especially the sites located in MTZ, WTZ, TPZ climate regimes. The satisfactory model performances at these sites are expressed by the maximum and minimum NSE of 0.92 and 0.70 for the calibration
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Fig. 1 Location of the surface solar radiance observed station, the general layout of the six major climates across China, and the spatial distribution of the efficiency criterion NSE of the solar radiation model in 2006
Table 1 Geographical information of the used stations in China Station
Latitude (°N)
Longitude (°E)
Altitude (m)
Station
Latitude (°N)
Longitude (°E)
Altitude (m)
Haerbin
45.75
126.77
142.3
Nanjing
32.00
118.80
35.2
Changchun
43.90
125.22
236.8
Hefei
31.78
117.30
27
Wulumuqi
43.78
87.65
935
Shanghai
31.40
121.45
5.5
Ejinaqi
41.95
101.07
940.5
Wenjiang
30.70
103.83
539.3
Shenyang
41.73
123.52
49
Wuhan
30.62
114.13
23.1
Beijing
39.80
116.47
Kashen
39.47
75.98
Leting
39.43
Tianjin
39.08
Taiyuan
31.3
Hangzhou
30.23
120.17
1,289.4
Lasa
29.67
91.13
118.88
10.5
Changsha
28.22
112.92
68
117.07
5,002.5
Fuzhou
26.08
119.28
84
37.78
112.55
778.3
Kunming
25.00
102.65
1,886.5
Jinan
36.60
117.05
170.3
Guangzhou
23.17
113.33
41
Geermu
36.42
94.90
2,807.6
Nanning
22.63
108.22
121.6
Yuzhong Zhengzhou
35.87 34.72
104.15 113.65
1,874.4 110.4
Sanya
18.23
109.52
6
period, respectively. Although the adaptability of model is not tested in the CTZ climate regime due to lack of reliable ground data, we guess that the performance of the model is also quite good in this area. However, the radiation model tends to produce larger errors in SZ and TZ zones,
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41.7 3,648.9
implying the accuracy of the model may be related to climate regimes. As an example, Fig. 2 shows a visual scatterplot for daily global radiation at 12 sites under different climate regimes. It can be seen that the estimated daily global
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comparison of estimated daily global radiation and the Chinese FY-2C satellite global radiation data during the period 2007–2008 at four stations under different climate regimes. Compared with the Chinese FY-2C satellite global radiation data, the solar radiation model yields a more excellent result at the Ejnaqi and Lasa, and a slight accurate result at the Beijing, as well as a disappointing result at Guangzhou. The values of the efficiency criterion NSE for the estimated global radiation and FY-2C satellite global radiation at the 27 stations from 2007 to 2008 are also listed in Table 2. It can be seen that the Chinese FY-2C satellite global radiation data provides lower NSE values at the stations located in MTZ, WTZ, TPZ climate zones and higher NSE values at most stations in SZ and TZ zones against the solar radiation model. Through the comparison of the annual variation of the NSE values, it is found that the relationships between the measured global radiation, the calculated global radiation, and the Chinese FY-2C satellite global radiation data in 2006–2007 are better than that in 2008. The averaged NSE of the solar radiation model for all stations in 2006 and 2007 are both 0.72, while the averaged value of NSE in 2008 is only 0.60. This result is repeated for the radiation product of FY-2C satellites. Moreover, as plotted in Fig. 3, there are a considerable number of abnormal values of the Chinese FY-2C satellite global radiation
2
Q est (MJ/m )
2
Q est (MJ/m )
2
Q est (MJ/m )
radiation agrees quite well to the observed data at Ejinaqi station, where the Chen’s model was claimed. At two other sites (Wulumuqi and Haerbin) in MTZ zone, the model also perform very well, although the model tends to slightly overestimate low radiation values and underestimate high radiation values at the two sites. For the stations in WTZ and TPZ zones, the estimated results over the period of 2006 are also matched closely to the measured global radiation. Comparison between the efficiency criteria NSE (as shown in Fig. 1) at these five validation sites(Geermu, Lasa, Kashen, Yuzhong and Beijing) shows that the model performs better at Geermu and Lasa since larger solar irradiances are received on the Qinghai-Tibet Plateau all over the year. The estimated global radiation at the stations (Shanghai, Kunming, Guangzhou, Sanya) located in SZ and TZ zones, however, is not well reproduced as reflected in scatterplots with little coherency and low NSE values (as shown in Fig. 1) due to humid climate. The model evidently overestimates the radiation in winter and spring (see points with low radiation values in Fig. 2) and underestimates the radiation during summer and autumn (see points with high radiation values in Fig. 2). It is always important to test and validate model with other data source such as satellite reanalysis data sets in a search for possible systematic difference caused by the modeling process or other problems. Figure 3 shows the
2
Q obs (MJ/m )
2
Q obs (MJ/m )
2
Q obs (MJ/m )
2
Q obs (MJ/m )
Fig. 2 Comparison between observed (Qobs) and estimated daily global radiation (Qest) at 12 sites for 2006
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Q FY (MJ/m2 )
Q est (MJ/m2 )
1686
2
Q obs (MJ/m )
2
2
Q obs (MJ/m )
2
Q obs (MJ/m )
Q obs (MJ/m )
Fig. 3 Comparison between observed measurements (Qobs), estimated global radiation (Qest) and FY-2C satellites daily global radiation (QFY) at four sites during the period 2007–2008
Table 2 Efficiency criterion NSE for the radiation transfer model and the Chinese FY-2C satellite global radiation data Station
NSE for the model
NSE for the FY-2C satellite data
2007
2008
2007
2008
Ejinaqi
0.91
0.82
0.90
0.56
Shenyang
0.82
0.63
0.79
0.53
Geermu
0.88
0.74
0.80
0.43
Wuhan
0.70
0.52
0.73
0.53
Kashen
0.84
0.82
0.53
0.33
Hefei
0.70
0.64
0.77
0.65
Lasa
0.82
0.69
0.76
-0.92
Nanjing
0.61
0.48
0.75
0.59
Beijing
0.75
0.64
0.72
0.52
Wenjiang
0.65
0.50
0.66
0.62
Tianjin
0.78
0.66
0.69
0.50
Changsha
0.60
0.58
0.72
0.61
Wulumuqi Zhengzhou
0.86 0.71
0.85 0.66
0.78 0.70
0.67 0.55
Shanghai Hangzhou
0.58 0.71
0.39 0.49
0.74 0.78
0.59 0.63
Jinan
0.74
0.62
0.67
0.45
Guangzhou
0.47
0.40
0.63
0.07
Yuzhong
0.71
0.64
0.83
0.64
Nanning
0.62
0.44
0.62
0.27
Leting
0.74
0.64
0.78
0.52
Kunming
0.59
0.46
0.77
0.36
Taiyuan
0.70
0.60
-0.23
0.48
Sanya
0.64
0.32
0.67
0.32
Changchun
0.82
0.67
0.75
0.54
Fuzhou
0.62
0.54
0.79
0.58
Haerbin
0.84
0.73
0.71
0.60
data that mainly occurred during the second half of 2008. It is probably caused by the satellite sensor degradation or other problems, which are the likely causes resulting in the negative NSE value of Taiyuan in 2007, when the satellite global radiation data is considerably overestimated, and the negative NSE value of Lasa in 2008, when the satellite global radiation data obviously deviated from its normal value during the late of this year (Fig. 4). Despite the estimated global radiation and FY-2C satellite
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Station
NSE for the model
NSE for the FY-2C satellite data
2007
2007
2008
2008
global radiation in 2008 leaves much to be desired, the accuracy of the model performance at some sites in MTZ, WTZ, and TPZ zones, especially in arid area of Northwest China (Wulumuqi, Ejinaqi, Kashen and Geermu), is acceptable. On the whole, the radiation transfer model yields more accurate estimations than the FY-2C satellite global radiation data in 2008, which suggests that the solar radiation model might be more appropriate for the study of global radiation estimation in China.
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2
Q est (MJ/m )
Q FY (MJ/m2)
2
Q est (MJ/m )
Q FY (MJ/m2)
References
Q obs (MJ/m2)
Q obs (MJ/m2)
Fig. 4 Comparison between observed measurements (Qobs), estimated global radiation (Qest), and FY-2C satellites daily global radiation (QFY) at Taiyuan in 2007 and Lasa in 2008
Conclusions Surface solar radiation is an important parameter in vegetative, biophysical, and hydrological model. A modified solar radiation model initially developed in an inland river basin of Northwest China, incorporating into several satellites remote sensing information, is presented to estimate solar irradiance all over China. The Chinese FY-2C satellite also has provided daily solar irradiance maps for China using remote sensing retrievals. Comparison between simulated global radiation and ground measured data indicates that the modified model can produce acceptable results at 27 stations in China. The satisfactory model performances at these sites are expressed by the maximum and minimum NSE of 0.92 and 0.70 in 2006, respectively. The solar radiation model performs better in CTZ, MTZ, WTZ, and TPZ zones than it does in SZ and TZ zones, implying the accuracy of the model may be related to climate regimes. Due to the satellite sensor degradation or other uncertain problems, the computed global radiation and FY-2C satellite global radiation in 2008 are dissatisfied. Results from comparison among estimated daily global radiation and FY-2C satellites product shows that the solar radiation model yields more accurate estimation than the FY-2C satellite product in CTZ, MTZ, WTZ, and TPZ climate regimes, while the FY-2C satellite product value is more reasonable in SZ and TZ climate regimes. Acknowledgments This work is supported by the Knowledge Innovation Project of National Natural Science Foundation of China (No. 91025011, No. 91125013 and No. 41222001), and Energy Efficiency Management and Innovation teamwork (PHR201007136). The author would also like to thank China Meteorological Administration for its data support.
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