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Mapping of global radiation with METEOSAT
Solar Energy Vol. 58, Nos 1-3, pp. 77-90, 1996
Pergamon
PII: S0038-092X (96)00017-5
Copyright © 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0038-092X/96 $15.00+0.00
M A P P I N G OF GLOBAL RADIATION WITH METEOSAT D. S C H U L Z E - K E G E L and F. D. H E I D T t Department of Physics, University of Siegen, D 57068 Siegen, Germany (Communicated by Frank Vignola) Abstract--For detailed mapping of global radiation received at ground the Heliosat method of l~cole Nationale Suprrieure des Mines de Paris, Sophia-Antipolis (France), is applied. Preliminary results of a validation for Germany are presented. Precision of Heliosat insolation estimates is assessed by comparison of monthly averaged hourly and daily sums and of weekly sums acquired in August 1994 and February 1995 with pyranometric measurements from 4 stations of the German Weather Service (DWD). The predictions RMS error amounts to 7% of the observed August 1994 monthly averaged daily global irradiation of 4285 Wh/m2 and to 15% of the observed February 1995 monthly averaged daily global irradiation of 1297 Wh/m2. The error of the estimation of monthly averaged hourly global irradiation is less than 100 Wh/m2. These values are within the limits of what has been reported hitherto. Copyright © 1996 Elsevier Science Ltd.
1. I N T R O D U C T I O N
Europe and in the USA for M E T E O S A T - and GOES-based estimates, respectively, indicate that, in the most favourable cases, prediction accuracies of both alternatives lie around an RMS e r r o r ~ 1 7 % of the observed mean for yearly sequences of daily sums (Zelenka et al., 1992). The spatial resolution of satellite derived insolation maps is by far superior to the resolution of any interpolation sustained by network stations only. So, when both sources of information are available, combining satellite and network data should be an adequate technique for mapping solar irradiation when the latter varies strongly at the regional scale. Creutin (1987) and Galle (1987) have suggested the use of cokriging, a multivariate geostatistical method, as the most effective combination technique. Recently, in a case study for Switzerland, D'Agostino and Zelenka (1992) have shown that use of (ANETZ) network data as primary variable, and of METEOSAT-based estimates (by the M r s e r and Raschke (1984) method) as secondary variable, helps reduce the prediction's RMS error below 14%, while predictions by each variable taken separately stagnate at the 17% precision level. Obviously, combination of the accurate network values with the less accurate, but spatially much better resolved, satellite-based estimates benefits the integrated interpolation procedure (Zelenka, 1992). There exists a great variety of methods to estimate solar radiation on the Earth's surface from satellite data. The first approach is represented by several models, dated between 1978 and 1986, based on statistical regressions
Numerous recent solar energy and climatological applications require access to a geographically continuous solar radiation input. For many years a radiation measurement network at ground level has existed in Europe, but, with an average distance between stations of around 150-200 km, its density is still low. This is not sufficiently detailed to take into account smaller scale variations which are important for the use of solar energy. In other parts of the world, the networks are even more coarse. Measurements from a satellite offer the unique opportunity for the simultaneous registration of global irradiance in large areas with reasonable resolution in time. Satellite data can serve to produce solar atlases and to provide radiation information particularly for those areas where no traditional observations are available. These data on spatial and temporal variability of the global radiation at ground level are also of great scientific value. Subtask D of IEA Task 9 (Zelenka et al., 1992) was evaluating techniques for estimating solar energy radiation resources at locations between network sites, using both measured and modelled data. In addition to the classical statistical techniques for deriving solar radiation data received on the Earth's surface, like interpolation by some adequate procedure (e.g. splines, moving averages, kriging), new methods, such as satellite-based techniques, were investigated. Comparative studies for several networks in rISES member. Author to whom correspondence should be addressed. 77
78
D. Schulze-Kegeland F. D. Heidt
between the digital count measured by the satellite radiometer in a given area and the simultaneous solar radiation value measured at the Earth's surface by a pyranometer in a station within the considered area. The second approach is represented by the models, published between 1980 and 1987, based on a radiative transfer model that explicitly describes the physical processes (i.e. scattering and absorption) operating in the Earth-atmosphere system. The best known statistical and physical models were recently reviewed by Noia et al. (1993). In the frame of a project of the European Community Solar Energy Research and Development Programme the methods of M6ser and Raschke (Cologne), Reinhardt et al. (Stuttgart) and Monget et al. (Sophia-Antipolis) were developed and compared (Grtiter et al., 1986). Whereas the first method is a physical approach, the others are statistical approaches to determine global irradiance at ground level using satellite imagery. Whatever the methodology proposed, the results are promising. In this project it has been demonstrated that hourly sums can be reconstructed from satellite data with an uncertainty of about 83 Wh/m 2 and daily sums with about 556 Wh/m 2. These values are smaller than 10% of averages; whereas the ground-based measurements themselves are already subject to uncertainties of about 5%. The intention of our paper is to report on first results of the current project "Recording Solar Radiation with METEOSAT". The project is part of a research and development programme of solar energy applications, the AG Solar, supported by the Ministry of Science and Technology of North-Rhine-Westfalia. The objectives of the presented project are: - receiving the readings of METEOSAT, -calculating the global irradiation using a brightness index with assistance of Heliosat software (Wald et al., 1992) of l~cole Nationale Sup6rieure des Mines de Paris, Sophia-Antipolis, France, validating the METEOSAT-based estimates of global irradiation with local pyranometer measurements in North-Rhine-Westfalia, working out a utilization concept for routine collection and distribution of data. Hence, radiation time series with high temporal and spatial resolution for large areas of Europe and Africa are at our disposal. The concept of utilization mentioned above offers a broad spectrum of applications for scientists
and engineers. Intended uses and research targets are: radiation mapping (e.g. for the assessment of energy potential), -investigation of possible sites for planned solar devices down to the level of districts, - recording fluctuations of power for photovoltaic devices which are spatially extended over large areas, analysing the temporal distribution of radiation during the day with the purpose of being able to simulate the stochastical behaviour of solar radiation, examining and further developing geostatistic theories for the interpolation of solar radiation on the Earth's surface, research in the spatial coherence of radiation. Through cooperation and exchange of experiences with other institutions in Germany, France and Switzerland which possess similar systems to record and analyze METEOSAT satellite data, the calculation and evaluation methods are examined and improved on an international basis. First results of the project are reported. In Section 2 first of all a brief description of the METEOSAT measurements as well as of hardware of the running system for acquisition and digital processing of images of WEFAX type follows. After that the Heliosat method, applied for detailed mapping of global irradiation on the Earth's surface, is discussed. Further the software used for Heliosat processing is described. Section 3 contains information about image data processing and the investigated sets of satellite and ground data. Furthermore results of the heliosat data processing, like satellitederived maps of apparent ground brightness and global irradiation, are shown and the method of comparison of estimated and measured data is discussed. In Section 4 the precision of Heliosat insolation estimates is assessed by comparison of monthly averaged hourly and daily sums and of weekly sums acquired in August 1994 and February 1995 with pyranometric measurements from four stations of the German Weather Service (DWD). The final Section 5 contains a conclusion drawn from the evaluation and further objectives of the project. 2.
M E T H O D
2.1. The M E T E O S A T s a t e l l i t e
The European METEOSAT satellite is part of an observational network of geostationary
Mapping of global radiation with meteosat
satellites permanently monitoring the major part of the Earth. These satellites are orbiting at an altitude of about 36,000 km above sea level with the same angular speed and direction as the Earth, so that they remain at a fixed position relative to the Earth over a given location at the equator. For METEOSAT this fixed position is (0°N; 0°E). Its four channel multispectral radiometer operates in three spectral bands with the characteristics given in Table 1. Because of the spinning motion of the satellite around its main axis, which is aligned nearly parallel to the Earth's north-south axis, the radiometer scans a line of the Earth's disk from east to west. Step by step the radiometer is tilted at each rotation of the satellite from south to north so that the full Earth's disk is scanned by 2500 rotations within 25 min. The following 5-min period includes retracing of the radiometer to its starting position, black-body calibration of the infrared channels and stabilisation of satellite rotation. Thus, an image in three spectral bands is generated during each 30 min period. Both infrared images (water-vapour and thermal) are composed of 2500 lines of 2500 picture elements (pixels) whilst the visible image consists of 5000 lines of 5000 pixels, since two visible channels are operated simultaneously, each looking at alternate image lines. For each pixel the radiometer system measures the incoming radiance for a period of 12/~s and stores the raw data in 8-bit resolution (0-255 counts). The spatial resolution at the subsatellite point is approximately 5 km for water-vapour and thermal infrared images and 2.5 km for visible images. As pixel size expands with decreasing satellite elevation angle for the pixel in question, the solar pixel sizes over Europe cover areas of 2.5 x 4 km 2 up to 3 x 6 km 2. The raw image data from the spacecraft are transmitted on a line by line basis to the controlling ground station in Darmstadt. First of all the data are pre-processed including separation into the different radiometric channels and correction of minor defects and optical or calibration errors. Then the composed images are
79
rectificated, to compensate image deformation caused by satellite motion relative to the Earth. After image segmentation the image data are again relayed by the METEOSAT satellite using two forms of transmission: high-resolution data, utilized by Primary Data User Stations (PDUS) and analogue data in WEFAX (Weather FACSimile) format, received by Secondary Data User Stations (SDUS) (EUMETSAT, 1987/1990). In the case of WEFAX format the satellite data are modified by the ESOC before transmission in order to enhance the display of the WEFAX image on to a video monitor. For the visible range, values between 0 and 20 are set to black, values over 220 are set to white and grey shades are linearly distributed between 20 and 220. The enhancement set up several years ago is constant no matter what the dynamics of an image is (EUMETSAT, 1990) and there is no change with time in the conversion of the satellite data into WEFAX format (Moussu et al., 1989). A series of 24 WEFAX images of the visible C-format covers approximately 90% of the Earth's disk as observed by the radiometer of METEOSAT. However only the European formats C02 and C03 are relayed by the ESOC. These are complemented by a series of WEFAX images of the visible CnD-format, which covers the Earth's disk with nine images. To standardise the start times of individual formats, 4-minute transmission slots have been introduced by the ESOC. The slot number is effectively the time to the nearest half hour at the end of the image. 2.2. The Heliosat station
A commercially available SDUS-oriented, PC-based "Heliosat station" was presented in 1988 by l~cole Nationale Sup6rieure des Mines de Paris, Sophia-Antipolis (Diabat6 et al., 1988), as a "cheap", operational alternative to the systems based on digital data which are (almost exclusively) operated by national (meteorological) organisations equipped with PDUS and large computing facilities, and which are, there-
Table 1. Radiometer characteristics Spectral band (#m) Detector type Dimension ~m) Field of view (mrad)
Visible 0.5-0.9
IR (water-vapour) 5.7-7.1
IR (thermal) 10.5-12.5
Si photodiodes 250 x 250 0.07
HgCdTe 70 x 70 0.14
HgCdTe 70 x 70 0.14
80
D. Schulze-Kegeland F. D. Heidt
fore, "not suitable for use and operation by people involved, e.g. in the solar energy business" (Diabat6 et al., 1989). Technical details of such a system were described by Wald et al. ( 1992 ), and Zelenka (1992) reported experiences and results from 1 year of operation. A brief description of hardware, software and operations of the system installed at the University of Siegen is discussed below.
using geometric correction with landmark correlation, noise filtering, and normalization of digitized counts by the solar irradiance which would be measured by the satellite radiometer after it has been reflected on a horizontal plane located at each pixel under clear sky. The corrected counts C C defined as (Moussu et al., 1989)
2.3. H a r d w a r e
result from this normalization. In this expression, DC is the actual WEFAX digitized count, DCo a constant offset (the minimum counts, e.g. for outer space), v and ~ are the elevation angles of the Sun and of the satellite for the pixel in question, while fj is the eccentricity correction factor of the Earth's orbit for the day j. The method's central parameter is the apparent brightness, which is proportional to the corrected counts CC. The apparent brightness pt at point (i,j) for a given time t defines a cloud cover index n t at the same point and same time according to
The Heliosat station comprises a unit that re-digitalizes the WEFAX formatted signal and, thus, allows further, PC-supported, pixelwise processing of the visible images into insolation maps following the Heliosat method described in the next subsection. A PC graphic board eases operation control as it allows display, on a second monitor, of the satellite images and of the resulting maps, as well as of all intermediary products. Other than for the antenna and the receiver, the whole system does not need more space than a desk top (Zelenka, 1992).
CC=(DC-DCo)/(fj
n t(i, j) = pt(i, j ) - pg(i, j) Pc--pg(i,j)
2.4. The Heliosat method
To construct maps of insolation for Germany we use the method developed by Cano (1982) and Cano et al. (1986), subsequently slightly modified by Diabat6 et al. (1988; 1989) and Moussu et al. (1989). This method represents the basis for the Heliosat Programme of the l~cole Nationale Sup6rieure des Mines de Paris, Sophia-Antipolis (France). The basic idea of the model is that the amount of cloud cover over a given area statistically determines the global radiation for that area. Consequently the processing is divided in the following steps: a map of the apparent ground brightness is constructed and updated daily, giving information on the clear-sky planetary albedo for every pixel; - a cloud cover index map is drawn from the comparison of the current satellite image and the apparent ground brightness map; finally the atmospheric transmittance factors, computed for every pixel from predetermined linear correlations with the cloud cover index, are used to build the global radiation map. This approach was developed to treat METEOSAT images in the visible channel. For that purpose the satellite data are received in the WEFAX analogue format by a SDUS whereupon the analogue signal is converted into digitized counts (DC). Each satellite image is preprocessed -
sin 1"15 ~ sin 1"15 v) (1)
(2)
with pg (i,j) being the minimum value corresponding to clear skies with clear and dry atmosphere (apparent ground brightness) at the same point, and Pc being the maximum brightness of opaque clouds. While Pc is derived once and then kept constant, the apparent ground brightness pg can vary (seasonally) within prescribed bounds. Possible variations of pg with varying solar elevation are not taken into account. Since the values assumed by n t range from 0 to 1 it can be interpreted as the percentage of the cloud cover per pixel. This cloud cover index is linearly related to the total atmospheric transmission factor K t (i,j) (Cano et al., 1986; Diabat6 et al., 1988; Michaud-Regas, 1986), which is defined as the ratio of global irradiance at ground on a horizontal surface Gt(i,j) to the horizontal irradiance outside the atmosphere G~(i,j):
-
g t ( i , j ) = G'(i,j)/G~(i,j)
(3)
and Kt(i,j)=a(i,j)
n'(i,j)+b(i,j)
(4)
with a (i,j)<0, i.e. low values of nt correspond to high values of K t, and with b describing the atmosphere for very clear sky. For the same hour keeping the same set of parameters (a, b) throughout the year and also
Mapping of global radiationwith meteosat constant in space gives only a slight increase in the retrieval error (Diabat6 et al., 1988; Michaud-Regas, 1986). Thus the Heliosat model only contains one set of parameters (a, b) per processed hour. Hourly global irradiation H[our is inferred from the cloud cover index nt by means of predetermined linear correlations (see Appendix A1) t " • __ t • " nhour(t,J)--na,hou~(1,J) (a nt(i,j)+b)
(5)
with H~.hour being the top of atmosphere insolation for the pertaining hour. Daily global irradiation H[ay is calculated from the hourly irradiation for the selected slots by
Hday(l'J)-~Ha,day(l'J)E t
•
•
t
•
•
H
t
hour/E
t
Ha,hour
(6)
with Hr.,day being the top of atmosphere daily irradiation.
2.5. Software The software consists of three distinct packages. The first manages data acquisition according to schedule as well as saving of the selected images. Meteosat images are accurately navigated before dissemination. However the unit re-digitalizing the WEFAX image line by line does not always take the same time to recognize the beginning of the data transmission, the phasing and the synchronization signals. Therefore, successive images of a given area are not geometrically superimposable and have to be properly translated (Wald et al., 1992). This step relies on 15 selected zones of the reference image (including crossings of latitude and longitude or landmarks), whose location is identified in the current image by a maximum cross correlation search technique. If at least three zones correlate with comparable shifts in lines, respectively in columns, then the image is translated by these amounts and released for further treatment. The second package processes the VIS images following the Heliosat method described above, including noise reduction by a median filter and the update of the apparent ground brightness image. In the present development stage 13 slots per day can be treated but calculation of the daily sum is limited to 12 slots. A running average beginning at the first day of the month is computed daily for each slot and for the daily sums. The third package supports the construction of the reference image of the apparent ground brightness. Theoretically, this procedure should
81
be necessary only once, after selection of the image format, the subsequent updating being done automatically. In fact, it appeared during operations that the ground brightness had to be recalculated from time to time and that this was better done in situ. This package, however, is not wholly mature yet (Zelenka, 1992). 3. RESULTS
3.1. Satellite data processing In order to compute solar irradiation maps, the processing steps of Heliosat are as follows: acquisition, navigation, computation of apparent brightness and cloud index, computation of solar global irradiation. This processing is done running a Heliosat station installed at the University of Siegen within the frame of the current project "Mapping solar radiation with METEOSAT". Thirteen C03 slots (17-29) have been scheduled for daily treatment. Radiation maps were computed using the hourly parameter sets of eqn (5) derived for Europe by l~cole Nationale Suprrieure des Mines de Paris (Appendix 1). The calculations presented refer to a set of METEOSAT images of C03 format taken in August 1994 and February 1995. Image losses were caused by switch-offs for software adaptations, SDUS failures and partly by unattended operation. Further image losses are inherent to the procedure itself. As already explained above, an image must be rejected when at least three of the zones necessary for automatic superposition with the reference apparent ground brightness image cannot be identified, and such weather situations are not rare for the rather confined geographical area contained in the C03 format. For disk storage reasons, a limited area located within the standard satellite-emitted C03-window was selected for evaluation, leading to images composed of 512 × 512 pixels. The remaining area approximately ranges from 2°E to 20°E and 40°N to 60°N. For every day the time slots 17 to 29 (equivalent to registration times 8.4 h to 14.4 h GMT for northern Europe) were processed if existent. Image coordinates were transformed to geographical coordinates and vice versa using the polynomal fit described in Appendix 2. 3.2. Maps of the apparent ground brightness In a preparatory step the reference map of the apparent ground brightness was constructed
82
D. Schulze-Kegeland F. D. Heidt
using a time series of satellite images with considerably cloud-free situations by iterative and adaptive filtering. In order to take into account the long-term seasonal variations, the map is further updated by weighted averaging a new cloud-free pixel with previously determined cloud-free pixels. A detailed description of this method is given by Cano et al. (1986) and Moussu et al. (1989). This processing was done for August 1994 and February 1995 separately. For example, Figure 1 shows the resulting map of the apparent ground brightness for the middle part of Europe obtained from a set of 10 M E T E O S A T C03-images of August 1994. As a result of a cloud layer remaining stationary in the timeseries, the apparent brightness in the upper left corner is not the ground brightness but the brightness of the cloud layer. Despite the size of pixels, details are clearly visible. The sea appears in black because of low brightness, while the plain of the river Po appears in clear tones. Lakes larger than 3 pixels can be seen displaying low brightness, such as Lago di Garda in Italy or V~inern and V/ittern in Sweden. 3.3. Maps of global radiation Monthly averages needed for validation could not be calculated with all the days of the months
because of image losses. The numbers of valid days amount to 17 in August 1994 and 16 in February 1995. Periods in which no radiation values could be estimated were also eliminated from the pyranometric data. Figure 2 shows a map of monthly averaged daily global irradiation for August 1994. The grey scale has been chosen in intervals such that interesting structures are enhanced. For the first lines at the top of the image the global radiation could not be calculated. Furthermore in the upper left corner estimation of irradiation failed due to the mistakes in the map of the apparent ground brightness mentioned above. It can be seen that convective activity in August enhances the differences between land and ocean. The image also shows strong variations of the global irradiation over the Alps with orographically induced cloudiness, while over Belgium, the Netherlands, west of Germany and Poland irradiation was reduced due to travelling disturbances. 3.4. Ground data The set of ground data used is formed by hourly global irradiation taken by the German Weather Service (DWD) at four sites in Germany: Bochum, Kassel, Giel3en and Bonn. The geographical coordinates of the pyranomet-
Fig. 1. Map of apparent ground brightness of the middle part of Europe obtained from METEOSAT data for August 1994. The brightness value increases from black to white. Because the sky was always cloudy in the upper left corner, the local brightness is very high, and does not represent the apparent ground brightness.
Mapping of global radiation with meteosat
36110
4100
4600
510(]
560(]
61011
83
6600 [Wh/m^2]
Fig. 2. Map of monthly averaged daily global irradiation for August 1994.
ric measurements are listed in Appendix 3. The hourly sums refer to time periods ending at the full hours of true solar time as given by the DWD. Usually C M l l Kipp & Zonen pyranometers of secondary standard are used in this measurements with an accuracy of 3% for hourly sums and of 2% in the case of daily sums.
3.5. Comparison When comparing the satellite data with ground solar radiation measurements two problems arise. The first problem is given by errors in the localization of the pyranometer sites on the satellite images. The second problem is that the satellite data are instantaneous measurements over a small solid viewing angle, while ground measurements are integrated over time (here 1 h) and upper hemisphere. Since short time variations of global irradiance are primarily due to the movement of the weather situation and less to locally fixed variations, temporal variations may be substituted by spatial variations. Averaging over several contigous pixels can also be favourable when location uncertainties occur (Noia et al., 1993; Beyer, 1992). Despite these problems, measurements of hourly global irradiation provided for the selected stations in the present phase of evaluation were
compared to the closest pixel in the irradiation image. To adapt the hourly sums recorded for full hours of true solar time (TST) to the satellite image times (slots 17-29, GMT), the global irradiation data have first been expressed in G MT and then interpolated between the nearest neighbours. As the monthly time integration corresponds to the time step for which the Heliosat method was originally developed, validations first have been performed for monthly averages of individual slots and daily totals, as well as for weekly totals. All averages and totals of pyranometric data have been calculated only with hours and days corresponding to availability of satellite-derived estimates. 4. DISCUSSION Figure 3 and Figure 4 show the comparison between pyranometric measurements of four German sites and satellite-derived values of monthly averaged hourly global irradiation acquired in August 1994 and February 1995. The slot number indicates image completion time ( G M T = 0.5 slot--5 min), and the number
84
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86
D. Schulze-Kegei and F. D. Heidt
in brackets indicates the quantity of days, which were used to calculate the average for this slot. The deviations between satellite-derived and ground-based monthly averaged hourly global irradiation are less than 103 Wh/m 2 in August 1994, and less than 91 Wh/m 2 in February 1995. This amounts to a mean deviation of 35 Wh/m 2 in August and 31 Wh/m 2 in February. For both months the worst case of estimation of monthly averaged hourly global irradiation appears at the station of GieBen. The comparison between measured ground data and satellite estimates on the basis of monthly averaged daily global irradiation acquired in August 1994 and February 1995 is shown in Figure 5 and Figure 6. In August 1994 (midsummer) the Heliosat method leads for all of the four sites to overestimated values of daily global irradiation, while in February 1995 (midwinter) the satellite-derived daily sums are always smaller than the measured ones. The deviations between satellite-derived and ground-based irradiation range from 183 Wh/m 2 to 379 Wh/m 2 in the case of August 1994, and from 138 Wh/m z to 264Wh/m 2 in February 1995. This amounts to a mean overestimation of 299 Wh/m 2 in August and a mean
underestimation of 196 W h / m 2 in February. For the four stations the systematic overestimation of the observed mean daily total of 4285 Wh/m 2 in August 1994 is expressed by a relative deviation of 7%, while the systematic underestimation of observed mean daily total of 1297 Wh/m 2 in February 1995 is expressed by a rela~!ve deviation of 15%. For individual days it can happen that the coincidence of data is much better. Figure 7 shows as an example the clear day comparison between the satellite- and ground-based data of hourly and daily global irradiation for the station of Bochum on the 4 August 1994. Figure 8 and Figure 9 display finally the comparison between ground and satellite data of weekly global irradiation. The number in brackets indicates the quantity of days, which were used to calculate the weekly sum. The trend of overestimation respective to underestimation appearing in the comparison of monthly averaged daily global irradiation is now displayed on a finer scale. In August 1994 most estimated weekly sums exceed the measured values, but in 3 of 16 cases the opposite occurs also. In February 1995 only in the third week the deviation for the site of GieBen runs against the trend of underestimation.
August 1S$4 (17 days)
Bochum
Kassel
Giei3en
Bonn
Fig. 5. Comparison between pyranometric measurements from four stations of the German Weather Service and satellite-derived values of monthly averaged daily global irradiation acquired in August 1994. Averages of pyranometric data have been calculated only with days corresponding to availability of satellite-derived estimates. In addition, standard deviation of daily values from their monthly average is shown by a bar.
Mapping of global radiation with meteosat
87
February 1695 (16 days)
Bochum
Kassel
Giel~,en
Bonn
Fig. 6. Comparison between pyranometric measurements from four stations of the German Weather Service and satellite-derived values of monthly averaged daily global irradiation acquired in February 1995. Averages of pyranometric data have been calculated only with days corresponding to availability of satellite-derived estimates. In addition, standard deviation of daily values from their monthly average is shown by a bar.
Bochurn, 4th of August 1994
,-..
Fig. 7. Clear day comparison between satellite- and ground-based data of hourly and daily global irradiation for the station of Bochum on the 4 August 1994. The r a n g e of the m e a s u r e d values is larger t h a n the range of satellite-derived values a n d the s t a n d a r d d e v i a t i o n of d a i l y values from their m o n t h l y average is in a l m o s t all cases larger for m e a s u r e d d a t a t h a n for satellite data.
5. C O N C L U S I O N A N D F U R T H E R OBJECTIVES T h e results of the p r e l i m i n a r y e v a l u a t i o n achieved in the first stage of the project to check
88
D. Schulze-Kegel and F. D. Heidt
August 1994
Bochum
Kassel
week (n-dayl~ sum)
Giel!en
Bonn
Fig. 8. Comparison between pyranometric measurements from four stations of the German Weather Service and satellite-derived values of weekly global irradiation acquired in August 1994. Totals of pyranometric data have been calculated only with days corresponding to availability of satellite-derived estimates.
February1995
Bochum
Kassel
week (n-days sum|
Gie~en
Bonn
Fig. 9. Comparison between pyranometric measurements from four stations of the German Weather Service and satellite-derived values of weekly global irradiation acquired in February 1995. Totals of pyranometric data have been calculated only with days corresponding to availability of satellite-derived estimates.
the applied method show that the Heliosat method for Germany also provides acceptable estimates of global irradiation. To achieve reliable results the experimental verification of the
satellite-derived values will take into account a wider set of data. For that purpose maps of global irradiation will be validated by means of pyranometric data of a greater number of sites
Mapping of global radiation with meteosat in G e r m a n y over a period of several years. By a following i m p r o v e m e n t of the e v a l u a t i o n m e t h o d resulting from this c o m p a r i s o n the estim a t i o n of global i r r a d i a t i o n will be optimized. The a d a p t i o n will include, for instance, change of coefficients of linear correlations between h o u r l y global i r r a d i a t i o n a n d cloud cover index, i m p r o v e m e n t of calculation of daily sums a n d averaging over several contigous pixels. The utilization concept for r o u t i n e processing a n d d i s t r i b u t i o n of r a d i a t i o n i n f o r m a t i o n developed in the first stage of the project will be realized now. The satellite-derived r a d i a t i o n data a n d derivatives will be applied to practiceoriented a n d scientific investigations. It is i n t e n d e d to inform a selected circle of potential users a b o u t the possibilities of Heliosat, to explore the need of the addressed groups a n d to inform the i n d i v i d u a l groups in detail a b o u t the available supply of the M E T E O S A T - p r o j e c t . It is p l a n n e d to offer the r a d i a t i o n i n f o r m a t i o n together with a toolbox which has to be developed to extract the regional a n d specific required values o n C D - R O M . Acknowledgements--This work was supported by the Ministry of Science and Technology of North-RhineWestfalia, Germany, under Contract No. 258-003-92. The authors are indebted to G. Moussu, L. Wald and A. Zelenka for helpful discussions about the theory of the Heliosatmethod and its application experiences.
REFERENCES Beyer H. G. (1992) Correlation of the cloud index derived from halfhourly METEOSAT images with ground measured irradiation--an example with data from German stations for July 1986. Report TM/92/R/04, Groupe Trlrdrtection et Modrlisation CENTRE D'ENERGETIQUE, l~coledes Mines de Paris. Cano D. (1982) Etude de rennuagement par analyse de srquences d'images de satellite:application ~ l'rvaluation du rayonnement solaire global au sol. Th~se 3~me Cycle, Ecole Nationale Suprrieure des Trlrcommunications. Cano D., Monget J. M., Albuisson M., Guillard H., Regas N. and Wald L. (1986) A method for the determination of the global solar radiation from meteorological satellite data. Solar Energy 37, 31-39. Creutin J. D. (1987) Validation et 6talonnage d'images de trlrdrtection/t l'aide de mesures ponctuelles et selon une approche grostatistique: application h la mesure des prdcipitations et de rinsolation. Thrse Sciences-Physiques, Universit6 J. Fourier et Institut National Polytechnique de Grenoble. D'Agostino V. and Zelenka A. (1992) Estimating Solar global irradiance by integration of satellite and network data: the Cokriging Approach. 2nd CODATA Conference on Geomathematics and Geostatistics, The University of Leeds, September 1990. Sci. de la Terre, S~r. Inf. 31, 461. Diabat6 L., Moussu G. and Wald L. (1988). An operational tool for the fine-scale mapping of the incident solar radiation using satellite images: the Heliosat station. Solar'88, Proc. ASES Annual Meeting, June 1988, Cambridge, MA, Coleman M.J. (Ed.), pp. 11-17.
89
Diabat6 L., Moussu G. and Wald L. (1989) Description of an operational tool for determining global solar radiation at ground using geostationary satellite images. Solar Energy 42, 201-207. EUMETSAT (1987/1990) ESA 001: Introduction to the Meteosat System, ESA 002: Meteosat WEFAX Transmissions. Technical Documents produced by ESA. Galle S. (1987) Mesures journalirres de l'insolation en France /~ l'aide d'un systrme de trlrdrtection et d'un rrseau conventionnel: 6tude statistique de la cohrrence et de la combinaison de ces mesures. Thrse SciencesPhysiques, Universit6 J. Fourier, Grenoble, 257. GrLiter W., Guillard H., Mrser W., Monget J. M., Palz W., Raschke E., Reinhardt R. E., Schwarzmann P. and Wald L. (1986) Solar Radiation Data from Satellite Images. Solar Energy R&D in the European Community, Series F: Solar Radiation Data, Vol. 4. D. Reidel, Dordrecht, Holland. Michaud-RegasN. (1986) Mise en oeuvre et validation d'une mrthode oprrationnelle et automatique pour rrvaluation d'atlas solaires en Europe/t l'aide de mesures satellitaires Meteosat (Annres 1983, 1984, 1985), Th~se de Doctorat de Sciences, Universit6 Paris VII. Mrser W. and Raschke E. (1984) Incident solar radiation over Europe estimated from METEOSAT data. J. Climate Appl. Meteorol. 23, 166-170. Moussu G., Diabat6 L., Obrecht D. and Wald L. (1989) A method for the mapping of the apparent ground brightness using visible images from geostationary satellites. Int. J. Remote Sensing, 10(7), 1207-1225. Noia M., Ratto C. F. and Festa R. (1993) Solar irradiance estimation from geostationary satellite data: 1. Statistical models and 2. Physical models. Solar Energy 51(6), 449-456, 457-465. Wald L., Wald J. L. and Moussu G. (1992) Technical note: a low-cost high-quality system for the acquisition and digital processing of images of WEFAX type provided by meteorological geostationary satellites. Int, J. Remote Sensing, 13(5), 911-916. Zelenka A. (1992) Estimating monthly average daily insolation with the SDUS-based Heliosat system: experiences and results from one year of operations. Proc. 9th Meteosat Scientific User's Meeting, Locarno, EUMETSAT Publ. #EUM Pll, pp. 109-114. Zelenka A., Czeplak G., D'Agostino V., Josefsson W., Maxwell E. W. and Perez R. (1992) Techniques for supplementing solar radiation network data. Draft Final Report Task 9, Subtask 9D, Solar Heating and Cooling Programme, International Energy Agency. APPENDIX 1 H o u r l y global irradiation H~o,r is inferred from the cloud cover index ntO by m e a n s of predetermined linear correlations nthour(i, j ) = nto.hour(i, j)(a n t (i, j ) + b)
using the following hourly sets of parameters (a, b) for E u r o p e derived by l~cole N a t i o n a l e Suprrieure des Mines de Paris, Sophia-Antipolis (France) (Table A1). Table A1. Correlation coefficients Slot
a
b
Time of day
< 23 23 > 29
- 0.540 -0.556 -0.554
0.602 0.667 0.598
Beginning of day Middle of day End of day
90
D. Schulze-Kegel and F. D. Heidt Table A2. Coefficients to be employed to convert row and column into latitude and longitude for the C03-zone
Z Latitude Longitude Row Column
Ao
A1
6.7301E+ 1 4.0813 2.5420E + 3 - 1.1400E + 2
-5.9481E-2 - 1.6343E-2 - 6.3650E + 1 4.6500
A2
2.7935E-5 3.0481E-5 3.9000E- 1 - 5.0000E-2
APPENDIX 2 T r a n s f o r m a t i o n of i m a g e c o o r d i n a t e s i n t o g e o g r a p h i c a l c o o r d i n a t e s a n d vice versa: R o w s a n d c o l u m n s are c o n v e r t e d to l a t i t u d e a n d l o n g i t u d e u s i n g t h e coefficients set o u t in T a b l e A2 in the e q u a t i o n
Z=Ao+A1 X+A2
X2+A3 X Y
+A 4 y2+A 5 Y If Z is l a t i t u d e o r l o n g i t u d e t h e n X is r o w a n d Y is c o l u m n , while, r e c i p r o c a l l y , if Z is r o w o r c o l u m n t h e n X is l a t i t u d e a n d Y is l o n g i t u d e . L a t i t u d e a n d l o n g i t u d e are e x p r e s s e d in d e c i m a l f o r m ( n e g a t i v e for s o u t h a n d west). T h e o r i g i n o f the r o w s a n d c o l u m n s is l o c a t e d t o w a r d s the
A4
A5
5.8382E-6 2.5514E-5 3.2500E-2 - 1.0250E-1
- 1.0058E-3 3.7591E-2 9.9167E-1 6.0575E + 1
A3
-6.3689E-6 - 5.3837E-5 - 2.0000E-2 - 6.1000E-1
t o p left of t h e m a p s . T h e coefficients for this e q u a t i o n w e r e d e r i v e d f r o m l a n d m a r k s determ i n e d by visual i n s p e c t i o n o f a satellite i m a g e a n d f r o m the l o c a t i o n in a g e o g r a p h i c a l m a p .
APPENDIX 3 Table A3. Ground data stations of Radiation Network of German Weather Service (DWD) whose data were used in the comparison Station
LAT [°]
LONG [°]
Bochum Kassel Giel3en Bonn
51.50 51.30 50.58 50.70
7.08 9.45 8.70 7.15
Pergamon
PII: S0038-092X (96)00017-5
Copyright © 1996 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0038-092X/96 $15.00+0.00
M A P P I N G OF GLOBAL RADIATION WITH METEOSAT D. S C H U L Z E - K E G E L and F. D. H E I D T t Department of Physics, University of Siegen, D 57068 Siegen, Germany (Communicated by Frank Vignola) Abstract--For detailed mapping of global radiation received at ground the Heliosat method of l~cole Nationale Suprrieure des Mines de Paris, Sophia-Antipolis (France), is applied. Preliminary results of a validation for Germany are presented. Precision of Heliosat insolation estimates is assessed by comparison of monthly averaged hourly and daily sums and of weekly sums acquired in August 1994 and February 1995 with pyranometric measurements from 4 stations of the German Weather Service (DWD). The predictions RMS error amounts to 7% of the observed August 1994 monthly averaged daily global irradiation of 4285 Wh/m2 and to 15% of the observed February 1995 monthly averaged daily global irradiation of 1297 Wh/m2. The error of the estimation of monthly averaged hourly global irradiation is less than 100 Wh/m2. These values are within the limits of what has been reported hitherto. Copyright © 1996 Elsevier Science Ltd.
1. I N T R O D U C T I O N
Europe and in the USA for M E T E O S A T - and GOES-based estimates, respectively, indicate that, in the most favourable cases, prediction accuracies of both alternatives lie around an RMS e r r o r ~ 1 7 % of the observed mean for yearly sequences of daily sums (Zelenka et al., 1992). The spatial resolution of satellite derived insolation maps is by far superior to the resolution of any interpolation sustained by network stations only. So, when both sources of information are available, combining satellite and network data should be an adequate technique for mapping solar irradiation when the latter varies strongly at the regional scale. Creutin (1987) and Galle (1987) have suggested the use of cokriging, a multivariate geostatistical method, as the most effective combination technique. Recently, in a case study for Switzerland, D'Agostino and Zelenka (1992) have shown that use of (ANETZ) network data as primary variable, and of METEOSAT-based estimates (by the M r s e r and Raschke (1984) method) as secondary variable, helps reduce the prediction's RMS error below 14%, while predictions by each variable taken separately stagnate at the 17% precision level. Obviously, combination of the accurate network values with the less accurate, but spatially much better resolved, satellite-based estimates benefits the integrated interpolation procedure (Zelenka, 1992). There exists a great variety of methods to estimate solar radiation on the Earth's surface from satellite data. The first approach is represented by several models, dated between 1978 and 1986, based on statistical regressions
Numerous recent solar energy and climatological applications require access to a geographically continuous solar radiation input. For many years a radiation measurement network at ground level has existed in Europe, but, with an average distance between stations of around 150-200 km, its density is still low. This is not sufficiently detailed to take into account smaller scale variations which are important for the use of solar energy. In other parts of the world, the networks are even more coarse. Measurements from a satellite offer the unique opportunity for the simultaneous registration of global irradiance in large areas with reasonable resolution in time. Satellite data can serve to produce solar atlases and to provide radiation information particularly for those areas where no traditional observations are available. These data on spatial and temporal variability of the global radiation at ground level are also of great scientific value. Subtask D of IEA Task 9 (Zelenka et al., 1992) was evaluating techniques for estimating solar energy radiation resources at locations between network sites, using both measured and modelled data. In addition to the classical statistical techniques for deriving solar radiation data received on the Earth's surface, like interpolation by some adequate procedure (e.g. splines, moving averages, kriging), new methods, such as satellite-based techniques, were investigated. Comparative studies for several networks in rISES member. Author to whom correspondence should be addressed. 77
78
D. Schulze-Kegeland F. D. Heidt
between the digital count measured by the satellite radiometer in a given area and the simultaneous solar radiation value measured at the Earth's surface by a pyranometer in a station within the considered area. The second approach is represented by the models, published between 1980 and 1987, based on a radiative transfer model that explicitly describes the physical processes (i.e. scattering and absorption) operating in the Earth-atmosphere system. The best known statistical and physical models were recently reviewed by Noia et al. (1993). In the frame of a project of the European Community Solar Energy Research and Development Programme the methods of M6ser and Raschke (Cologne), Reinhardt et al. (Stuttgart) and Monget et al. (Sophia-Antipolis) were developed and compared (Grtiter et al., 1986). Whereas the first method is a physical approach, the others are statistical approaches to determine global irradiance at ground level using satellite imagery. Whatever the methodology proposed, the results are promising. In this project it has been demonstrated that hourly sums can be reconstructed from satellite data with an uncertainty of about 83 Wh/m 2 and daily sums with about 556 Wh/m 2. These values are smaller than 10% of averages; whereas the ground-based measurements themselves are already subject to uncertainties of about 5%. The intention of our paper is to report on first results of the current project "Recording Solar Radiation with METEOSAT". The project is part of a research and development programme of solar energy applications, the AG Solar, supported by the Ministry of Science and Technology of North-Rhine-Westfalia. The objectives of the presented project are: - receiving the readings of METEOSAT, -calculating the global irradiation using a brightness index with assistance of Heliosat software (Wald et al., 1992) of l~cole Nationale Sup6rieure des Mines de Paris, Sophia-Antipolis, France, validating the METEOSAT-based estimates of global irradiation with local pyranometer measurements in North-Rhine-Westfalia, working out a utilization concept for routine collection and distribution of data. Hence, radiation time series with high temporal and spatial resolution for large areas of Europe and Africa are at our disposal. The concept of utilization mentioned above offers a broad spectrum of applications for scientists
and engineers. Intended uses and research targets are: radiation mapping (e.g. for the assessment of energy potential), -investigation of possible sites for planned solar devices down to the level of districts, - recording fluctuations of power for photovoltaic devices which are spatially extended over large areas, analysing the temporal distribution of radiation during the day with the purpose of being able to simulate the stochastical behaviour of solar radiation, examining and further developing geostatistic theories for the interpolation of solar radiation on the Earth's surface, research in the spatial coherence of radiation. Through cooperation and exchange of experiences with other institutions in Germany, France and Switzerland which possess similar systems to record and analyze METEOSAT satellite data, the calculation and evaluation methods are examined and improved on an international basis. First results of the project are reported. In Section 2 first of all a brief description of the METEOSAT measurements as well as of hardware of the running system for acquisition and digital processing of images of WEFAX type follows. After that the Heliosat method, applied for detailed mapping of global irradiation on the Earth's surface, is discussed. Further the software used for Heliosat processing is described. Section 3 contains information about image data processing and the investigated sets of satellite and ground data. Furthermore results of the heliosat data processing, like satellitederived maps of apparent ground brightness and global irradiation, are shown and the method of comparison of estimated and measured data is discussed. In Section 4 the precision of Heliosat insolation estimates is assessed by comparison of monthly averaged hourly and daily sums and of weekly sums acquired in August 1994 and February 1995 with pyranometric measurements from four stations of the German Weather Service (DWD). The final Section 5 contains a conclusion drawn from the evaluation and further objectives of the project. 2.
M E T H O D
2.1. The M E T E O S A T s a t e l l i t e
The European METEOSAT satellite is part of an observational network of geostationary
Mapping of global radiation with meteosat
satellites permanently monitoring the major part of the Earth. These satellites are orbiting at an altitude of about 36,000 km above sea level with the same angular speed and direction as the Earth, so that they remain at a fixed position relative to the Earth over a given location at the equator. For METEOSAT this fixed position is (0°N; 0°E). Its four channel multispectral radiometer operates in three spectral bands with the characteristics given in Table 1. Because of the spinning motion of the satellite around its main axis, which is aligned nearly parallel to the Earth's north-south axis, the radiometer scans a line of the Earth's disk from east to west. Step by step the radiometer is tilted at each rotation of the satellite from south to north so that the full Earth's disk is scanned by 2500 rotations within 25 min. The following 5-min period includes retracing of the radiometer to its starting position, black-body calibration of the infrared channels and stabilisation of satellite rotation. Thus, an image in three spectral bands is generated during each 30 min period. Both infrared images (water-vapour and thermal) are composed of 2500 lines of 2500 picture elements (pixels) whilst the visible image consists of 5000 lines of 5000 pixels, since two visible channels are operated simultaneously, each looking at alternate image lines. For each pixel the radiometer system measures the incoming radiance for a period of 12/~s and stores the raw data in 8-bit resolution (0-255 counts). The spatial resolution at the subsatellite point is approximately 5 km for water-vapour and thermal infrared images and 2.5 km for visible images. As pixel size expands with decreasing satellite elevation angle for the pixel in question, the solar pixel sizes over Europe cover areas of 2.5 x 4 km 2 up to 3 x 6 km 2. The raw image data from the spacecraft are transmitted on a line by line basis to the controlling ground station in Darmstadt. First of all the data are pre-processed including separation into the different radiometric channels and correction of minor defects and optical or calibration errors. Then the composed images are
79
rectificated, to compensate image deformation caused by satellite motion relative to the Earth. After image segmentation the image data are again relayed by the METEOSAT satellite using two forms of transmission: high-resolution data, utilized by Primary Data User Stations (PDUS) and analogue data in WEFAX (Weather FACSimile) format, received by Secondary Data User Stations (SDUS) (EUMETSAT, 1987/1990). In the case of WEFAX format the satellite data are modified by the ESOC before transmission in order to enhance the display of the WEFAX image on to a video monitor. For the visible range, values between 0 and 20 are set to black, values over 220 are set to white and grey shades are linearly distributed between 20 and 220. The enhancement set up several years ago is constant no matter what the dynamics of an image is (EUMETSAT, 1990) and there is no change with time in the conversion of the satellite data into WEFAX format (Moussu et al., 1989). A series of 24 WEFAX images of the visible C-format covers approximately 90% of the Earth's disk as observed by the radiometer of METEOSAT. However only the European formats C02 and C03 are relayed by the ESOC. These are complemented by a series of WEFAX images of the visible CnD-format, which covers the Earth's disk with nine images. To standardise the start times of individual formats, 4-minute transmission slots have been introduced by the ESOC. The slot number is effectively the time to the nearest half hour at the end of the image. 2.2. The Heliosat station
A commercially available SDUS-oriented, PC-based "Heliosat station" was presented in 1988 by l~cole Nationale Sup6rieure des Mines de Paris, Sophia-Antipolis (Diabat6 et al., 1988), as a "cheap", operational alternative to the systems based on digital data which are (almost exclusively) operated by national (meteorological) organisations equipped with PDUS and large computing facilities, and which are, there-
Table 1. Radiometer characteristics Spectral band (#m) Detector type Dimension ~m) Field of view (mrad)
Visible 0.5-0.9
IR (water-vapour) 5.7-7.1
IR (thermal) 10.5-12.5
Si photodiodes 250 x 250 0.07
HgCdTe 70 x 70 0.14
HgCdTe 70 x 70 0.14
80
D. Schulze-Kegeland F. D. Heidt
fore, "not suitable for use and operation by people involved, e.g. in the solar energy business" (Diabat6 et al., 1989). Technical details of such a system were described by Wald et al. ( 1992 ), and Zelenka (1992) reported experiences and results from 1 year of operation. A brief description of hardware, software and operations of the system installed at the University of Siegen is discussed below.
using geometric correction with landmark correlation, noise filtering, and normalization of digitized counts by the solar irradiance which would be measured by the satellite radiometer after it has been reflected on a horizontal plane located at each pixel under clear sky. The corrected counts C C defined as (Moussu et al., 1989)
2.3. H a r d w a r e
result from this normalization. In this expression, DC is the actual WEFAX digitized count, DCo a constant offset (the minimum counts, e.g. for outer space), v and ~ are the elevation angles of the Sun and of the satellite for the pixel in question, while fj is the eccentricity correction factor of the Earth's orbit for the day j. The method's central parameter is the apparent brightness, which is proportional to the corrected counts CC. The apparent brightness pt at point (i,j) for a given time t defines a cloud cover index n t at the same point and same time according to
The Heliosat station comprises a unit that re-digitalizes the WEFAX formatted signal and, thus, allows further, PC-supported, pixelwise processing of the visible images into insolation maps following the Heliosat method described in the next subsection. A PC graphic board eases operation control as it allows display, on a second monitor, of the satellite images and of the resulting maps, as well as of all intermediary products. Other than for the antenna and the receiver, the whole system does not need more space than a desk top (Zelenka, 1992).
CC=(DC-DCo)/(fj
n t(i, j) = pt(i, j ) - pg(i, j) Pc--pg(i,j)
2.4. The Heliosat method
To construct maps of insolation for Germany we use the method developed by Cano (1982) and Cano et al. (1986), subsequently slightly modified by Diabat6 et al. (1988; 1989) and Moussu et al. (1989). This method represents the basis for the Heliosat Programme of the l~cole Nationale Sup6rieure des Mines de Paris, Sophia-Antipolis (France). The basic idea of the model is that the amount of cloud cover over a given area statistically determines the global radiation for that area. Consequently the processing is divided in the following steps: a map of the apparent ground brightness is constructed and updated daily, giving information on the clear-sky planetary albedo for every pixel; - a cloud cover index map is drawn from the comparison of the current satellite image and the apparent ground brightness map; finally the atmospheric transmittance factors, computed for every pixel from predetermined linear correlations with the cloud cover index, are used to build the global radiation map. This approach was developed to treat METEOSAT images in the visible channel. For that purpose the satellite data are received in the WEFAX analogue format by a SDUS whereupon the analogue signal is converted into digitized counts (DC). Each satellite image is preprocessed -
sin 1"15 ~ sin 1"15 v) (1)
(2)
with pg (i,j) being the minimum value corresponding to clear skies with clear and dry atmosphere (apparent ground brightness) at the same point, and Pc being the maximum brightness of opaque clouds. While Pc is derived once and then kept constant, the apparent ground brightness pg can vary (seasonally) within prescribed bounds. Possible variations of pg with varying solar elevation are not taken into account. Since the values assumed by n t range from 0 to 1 it can be interpreted as the percentage of the cloud cover per pixel. This cloud cover index is linearly related to the total atmospheric transmission factor K t (i,j) (Cano et al., 1986; Diabat6 et al., 1988; Michaud-Regas, 1986), which is defined as the ratio of global irradiance at ground on a horizontal surface Gt(i,j) to the horizontal irradiance outside the atmosphere G~(i,j):
-
g t ( i , j ) = G'(i,j)/G~(i,j)
(3)
and Kt(i,j)=a(i,j)
n'(i,j)+b(i,j)
(4)
with a (i,j)<0, i.e. low values of nt correspond to high values of K t, and with b describing the atmosphere for very clear sky. For the same hour keeping the same set of parameters (a, b) throughout the year and also
Mapping of global radiationwith meteosat constant in space gives only a slight increase in the retrieval error (Diabat6 et al., 1988; Michaud-Regas, 1986). Thus the Heliosat model only contains one set of parameters (a, b) per processed hour. Hourly global irradiation H[our is inferred from the cloud cover index nt by means of predetermined linear correlations (see Appendix A1) t " • __ t • " nhour(t,J)--na,hou~(1,J) (a nt(i,j)+b)
(5)
with H~.hour being the top of atmosphere insolation for the pertaining hour. Daily global irradiation H[ay is calculated from the hourly irradiation for the selected slots by
Hday(l'J)-~Ha,day(l'J)E t
•
•
t
•
•
H
t
hour/E
t
Ha,hour
(6)
with Hr.,day being the top of atmosphere daily irradiation.
2.5. Software The software consists of three distinct packages. The first manages data acquisition according to schedule as well as saving of the selected images. Meteosat images are accurately navigated before dissemination. However the unit re-digitalizing the WEFAX image line by line does not always take the same time to recognize the beginning of the data transmission, the phasing and the synchronization signals. Therefore, successive images of a given area are not geometrically superimposable and have to be properly translated (Wald et al., 1992). This step relies on 15 selected zones of the reference image (including crossings of latitude and longitude or landmarks), whose location is identified in the current image by a maximum cross correlation search technique. If at least three zones correlate with comparable shifts in lines, respectively in columns, then the image is translated by these amounts and released for further treatment. The second package processes the VIS images following the Heliosat method described above, including noise reduction by a median filter and the update of the apparent ground brightness image. In the present development stage 13 slots per day can be treated but calculation of the daily sum is limited to 12 slots. A running average beginning at the first day of the month is computed daily for each slot and for the daily sums. The third package supports the construction of the reference image of the apparent ground brightness. Theoretically, this procedure should
81
be necessary only once, after selection of the image format, the subsequent updating being done automatically. In fact, it appeared during operations that the ground brightness had to be recalculated from time to time and that this was better done in situ. This package, however, is not wholly mature yet (Zelenka, 1992). 3. RESULTS
3.1. Satellite data processing In order to compute solar irradiation maps, the processing steps of Heliosat are as follows: acquisition, navigation, computation of apparent brightness and cloud index, computation of solar global irradiation. This processing is done running a Heliosat station installed at the University of Siegen within the frame of the current project "Mapping solar radiation with METEOSAT". Thirteen C03 slots (17-29) have been scheduled for daily treatment. Radiation maps were computed using the hourly parameter sets of eqn (5) derived for Europe by l~cole Nationale Suprrieure des Mines de Paris (Appendix 1). The calculations presented refer to a set of METEOSAT images of C03 format taken in August 1994 and February 1995. Image losses were caused by switch-offs for software adaptations, SDUS failures and partly by unattended operation. Further image losses are inherent to the procedure itself. As already explained above, an image must be rejected when at least three of the zones necessary for automatic superposition with the reference apparent ground brightness image cannot be identified, and such weather situations are not rare for the rather confined geographical area contained in the C03 format. For disk storage reasons, a limited area located within the standard satellite-emitted C03-window was selected for evaluation, leading to images composed of 512 × 512 pixels. The remaining area approximately ranges from 2°E to 20°E and 40°N to 60°N. For every day the time slots 17 to 29 (equivalent to registration times 8.4 h to 14.4 h GMT for northern Europe) were processed if existent. Image coordinates were transformed to geographical coordinates and vice versa using the polynomal fit described in Appendix 2. 3.2. Maps of the apparent ground brightness In a preparatory step the reference map of the apparent ground brightness was constructed
82
D. Schulze-Kegeland F. D. Heidt
using a time series of satellite images with considerably cloud-free situations by iterative and adaptive filtering. In order to take into account the long-term seasonal variations, the map is further updated by weighted averaging a new cloud-free pixel with previously determined cloud-free pixels. A detailed description of this method is given by Cano et al. (1986) and Moussu et al. (1989). This processing was done for August 1994 and February 1995 separately. For example, Figure 1 shows the resulting map of the apparent ground brightness for the middle part of Europe obtained from a set of 10 M E T E O S A T C03-images of August 1994. As a result of a cloud layer remaining stationary in the timeseries, the apparent brightness in the upper left corner is not the ground brightness but the brightness of the cloud layer. Despite the size of pixels, details are clearly visible. The sea appears in black because of low brightness, while the plain of the river Po appears in clear tones. Lakes larger than 3 pixels can be seen displaying low brightness, such as Lago di Garda in Italy or V~inern and V/ittern in Sweden. 3.3. Maps of global radiation Monthly averages needed for validation could not be calculated with all the days of the months
because of image losses. The numbers of valid days amount to 17 in August 1994 and 16 in February 1995. Periods in which no radiation values could be estimated were also eliminated from the pyranometric data. Figure 2 shows a map of monthly averaged daily global irradiation for August 1994. The grey scale has been chosen in intervals such that interesting structures are enhanced. For the first lines at the top of the image the global radiation could not be calculated. Furthermore in the upper left corner estimation of irradiation failed due to the mistakes in the map of the apparent ground brightness mentioned above. It can be seen that convective activity in August enhances the differences between land and ocean. The image also shows strong variations of the global irradiation over the Alps with orographically induced cloudiness, while over Belgium, the Netherlands, west of Germany and Poland irradiation was reduced due to travelling disturbances. 3.4. Ground data The set of ground data used is formed by hourly global irradiation taken by the German Weather Service (DWD) at four sites in Germany: Bochum, Kassel, Giel3en and Bonn. The geographical coordinates of the pyranomet-
Fig. 1. Map of apparent ground brightness of the middle part of Europe obtained from METEOSAT data for August 1994. The brightness value increases from black to white. Because the sky was always cloudy in the upper left corner, the local brightness is very high, and does not represent the apparent ground brightness.
Mapping of global radiation with meteosat
36110
4100
4600
510(]
560(]
61011
83
6600 [Wh/m^2]
Fig. 2. Map of monthly averaged daily global irradiation for August 1994.
ric measurements are listed in Appendix 3. The hourly sums refer to time periods ending at the full hours of true solar time as given by the DWD. Usually C M l l Kipp & Zonen pyranometers of secondary standard are used in this measurements with an accuracy of 3% for hourly sums and of 2% in the case of daily sums.
3.5. Comparison When comparing the satellite data with ground solar radiation measurements two problems arise. The first problem is given by errors in the localization of the pyranometer sites on the satellite images. The second problem is that the satellite data are instantaneous measurements over a small solid viewing angle, while ground measurements are integrated over time (here 1 h) and upper hemisphere. Since short time variations of global irradiance are primarily due to the movement of the weather situation and less to locally fixed variations, temporal variations may be substituted by spatial variations. Averaging over several contigous pixels can also be favourable when location uncertainties occur (Noia et al., 1993; Beyer, 1992). Despite these problems, measurements of hourly global irradiation provided for the selected stations in the present phase of evaluation were
compared to the closest pixel in the irradiation image. To adapt the hourly sums recorded for full hours of true solar time (TST) to the satellite image times (slots 17-29, GMT), the global irradiation data have first been expressed in G MT and then interpolated between the nearest neighbours. As the monthly time integration corresponds to the time step for which the Heliosat method was originally developed, validations first have been performed for monthly averages of individual slots and daily totals, as well as for weekly totals. All averages and totals of pyranometric data have been calculated only with hours and days corresponding to availability of satellite-derived estimates. 4. DISCUSSION Figure 3 and Figure 4 show the comparison between pyranometric measurements of four German sites and satellite-derived values of monthly averaged hourly global irradiation acquired in August 1994 and February 1995. The slot number indicates image completion time ( G M T = 0.5 slot--5 min), and the number
84
D. Schulze-Kegel and F. D. Heidt
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Mapping of global radiation with meteosat
85
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86
D. Schulze-Kegei and F. D. Heidt
in brackets indicates the quantity of days, which were used to calculate the average for this slot. The deviations between satellite-derived and ground-based monthly averaged hourly global irradiation are less than 103 Wh/m 2 in August 1994, and less than 91 Wh/m 2 in February 1995. This amounts to a mean deviation of 35 Wh/m 2 in August and 31 Wh/m 2 in February. For both months the worst case of estimation of monthly averaged hourly global irradiation appears at the station of GieBen. The comparison between measured ground data and satellite estimates on the basis of monthly averaged daily global irradiation acquired in August 1994 and February 1995 is shown in Figure 5 and Figure 6. In August 1994 (midsummer) the Heliosat method leads for all of the four sites to overestimated values of daily global irradiation, while in February 1995 (midwinter) the satellite-derived daily sums are always smaller than the measured ones. The deviations between satellite-derived and ground-based irradiation range from 183 Wh/m 2 to 379 Wh/m 2 in the case of August 1994, and from 138 Wh/m z to 264Wh/m 2 in February 1995. This amounts to a mean overestimation of 299 Wh/m 2 in August and a mean
underestimation of 196 W h / m 2 in February. For the four stations the systematic overestimation of the observed mean daily total of 4285 Wh/m 2 in August 1994 is expressed by a relative deviation of 7%, while the systematic underestimation of observed mean daily total of 1297 Wh/m 2 in February 1995 is expressed by a rela~!ve deviation of 15%. For individual days it can happen that the coincidence of data is much better. Figure 7 shows as an example the clear day comparison between the satellite- and ground-based data of hourly and daily global irradiation for the station of Bochum on the 4 August 1994. Figure 8 and Figure 9 display finally the comparison between ground and satellite data of weekly global irradiation. The number in brackets indicates the quantity of days, which were used to calculate the weekly sum. The trend of overestimation respective to underestimation appearing in the comparison of monthly averaged daily global irradiation is now displayed on a finer scale. In August 1994 most estimated weekly sums exceed the measured values, but in 3 of 16 cases the opposite occurs also. In February 1995 only in the third week the deviation for the site of GieBen runs against the trend of underestimation.
August 1S$4 (17 days)
Bochum
Kassel
Giei3en
Bonn
Fig. 5. Comparison between pyranometric measurements from four stations of the German Weather Service and satellite-derived values of monthly averaged daily global irradiation acquired in August 1994. Averages of pyranometric data have been calculated only with days corresponding to availability of satellite-derived estimates. In addition, standard deviation of daily values from their monthly average is shown by a bar.
Mapping of global radiation with meteosat
87
February 1695 (16 days)
Bochum
Kassel
Giel~,en
Bonn
Fig. 6. Comparison between pyranometric measurements from four stations of the German Weather Service and satellite-derived values of monthly averaged daily global irradiation acquired in February 1995. Averages of pyranometric data have been calculated only with days corresponding to availability of satellite-derived estimates. In addition, standard deviation of daily values from their monthly average is shown by a bar.
Bochurn, 4th of August 1994
,-..
Fig. 7. Clear day comparison between satellite- and ground-based data of hourly and daily global irradiation for the station of Bochum on the 4 August 1994. The r a n g e of the m e a s u r e d values is larger t h a n the range of satellite-derived values a n d the s t a n d a r d d e v i a t i o n of d a i l y values from their m o n t h l y average is in a l m o s t all cases larger for m e a s u r e d d a t a t h a n for satellite data.
5. C O N C L U S I O N A N D F U R T H E R OBJECTIVES T h e results of the p r e l i m i n a r y e v a l u a t i o n achieved in the first stage of the project to check
88
D. Schulze-Kegel and F. D. Heidt
August 1994
Bochum
Kassel
week (n-dayl~ sum)
Giel!en
Bonn
Fig. 8. Comparison between pyranometric measurements from four stations of the German Weather Service and satellite-derived values of weekly global irradiation acquired in August 1994. Totals of pyranometric data have been calculated only with days corresponding to availability of satellite-derived estimates.
February1995
Bochum
Kassel
week (n-days sum|
Gie~en
Bonn
Fig. 9. Comparison between pyranometric measurements from four stations of the German Weather Service and satellite-derived values of weekly global irradiation acquired in February 1995. Totals of pyranometric data have been calculated only with days corresponding to availability of satellite-derived estimates.
the applied method show that the Heliosat method for Germany also provides acceptable estimates of global irradiation. To achieve reliable results the experimental verification of the
satellite-derived values will take into account a wider set of data. For that purpose maps of global irradiation will be validated by means of pyranometric data of a greater number of sites
Mapping of global radiation with meteosat in G e r m a n y over a period of several years. By a following i m p r o v e m e n t of the e v a l u a t i o n m e t h o d resulting from this c o m p a r i s o n the estim a t i o n of global i r r a d i a t i o n will be optimized. The a d a p t i o n will include, for instance, change of coefficients of linear correlations between h o u r l y global i r r a d i a t i o n a n d cloud cover index, i m p r o v e m e n t of calculation of daily sums a n d averaging over several contigous pixels. The utilization concept for r o u t i n e processing a n d d i s t r i b u t i o n of r a d i a t i o n i n f o r m a t i o n developed in the first stage of the project will be realized now. The satellite-derived r a d i a t i o n data a n d derivatives will be applied to practiceoriented a n d scientific investigations. It is i n t e n d e d to inform a selected circle of potential users a b o u t the possibilities of Heliosat, to explore the need of the addressed groups a n d to inform the i n d i v i d u a l groups in detail a b o u t the available supply of the M E T E O S A T - p r o j e c t . It is p l a n n e d to offer the r a d i a t i o n i n f o r m a t i o n together with a toolbox which has to be developed to extract the regional a n d specific required values o n C D - R O M . Acknowledgements--This work was supported by the Ministry of Science and Technology of North-RhineWestfalia, Germany, under Contract No. 258-003-92. The authors are indebted to G. Moussu, L. Wald and A. Zelenka for helpful discussions about the theory of the Heliosatmethod and its application experiences.
REFERENCES Beyer H. G. (1992) Correlation of the cloud index derived from halfhourly METEOSAT images with ground measured irradiation--an example with data from German stations for July 1986. Report TM/92/R/04, Groupe Trlrdrtection et Modrlisation CENTRE D'ENERGETIQUE, l~coledes Mines de Paris. Cano D. (1982) Etude de rennuagement par analyse de srquences d'images de satellite:application ~ l'rvaluation du rayonnement solaire global au sol. Th~se 3~me Cycle, Ecole Nationale Suprrieure des Trlrcommunications. Cano D., Monget J. M., Albuisson M., Guillard H., Regas N. and Wald L. (1986) A method for the determination of the global solar radiation from meteorological satellite data. Solar Energy 37, 31-39. Creutin J. D. (1987) Validation et 6talonnage d'images de trlrdrtection/t l'aide de mesures ponctuelles et selon une approche grostatistique: application h la mesure des prdcipitations et de rinsolation. Thrse Sciences-Physiques, Universit6 J. Fourier et Institut National Polytechnique de Grenoble. D'Agostino V. and Zelenka A. (1992) Estimating Solar global irradiance by integration of satellite and network data: the Cokriging Approach. 2nd CODATA Conference on Geomathematics and Geostatistics, The University of Leeds, September 1990. Sci. de la Terre, S~r. Inf. 31, 461. Diabat6 L., Moussu G. and Wald L. (1988). An operational tool for the fine-scale mapping of the incident solar radiation using satellite images: the Heliosat station. Solar'88, Proc. ASES Annual Meeting, June 1988, Cambridge, MA, Coleman M.J. (Ed.), pp. 11-17.
89
Diabat6 L., Moussu G. and Wald L. (1989) Description of an operational tool for determining global solar radiation at ground using geostationary satellite images. Solar Energy 42, 201-207. EUMETSAT (1987/1990) ESA 001: Introduction to the Meteosat System, ESA 002: Meteosat WEFAX Transmissions. Technical Documents produced by ESA. Galle S. (1987) Mesures journalirres de l'insolation en France /~ l'aide d'un systrme de trlrdrtection et d'un rrseau conventionnel: 6tude statistique de la cohrrence et de la combinaison de ces mesures. Thrse SciencesPhysiques, Universit6 J. Fourier, Grenoble, 257. GrLiter W., Guillard H., Mrser W., Monget J. M., Palz W., Raschke E., Reinhardt R. E., Schwarzmann P. and Wald L. (1986) Solar Radiation Data from Satellite Images. Solar Energy R&D in the European Community, Series F: Solar Radiation Data, Vol. 4. D. Reidel, Dordrecht, Holland. Michaud-RegasN. (1986) Mise en oeuvre et validation d'une mrthode oprrationnelle et automatique pour rrvaluation d'atlas solaires en Europe/t l'aide de mesures satellitaires Meteosat (Annres 1983, 1984, 1985), Th~se de Doctorat de Sciences, Universit6 Paris VII. Mrser W. and Raschke E. (1984) Incident solar radiation over Europe estimated from METEOSAT data. J. Climate Appl. Meteorol. 23, 166-170. Moussu G., Diabat6 L., Obrecht D. and Wald L. (1989) A method for the mapping of the apparent ground brightness using visible images from geostationary satellites. Int. J. Remote Sensing, 10(7), 1207-1225. Noia M., Ratto C. F. and Festa R. (1993) Solar irradiance estimation from geostationary satellite data: 1. Statistical models and 2. Physical models. Solar Energy 51(6), 449-456, 457-465. Wald L., Wald J. L. and Moussu G. (1992) Technical note: a low-cost high-quality system for the acquisition and digital processing of images of WEFAX type provided by meteorological geostationary satellites. Int, J. Remote Sensing, 13(5), 911-916. Zelenka A. (1992) Estimating monthly average daily insolation with the SDUS-based Heliosat system: experiences and results from one year of operations. Proc. 9th Meteosat Scientific User's Meeting, Locarno, EUMETSAT Publ. #EUM Pll, pp. 109-114. Zelenka A., Czeplak G., D'Agostino V., Josefsson W., Maxwell E. W. and Perez R. (1992) Techniques for supplementing solar radiation network data. Draft Final Report Task 9, Subtask 9D, Solar Heating and Cooling Programme, International Energy Agency. APPENDIX 1 H o u r l y global irradiation H~o,r is inferred from the cloud cover index ntO by m e a n s of predetermined linear correlations nthour(i, j ) = nto.hour(i, j)(a n t (i, j ) + b)
using the following hourly sets of parameters (a, b) for E u r o p e derived by l~cole N a t i o n a l e Suprrieure des Mines de Paris, Sophia-Antipolis (France) (Table A1). Table A1. Correlation coefficients Slot
a
b
Time of day
< 23 23 > 29
- 0.540 -0.556 -0.554
0.602 0.667 0.598
Beginning of day Middle of day End of day
90
D. Schulze-Kegel and F. D. Heidt Table A2. Coefficients to be employed to convert row and column into latitude and longitude for the C03-zone
Z Latitude Longitude Row Column
Ao
A1
6.7301E+ 1 4.0813 2.5420E + 3 - 1.1400E + 2
-5.9481E-2 - 1.6343E-2 - 6.3650E + 1 4.6500
A2
2.7935E-5 3.0481E-5 3.9000E- 1 - 5.0000E-2
APPENDIX 2 T r a n s f o r m a t i o n of i m a g e c o o r d i n a t e s i n t o g e o g r a p h i c a l c o o r d i n a t e s a n d vice versa: R o w s a n d c o l u m n s are c o n v e r t e d to l a t i t u d e a n d l o n g i t u d e u s i n g t h e coefficients set o u t in T a b l e A2 in the e q u a t i o n
Z=Ao+A1 X+A2
X2+A3 X Y
+A 4 y2+A 5 Y If Z is l a t i t u d e o r l o n g i t u d e t h e n X is r o w a n d Y is c o l u m n , while, r e c i p r o c a l l y , if Z is r o w o r c o l u m n t h e n X is l a t i t u d e a n d Y is l o n g i t u d e . L a t i t u d e a n d l o n g i t u d e are e x p r e s s e d in d e c i m a l f o r m ( n e g a t i v e for s o u t h a n d west). T h e o r i g i n o f the r o w s a n d c o l u m n s is l o c a t e d t o w a r d s the
A4
A5
5.8382E-6 2.5514E-5 3.2500E-2 - 1.0250E-1
- 1.0058E-3 3.7591E-2 9.9167E-1 6.0575E + 1
A3
-6.3689E-6 - 5.3837E-5 - 2.0000E-2 - 6.1000E-1
t o p left of t h e m a p s . T h e coefficients for this e q u a t i o n w e r e d e r i v e d f r o m l a n d m a r k s determ i n e d by visual i n s p e c t i o n o f a satellite i m a g e a n d f r o m the l o c a t i o n in a g e o g r a p h i c a l m a p .
APPENDIX 3 Table A3. Ground data stations of Radiation Network of German Weather Service (DWD) whose data were used in the comparison Station
LAT [°]
LONG [°]
Bochum Kassel Giel3en Bonn
51.50 51.30 50.58 50.70
7.08 9.45 8.70 7.15