Satellite Application Facilities irradiance products: hourly time step comparison and validation

(1)

Report

Reference

Satellite Application Facilities irradiance products: hourly time step comparison and validation

INEICHEN, Pierre

Abstract

Eumetsat is an organisation providing data and services to the different national meteorological offices and is partner in number of climate monitoring programs. The data sources are images from geostationnary and polar orbit satellites. In order to benefit from members specialized expertise, Eumetsat created in 1999 Satellite Application Facilites (SAFs), based on cooperation between several offices and hosted by a national meteorological service (www.eumetsat.int). Three SAFs working on climate monitoring retrieve mainly surface solar irradiance (SSI) and downward longwave irradiance (DLI) from meteosat new generation satellite (MSG) and NOAA-AVHRR satellites. To obtain the same parameters, the different SAFs use their own algorithms and different sources of secondary input data. It was therefore suitable to conduct a common validation of the produced values against ground measurements, and to do an intercomparison of the different products. The benefit will be an assessement of the different methods. The study will provide informations on possible biases and noise, point out specific dependencies, and bring some [...]

INEICHEN, Pierre. Satellite Application Facilities irradiance products: hourly time step comparison and validation . Geneva : 2007

Available at:

http://archive-ouverte.unige.ch/unige:23668

Disclaimer: layout of this document may differ from the published version.

1 / 1

(2)

Satellite Application Facilities irradiance products:

hourly time step comparison and validation

Pierre Ineichen University of Geneva Centre Universitaire d’étude des problèmes de l’énergie January 2007

(3)

University of Geneva - 36 - Pierre Ineichen

(4)

University of Geneva - 37 - Pierre Ineichen Table of content

1. Introduction 1.

2. Satellite application facilities 1.

3. Ground stations 2.

4. The clearness index Kt and sky type determination 5.

5. Outgoing longwave model 6.

6. Atmospheric transmission and aerosol optical depth 8.

7. Comparison method 10.

8. Aerosol optical depth comparison 10.

9. Surface solar irradiance 13.

10. Downward longwave irradiance 20.

11. Outgoing longwave irradiance 25.

12. Secondary parameters comparison 26.

13. Conclusions 29.

14. Acknowledgements 30.

15. Nomenclature 30.

16. References 31.

(5)

(6)

University of Geneva - 1 - Pierre Ineichen 1. Introduction

Eumetsat is an organisation providing data and services to the different national meteorological offices and is partner in number of climate monitoring programs. The data sources are images from geostationnary and polar orbit satellites. In order to benefit from members specialized expertise, Eumetsat created in 1999 Satellite Application Fa- cilites (SAFs), based on cooperation between several offices and hosted by a national meteorological service (www.eumetsat.int).

Three SAFs working on climate monitoring retrieve mainly surface solar irradiance (SSI) and downward longwave irradiance (DLI) from meteosat new generation satellite (MSG) and NOAA-AVHRR satellites. To obtain the same parameters, the different SAFs use their own algorithms and different sources of secondary input data. It was therefore suitable to conduct a common validation of the produced values against ground measurements, and to do an intercomparison of the different products. The benefit will be an assessement of the different methods. The study will provide informations on possible biases and noise, point out specific dependencies, and bring some recommendations to improve the methods.

To do the comparison and validation, data from eight european ground stations covering latitudes from 44° to 58° north will be used. The work will be done on 4 separate months representative of the annual declination variation. The solar surface and downward longwave irradiances will mainly be analyzed, and if available, secondary parameters like the dry bulb temperature and the atmospheric humidity.

2. Satellite Application Facilites

Products from tree satellite facilities will be compared and validated against ground measurements:

- The Satellite Application Facility on Climate Monitoring (cm-SAF) is dedicated to the high-quality long-term monitoring of the climate system‘s state and variability, partly on the regional level. It supports the analysis and diagnosis of climate parameters in order to detect and understand changes in the climate system. The climate SAF serves the modelling of the atmospheric system as well as planning and manage- ment purposes. Data must be measured operationally in a continuous long-term manner covering all dimensions. Satellite data are - in combination with in-situ data and model output - particularly suitable for this task. It is hosted by the german meteorological office: Deutcher Wetterdienst (DWD, www.dwd.de).

- The Ocean and Sea Ice Satellite Application Facility (osi-SAF) is an answer to the common requirements of meteorology and oceanography for a comprehensive in- formation on the ocean-atmosphere interface. One of the objectives of the osi SAF is to produce, control and distribute operationally in near real-time products using available satellite data with the necessary users support activities. Meteo-France is hosting the osi-SAF (www.meteofrance.com, www.osi-saf.org).

- The main purpose of the Land Surface Analysis Satellite Application Facility (lsa or land-SAF) is to increase the benefits from MSG and Eumetsat Polar System satellite (EPS) data related to land, land-atmosphere interactions and biophysical applications, namely by developing techniques, products and algorithms that will allow a more effective use of data from the EUMETSAT satellites. Although directly designed to improve the observation of meteorological systems, the spectral characteristics, time resolution and global coverage offered by MSG and EPS allow for their use in a broad spectrum of other applications, namely within the scope of land biophysical

(7)

University of Geneva - 2 - Pierre Ineichen applications. It is hosted by the Institute of Meteorology of Portugal (www.meteo.pt, landsaf.meteo.pt).

3. Ground stations

Data acquired at 8 european locations are used to conduct the validation. The station latitudes vary from 44°N to 58°N and the longitudes from 5°W to 26°E. The study is restricted to four months, representative of the annual variation of the different parameters: July and October 2005, and January, and April 2006. The original ground data acquisition time steps of the different data banks vary from one to ten minutes.

Therefore, all the ground data are integrated to bring them to a ten minutes time step.

The stations are the following:

- Cabauw (the Netherland, latitude 51.97°N, longitude 4.93°E, altitude 2 meters, temperate maritime climate). The Cabauw tower station is operated by KNMI (The Netherlands Institut of Meteorology) and is part of the Baseline Surface Radiation Network (BSRN).

- Camborne (Great Britain, lat. 50.22°N, long 5.32°W, alt. 70 m., temperate maritime climate). The station is part of BSRN network ans is operated by the UK meteorological office.

- Carpentras (France, lat. 44.08°N, long. 5.06°E, alt. 100 m., mediteranean climate), also a BSRN network station, it is operated by Meteo-France.

- Geneva (Switzerland, lat 46.20°N, long. 6.01°E, alt. 420 m., moderate maritime climate with continental influence). The station is maintained by the Centre Univer- sitaire d’étude des problèmes de l’énergie (CUEPE), University of Geneva. It is part of the International Daylight Measurements Prgram (IDMP) network.

- Lindenberg (Germany, lat. 52.22°N, long. 14.12°E, alt. 125 m., moderate maritime climate). The station is operated by DWD and is part of the BSRN network.

- Lyon (France, lat. 45.78°N, long. 4.93°E, alt. 170 m., moderate maritime climate with mediteranean influence). Part of the IDMP network, the station is maintained

Figure 1 Ground measurements sites

(8)

University of Geneva - 3 - Pierre Ineichen by the Ecole Nationale des Travaux Publics (ENTPE).

- Payerne (Switzerland, lat. 46.82°N, long. 7.94°E, alt. 491 m., moderate maritime climate with continental influence), BSRN station maintained by the Swiss Meteorological Office.

- Toravere (Estonia, lat. 58.27°N, long. 26.47°E, alt. 70 m., cold and humid climate).

The station is part of the BSRN network since January 1999, it is under the responsibility of the Estonian Meteorological and Hydrological Institute (EMHI).

The different ground measurement parameters and the corresponding evaluated parameters are given in Table I for the different SAFs, where the measurements and and the produced values are marked with a «x» and the calculated or retrieved from a measurements with a «c». The yellow boxes are representative of the variables taken into account in the validation process.

All the stations are part of either the BSRN or the IDMP network; the acquisition instru- ments are of high quality, their specifications are strictly defined and the data quality control procedures follow the corresponding recommendations (www.idmp.fr and bsrn.ethz.ch).

To ensure the comparisons and validations are done between comparable data, a special attention is given to the coherence of the data, the precision of the time of acquisition, Table I List of the different available parameters for the 8 considered stations

(9)

University of Geneva - 4 - Pierre Ineichen Figure 3b Modified clearness index versus solar elevation angle, for the 8 stations and 4 months.

Figure 3a Clearness index versus solar elevation angle, for the 8 stations and 4 months.

Figure 2 Global horizontal and normal beam irradiance components versus the sine of the solar elevation angle for a clear day in Carpentras.

(10)

University of Geneva - 5 - Pierre Ineichen and the synchronization of the different data sets with the ground measurements. The best method to assess the time stamp in a data bank is to select days with very clear and stable conditions (i.e. symetric to solar noon), and to plot the radiation components versus the solar elevation angle or its sine: the morning and afternoon curves have to be superposed. An example is given on Figure 2 where the global and beam components are plotted versus the sine of the solar elevation angle for a clear day in Carpentras. If necessary, the time stamp is then adjusted and the geometric parameters calculated in correspondance.

4. The clearness index K_t and sky type classification

As it is the case for the majority of the national networks, the global irradiance is the only available measured parameter concerning the solar radiation. Even if nowadays it is possible to obtain the beam component for specific locations, the global irradiance and the corresponding clearness index K_t are key parameters in the field of irradiance modelization. K_tis defined as the surface solar irradiance normalized by the corresponding extraterrestrial solar irradiance:

K_t= G_h / I_o sin h

Where I_ois the extraatmospheric normal irradiance and h the solar elevation angle above the horizon. K_t was introduced as a norm [Black 1954] to characterize the insulation conditions at a given point in time when only the global component is known. Unfortunately, this parameter is not independent of the solar elevation angle as it is shown on Figure 3a where the clearness index K_tis plotted versus the solar elevation angle for ten minutes data, the 8 stations and the four months considered in the present study. It can be seen on this Figure that clear sky conditions, determined by the upper limit of the clearness index values, are not equally represented by K_tfor the different solar elevation angles.

In order to use the clearness index as a reliable sky condition descriptor, Perez et al.

[Perez, 1990] modified this parameter to make it independent of the solar elevation angle. The formulation is the following:

K’_t= K_t/ ( 1.031 exp(-1.4 / (0.9 + 9.4 / am)) + 0.1)

where am is the optical air mass as defined by Kasten [1980]. This modified clearness

Figure 4a Cloud Free In- dex saturation (CFIsat) versus clearness index for Carpentras

(11)

University of Geneva - 6 - Pierre Ineichen index is represented on Figure 3b for the same points than above. It can clearly be seen on this Figure that even if some patterns are still present, the modified clearness index is relatively independent from the solar elevation angle. Therefore, it is now possible to define three zones to characterize three sky types:

clear sky conditions 0.65 < K’_t≤^1.00 intermediate sky conditions 0.30 < K’_t≤^0.65 cloudy sky conditions 0.00 < K’_t≤^0.30

These limits are arbitrary, but are coherent with other classifications, like for example the Cloud Free Index saturation (CFIsat) as defined by Dürr [2006]:

CFIsat = 100 (CFI - 1) / z

where z and CFI are defined in Dürr [2004]. CFIsat is a function of the downward surface longwave irradiance and the dry bulb temperature. The comparison between the CFIsat and K’_t is illustrated on Figure 4a for the station of Carpentras. The red dashed lines represent the limits used in the present study and applied on the modified clearness index. In the CFIsat classification, clear sky conditions are defined by a CFIsat below 0%, and cloudy conditions above 50%. The corresponding limits are represented in green dashed lines. It can be seen on Figure 4a that the majority of the points are situated in the intersections of the corresponding three zones.

Another comparison can be done with the cloud cover as retrieved by the climate SAF and illustrated on Figure 4b. Here also, the limits used in the present study (in red dashed lines) are well correlated with the cloud cover (0% cloud cover for the clear sky conditions and 100% cloud cover for the overcast sky conditions).

5. The outgoing longwave irradiance and the OLR model

Outgoing longwave irradiance is one of the parameters produced by the climate SAF. In order to evaluate the produced values where longwave ground measurements are not available, it is possible to evaluate it from the surface temperature with the help of Stefan-Boltzmann law defined as:

Figure 4b Cloud cover as retrieved by cm-SAF versus modified clearness index for Carpentras.

(12)

University of Geneva - 7 - Pierre Ineichen Figure 5 Outgoing infrared radiation calculated from T_a with the Stefan-Boltzman law versus measurements for the station of Cabauw and Payerne.

Figure 6 Bias between Ste- fan-Boltzman law based on the dry bulb temperature and the ground measurements before correction in blue and after correction in red.

Figure 7 Outgoing infrared radiation calculated from T with the Stefan-Boltzman law plotted versus measurements.

(13)

University of Geneva - 8 - Pierre Ineichen Ir_up= ε σ T_s⁴ expressed in [W/m²]

whereεis the ground emissivitiy and can be taken as unity,σis the Boltzmann constant (σ = 5.67 10^-8 [W/m² K⁴]) and T_s the surface temperature in Kelvin. By definition, the Stefan-Boltzman law permit the evaluation of the irradiance emitted by a surface at a given tempertaure Ts. Here, only the dry bulb or ambiant temperature (air temperature taken at 2 meters from the ground) is available and will be used. On Figure 5, the outgoing irradiance is represented versus the corresponding measurements. It is clear from the Figure that a correction has to be applied on the dry bulb temperature to take into account the deviation from the diagonal line. The main effect comes from the surface solar irradiance as shown on Figure 6 where the bias is plotted against the solar surface irradiance (blue dots). A correction proportional to the surface irradiance is applied and the resulting deviation is plotted on the same graph in red dots.

The correction brings high longwave irradiance values back to the diagonal line. Its formulation is given in the Appendix. A less important corrections can be done based on the atmospheric humidity; it slightly correct the overestimation for low longwave irradiance values (see Appendix). A much better model-measurements scatterplot is then obtained and is given on Figure 7.

The model is developped and tested on the same data, it should be extended and validated on independent data. Nevertheless it is a help in the validation procedure because of the lack of outgoing irradiance measurements.

6. The aerosol optical depth

The evaluation of the surface solar irradiance from satellite data is subject to the knowledge of the corresponding highest possible irradiance value for the considered time and location. It is calculated with the help of atmospheric transmission models (ATM) and is used as normalization value. The main input for the ATM is the aerosol optical depth at 550 nm (aod). Unfortunately, it is the most difficult to obtain or to retrieve. If tables and atlases exist, its variability is often non negligible from day to day, and even during the day.

The aerosol optical depth can be retrieved from ground measurements if the beam component is available, the method is the following:

- with the use of Kasten pyrheliometric formula [Kasten 1980] for an air mass equal

Figure 8 Daily aerosol optical depth at 550 nm obtained from s u n p h o t o m e t e r measurements versus aod obtained from normal beam broadband irradiance in Payerne for the year 2004.

(14)

University of Geneva - 9 - Pierre Ineichen to two, or Ineichen’s air mass independent formulation [Ineichen 2002], the Linke turbidity coefficient T_L can be obtained for clear days on a daily basis,

- from this Linke turbidity coefficient and the water vapor content of the atmosphere, it is possible to evaluate the aerosol optical depth using a model developped by Ineichen [2006].

The above method is validated with spectral and broadband beam measurements acquired in Payerne during the year 2004 [Vuilleumier 2006]. It is illustrated on Figure 8 where the spectral retrieved aod at 550 nm is plotted against the aod obtained from T_L. Considering that the values are daily averages, and that the method cumulates several models, the correlation between the aerosol optical depth obtained from the normal beam irradiance and from spectral measurements is satisfying.

The atmospheric water vapour content is part of the ground measurements (by the help of the relative humidity or the dew point temperature) for all the stations except Camborne and Toravere. For Camborne, monthly values obtained from the Meteonorm 5.1 software [Meteonorm 2006] are used (monthly mean values from measurements obtained through the internet). For Toravere, monthly mean are taken from the ESRA atlas [2000].

Cm-SAF and osi-SAF provide the clear sky surface solar irradiance (G_hc). From these

Figure 11 Scatter plot representting modelled global irradiances versus measurements for the station of Carpentras

Figure 12 Relative frequency of occurence of a given surface solar irradiance for the station of Cabauw.

(15)

University of Geneva - 10 - Pierre Ineichen values, with the knowledge of the atmospheric water vapour content and the hypothesis that the aerosols are of urban type, it is possible to retrocalculate by iteration and best fit an aerosol optical depth value, using the solis clear sky scheme developped by Müller [2005] (see Appendix).

7. Comparison method

In term of validation, when evaluating satellite derived parameters with the same time step, the comparison is generally done by means of scatter plots, mean and absolute bias differences, and root mean square differences.

The scatter plot, or representation of the modelled parameter versus the corresponding measured value is illustrated on Figure 11. A perfect model should align the dots on the diagonal line.

The statistical parameters like the mean bias difference (mbd), the absolute mean bias difference (ambd) and the root mean square difference (rmsd) represent a quantifica- tion of the model’s precision. In the present study, as the majority of the mean bias differences are very small, it is not necessary to differentiate the standard deviation (dispersion around the bias) and the root mean square difference.

In the field of solar radiation and natural light, the comparison is often done in term of frequency of occurrence. The obtained graph is a line (or a bar chart) representative of the relative frequency of occurrence of a given parameter. This is illustrated on Figure 12 for the surface solar irradiance where for example a value of 200 [W/m²] occurs 2 times more than a value of 500 [W/m²]. A good visualisation of the model precision is to plot the modelled values and the corresponding ground measurements on the same graph.

8. Aerosol optical depth and clear sky irradiance

Using the method described in section 6, it is possible to extract the atmospheric aerosol optical depths for the different stations and SAFs products. The aod are retrieved on a daily basis and for the 123 days concerned with the comparison. For the ground based values, when the sky conditions did not permit to evaluate the aod, an average value between the preceeding and the next value is taken. The statistics are given in Table II.

Figure 13 Daily aerosol optical depth at 550 nm obtained from cm-SAF and osi-SAF global clear sky irradiance versus aod obtained from normal beam broadband irradiance in Lindenberg.

(16)

University of Geneva - 11 - Pierre Ineichen Figure 14 Daily aerosol optical depth at 550 nm obtained from ground measurements, cm-SAF and osi-SAF versus the day of the year in Lindenberg.

Table II statistical parameters for the optical depth at 550 nm and the clear sky surface solar irradiance comparisons.

ground average 0.084 0.084 0.084 475 466 470

nb 123 123 246 1037 1119 2156

mean abs. diff. 0.048 0.034 0.041 34 26 30

MBD -0.045 -0.012 -0.029 31 24 28

RMSD 0.057 0.042 0.049 40 32 36

ground average 0.083 0.083 0.083 514 514 514

nb 123 123 246 1076 1134 2210

mean abs. diff. 0.048 0.031 0.039 16 12 14

MBD -0.039 0.000 -0.020 -5 0 -2

RMSD 0.058 0.038 0.049 19 15 17

ground average 0.084 0.084 491 491

nb 123 123 1193 1193

mean abs. diff. 0.038 0.038 12 12

MBD 0.029 0.029 1 1

RMSD 0.046 0.046 15 15

ground average 0.097 0.097 0.097 457 459 458

nb 123 123 246 1095 1147 2242

mean abs. diff. 0.030 0.042 0.036 14 14 14

MBD -0.016 0.028 0.006 0 -8 -4

RMSD 0.037 0.052 0.045 17 18 18

ground average 0.115 0.115 0.115 497 497 497

nb 123 123 246 1136 1195 2331

mean abs. diff. 0.053 0.026 0.040 13 12 12

MBD -0.052 0.006 -0.023 6 -2 2

RMSD 0.060 0.032 0.046 16 14 15

ground average 0.106 0.106 494 494

nb 123 123 1194 1194

mean abs. diff. 0.029 0.029 10 10

MBD 0.015 0.015 -9 -9

RMSD 0.037 0.037 13 13

ground average 0.077 0.077 443 443

nb 123 123 1044 1044

mean abs. diff. 0.050 0.050 16 16

MBD 0.035 0.035 -9 -9

RMSD 0.059 0.059 19 19

ground average 0.095 0.092 0.093 486 481 483

nb 492 861 1353 4344 8026 12370

mean abs. diff. 0.045 0.036 0.039 19 15 16

MBD -0.038 0.014 -0.005 8 0 2

RMSD 0.053 0.044 0.047 25 19 21

Toravere 58.27°N 26.47°E

all stations Payerne 46.82°N 7.94°E Carpentras

44.08°N 5.06°E

Geneva 46.20°N 6.01°E

Lindenberg 52.22°N 14.12°E

Lyon 45.78°N

4.93°E Camborne

50.22°N 5.32°W

all SAFs all SAFs

osi SAF climate SAF osi SAF

Aerosol optical depth at 550 nm Surface solar clear sky irradiance [W/m²]

climate SAF

(17)

University of Geneva - 12 - Pierre Ineichen An illustration of the results is given on Figure 13 and 14 for the station of Lindenberg. It can be observed on the scatter plot (Figure 13) that if the order of magnitude is correct, the correlation between the SAFs’ products and ground values is almost inexistant. On Figure 14, the different aod are represented versus the day of the year (i.e. the season).

It is interresting to observe here that the tendency is to have higher aod in summer for the the osi-SAF product, and the opposite trend for the cm-SAF. These pattern are illustrated here for the station of Lindenberg, but can be outlined similarly for all the other stations where the aod is available (see appendix, Figure a-1.2 to a-1.8). This can explain the differences observed by Marsouin [2006] where monthly integrated values from osi and climate SAF were compared over the full meteosat disk.

Using the retrieved aod and the ground water vapour content of the atmosphere measurements, it is then possible to calculate a «ground based» clear sky surface solar irradiance with the Solis clear sky model [Müller 2004, Ineichen 2006] and to compare it to the SAFs’ produced corresponding irradiance.

An illustration for the station of Carpentras is given on Figure 15, and the comparison statistics are given in Table II. It can be seen that despite of the bad aod correlations, the clear sky surface irradiance is evaluated with an average rmsd of about 20 [W/m²] or 4%. The results are very similar for all the sites, except for the station of Camborne.

Camborne is the only site where ground measurements of the water vapour content of the atmosphere are not available, and four monthly average values were taken from ESRA [2000] to evaluate the irradiance with the solis scheme. But the influence of the water vapour content in the atmospheric transmission is not so important and this is not sufficient to explain the difference.

No direct conclusion can be drawn from these results. In fact, the aim of knowing the aod and the atmospheric water wapour content is to use these parameters to determine the clear sky reference. At the end, the precision of the different models depends on the complete scheme. Nevertheless, it will be shown in the next section that the aod seasonal bias may be the source of a seasonal trend of the surface solar irradiance bias.

If the aod and the water vapour content have not an essential importance on the global surface irradiance evaluation, their influence becomes not negligible when considering the atmospheric diffusion and transmission, and therefore, their precise knowledge will Figure 15 Clear sky surface solar irradiance scatter plot for the station of Carpentras and the climate SAF product.

(18)

University of Geneva - 13 - Pierre Ineichen Figure 16 Modelled surface solar irradiance versus the corresponding ground measurements for the station of Carpentras.

be important when splitting the global component into the beam and the diffuse [Ineichen 2006].

9. Surface solar irradiance

The main component produced from the satellite images is the surface solar or global horizontal irradiance. It is widely used in the field of passive solar heating and cooling, solar thermal installation, photovoltaic powerplants etc. It is also the input parameter for other models and applications like beam/diffuse splitting models, photosyntetic active radiation, visible irradiance, etc. It is therefore important to assess the precision of this modelled parameters and to study its behaviour with the season, the climate, the geographic location, etc.

To ensure a correct and comparable validation of the different products, the following method was used to merge the SAFs’ products and the ground measurements: for each generated value, the nearest time stamped corresponding ground value is searched in the data base; this means that the satellite image was taken within the ground integration period. Due to missing values in the different data banks, the number of points taken into account in the validation is not the same for all products, and the average irradiance value can be slightly different. Furthermore, osi-SAF produces only value for a solar elevation greater than 10°, this will therefore be a lower limit for the whole comparison.

It can also be noted that the land- and osi-SAF provided the 9 nearest pixels around the station and the climate SAF a 3x5 pixel average. In the case of the 9 pixels, the pixel giving the best results was kept in the comparison. A mathematical and geometrical averaging of the 9 pixels was also tested, but did not give better results.

Figure 16 illustrates the comparison by means of a scatter plot for the station of Carpen- tras (all the SAFs and stations scatter plots are given in the Appendix, Figures a-2.1 to a- 2.8). The statistical parameters are given on Table III where the number of points concerned by the validation, the ground surface solar irradiance average, the absolute mean bias difference, the mean bias difference and the root mean square difference are given for all the stations and SAFs.

From the Figures and the Table, it can be seen that Carpentras, the station with the

(19)

University of Geneva - 14 - Pierre Ineichen Table III Statistical comparison of the SAFs products with the ground measurements, by stations, SAFs, all stations average, all SAFs average and overall comparison.

ground average 297 294 296 295

model average 297 287 301 294

nb 1091 1826 1141 4058

abs. difference 54 67 69 64

MBD 1 -7 5 -2

RMSD 81 95 104 94

ground average 315 317 306 313

model average 317 325 326 323

nb 1025 1760 1119 3904

MBD 2 7 20 9

RMSD 87 98 96 95

ground average 437 438 426 434

model average 439 443 439 441

nb 1054 1774 1134 3962

MBD 3 5 13 7

RMSD 59 64 78 67

ground average 363 354 359

model average 374 354 366

nb 1873 1192 3065

abs. difference 71 65 69

MBD 11 0 7

RMSD 110 102 107

ground average 307 303 304 305

model average 292 297 306 298

nb 1081 1820 1148 4049

MBD -16 -7 2 -7

RMSD 85 101 94 95

ground average 365 360 352 359

model average 373 376 364 372

nb 1111 1875 1195 4181

MBD 9 16 12 13

RMSD 102 131 112 119

nb 1776 1185 2961

MBD 1 -4 -1

RMSD 103 112 106

nb 1694 1044 2738

MBD 11 3 8

RMSD 100 99 100

ground average 344 343 337 341

model average 344 348 344 346

nb 5362 14398 9158 28918

abs. difference 53 67 64 63

MBD 0 5 6 4

RMSD 84 102 100 98

osi SAF land SAF climate SAF Surface solar irradiance

[W/m²]

all SAFs All months

All sky conditions

Camborne 50.22°N

5.32°W

Solar elevation >= 10°

Lyon 45.78°N

4.93°E

Toravere 58.27°N 26.47°E Cabauw 51.97°N 4.93°E

all stations Payerne

46.82°N 7.94°E Carpentras

44.08°N 5.06°E

Geneva 46.20°N 6.01°E

(20)

University of Geneva - 15 - Pierre Ineichen clearest conditions, gives also the best results for all the SAFs products. A higher dispersion for the other stations (scatter plots and rmsd) is due to the different climate (more intermediate sky conditions). For Geneva, the proximity of the lake could influence the albedo determination and increase the dispersion.

Not only the absolute solar surface irradiance should correspond to the ground measurements, but also the relative value represented by the modified clearness index (this means that the right irradiance value occures at the right moment during the day).

An illustration of the behaviour of this parameter is given on Figure 17 for the station of Lindenberg and land SAF’s product. It is interresting to underline the upper limit due to the normalization method (clear sky) that doesn’t take into account the particular conditions where broken clouds are present and higher values of surface solar irradiance can occur. The scatter plots for all the SAFs and stations are given in the appendix (Figures a- 3.1 to a-3.8).

The dependence of the surface solar irradiance with available ground and satellite parameters was studied, in particular the seasonal dependence, the dependence with the clearness index, the Perez weather type parameters delta and epsilon [Perez 1990],

Figure 18 Model- measurements mean bias difference versus the modified clearness index for the station of Lindenberg.

Figure 17 Modelled surface solar irradiance clearness index versus the corresponding ground measurements for the station of Lindenberg and land SAF.

(21)

University of Geneva - 16 - Pierre Ineichen Figure 20 Model- measurements mean bias difference versus the surface albedo retrieved from satellite data for the station of Geneva by the climate SAF.

Figure 21 Model- measurements mean bias difference versus the solar elevation angle for the station of Camborne and the land-SAF solar surface irradiance.

Figure 19 Model- measurements mean bias difference versus the cloud cover retrived from satellite data at the station of Lyon by the climate SAF.

(22)

University of Geneva - 17 - Pierre Ineichen the solar elevation angle, the cloud cover and cloud top height.

Figure 17 suggest to look at the clearness index dependence; it is represented on Fi- gure 18 where the model-measurements bias is plotted versus the modified clearness index. A slight dependence can be outlined, but difficult to quantify due to the dispersion of the points. The general pattern of the dependence is similar for all the SAFs and stations, the complete set of Figures is given in the appendix (Figures a-4.1 to a-4.8).

No particular pattern could be pointed out with delta, epsilon, cloud cover and cloud top height. This is illustrated on Figure 19 and 20 for two of these parameters.

On the other hand, for the land SAF product, a slight dependence with the solar elevation angle can be seen on Figure 21 (here, all solar elevation angles are plotted, even if only angles greater than 10° are considered in the comparison). The model underestimation for low solar elevation angles shows the same pattern for all the stations and the land SAF product as illustrated in the appendix on Figure a-5. No particular pattern can be seen for the same parameter on the products from the other SAFs.

Figure 22 Relative frequency of occurence of the surface solar irradiance at the station of Payerne and for climate SAF’s product.

Figure 23 Relative frequency of occurence of the modified clearness index at the station of Lindenberg and for climate SAF’s product.

(23)

University of Geneva - 18 - Pierre Ineichen Figure 25 Model- measurements root mean square difference.

Figure 24 Model- measurements mean bias difference

Figures a-6.1 to a-6.8 represent the relative frequency of occurrence of a certain value of surface solar irradiance. The grey bars indicate the ground measurements and the red line the corresponding SAFs’ product. An illustration is given on Figure 22 for the station of Payerne and climate SAF’s product. As previously seen on the scatter plots and from the mean bias differences, no particular pattern can be outlined from the Figures.

If the relative frequency of occurence of the surface solar irradiance is correctly represented by the different products, it is slightly different when considering the modified clearness index. The upper limit in the modelled values observed on Figures 17 and 18 can clearly be seen on the frequency of occurrence, as for example at Lindenberg (Figure 23). Here, the model is not able to produce values higher than K’_t = 0.8. These conditions are particular to broken cloud situations where the beam irradiance is reflected by the clouds and the surface solar irradiance is higher than the clear sky surface solar irradiance used for the normalization. Except for specific situations at particular stations, the behaviour is the same for all SAFs and stations (cf. Figures a-7.1 to a-7.8)

A graphical visualisation of the results from Table III is given on Figures 24 and 25 for all the stations, SAFs and overall results. It can be seen that the performances of the different SAFs’ products are very similar, with some specific biases. These particular

(24)

University of Geneva - 19 - Pierre Ineichen differences could be due to a slight spatial shift, ground instrument calibration shifts, specific albedo configuration like in Geneva, etc.

The evaluation was done for the 3 different sky conditions described in section 4: clear, intermediate and cloudy skies. Even if the overall bias is negligible, the observed biases for the different sky types (Table a-1.2 to a-1.4) confirm the behaviour previously pointed out on Figure 18. The pattern is the same for all SAFs products, that is a modell overestimation for cloudy conditions and an underestimation for clear skies (respectively bias = 50 [W/m²] and -33 [W/m²]). The dispersion (root mean square) remains in the same order of magnitude for all conditions (rmsd = 80-100 [W/m²]). The charts for the different sky types are given in the appendix (Chart a-1.1 and a-1.2)

The seasonal effect is illustrated in the appendix, Tables a-2 to a-5, where the statistics are given month by month in the same way than on Table III. The trend for the climate SAF product to underestimate in winter and overestimate in summer can be explained by the tendency of the aod to be to high in winter and to low in summer as shown on Figure 26. A high aod will produce a low clear sky irradiance used as reference, and therefore also a low surface solar irradiance. The corresponding mean bias differences are illustrated Figure 26 Daily aerosol optical depth at 550 nm obtained from ground measurements SAFs’

products versus the day of the year in Lindenberg. The seasonnal trends are also represented.

Figure 27 Surface solar irradiance model- measurements mean bias and root mean square difference for the 4 months, cm-SAF, and for the station of Lindenberg.

(25)

University of Geneva - 20 - Pierre Ineichen on Figure 27.

The opposite tendency presentend by the aod produced by the climate SAF and the osi SAF is a systematic pattern (Figure a-1.2 to a-1.7). The consequence can be seen on Figure 28 where the surface solar irradiance mean bias difference is represented for the four months and the different SAFs, as average over the 8 stations. Here again, the pattern confirms the conclusion drawn for the station of Lindenberg from Figure 26 and 27.

In conclusion, no important dependence could be pointed out, and the overall perfor- mance of the models is satisfying. Nevertheless, the precison depends on the climate and a higher dispersion is present by intermediates sky conditions. A slight correlation of the bias with the clearness index and the solar elevation could be underlined, but they are very small as compared to the dispersion.

An improvement of the models could come from a better knowledge of the turbidity of the atmosphere, in particular if in a further step, the global component has to be splitted into its two components: the beam and diffuse.

Due to the methodology, particular conditions like broken clouds are not taken into account in the different models and the consequence is that in climates where intermediate conditions predominate like in Geneva or Lyon, the dispersion is higher.

10. Downward longwave irradiance

The second important parameter used in the calculation of the energy balance is the downward longwave irradiance. This parameter is produced by all SAFs, it is investigated in the same way with the same indicators than the surface solar irradiance.

It is not possible to apply a simple and reliable quality control on the downward longwave irradiance like for the shortwave irradiance, where clear sky conditions form a physical upper limit. Nevertheless, the CFIsat method [Dürr 2006] described in a previous section can be applied to evaluate the quality of the measurements. The evaluation of the CFIsat parameter needs the knowledge of the dry bulb temperature and the water vapour content of the atmosphere. Therefore, it can only be applied on 4 of the 8 data sets. An example is given on Figure 29 for the data from Payerne. Following the method from Dürr, clear sky conditions should have a CFIsat value below 0%, and cloudy conditions higher then 50%. This quality check pointed out some measurement artifacts on the Figure 28 Surface solar irradiance model- measurements mean bias difference for the 4 months and the 3 SAFs, as average over all stations.

(26)

University of Geneva - 21 - Pierre Ineichen Table IV Statistical comparison of the SAFs products with the ground measurements, by stations, SAFs, all stations average, all SAFs average and overall comparison.

ground average 329 330 330 329

nb 2786 2296 2784 7866

MBD -15 -13 -10 -13

RMSD 23 27 23 24

ground average 328 327 329 328

nb 2472 4364 2539 9375

MBD 5 2 3 3

RMSD 28 33 27 30

ground average 325 323 325 324

nb 2636 2211 2665 7512

MBD -15 -18 -13 -15

RMSD 21 27 23 24

ground average nb abs. difference

MBD RMSD

ground average 309 308 310 309

nb 2760 2238 2736 7734

MBD -2 -2 5 0

RMSD 21 24 25 24

ground average nb abs. difference

MBD RMSD

nb 2290 2739 5029

MBD -15 -15 -15

RMSD 28 28 28

ground average 307 307

nb 3814 3814

abs. difference 24 24

MBD -11 -11

RMSD 31 31

ground average 322 318 322 320

nb 10654 17213 13463 41330

abs. difference 18 22 20 20

MBD -7 -8 -6 -7

RMSD 23 29 26 27

land SAF

Downward longwave irradiance [W/m²]

osi SAF climate SAF all SAFs

All months All sky conditions

Camborne 50.22°N

5.32°W

Solar elevation >= 10°

Lyon 45.78°N

4.93°E

Toravere 58.27°N 26.47°E Cabauw 51.97°N 4.93°E

44.08°N 5.06°E

Geneva 46.20°N 6.01°E

(27)

University of Geneva - 22 - Pierre Ineichen Figure 30 Modelled downward longwave irradiance versus the corresponding ground measurements for the station of Cabauw.

Figure 31 Relative frequency of occurence of the downward longwave irradiance at the station of Carpentras and for the land SAF.

Figure 29 Cloud Free Index saturation (CFIsat) calculated from ground measurements versus clearness index for the station of Payerne.

(28)

University of Geneva - 23 - Pierre Ineichen Figure 32 Downward longwave irradiance model- measurements mean bias difference versus the modified clearness index for the station of Camborne and the land SAF.

Figure 34 Downward longwave irradiance model- measurements mean bias difference versus the solar elevation angle for the station of Camborne and the land SAF.

Figure 33 Downward longwave irradiance model- measurements mean bias difference versus the cloud cover for the station of Car- pentras and the climate SAF.

(29)

University of Geneva - 24 - Pierre Ineichen Figure 35 Downward longwave irradiance model- measurements mean bias difference versus the cloud top height for the station of Toravere.

data from Cabauw, where not all the data were acquired with a shaded pyrgeometer. A correction was then applied (see Appendix).

The observation of the model-measurements scatter plots for the downward longwave irradiance show a general tendency to underestimate this parameter (Figure a-9.1 to a- 9.8) as it can be seen for example on Figure 30 for the measurements acquired in Cabauw. It can also be outlined from the Figures in the annexe that there are some strange patterns (i.e. Figure a-9.2) that cannot be explained or isolated by a parameter dependance study. These can be attributed either to ground measurements imprecisions or to model biases (or both).

The model tendency to underestimate is corroborated by the relative frequency of occurence illustrated on Figure 31, where it can be seen that the shape of the model is correctly reproduced but shifted to the left. The complete set of Figures is given in the appendix (Figure a-10.1 to a-10.8).

The validation statistics are given on Table IV for all the stations, SAFs and overall perfor- mance. The bias seems to be station dependent but is less than 5%. There are no differences between nightime and daytime statistics as illustrated in the appendix on Charts a-1.5.

The model dependence with geometric and climatic parameters is illustrated on Figure 32 to 34, where no particular effect could be pointed out. If specific patterns are present (like for example on Figure 34 for the station of Camborne with the solar elevation angle), these are not systematic for all the stations or SAFs, neither dependent on the classification based on the sky conditions as shown in the appendix on Chart a-1.3 and a- 1.4. These effects are certainly artifacts (it has to be noted that the classification for different sky conditions takes into account only daytime values).

On the other hand, a systematic dependence with the cloud top height is clearly present for all the stations. An example is given on Figure 35 for the station of Toravere. But here again, if the trend is present for all stations, it is difficult to quantify because of the dispersion. It has also to be noted that the cloud top height is not a ground measurements but also a satellite product.

In conclusion, despite the fact that a quality control is difficult to apply and that some strange pattern are present on the graphs, the modelled downward longwave irradiance

(30)

University of Geneva - 25 - Pierre Ineichen is of good quality, with a small negative bias and a root mean square difference of less than 8%.

Except a slight dependence with the cloud top height, no particular effect was found. If some effects can be seen on specific graphs, they are station or SAF dependent.

11. Outgoing longwave irradiance

Only the climate SAF produces outgoing longwave irradiance, and only 2 stations do the measurements of this parameter. In order to validate on more stations data, the OLR model described in a previous section was used to evaluate the outgoing irradiance from the dry bulb temperature, corrected with the help of the surface solar irradiance and the atmospheric water vapour content for the two stations where these parameters are available. Scatter plots for the station of Cabauw are given on Figure 36 for measurements and Figure 37 for calculated values of outgoing irradiance.

The results for the station of Carpentras, Lindenberg and Payerne are given on the Figures a-12 in the appendix. Concerning the stations of Payerne, as the two plots present

Figure 37 Modelled outgoing longwave irradiance versus the corresponding evaluated from T_a irradiance for the station of Cabauw.

Figure 36 Modelled outgoing longwave irradiance versus the c o r r e s p o n d i n g measurements for the station of Cabauw.

(31)

ground from Ta

nb 2688 2688 5376

MBD -3 -3 -3

RMSD 11 13 12

nb 2545 2545

MBD -14 -14

RMSD 19 19

nb 1978 1978

MBD -1 -1

RMSD 13 13

nb 2652 2585 5237

MBD -21 -23 -22

RMSD 28 28 28

ground average 368 373 371

nb 5340 9796 15136

abs. difference 15 15 15

MBD -12 -11 -11

RMSD 21 20 20

Outgoing longwave irradiance [W/m²]

climate SAF

Cabauw 51.97°N 4.93°E

44.08°N 5.06°E

Table V Statistical comparison of the climate SAF outgoing longwave irradiance with the corresponding ground measurements and evaluation.

a similar patteren, i.e. underestimation for high irradiances, no conclusion can be drawn on the validity of the modelled values. Here, it could be a ground measurements shift.

The comparison statistics are given on Table V. The results are compatible with the corresponding values obtained for the downward longwave irradiance, that is the biases are of the same order of magnitude for the same stations. The overall bias is less than 3% and the corresponding root mean square less than 6%.

12. Secondary parameters comparison

The secondary parameters produced by only osi SAF are the surface temperature, the water vapour content of the atmosphere and the relative humidity. They are produced with the ARPEGE model from Météo-France. In the same way than for the main components, they can be evaluated against ground measurements through scatter plots, frequency of occurrence and statistics.

Temperature, water vapor and relative humidity are simultaneously acquired and modelled for 3 data banks, and the sole relative humidity for the station of Lyon.

Figure 38 is an illustration of the dry bulb temperature validation for the station of

(32)

University of Geneva - 27 - Pierre Ineichen Figure 39 Dry bulb temperature model- measurements bias versus solar time for the station of Carpentras.

Figure 40 Modelled atmospheric water vapour content versus the c o r r e s p o n d i n g measurements for the station of Lyon.

Figure 38 Modelled dry bulb temperature versus the c o r r e s p o n d i n g measurements for the station of Lindenberg.

(33)

University of Geneva - 28 - Pierre Ineichen Table VI Statistical comparison of the osi SAF climatic products with the ground

measurements, by stations, and average.

Water vapour [cm]

Dry bulb temp.

[°C]

Relative humidity [%]

ground average 1.75 10.5 81

nb 2775 2770 2786

abs. difference 0.19 1.4 9

MBD -0.03 -0.6 7

RMSD 0.27 1.8 12

nb 2491 2636 2636

MBD 0.13 -2.7 13

RMSD 0.38 3.5 18

nb 1477 1813 2106

MBD 0.00 -0.6 2

RMSD 0.32 1.7 9

ground average 1.52

nb 2896

abs. difference 0.43

MBD 0.38

RMSD 0.55

ground average 1.72 12.4 75

nb 9639 7219 7528

abs. difference 0.29 2.0 10

MBD 0.14 -1.4 8

RMSD 0.41 2.5 14

Cabauw 51.97°N 4.93°E

all stations Carpentras 44.08°N

5.06°E

All months

Lyon 45.78°N

4.93°E

osi SAF

Lindenberg. The statistics are given in Table VI for the 3 climatic parameters. The plots for the 3 stations are given in the appendix (Figures a-13.1, a-13.3 and a-13.5). It can be seen on the Figure for Carpentras that for high temperatures corresponding to a high level of insolation, the modelled temperature is underestimated. This is assessed by the higher negative bias and its correlation with the surface solar irradiance as shown on Figure 39, where the bias is plotted against the solar time. The underestimation happens clearly during the daytime.

The water vapour content of the atmosphere is a secondary input parameter for the radiative transfert models. Its knowledge in conjonction with the atmospheric aerosol optical depth is nevertheless important in order to improve the precision of the clear sky model, which is used as normalization for the solar irradiance evaluation. An example is given on Figure 40 for the station of Lyon, the Figures for the other stations are given in the appendix.

Not only the water vapour content of the atmosphere is a secondary input parameter for the RTM, but also the relative humidity. It is a normalized with the temperature water content, and has its importance in the condensation of the aerosols, and therefore in the atmospheric transmitivity. Figure 41 illustrates the modelled values for the station of Lindenberg, the other graphs are given in the appendix (Figure a-16.1, a-16.3 and a- 16.5)

In conclusion, the secondary parameters are retrieved with a relatively satisfying precision,

(34)

University of Geneva - 29 - Pierre Ineichen Figure 41 Modelled relative humidity versus the c o r r e s p o n d i n g measurements for the station of Lindenberg.

taking into account the difficulty to evaluate them and their relative importance in the main model inputs.

13. Conclusions

A complete comparison and validation over 4 representative months and data from eight european ground stations was conducted. The general conclusion is that the products from the different SAFs have comparable biases and precisions. If some trends appear for specific sites or SAFs, they are difficult to isolate and are much smaller than the dispersion. The overall precision is of the order of 80-100 [W/m²] for the solar surface irradiance with a negligible bias, and of 25 [W/m²] with a slight negative bias for the downward longwave irradiance.

Concerning thesurface solar irradiance, a slight correlation of the bias with the modified clearness index and the solar elevation could be underlined, but these trends are very small compared to the dispersion. An improvement of the results could come from a better knowledge of the secondary atmopsheric transmission models’ inputs, like the atmospheric turbidity. It becomes important if the global component has to be splitted into its two components: the beam and diffuse.

Furthermore, due to the methodology, particular conditions like broken clouds are not taken into account in the different models; therefore in climates where intermediate conditions predominate like in Geneva or Lyon, the dispersion is higher.

For the downward longwave irradiance, despite the fact that a quality control is difficult to apply and that some strange pattern are present on the graphs, the modelled downward longwave irradiance is of good quality, with a small negative bias and a root mean square difference of less than 8%. Except a slight dependence with the cloud top height, no particular effect was found.

The secondary parameterslike the dry bulb temperature and the water vapour content of the atmosphere are retrieved with a relatively satisfying precision. They are mainly used as input to the radiation models.