1.0 - CM SAF

Satellite Application Facility on Climate Monitoring
Scientific Prototype Report
WP 2.2 Surface Fluxes:
Surface Incoming Shortwave
Radiation
DWD, GKSS, 07. November 2003
Reference Number:
SAF/CM/DWD/PR/1.2A2_3
Issue/Revision Index 1.0
Date: 07 November 2003

SAF on Climate
Monitoring
Scientific Prototype Report
Annex 2:
WP 2.2 Surface Fluxes
Doc.No.: SAF/CM/DWD/PR/1.2A2_3
Issue: 1.0
Date: 7 November 2003
TABLE OF CONTENTS
1. SHORTWAVE FLUXES ............................................................................................................... 3
1.1 MODEL DESCRIPTION.................................................................................................................. 3
1.2 INPUT DATA FOR PROTOTYPE ACTIVITES ................................................................................... 3
1.2.1 ISCCP-DX data ................................................................................................................. 4
1.2.2 ECMWF data..................................................................................................................... 4
1.3 NARROW-TO-BROADBAND CONVERSION................................................................................... 4
1.3.1 Conversion for METEOSAT for prototype development ................................................. 5
1.3.2 Conversion for the NOAA-AVHRR instruments.............................................................. 5
1.3.3 Description of method of Hucek and Jacobowitz (1995) .................................................. 6
1.4 CALCULATION OF LOOK-UP TABLES FOR SIS............................................................................ 7
1.4.1 Sensitivty studies for generation of look-up tables............................................................ 7
1.4.2 Definition of look-up tables...............................................................................................9
1.4.3 Comparison with other RTM results ............................................................................... 10
1.5 PROTOTYPE DEVELOPMENT...................................................................................................... 12
1.5.1 Usage of look-up tables within the prototype.................................................................. 12
1.5.2 Surface albedo ................................................................................................................. 14
1.5.3 Processing steps............................................................................................................... 14
1.6 CALCULATION OF DAILY AVERAGES......................................................................................... 15
1.7 PRELIMINARY VALIDATION RESULTS....................................................................................... 15
1.8 FIRST RESULTS BASED ON AVHRR DATA................................................................................ 18
1.9 NEXT STEPS .............................................................................................................................. 20
1.10 REFERENCES ........................................................................................................................ 20
2

SAF on Climate
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Scientific Prototype Report
Annex 2:
WP 2.2 Surface Fluxes
Doc.No.: SAF/CM/DWD/PR/1.2A2_3
Issue: 1.0
Date: 7 November 2003
1. SHORTWAVE FLUXES
The following chapters describe the prototyping activities for the surface incoming shortwave flux.
1.1 MODEL DESCRIPTION
For the calculation of the surface incoming shortwave flux (SIS) an algorithm similar to the one
developed by Pinker (e.g. Pinker and Laszlo, 1992) was developed. The basic idea for the algorithm is
that a relationship exists between the broadband (0.2-4.0 µm) atmospheric transmittance and the
reflectance at the top of the atmosphere (TOA). Once the transmittance is determined from the TOA
albedo, the surface irradiance SIS can be computed from the incoming solar flux at the top of the
atmosphere E
0
and the atmospheric transmittance T .
0
is the sun zenith angle.
SIS E0 cos( 0
) T
With a radiative transfer model, the broadband atmospheric transmittance is calculated once in
relationship to the broadband TOA albedo for a variety of atmospheric and surface states. The actual
computation of the surface irradiance involves two steps. First the broadband TOA albedo is
determined from the satellite measurement. Then the atmospheric transmittance is determined from the
TOA albedo together with information on the atmospheric and surface state from the precomputed
tables.
Input parameters into the algorithm are the broadband TOA albedo, solar zenith angle, surface albedo,
the cloud mask, additional cloud properties as e.g. the cloud optical depth if available, aerosol and
ozone data and the total water vapour content of the atmosphere. The broadband TOA albedo for the
area covered by the MSG satellite, the surface albedo and the cloud properties are derived by other
groups within the CM-SAF. All other input data are either from climate data sets (aerosol, ozone) or
NWP-data (water vapour). Depending on the availability of appropriate data sets, satellite derived
values could be used instead of the climate values. To calculate the TOA albedo for the CM-SAF
region not covered by the MSG satellite, a narrow-to-broadband conversion of the satellite data and
the application of an angular directional model is necessary. In using broadband fluxes at the top of the
atmosphere, the algorithm is independent of the satellite instrument and allows for the application to
multiple sensors (here MSG/SEVIRI and AVHRR).
1.2 INPUT DATA FOR PROTOTYPE ACTIVITES
In order to have consistency between the cloud and radiation products, CM-SAF cloud products will
be used as input into the surface flux calculations. The CM-SAF cloud products will be based on MSG
data for the CM-SAF area within the MSG field of view and on AVHRR data for the CM-SAF area
outside the MSG field of view. As the cloud software was still in the tuning and evaluation stage
during the development of the SIS prototype, alternative cloud products have been selected for the
present prototyping activities. They are the ISCCP-DX datasets from 5 different months from 1993 to
December 1997 and corresponding ECMWF analysis data from the same time period. These data sets
have been used to test the prototype software.
Figure 1 gives an overview of the general processing within the SIS prototype:
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SAF on Climate
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Scientific Prototype Report
Annex 2:
WP 2.2 Surface Fluxes
Doc.No.: SAF/CM/DWD/PR/1.2A2_3
Issue: 1.0
Date: 7 November 2003
Background data
& maps
e.g. spec. albedo maps,
surface type
Satellite Data
sza,
_sfc
toa
ECMWF analyses
Int. water vapour
Lookuptable for
SIS = f (sza, _sfc_toa,...)
Precalculated
Model runs
of SHDOM
for N atmosph.
conditions
Inst. SIS
Figure 1: Overview of processing for SIS-calculations.
1.2.1 ISCCP-DX DATA
The International Satellite Cloud Climatology Project (ISCCP, see e.g. Rossow and Schiffer, 1991)
uses data from geostationary and polar orbiting weather satellites to derive a global cloud climatology.
The ISCCP-DX data are sampled in space and time to reduce the data volume. The satellite
observations are available every 3 hours. The pixels of the satellites are sampled to a spacing of about
30 km. For Meteosat, this means every 6 th pixel is used, which gives a spacing of about 50 km and a
pixel size of about 8 km in midlatitudes. The SIS algorithm uses the cloud flag, the narrowband
radiance of the visible channel and corresponding viewing geometry information.
For the validation, so far only Meteosat cloud data have been used. For the monthly mean gridded
image, all available (in space and time) Meteosat data have been used. At first the instantaneous data
were gridded to a 0.167° x 01.67° rectangular latitude/longitude grid. All pixels in a grid box were
averaged. The final results are presented in 0.5° x 0.5° rectangular grid.
1.2.2 ECMWF DATA
ECMWF analysis data were used for the atmospheric variables temperature and humidity. ECMWF
analysis data are available in a 0.5625° x 0.5625° rectangular latitude/longitude grid in 31 levels,
which can be related to pressure levels. From the atmospheric profiles, total precipitable water was
calculated. Additionally, the surface temperature is used for classification of the atmospheric state. For
the MSG and AVHRR processing NWP data from the DWD global forecast model GME will be used.
1.3 NARROW-TO-BROADBAND CONVERSION
The calculation of the TOA albedo from satellite radiances requires a narrow-to-broadband (NTB)
radiance conversion and the use of angular distribution models to convert radiance into flux. For
SEVIRI the TOA flux will be provided by another group within the CM-SAF (RMIB). For other
sensors the conversion from radiance to flux needs to be included in the surface flux processing. The
4

SAF on Climate
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Scientific Prototype Report
Annex 2:
WP 2.2 Surface Fluxes
Doc.No.: SAF/CM/DWD/PR/1.2A2_3
Issue: 1.0
Date: 7 November 2003
following two chapters describe the NTB conversion for METEOSAT and AVHRR. For both sensors
the ERBE angular distribution models were used (Suttles, et al., 1988).
1.3.1 CONVERSION FOR METEOSAT FOR PROTOTYPE DEVELOPMENT
For prototype testing of SIS METEOSAT-5 data of the ISCCP-DX data set from the August 1995
period was primarily used. For the narrow-to-braodband conversion of the METEOSAT-5 radiances
the conversion developed by Gimeno-Ferrer et al. (2001) was used. The authors derived the broadband
shortwave radiance (L SW ) as a function of the visible radiance of METEOSAT channel 1 (L VIS ) and the
cosine of the solar zenith angle µ. The conversion is based on radiative transfer calculations with the
STREAMER model (Key, 1998) and is independent of scene type. The following equation was
derived:
L
SW
a
1
0
a1
a2
ln(
)
L
VIS
a
4
L
2
VIS
where the a i are regression coefficients:
a 0 =0.99 a 1 =2.428
a 3 =-0.220 a 4 =-0.00328
Table 1: Regression coefficients for the NTB-conversion.
The calculations for 1993 with METEOSAT-4 data and the calculations for 1997 with METEOSAT-6
were also performed with this NTB conversion.
1.3.2 CONVERSION FOR THE NOAA-AVHRR INSTRUMENTS
For areas outside the field of view of MSG, the broadband albedo will be calculated from the AVHRR
channel 1 and channel 2 radiances. Based on a theoretical study conducted by the OSI-SAF (Øystein
and Eastwood, 2002) a validation study was performed in the CM-SAF to investigate which narrowto-broadband
conversion method performs best (Kryvobok and Hollmann, 2003). Four different NTB
conversion methods were compared with co-located and co-aligned measurements of the broadband
measuring instrument ScaRaB (Scanner for Radiation Budget) for some months in 1994. For the
comparison, all AVHRR pixels within each ScaRab pixel were averaged, weighted by the ScaRab
point spread function. The area of investigation concentrated mainly on Europe (Figure 2).
Figure 2: Geographical distribution of pixels (area is indicated by black horizontal lines) used to test
narrow to broadband conversions.
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SAF on Climate
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WP 2.2 Surface Fluxes
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Date: 7 November 2003
Testing of the four different NTB corrections shows that the method of Hucek and Jacobowitz (1995)
gives the best estimation of reflected shortwave flux at the top of atmosphere from AVHRR narrowband
channel data. This method gives a mean difference of 1.8 W/m 2 , the standard deviation is 48.0
W/m 2 and the relative error is 0.12. The one channel NTB correction of Hollmann et al. (1998) is
better than the non scene-dependent method of Wydick (1987) (Table 2). It has to be noted, that in this
comparison also the errors of angular directional model after Suttles et al (1988) are implicitly
included.
NTB correction
M_DIFF,
R_ER
W/m 2 ,
W/m 2
Wydick -37.2 73.0 0.20
Li & Leighton 43.1 75.6 0.21
Hucek & Jacobowitz -1.8 48.0 0.12
Hollmann -12.3 67.7 0.16
Table 2: Summary of statistics of AVHRR prediction of broadband flux for different NTB corrections,
all measurement. M_DIFF=average mean difference, =standard deviation, R_ER=relative error.
From the comparison it could be shown that the accuracy of a method depends more on the surface
type and on cloud cover than on the number of channels used. AVHRR – ScaRaB flux differences
were also evaluated with respect to different observational conditions, using the Hucek and Jacobowitz
NTB correction. The smallest errors appear for those measurements which are coincident in time,
collocated, co-angular and over the same scene type . For the case when the difference in time was less
than 10 min, difference in space geolocation angles was less than 10 and for land cases an mean
difference of 1.13 W/m 2 , a standard deviation of 6.9 W/m 2 and a relative error of 0.04 was found.
Based upon the validation results the method of Hucek and Jacobowitz (1995) was selected as NTB
correction for the developed software to calculate the top of atmosphere broadband albedo from
AVHRR data.
1.3.3 DESCRIPTION OF METHOD OF HUCEK AND JACOBOWITZ (1995)
The NTB conversions of Hucek and Jacobowitz (1995) were developed based upon coincident
narrowband (AVHRR) and broadband (ERBE) observations made by NOAA-9. The model takes into
account surface geography type and cloud amount (Table 3).
Coefficient Surface type
Snow Ocean Land Desert Coast
Clear
A 3.8995 1.78 2.17 2.60 2.77
B 1 0.0520 1.3302 0.3999 0.3896 0.3779
B 2 0.7423 -0.6250 0.4333 0.3873 0.4168
Partly cloudy
A - 4.11 4.24 3.12 4.65
B 1 - 0.9029 0.3166 0.2705 0.3085
B 2 - -0.244 0.3948 0.4811 0.3856
Mostly cloudy
A - 5.08 4.75 5.49 5.36
B 1 - 0.4711 0.3757 0.3255 0.4362
B 2 - 0.2983 0.3870 0.3961 0.3227
Overcast
A -0.1174 8.19 6.98 7.50 7.79
B 1 -0.0650 0.2301 0.2566 0.7564 0.2930
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Annex 2:
WP 2.2 Surface Fluxes
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Coefficient Surface type
Snow Ocean Land Desert Coast
B 2 0.8671 0.5032 0.4907 -0.0136 0.4446
Table 3: Regression coefficients for the surface type and cloud amount dependent model of Hucek and
Jacobowitz (1995)
1.4 CALCULATION OF LOOK-UP TABLES FOR SIS
In the CM-SAF shortwave radiative transfer calculations in cloudy skies were needed for the
development of the cloud optical thickness prototype and the surface incoming shortwave radiation
prototype. In order to keep a high degree of consistency, it was decided to use the same radiative
transfer for both purposes. Based on an comparison study (Roebeling et al., 2002; Frerichs et al.,
2002) the radiative transfer model SHDOM was selected as the baseline algorithm. The radiative
transfer model to be used in the look-up table (LUT) generation is therefore the Spherical Harmonics
Discrete Ordinate Method (SHDOM), a highly efficient and flexible algorithm suitable for radiative
transfer modelling (RTM) in inhomogeneous three-dimensional media (Evans, 1998). Some of the
major features of this RTM can be summarised as follows:
The radiance field is represented both in spherical harmonics and discrete ordinates during different
parts of the iterative solution algorithm.
The model can perform 1D, 2D and 3D unpolarised radiative transfer, and the optical properties of
the medium can be specified at every grid point within the atmosphere: extinction, single-scattering
albedo, Legendre series of the phase function and temperature.
The source function and radiance are defined on discrete grid points.
After implementation of the SHDOM program, the look-up table generation followed several steps
(Gimeno-Ferrer and Hollmann, 2002):
1. Analysis of the trade-off between the accuracy in the results and the computing time,
2. Study of the sensitivity of key parameters in the results,
3. Selection of appropriate variables and their variation range, and
4. Running the RTM to generate the look-up tables.
1.4.1 SENSITIVTY STUDIES FOR GENERATION OF LOOK-UP TABLES
In order to select the adequate variables and their variation range for the LUT generation, some
sensitivity studies have been carried out. SHDOM was run in 1D mode (plane-parallel atmosphere).
Broadband simulations were carried out in the six Fu´s shortwave bands (Table 4) and the model
output were hemispheric fluxes and net flux divergences (heating rates). Atmospheric transmissivity
and TOA albedo was also computed. Three general atmospheric situations were considered: clear sky
without aerosols (Rayleigh atmosphere), clear sky with aerosol layers and aerosol-free cloudy sky.
Aerosols have been taken into account in the radiative transfer simulations by filling the troposphere
with two aerosol layers, one layer of homogeneous number concentration in the planetary boundary
layer and another one just above extending to the tropopause with an exponentially decreasing number
concentration profile. Clouds are considered setting one single water cloud layer between 1 and 2 km
height, with cloud droplets following a modified gamma size distribution with an effective variance of
0.15.
The variables of interest for the sensitivity studies are water vapour content, ozone column, cloud
droplet effective radius, cloud optical depth, and aerosol optical depth. Other parameters such as sun
zenith angle, surface lambertian albedo, atmospheric profiles and aerosol type were selected without
previous in-depth studies. The sensitivity studies have been performed computing the transmissivity
and TOA albedo for the first three variables above, whereas for optical depths, cloud (aerosol)
radiative forcing at the TOA and the surface was computed:
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Annex 2:
WP 2.2 Surface Fluxes
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Issue: 1.0
Date: 7 November 2003
0 0
RF F F F F
where the equation above should be applied at the level of interest. Figure 3 displays some results, in
whose interpretation it should be taken into account that increasing bands contain decreasing incoming
energy at the TOA (Table 4).
Band Spectral interval (m) TOA normal flux (W/m 2 )
1 0.2 - 0.7 627.50
2 0.7 - 1.3 490.50
3 1.3 - 1.8 151.80
4 1.8 - 2.5 49.35
5 2.5 - 3.5 32.06
6 3.5 - 4.0 5.87
Shortwave 0.2 - 4.0 1357.08
Table 4: Incoming TOA normal fluxes for Fu´s bands.
0.5
Band 1
0.15
Band 2
0.14
0.4
0.13
0.12
TOA Albedo
0.3
0.2
TOA Albedo
0.11
0.10
0.09
0.1
SZA=0
SZA=20
SZA=40
SZA=50
SZA=80
0.08
0.07
0.06
SZA=0
SZA=20
SZA=40
SZA=50
SZA=80
0.0
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
0.05
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Water vapour (g/cm 2 )
Water vapour (g/cm 2 )
0.060
Band 3
0.08
Band 4
0.055
0.07
0.050
TOA Albedo
0.045
0.040
0.035
SZA=0
SZA=20
SZA=40
SZA=50
SZA=80
TOA Albedo
0.06
0.05
SZA=0
SZA=20
SZA=40
SZA=50
SZA=80
0.030
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
0.04
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Water vapour (g/cm 2 )
Water vapour (g/cm 2 )
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Date: 7 November 2003
0.020
Band 5
0.10
Band 6
0.09
0.015
TOA Albedo
0.010
TOA Albedo
0.08
0.07
0.005
SZA=0
SZA=20
SZA=40
SZA=50
SZA=80
0.06
SZA=0
SZA=20
SZA=40
SZA=50
SZA=80
0.000
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
0.05
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Water vapour (g/cm 2 )
Water vapour (g/cm 2 )
Figure 3:TOA albedo vs. water vapour content for different bands and sun zenith angles. The fixed
scene parameters are: surface albedo of 0.1 and clear-sky mid-latitude summer atmosphere with 335
DU of ozone column without aerosols.
Water vapour (Figure 3) shows an important influence in the results for all bands, excepting band 1.
Cloud optical properties impact on the results seem to saturate within the studied range of variation.
Finally, aerosol optical depth variation range does not saturate the simulated results. However, this
range covers the typically observed values for this magnitude (e.g. Dubovik et al., 2002).
1.4.2 DEFINITION OF LOOK-UP TABLES
The definition of the LUT is based on the performed sensitivity studies and on other physical and
mathematical reasons, e.g., the physically meaningful sampling of variables for further interpolation
when the LUT is used for SIS retrieval. Tables Table 5 and Table 6 show the definition of the LUT for
both clear and cloudy sky.
Parameter Variation range Number of cases
Band 6 Fu´s bands 6
Atmosphere TRO, MLS, MLW, SAS, SAW, USS 6
SZA 0, 10, 20, 30, 40, 50, 60, 70, 80° 9
Surface albedo 0, 0.05, 0.1, 0.2, 0.3, 0.5, 1 8
Aerosol type 10 pure standard components + no aerosols 10 + 1
Aerosol OD 0.01, 0.05, 0.1, 0.5, 1, 5 + no aerosols 6 + 1
Water vapour 25, 50, 75 and 100% of standard column + saturation 5
Ozone column 75, 100 and 125% of standard column 3
2371680
Table 5: Look-up table definition for clear sky.
Parameter Variation range Number of cases
Band 6 Fu´s bands 6
Atmosphere TRO, MLS, MLW, SAS, SAW, USS 6
SZA 0, 10, 20, 30, 40, 50, 60, 70, 80° 9
Surface albedo 0, 0.05, 0.1, 0.2, 0.3, 0.5, 1 8
Cloud OD 0.5, 1, 2, 4, 8, 16, 32, 64, 128 9
Effective radius 1, 3, 5, 8, 12, 16, 24 µm 7
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Parameter Variation range Number of cases
Water vapour 25, 50, 75 and 100% of standard column + saturation 5
Ozone column 75, 100 and 125% of standard column 3
2449440
Table 6: Look-up table definition for cloudy sky.
The aerosol optical and physical properties are taken from the OPAC (Optical Properties of Aerosols
and Clouds) database (Hess et al., 1998). Aerosol particles are supposed to follow a log-normal size
distribution.
The size distribution parameters of the aerosol types to be simulated are summarised in Table 7. The
integrated columns of water vapour and ozone are varied as a rounded percentage of the standard
values of the different atmospheres. Thus, their actual values are different for each of these
atmospheres (Table 8). The saturation value for water vapour is included as the higher amount of
water vapour that every atmosphere is able to hold before reaching saturation at any level. The
variation ranges of all the variables are expected to cover realistic and typical values of these
magnitudes, though some extreme values are also included, like high optical depths, low cloud droplet
effective radius or non-usual aerosol types.
Aerosol type Effective radius (µm) Effective variance r max (µm) r min (µm)
Insoluble 0.702 0.173 0.005 20.0
Water-soluble 0.032 0.178 0.005 20.0
Soot 0.015 0.095 0.005 20.0
Sea salt (acc. mode) 0.265 0.099 0.005 20.0
Sea salt (coa. mode) 2.217 0.099 0.005 60.0
Mineral (nuc. mode) 0.086 0.088 0.005 20.0
Mineral (acc. mode) 0.489 0.095 0.005 20.0
Mineral (coa. mode) 2.505 0.117 0.005 60.0
Mineral-transported 0.670 0.124 0.020 5.0
Sulfate droplets 0.088 0.099 0.005 20.0
Table 7: Size distribution parameters for the simulated aerosol types in dry state. r max and r min are the
upper and lower limits of the particle sizes taken into account for Mie calculations.
Atmosphere Water vapour columns (g/cm 2 ) Ozone columns (DU)
TRO 1.00, 2.00, 3.00, 4.00, 5.55 215, 285, 355
MLS 0.75, 1.50, 2.25, 3.00, 3.95 250, 335, 420
MLW 0.20, 0.40, 0.60, 0.80, 1.10 285, 380, 475
SAS 0.50, 1.00, 1.50, 2.00, 2.80 260, 350, 440
SAW 0.10, 0.20, 0.30, 0.40, 0.50 280, 375, 470
USS 0.35, 0.70, 1.05, 1.40, 2.75 260, 345, 430
Table 8: Water vapour and ozone columns used for the look-up table generation for every atmosphere.
1.4.3 COMPARISON WITH OTHER RTM RESULTS
As a quality check some of the RTM results have been compared to results from another radiative
transfer model. The comparison was done with tabulated values of calculations performed with the Fu-
Liou (Fu and Liou, 1992) radiative transfer model (http://snowdog.larc.nasa.gov/~rose/flp0602/).
10

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Generally the comparison showed a good agreeement. Figure 4 and Figure 5 show two selected results
from the comparison, one for clear-sky and one for cloudy sky situation.
MLS; WASO = Aerosol Typ: 2; Transmissionen
Albedo: 0.3; WD-Gehalt: 1.5; O 3 : 335 ppm v
Aers. op.T. 0.01 Aers. op.T. 0.05
Aers. op.T. 0.1 Aers. oT. 0.25
Aers. oT. 0.4 Aers. oT. 0.6
Aers. op.T. 1 Aers. op.T. 5
Diagonale
90%
80%
70%
60%
SHDOM
50%
40%
30%
20%
10%
0%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Fu-Liou
Figure 4: Comparison of atmospheric transmission for midlatitude summer atmosphere with a water
soluble aerosol and different values of the aerosol optical depth
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MLS; cloud vis. optical depth: 8.0;
Albedo: 0.3; WD-Gehalt: 2.8; O 3 : 344 ppm v
Re = 5
Re = 8
Re = 12
Diagonale
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Figure 5: Comparison of atmospheric transmission for midlatitude summer atmosphere with a cloud
optical depth of 8 and three different values of the cloud particel effective radius R e
1.5 PROTOTYPE DEVELOPMENT
1.5.1 USAGE OF LOOK-UP TABLES WITHIN THE PROTOTYPE
For use in the prototype, the look-up tables were further processed.
1. Integration over the 6 spectral Fu-Liou bands
The above defined LUT’s were integrated for each of the 20 IGBP spectral surface albedos to come to
a an easier and faster accessible LUT. For use with the MSG /NOAA prototype, the IGBP classes
were related to the USGS land use classes.
2. Definition of aerosol composit types for clear sky cases
The RTM results for the basic aerosol types were combined to typical results for aerosol composits as
e.g. “continental average” or “maritime clean”. The composition of the compound aerosol types from
the pure aerosol types is based on Hess et al. (1998). The following Table 9 gives an overview of the
10 derived aerosol types and their composition.
Aerosoltype Nr. Number weighted composition of basic aerosols
Continental clear 1 Insoluble aerosol/ water-soluble aerosol
Continental average 2 Insoluble aerosol/ water-soluble aerosol/ soot
Continental polluted 3 Insoluble aerosol/ water-soluble aerosol/ soot
Urban 4 Insoluble aerosol/ water-soluble aerosol/ soot
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Aerosoltype Nr. Number weighted composition of basic aerosols
Desert 5 Water-soluble aerosol/ mineral (nucl. mode)/ mineral (acc.
mode)/ mineral (coarse mode)
Maritim clear 6 Water-soluble aerosol/ Sea salt (acc. mode)/sea salt (coarse
mode)
Maritim polluted 7 Water-soluble aerosol/ soot/ sea salt (acc. mode) /
sea salt (coarse mode)
Maritim tropical 8 Water-soluble aerosol/ Sea salt (acc. mode)/sea salt (coarse
mode)
Arctic 9 Insoluble aerosol/ water-soluble aerosol/ soot/ sea salt (acc.
mode)
Antarctic 10 Sea salt (acc. mode)/ mineral transported/ sulfate droplets
Table 9: Composition of different aerosol types after OPAC.
For the time being to each IGBP surface class a aerosol type after Table 10 is assigned.
IGBP Surface Type Nr. Assigned aerosol type
EVERGREEN NEEDLE FOREST 1 Continental clear
EVERGREEN BROAD FOREST 2 Continental clear
DECIDUOUS NEEDLE FOREST 3 Continental average
DECIDUOUS BROAD FOREST 4 Continental average
MIXED FOREST 5 Continental average
CLOSED SHRUBS 6 Continental average
OPEN/SHRUBS 7 Continental average
WOODY SAVANNA 8 Continental average
SAVANNA 9 Continental average
GRASSLAND 10 Continental average
WETLAND 11 Maritime polluted
CROPLAND (CAGEX-APR) 12 Continental average
URBAN 13 Urban
CROP MOSAIC 14 Continental average
ANTARCTIC SNOW 15 Antarctic
BARREN/DESERT 16 Desert
OCEAN WATER 17 Maritime clear
TUNDRA 18 Continental clear
FRESH SNOW 19 Antarctic
SEA ICE 20 Arctic
Table 10: Aerosol type for each surface type.
For the prototype processing this gives finally LUT’s depending an 20 IGBP surface types. The
advantage of this preprocessing is a a much faster processing within the prototype.
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Date: 7 November 2003
1.5.2 SURFACE ALBEDO
For the prototype algorithm a synthetical background broadband surface albedo based on spectral
surface properties results obtained by the CERES SRB team is used (http://wwwsurf.larc.nasa.gov/surf).
They collected spectral surface properties from a broad range of authors
and combined them for the 20 IGBP classes and the 6 Fu-Liou bands covering the solar
spectral domain. This gives the spectral shape of the surface albedo and an integrated
broadband surface albedo for a fixed sun zenith angle of 60°. To account for changes in the
sun zenith angle the broadband surface albedo is varied with the cosine of the sun zenith
angle µ:
1
d(
igbp)
SFC
60
1
2d
( igbp)
where d depends on the chosen IGBP-surface type. For cloudy conditions no sun zenith angle
correction is done and an integrated value for the surface albedo is used. Within the MSG/NOAA
prototype, this background surface albedo will be scaled by the surface albedo derived within the CM-
SAF.
1.5.3 PROCESSING STEPS
Two versions of the prototype software are presently available, one to process ISCCP-DX and the
other to process MSG/SEVIRI or NOAA/AVHRR data. The basic modules for the calculation of the
solar radiation are the same, but the interfaces are different.
The following input data are required for processing:
- cloud mask data
- broadband TOA albedo
- surface albedo (if available)
- land use data
- latitude, longitude data for every satellite pixel
- sun zenith angle
- NWP data
- precipitable water calculated from NWP data
- solar constant
- ozone content (presently static)
- cloud effective radius (presently static)
- aerosol type (presently only depending on the land use class)
- look-up tables
For each satellite pixel, the atmosphere to be used for interpolation in the LUTs is determined from
surface temperature and water vapour content of the atmosphere. From the surface type the surface
albedo is determined, which, as mentionend in chapter 1.5.2 will be scaled to the actual surface albedo
if it is available. Then the atmospheric transmittance is interpolated from the LUTs with a linear
interpolation scheme for clear sky or cloudy sky cases dependent on the cloud mask. From the
transmittance SIS is calculated according to equation 1. Interpolation in the clear and cloudy LUTs
returns values of the aerosol and cloud optical thickness. They may not represent realistic aerosol or
cloud optical thickness values but instead include residual errors made by assuming certain
atmospheric conditions at a pixel location and trying to match the measured radiances to the assumed
atmospheric state. However a comparison between cloud optical thickness derived from the
interpolation with the ISCCP derived cloud optical thickness values shows comparable results.
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For each satellite pixel not only SIS is derived but also the clear sky solar radiation reaching the
surface, which is needed for the calculation of the daily average (see next chapter). For this a fixed
aerosol optical thickness of presently 0.5 is assumed.
1.6 CALCULATION OF DAILY AVERAGES
Daily averages are calculated following a method by Möser (1983) (also published in Diekmann et al.,
1988).
SIS
DA
SIS
CLSDA
n
i1
n
i1
SIS
SIS
i
CLS i
SIS
DA
is the daily average of SIS. SIS
CLSDA
is the daily averaged clear sky SIS, SIS
i
the calculated
SIS for satellite image i and SIS
CLS i
the corresponding calculated clear sky SIS. n is the number of
images available during a day. The larger the number of available images per day, the better the daily
cycle of the cloud coverage can be approximated and the better the calculated daily average. The
minimum number of available data per day required to calculate a daily average can be set. The daily
averaged clear sky incoming solar radiation is calculated by integrating hourly values.
1.7 PRELIMINARY VALIDATION RESULTS
The validation activities conducted so far are based on results of the prototype runs with ISCCP DX
data (only METEOSAT field of view) and ECMWF analyses data for several months of ISCCP DX
data. For validation available have been the months of October 1993, March 1994, May 1994, August
1995 and December 1997. Surface radiation measurements were available from DWD, SMHI, KNMI
(Cabauw, see Beljaars and Bosveld, 1997), RMIB and from the Baseline Surface Radiation Network
(BSRN, Ohmura et al., 1998). As the surface measurements origin from different station operators,
they also are provided in different formats. Data sets came as ASCII-files, excel-tables and htmlpages.
They are hourly averages, half-hourly averages, 10-minute averages or minute values. Time is
reported in local solar time, Mean European Time (MET) and UTC. Units are W/m 2 or J/cm 2 . To cope
with the different formats it was necessary to define a common format. All data was converted to
hourly values. If integrated values were reported in local solar time, the measurement was assigned to
the nearest passed hour in UTC. Additional information as cloud cover (if available) was included.
As the surface incoming shortwave radiation is strongly dependent on the cloud cover which is highly
variable in time and space , validation results for instantaneous SIS values are expected to improve if
in-situ data are available for a shorter time period and centred around the time of satellite observation.
One also has to keep in mind that the ISCCP-DX data have a spacing of about 50 km and a pixel size
of about 8 km in midlatitudes. For the validation the nine pixels closest to the station have been
averaged. The selected ISCCP-DX pixels might in fact not be very representative for the location of
the surface station, particularly in orographically structured terrain.
Unfortunately at this time validation results can only be shown for a previous version of the SIS
program. The previous version needed much processing time. Therefore some processing steps which
were previously performed within the program as e.g. integration over the spectral bands were done
once in advance. The version described in this document is the modified one (see also chapter 1.5.1).
This modification increased the size of the look-up tables but decreased the processing time by a factor
of 20-100. Some tests were made to ensure that both version produce identical results. However, the
validation process showed that there are cases where the results from the previous version and the
faster version do not agree. This is most likely due to some coding error. The encountered differences
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Annex 2:
WP 2.2 Surface Fluxes
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Issue: 1.0
Date: 7 November 2003
of daily averages seem to mainly affect the bias. Bias corrected RMS differences show acceptable
ranges. Therefore the results from the latest version will not be presented at this stage. Table 11 shows
validation results for instantaneous values of SIS based on the previous software version.
YEAR MONTH NSTAT MA_ME
[W/m2]
SI_ME
[W/m2]
MA_CA
[W/m2]
SI_CA
[W/m2]
BIAS
[W/m2]
BIAS
[%]
1994 3 50 214.5 148.0 228.6 136.2 14.1 7.6
1994 5 50 369.2 237.3 392.1 213.8 22.8 6.5
1995 8 46 398.0 234.9 399.8 213.1 1.7 0.6
1993 10 47 179.2 130.4 202.9 116.5 23.7 13.8
1997 12 38 93.6 67.5 86.5 56.7 -7.2 -6.4
Table 11: Validation results for previous program version. NSTAT: Number of available validation
stations, MA_ME: Measured monthly average, SI_ME: standard deviation of measured data,
MA_CA.: Calculated monthly average, SI_CA: standard deviation of calculated data, BIAS calculated
- measured and % of measured average.
SIS in situ - SIS sat Arkona Version 8x
1000
900
800
700
SIS sat (W/m²)
600
500
400
300
200
100
0
0 100 200 300 400 500 600 700 800 900 1000
SIS in situ (W/m²)
Figure 6: Preliminary validation results for station Arkona, Germany (based on previous software
version)
One important factor affecting the results is the aerosol optical thickness. A study with fixed aerosol
optical thickness values of 0.1 and 0.5 showed large differences in the results for the August 1995
case.
YEAR MONTH τ(Aer) MA_ME
[W/m2]
SI_ME
[W/m2]
MA_CA
[W/m2]
SI_CA
[W/m2]
BIAS
[W/m2]
BIAS
[%]
1995 8 0.1 398.0 234.9 412.3 224.3 14.4 3.7
1995 8 0.5 398.0 234.9 369.1 200.0 -28.9 -7.1
Table 12:Influence of aerosol optical thickness τ(Aer) on SIS (previous version).
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Annex 2:
WP 2.2 Surface Fluxes
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Date: 7 November 2003
Figure 7: Calculated surface incoming shortwave radiation (preliminary results).
Figure 8: GEWEX SRB surface incoming shortwave radiation
Figure 7 shows the monthly average of SIS for August 1995 calculated with the latest version (which
needs to be checked for coding errors). The surface incoming radiation was calculated for all available
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Annex 2:
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Date: 7 November 2003
METEOSAT ISCCP-DX pixels. A rectangular grid was selected, and all pixels within a gridbox were
averaged. The final results are gridded in a 0.5°x0.5° rectangular grid, covering the baseline CM-SAF
region (30° N - 80° N, and 60° W - 60° E).
Figure 8 shows the surface incoming flux derived by the GEWEX SRB project for the same month .
The flux in Figure 8 is calculated from ISCCP-DX data and Goddard Earth Observing System
(GEOS) v.1 reanalysis data and has a spatial resolution of 1°x1° degrees (Stackhouse et al., 2000).
Basic structures do agree, but CM-SAF values tend to be higher (see also Figure 9). Some of the
scatter seen in Figure 9 can probably be attributed to the higher resolution of the CM-SAF product.
Fit Results
Y = 0.8799120291 * X + 16.22678981
Average X = 242.144
Average Y = 229.292
Figure 9: Comparison of CM-SAF fluxes with GEWEX-SRB shortwave fluxes for the CM-SAF
baseline area
1.8 FIRST RESULTS BASED ON AVHRR DATA
AVHRR data are being processed on tiles in a fixed projection. Processing of fluxes is also based on
individual satellite images and is done on the same tiles. Figure 10 and Figure 11show results from
single NOAA overpasses, Figure 10 gives the top of atmosphere albedo calculated from the AVHRR
reflectances for NOAA 15, 08.10.2003 ,6:36 UTC, and Figure 11 the calculated surface incoming
shortwave radiation for NOAA-16, 29.04.2002, 11:28 UTC.
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Date: 7 November 2003
Figure 10: Estimation of top of atmosphere albedo using Hucek and Jacobowitz (1995) NTB
conversion.
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Annex 2:
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Date: 7 November 2003
Figure 11: Instantaneous suface incoming shortwave radiation for 29.04.2002, 11:28 UTC, derived
from NOAA 16 data
1.9 NEXT STEPS
Cloud data derived with the CM-SAF cloud software and based on AVHRR data are now available for
a few selected months. After finishing the validation activities for the results based on ISCCP-DX
data, the next step then will be the validation of surface radiation fluxes based on AVHRR data.
Aerosols play an important role in the determination of the surface incoming shortwave flux,
particularily tthe clear sky fluxes. One of the next steps will be to check how more realistic aerosol
loads can be incorporated into the model. A study presently under investigation is to include a height
dependency for SIS.
1.10 REFERENCES
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parameterization schemes. J. of Climate, 10, 1172-1193.
Bodas, A., R. Hollmann, R. Stuhlmann, K. Damman, 2000: The surface net flux from the scanner for
radiation budget (ScaRaB). CM-SAF visiting scientist report.
Diekmann, F.-J. et al., 1988: An operational estimate of global solar irradiance at ground level from
METEOSAT data: results from 1985 to 1987. Meteorol. Rdsch., 41, 65-79.
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Dubovik, O., B. Holben, T. F. Eck, A. Smirnov, Y. J. Kaufman, M. D. King, D. Tanré, and I.
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Fu, Q., and K.-N. Liou, 1992: On the correlated k-distribution method for radiative transfer in
nonhomogenous atmospheres. J. Atmos. Sci., 49, 2139-2156.
Gimeno-Ferrer, J. F., R. Hollmann: RTM calculations for surface incoming shortwave radiation.
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Hess, M., P. Koepke, and I. Schult, 1998: Optical Properties of Aerosols and Clouds: The Software
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Hollmann, R., Feng, J., Müller, J., Leighton, H.G., Stuhlmann, R., 1998: Application of Broadband
Fluxes From ScaRab and AVHRR Narrowband Radiances for Baltex and Mags, Proceedings of the 9 th
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Reflectance Measurments, J. Appl. Meteor., 31,. 421- 432.
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und Meteorologie der Universität zu Köln, Köln 1983.
Ohmura, A. et al., 1998: Baseline surface radiation network (BSRN/WCRP): new precision
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Roebeling, R., Jolivet, D., Macke, A., Frerichs, W., Berk, L., Feijt, A.,2002: Intercomparison of
models for radiative transfer in clouds; Proceedings of the 11 th AMS conference on cloud physics.
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Stackhouse P. W. , Jr., S. K. Gupta, S. J. Cox, M. Chiacchio and J. Mikovitz, 2000: The
WCRP/GEWEX Surface Radiation Budget Project Release 2: An Assessment of Surface Fluxes at 1
Degree Resolution , International Radiation Symposium 2000, St. Petersburg, Russia, July 24-29.
Suttles, J. T., R. N. Green, P. Minnis, G. L. Smith, W. F. Staylor, B. A. Wielicki, I. J. Walker, D. F.
Young, V. R. Taylor and L. L. Stowe, 1988: Angular radiation models for earth-atmosphere system,
Vol.1, NASA Reference Publication 1184.
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