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Comparison of rain attenuation models of satellite communication channels
based on measured point rain intensity
Conference Paper · January 2004
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2. COMPARISON OF RAIN ATTENUATION MODELS OF
SATELLITE COMMUNICATION CHANNELS BASED ON
MEASURED POINT RAIN INTENSITY
Róbert Singliar(1)
, Balázs Héder(2)
, László Csurgai(3)
, Uwe-Carsten Fiebig(4)
,
F.Perez-Fontan(5)
and János Bitó(6)
(1,2,3,6)
Budapest University of Technology and Economics,
Department of Broadband Infocommunications and Electromagnetic Theory,
Budapest, Hungary,
E-mails: roberto@docs.mht.bme.hu, balazs@docs.mht.bme.hu, laszlo.csurgai@mht.bme.hu,
bito@mht.bme.hu
(4)
German Aerospace Centre (DLR), Institute for Communications and Navigation,
Oberpfaffenhofen, Germany,
E-mail: uwe.fiebig@dlr.de
(5)
Ingenieros de Telecomunicacion (ETS), Campus universitario,
Vigo, Spain.
E-mail: fpfontan@tsc.uvigo.es
Abstract
Satellite communication plays a very important role in the global coverage vision, because it has several
advantages compared to the terrestrial radio systems. However, rain can cause significant attenuation on
earth-space paths. This paper will present a comparison study of rain attenuation models of satellite
communication channels based on measured point rain intensity in Hungary. Results will be calculated
exemplarily for locations in Hungary, Germany and Spain, where rain intensity values are available. In
this paper our first results on rain attenuation modeling in satellite communication channel will be
presented.
Keywords
Rain attenuation, millimetre wave propagation, rain intensity, satellite communication.
1. INTRODUCTION
Point rainfall rates have been measured in different locations. From these measured data we are able to
calculate various statistics such as the complement cumulative rain distribution and the complement
cumulative attenuation distribution. We are using different models of rain attenuation statistics. We can
use the measured values for both terrestrial and satellite rain attenuation models as well. From the
attenuation distributions we can provide efficient planning of the fading margin. This paper presents a
comparison study of rain attenuation models of satellite communication channels based on measured
point rain intensity in Hungary, Germany and Spain.
2. MEASUREMENT SYSTEM
The precise knowledge of rain attenuation distribution throughout the investigated region improves the
estimation of link availability, since it will be based on an accurate knowledge of outage time. We have
rain rate data from our weather stations, which are part of our countrywide measuring system [1]. The
measurement system records the received power signal level of terrestrial, point-to-point microwave radio
links and data of weather stations. The weather stations installed in the neighborhood of the microwave
links is measuring rain intensity, wind direction, wind speed, wind gust, air temperature and relative air
humidity. A central computer unit collects the data from the measurement system with a sampling time of
one minute in case of weather data. The measurement system was set up in 1998. Currently we are
collecting data of rain intensity at 5 measuring nodes (Budapest, Győr, Miskolc, Pécs and Szeged). We
have two types of different rainfall sensors. One of them measures the rain rate by electrically counting
equally sized droplets with 0.02 mm resolution. The other one measures the rain rate by a tipping bucket
with 4.0 gram/bucket (0.20 mm/bucket) resolution. In this paper we are collaborating with other European

3. institutes so we are using rain rate data also from Oberpfaffenhofen (Germany), La Coruña, Santiago de
Compostela and Vigo ( Spain).
Fig. 1. Rain intensity (R0.01) distribution in Hungary
based on measured values
Fig. 2. Rain intensity (R0.01) distribution in Hungary
based on ITU-R 837-3 [1]
Fig. 1 shows the rain intensity distribution in Budapest and Józsa calculated from our measured data. The
rain intensity map for Hungary is presented in Fig. 2.
Table 1: Rain intensity exceeded in 0.01% of time in average year for different stations
Budapest Józsa Oberpfaffenhofen
La
Coruña
Santiago de
Compostela
Vigo
2003 24.5 N/A N/A N/A N/A N/A
2002 41.8 48.1 N/A N/A N/A N/A
1995 N/A N/A 22.5 N/A N/A N/A
1989 N/A N/A N/A 28 48 39
1990 N/A N/A N/A 29 24 34
ITU 34 38 29 35 35 40
3. RAIN ATTENUATION MODELS
Several methods have been developed and tested against available data. The following rain attenuation
models are briefly described in this section: 1. ITU-R 618-5, 2. Garcia, 3. Svjatogor.
Table 2 gives the geometrical parameters for Budapest and in Table 3 the geometrical parameters of the
investigated satellites can be found. Below are described the input parameters for rain attenuation
prediction models: φ – station latitude [degree],
θ – elevation of the antenna [degree],
hs – station latitude [km], p– probability [%],
Rp – point rainfall rate exceeded at percentage of time [mm/h],
R0.01 – rain intensity exceeded for 0.01% of an average year [mm/h],
k, α – frequency and polarization dependent factors [ -] and f–frequency [GHz].
Table 2. Geographical parameters for locations
Budapest Józsa Oberpfaffenhofen La Coruña
Santiago de
Compostela
Vigo
Latitude 47.52°N 47.54°N 48.1°N 43.36°N 42.86°E 42.25°E
Longitude 19.07°E 21.64°E 11.3°E 8.38°W 8.55°W 8.72°E
Altitude 102 m 149 m 590 m 58 m 364 m 255 m
Table3. Geographical parameters of the investigated satellites regarding to Budapest
Satellite Position Elevation Distance
Hot Bird 13ºE 35.42º 38168 km
HispaSat 30ºW 18.06º 39750 km
PanamSat 45ºW 8.70º 40734 km
29mm/h
40mm/h
35mm/h
45mm/h
33mm/h

4. Table 4. Frequency and polarization of the used transponders
Satellite Frequency [GHz] Polarization
HotBird 12.539 and 19.842 Horizontal
HispaSat 12.662 Horizontal
PanamSat 11.510 Horizontal
3.1 ITU-R model
To estimate the long-term statistics (The estimation procedure is based on mapping of rain statistics onto
attenuation statistics.) of the slant-path rain attenuation at a given location for frequencies up to 30 GHz
the following steps have to be carried out [3] :
Step 1: Calculate the effective rain height, hR, for the latitude of the station φ:
hR = 5 – 0.075(φ – 23) for φ > 23° for the northern hemisphere (1)
Step2: For θ ≥ 5° compute the slant-path length, Ls, below the rain height from:
Ls = (hR - hs)/sinθ (2)
Step 3: Calculate the horizontal projection, LG, of the slant-path length from:
LG = LS cosθ (3)
Step 4: Obtain the rain intensity, R0.01, exceeded for 0.01% of an average year (with an integration time of
1 min). If that information cannot be obtained from local data sources, an estimate can be obtained from
the map of rain climates given in Recommendation ITU-R P.837.
Step 5: Calculate the reduction factor, r0.01, for 0.01% of the time for R0.01 ≤ 100 mm/h:
)
/
1
/(
1
01
.
0 O
G L
L
r +
= (4)
where Lo = 35 exp (-0.015 R0.01) (5)
Step 6: Obtain the specific attenuation, γR, using he frequency dependent coefficients given in
Recommendation ITU-R P.838 and the rainfall rate, R0.01, determined from Step 4, by using:
R
γ = 0.01
( )
k R α
[dB/km] (6)
Step 7: The estimated attenuation exceeded for 0.01% of an average year is obtained from:
0.01
A = 0.01
R s
L r
γ [dB] (7)
Step 8: The estimated attenuation to be exceeded for other percentage of an average year, in the range
0.001% to 1%, is determined from the attenuation to be exceeded for 0.01% for an average year by using:
(0.546 0.043log )
0.010.12 p
p
A A p− +
= [dB] (8)
3.2 Garcia model
Rain attenuation As in a satellite link is obtained by [4]:
]}
/
)
(
[
/{ e
d
cL
bR
L
a
L
kR
A s
s
s
p
s +
+
+
= α
[dB] (9)
where Ls is the equivalent path length, in km, given by (1),
with hR =4 – 0.075(φ – 36) [km] φ ≥ 36º (10)
The coefficients a, b, c and d are constants depending on the geographical area. Coefficient e is only a
scaling factor. Taking e = 104
, the “worldwide” coefficients are: a=0.7, b=18.35, c=-16.51, d=500.
3.3 Svjatogor model
The effective rain height hR for the Svjatogor model is depending on the rain intensity [4]:
=
R
h p
p R
R 0015
.
0
)
5
.
1
3
.
0
(
log
/
7
.
2 10 +
+ [km] (11)
The path length reduction factor is formulated in the following expression:
( ) 6
.
0
68
.
0
tan
/
0045
.
0 θ
R
p h
R
Y −
= (12)
The rain attenuation As is given by:
rs
s
p
s k
L
kR
A α
= [dB] (13)
Where the path length reduction factor is formulated in the following expression:
Y
rs e
k = (14)
θ
sin
/
)
( s
R
s h
h
L −
= [km] if °
≥ 5
θ (15)

5. 4. COMPARISM OF RAIN ATTENUATION DISTRIBUTION OF SATELLITE
LINKS
Fig. 3. CCDFs for Budapest based on the ITU
model
Fig. 4. CCDFs for Józsa and Oberpfaffenhofen
based on the ITU model
Fig. 5. CCDFs for Spain locations and HispaSat
based on the ITU model
Fig. 6. CCDFs for Spain locations and PanAmsat
based on the ITU model
Fig. 7. CCDFs for Budapest based on the
Svjatogor model using 2 different elevations
Fig. 8. CCDFs for Budapest based on the Garcia
model using 3 different elevations

6. Figures 3-4 show the complement cumulative attenuation distribution function (CCDF) calculated for
Budapest, Józsa and Oberpfaffenhofen. The CCDF gives the percentage of time when Ai is exceeded:
P(Ai) = probability (A>Ai). Fig. 3 gives the frequency and elevation dependence of the CCDF based on
the ITU model. Results are presented for the time period from 2002 to 2003. All curves were calculated
from the measured rainfall point rate. We can obtain higher attenuation in the Ka band and also higher
attenuation for the Hispasat satellite which has lower elevation when seen from Budapest. We can see
also the differences between the curves calculated from the measured and the recommended R0.01 values.
Comparing Fig. 4 to Fig. 3 it is relevant that the differences in attenuation between the HotBird and
HispaSat curves are lower than between HotBird and PanamSat. That is due to the higher elevation angle
for HispaSat and the much lower elevation angle for PanamSat.
Fig. 5-6 shows almost the same tendencies. The curves were calculated for the same locations but
different satellites and frequencies. Although the elevation for PanamSat is lower than for HisPasat the
attenuation is also lower. This apparent contradiction is caused the fact that the calculations for Panamsat
were made for a frequency which is 1 GHz lower than the frequency for HispaSat. The lower frequency is
less sensitive to the attenuation due to rain than the higher frequency. For all the 3 locations the recorded
data is from 1989 and 1990 as well as the curves from ITU-R Recommendation are shown.
Figures 7-8 are presenting the two alternative models (Svjatogor and Garcia). The calculations for these
models were made from measured data in Budapest in years 2002 and 2003. In both figures we can obtain
the elevation and frequency dependence. Increasing the frequency and decreasing the elevation the
attenuation will be higher. Compared the two models it is obvious that the Svjatogor model is more
sensitive to the attenuation but on the other hand the Garcia model is sensitive to the elevation.
5. CONCLUSION
In this contribution a comparison between rain attenuation models is made. Measurement results are
presented for different geographical locations in Hungary, Germany and Spain. We have focused our
attention on the measured point rate intensity to compare the models using our measured data. The
elevation dependence provides interesting insights to fade margin design. Comparing the 3 presented rain
attenuation models we can find out that Svjatogor model gives at a given percentage much higher
attenuation than the ITU and Garcia model. We can obtain that the ITU model is much more sensitive for
the Ka band than the others. Finally from the results is clear that the most sensitive model for the
elevation angle is the Garcia model. We draw the conclusion that for investigating the Ka band we should
take the ITU model, because this turned out to be the best one. We determined that the Garcia model has
to be modified in order to get appropriate results.
6. ACKNOWLEDGEMENT
The results are carried out in the framework of SATNEX FP6-IST-507554.
References
[1] ITU-R PN.837, “Characteristics of Precipitation for Propagation Modelling”,
[2] Zsolt Kormányos, Lena Pedersen, Cyril Sagot, János Bitó, “Rain Attenuation and Fade Duration
Statistics at 38 GHz Derived From Long Term Radio Link Measurements in Hungary, Norway and
Ireland”, AP2000 Conference, Davos 13.April 2000.
[3] ITU-R P.618; Propagation Data and Prediction Methods Required for the Design of Earth-Space
Telecommunication Systems,
[4] Cost Action 255 Final Report, “Radiowave Propagation Modelling for SatCom Services at Ku-Band
and Above”, ESA Publications Division, Noordwijk, The Netherlands, 2002.
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