cloud attenuation modelling for shf and ehf applications

*Correspondence to: Asoka Dissanayake, Comsat Laboratories, 22300 Comsat Drive, Clarksburg, MD 20871, U.S.A.�E-mail: [email protected]

Copyright � 2001 John Wiley & Sons, Ltd.

INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONSInt. J. Satell. Commun. 2001; 19:335}345 (DOI: 10.1002/sat.671)

Cloud attenuation modelling for SHF and EHF applications

Asoka Dissanayake��*��, Jeremy Allnutt� and Fatim Haidara�

�Comsat Laboratories, 22300 Comsat Drive, Clarksburg, MD 20871, U.S.A.�Virginia Tech/NVC, 7054 Haycock Road, Falls Church, VA 22043, U.S.A.� INTELSAT, 3400 International Drive NW, Washington, DC 20008, U.S.A.

SUMMARY

Tropospheric propagation impairments that a!ect earth}space communication signals increase in severitywith the increase of frequency. Introduction of satellite services using higher frequency bands such as Ka-and V-band requires the characterization of propagation factors that are normally considered negligible atlower frequency bands. Cloud attenuation is considered one such factor. Clouds are present during a largefraction of an average year and cloud attenuation, together with gaseous absorption, will determine thesystem performance under non-rainy conditions. A cloud attenuation model that has global applicabilityis presented in this paper. The model is based on average properties of four cloud types and their occur-rence probabilities. The occurrence probabilities are derived from long-term observations of cloud covercarried out by several thousand meteorological stations throughout the world. Predictions made with themodel are compared with cloud attenuation data gathered using Ka-band beacon signals and radiometers.Copyright � 2001 John Wiley & Sons, Ltd.

KEY WORDS: cloud attenuation modelling; propagation impairments

1. INTRODUCTION

Propagation impairments produced by the troposphere are a limiting factor for the e!ective useof the frequency bands above about 10 GHz for satellite communication applications. In general,propagation impairments of the troposphere increase in severity with the increase of frequency.The rapid growth of satellite services using frequency bands above 10 GHz has highlighted a needfor estimating propagation factors that are normally considered benign or negligible at the lowerfrequency bands. Rain attenuation is considered the dominant factor in these frequency bands.However, many projected Ka- and V-band services will use very small terminals and, for these,rain e!ects may only form a relatively small part of the total propagation link margin. It istherefore necessary to identify and predict the impact of every signi"cant attenuating e!ect alongany given path. Cloud attenuation is one of the components that need to be characterized for lowavailability satellite links.Although several models are available for the prediction of cloud attenuation [3}5], an

evaluation of these models by the authors revealed that they are either poor estimators or theinput data required are di$cult to obtain for a general purpose application. The best of these

models, due to Salonen [5], su!ers from a cosecant elevation angle dependence, which rendersits use on low elevation angle paths questionable. In view of this, a cloud attenuation modelbased on available cloud cover data and the average properties of di!erent cloud typeswas developed. A brief description of the model was presented in Reference [1]. Thispaper expands on the presentation given in Reference [1] and attempts to improve itsperformance under low elevation angle conditions. Also, model predictions are comparedwith some of the recently available measured cloud attenuation data at Ka-band and withthe Salonen model.The model is developed on the basis of long-term observation of cloud cover data and average

properties of several types of clouds that contain liquid water. A cloud cover atlas based on dailyvisual observations of di!erent cloud types can be found in Reference [2]. A 10 year observationperiod has been used in constructing the atlas. Four cloud types were selected for the develop-ment of the cloud attenuation model: cumulus, cumulonimbus, stratus, and nimbostratus.Average properties of these clouds and their occurrence probabilities are used in characterizingthe cloud attenuation. Log-normal probability distribution is used to describe the attenuationdistribution.A description of the cloud cover data used for the model development is presented in the

next section. Characteristics of the cloud types included in the model are given inSection 3. Details of the cloud attenuation model are presented in Section 4. A comparison ofthe model predictions with some of the cloud attenuation data measured at Ka-band is givenin Section 5.

2. CLOUD COVER DATA

Routine weather observations that include cloud cover estimations are being carried outthroughout the world under the auspices of World Meteorological Organization (WMO).Observations pertaining to clouds include:

� present weather (clear, fog, rain, snow, thunderstorm, etc.),� total cloud cover (in units of 1/8),� low cloud type and amount,� low cloud height,� middle cloud type,� high cloud type.

Nine di!erent cloud types are de"ned for each of the three cloud heights: low, middle and high.More than 4000 stations scattered throughout the world make these observations on a dailybasis.The observations are made on the ground by visual means at 6 h intervals (0, 6, 12, and

18 GMT). Approximately, three-quarters of the stations also report at 3, 9, 15, and 21 GMT.Taken over a long observation period (more than 10 years), this data are thought to providereliable information on cloud cover and cloud-type distributions.Based on these observations, maps of global distribution of cloud cover and cloud types have

been prepared [2]. Although the data sampling, in both time and space, is rather coarse,averaging over a long observation period can reduce a large portion of the sampling uncertainty.The atlas presents the average fractional coverage and the average cloud amount for six di!erent

336 A. DISSANAYAKE, J. ALLNUTT AND F. HAIDARA

Copyright � 2001 John Wiley & Sons, Ltd. Int. J. Satell. Commun. 2001; 19:335}345

cloud types, as well as the average total cloud cover. The di!erent cloud types are:

� Cumulus,� Cumulonimbus,� Stratus and stratocumulus,� Nimbostratus,� Altostratus and altocumulus,� Cirrus.

Maps given in the cloud atlas have been prepared for each of four seasons at 53�53 latitude}longitude resolution (to keep the cell areas almost constant, for latitudes greater than 503a longitude grid resolution of 103 has been used). Although it is accepted that in many regions thecloud climatology varies on spatial scales smaller than 53, for the present purpose, this resolutionappears to be adequate.Three types of statistics are presented under each cloud classi"cation: the amount when

present, the frequency of occurrence, and the average cloud amount, all as percentages. Theaverage cloud amount is the product of frequency of occurrence and the amount when present. Inaddition, the average total cloud cover is also used in the development of the model. For eachcloud-type annual average amounts were derived from the seasonal data.In order to develop a cloud attenuation model, the cloud cover data need to be combined with

other average parameters of individual cloud types. These parameters are: water content,temperature, vertical extent and horizontal extent. These properties as well as electromagneticabsorption properties of clouds are considered in the following section.

3. CHARACTERISTICS OF DIFFERENT CLOUDS

Clouds are composed of either water droplets or ice crystals. Ice clouds, by virtue of the lowdielectric constant of ice and the small size of the constituent particles, are not expected to causeappreciable attenuation to radiowaves in the frequency range below about 50 GHz. Therefore,cirrus-type clouds, which are mainly composed of ice crystals, are not considered for attenuationmodelling. Absorption from water clouds depends on the cloud water content, the temperature,and the path length through the cloud. Average values of these parameters and the way theydetermine absorption levels are discussed below.Water clouds exist in di!erent shapes and sizes. Table I gives cloud models representative of

mid-latitude conditions [6]. Cloud parameters are highly variable and the values given in thetable can be considered average.The maximum diameter of the water droplets found in these clouds is of the order of 50 �m.

The Rayleigh approximation [7] is adequate for the calculation of absorption caused by suchparticles at the frequencies of interest. The absorption cross-section of a water drop of radius,a (m), is given by

Q�(a)"

��a�

8�Im�

!(�!1)

(�#2) � (m�) (1)

where � is the wavelength (m), � the complex relative dielectric constant of water and Im[x] theimaginary part of x.

CLOUD ATTENUATION MODELLING 337


Table I. Average properties of di!erent cloud types.

Average water content Average vertical heightCloud type (g/m�) (km)

Cumulus 1.0 2.0Cumulonimbus 2.5 2.0Stratus 0.15 0.5Stratocumulus 0.3 0.5Nimbostratus 0.6 0.8Altostratus 0.4 0.5

The attenuation su!ered by a radiowave having a free space wavelength of �, when passingthrough a layer of cloud is

��"0.4343�10� [�Q

�(a)N(a)] l

�(dB) (2)

where l�is the path length through the cloud (km) andN(a) the number density of particles (m��),

having radius a. In Equation (2), a summation of absorption cross-sections of all particles presentin a unit volume is taken.Since the absorption cross-section of a single cloud particle is proportional to its volume, the

total attenuation through the cloud can be related to the cloud water content via

��"0.4343 �

3�w50��

�� Im�

!(�!1)

(�#2) �l� (dB) (3)

where w is the cloud water content (g/m�) and ��the density of water (g/cm�).

The density of water, ��, is a function of temperature, but over the temperature range of cloud

droplets (!20 to #103C) its value can be approximately taken as 1.0. The dielectric constant ofwater is also a function of temperature. A model for calculating temperature-dependent complexpermittivity of water across the frequency range 1}1000 GHz which uses a double-Debyerelaxation approximation is given in Reference [8]. This model is based on non-linear, multi-parameter, least-squares "tting of experimental data to the dielectric properties of water. The realand the imaginary parts of the complex refractivity are given by

��"��#

��!�

�1#( f/ f

�)�

#

��!�

�1#( f/f

)�

(4)

��"f (�

�!�

�)

f�[1#( f/f

�)�]

#

f (��!�

�)

f[1#( f/ f

)�]

(5)

where f is the frequency (GHz), �� the real part of complex permittivity, � and �� the imaginary partof complex permittivity.

��"77.66#103.3(�!1), �

�"5.48, �

�"3.51

f�"20.09!142.0(�!1)#294.0(�!1)� (GHz)



f"590!1500(�!1) (GHz)

�"300/¹; ¹ is temperature in K

The above relations are valid for frequencies up to 1000 GHz over a temperature range from 10to 303C.Equation (3) assumes a uniform distribution of cloud liquid water density and constant

temperature along the path, neither of which is true in practice. Clouds are not homogeneousmasses of air containing uniformly distributed droplets of water; the liquid water content can varywidely with location in a single cloud. The speci"c attenuation increases with the decrease oftemperature. In order to incorporate temperature dependence on cloud attenuation modelling,statistics on cloud base height and temperature lapse rate are required. Considering the some-what benign temperature dependency of speci"c attenuation, and not to make the predictionmodel too complex, an average cloud temperature of 03C will be used.The e!ective radio path length through a cloud is not easy to de"ne. The distribution of

liquid water content is not uniform in both horizontal and vertical directions. Cumulusclouds have a horizontal extent similar to their vertical extents. Stratus-type clouds arewide spread in the horizontal and normally patchy in nature; their vertical dimensionsare much smaller than the horizontal extent. To a "rst approximation, cumulus cloudsmay be modelled as vertical cylinders with the diameter equal to the height. Cumulonimbusclouds normally have larger vertical extents compared to their horizontal dimensions.Stratus cloud types have a much larger horizontal extent compared to the vertical extent.They may spread over tens of kilometers in the horizontal compared to several hundreds ofmeters in the vertical.The model clouds are assumed to be homogeneous in terms of the distribution of the liquid

water content. The elevation angle dependence of attenuation is determined using the cosecantlaw applied to the assumed cloud dimensions. The cloud attenuationmodel, together with furtherconsiderations that went into its development, is presented in the next subsection.

4. CLOUD ATTENUATION MODEL

A cloud attenuation model based on available cloud cover data and the average properties of fourdi!erent cloud types is presented here. The model is derived using the average cloud propertiesmentioned in the previous sections together with the assumption that the statistical distribut-ion of cloud attenuation has the log-normal form. This assumption is thought to bereasonable considering that most measured slant-path attenuation data conform to the log-normal distribution.Four cloud types are used for the model and their average properties are given in Table II. The

cloud parameters selected are a compromise between the values in Table I and the descriptionsgiven in the cloud atlas. Upper level cloud classi"cations given in the cloud atlas, alto stratus andalto cumulus, which might possibly produce noticeable attenuation were not considered here forthe following reasons: the lower cloud types considered for the modelling purpose alreadyaccount for the presence of some amount of upper cloud layers, and the model makes someallowance for the presence of other cloud types producing lower attenuation by taking account ofthe total cloud cover.



Table II. Average properties of the four cloud types used in attenuation model.

Vertical extent (km) Horizontal extent (km)Cloud type ¸

�¸�

Water content (g/m�)

Cumulonimbus 3.0 4.0 1.0Cumulus 2.0 3.0 0.6Nimbostratus 0.8 10.0 1.0Stratus 0.6 10.0 0.4

Attenuation distribution along the zenith at a given location is obtained from the total cloudcover, individual cloud cover amount for the four cloud types, their vertical dimensions, and thespeci"c attenuations. The four cloud types together with the total cloud cover provide "ve pointson the distribution curve, and the best-"t curve is found from these points. An adjustment to theattenuation of each cloud type must be made to account for the fact that the cloud cover amountsin the cloud atlas applies to the complete sky and the cloud attenuation is calculated along a givendirection. The required adjustment factor was determined using cloud attenuation data inReference [9] and found to be approximately equal to 0.52. Before carrying out the log-normal "tthe four attenuation values must be multiplied by the above factor. The log-normal law used forthe cloud attenuation distribution has the following form:

P (A'a)"P�2

erfc �ln a!ln

�2 � (6)

where P is probability of a (attenuation in dB) not exceeding A, P�is probability of a being

present, is the mean value of a and is the standard deviation of a.For other directions, the e!ective path length through each cloud must be calculated. For

relatively high elevation angles the path length is assumed to be contained within the cylindricalvolume de"ned by the dimensions given in Table II. For low elevation angles, the clouddimensions are adjusted to account for the possibility of encountering multiple cloud structuresalong the path. This is carried out using a horizontal path reduction factor similar to the one usedfor rain attenuation modelling [1]. The calculation method is illustrated in Figure 1. Using thehorizontal projection of the path length, ¸

�, through an in"nite cloud layer, the horizontal

projection of the e!ective path length, ¸�, is determined as follows:

¸�

"¸�

for ¸�)¸

�(7a)

For ¸�'¸

�

¸�

"¸�#

(¸�!¸

�)

1#0.78�¸�!0.38[1!exp(!2¸

�)]

(7b)

Next, calculate the path length through each cloud type, ¸, using the geometry shown in

Figure 1:

¸"¸

�/sin � �*�

¸"¸

�/cos � �(�



Figure 1. Geometry of propagation through a cloud.

The model assumes that a single cloud-type accounts for di!erent parts of the distribution. Ingeneral, this is not true since cloud properties vary widely and the same attenuation level can beexpected frommore than one cloud type. However, available data do not permit the developmentof a model that accounts for the attenuation distribution of each individual cloud type.Attenuation caused by clouds is only a part of the total attenuation su!ered by a radiowave.

Therefore, the start and end points of the cloud attenuation distribution need to be de"ned. Onone side, the end point is de"ned by the clear-sky conditions, and at the other extreme, presence ofrain de"nes the cut-o! point. Average total cloud cover is used to set the lower cut-o! point of theattenuation distribution. Below this point the path attenuation is determined by absorption dueto oxygen and water vapour. The upper end of the cloud distribution can be considered as thepoint where the rain and melting layer attenuation merge with cloud attenuation. This is nota well-de"ned boundary and will depend on both the geographic location and the elevation angle.

5. MODEL EVALUATION

Figures 2 and 3 show cloud attenuation distributions for New York, NY, and Singapore,respectively, calculated at 20 GHz for two elevation angles (80 and 203). Pertinent cloud amountsare shown in Table III. In general, the data points follow the log-normal form reasonably well.However, under certain conditions, the transition from one cloud type to another may not besmooth due to large deviations in the path length when going from cumulus to stratus types.These plots illustrate the location dependence of the predicted attenuation. In Singapore, the

occurrence of cumulus and cumulonimbus clouds is much more frequent than in New York. Athigh elevation angles attenuation from cumulus clouds dominates over stratiform clouds and theopposite occurs at lower elevations. At lower elevation angles the rain attenuation distributionextends toward higher percentage times and the cloud attenuation distribution needs to betruncated accordingly. The 203 elevation curves shown in Figures 2 and 3 serve only to illustratethe trend in the distribution function.The model can be validated only by comparing withmeasured data. In general, not many cloud

attenuation observations are available for comparison. Radiometers are the most reliable instru-ments to measure cloud attenuation. However, quality of the measured data depends on anumber of factors including instrument stability, calibration interval and removal of groundcontributions. These factors are especially important when measuring low levels of attenuation



Figure 2. Cloud attenuation distribution at 20 GHz; New York, NY.

Figure 3. Cloud attenuation distribution at 20 GHz; Singapore.

such as those produced by clouds. The other common technique of measuring cloud attenuationis to use a satellite beacon signal. Attenuation derived from beacon receivers has an inherentinaccuracy close to $0.5 dB and therefore, cloud attenuation data obtained with beaconreceivers alone may not be suitable for validating attenuation models.Cloud attenuation measurements made in Darmstadt at 20 and 30 GHz frequencies are

reported in Reference [9]. Figure 4 shows the comparison of the model against the measureddata. The measurements were made with radiometers at an elevation angle of 283. The measure-ments pertain only to non-rainy conditions, and the cloud contribution to the total attenuationhas been extracted allowing an easy comparison with the model predictions. It is seen that at bothfrequencies reasonably good agreement exists between the model and the measurements.A second set of measured cloud attenuation data is available for Clarksburg, MD [10]. The

measurements have been made with the 20 and 27.5 GHz beacon signals from the AdvancedCommunication Technology (ACTS) satellite. The measurement system included radiometers atthe same frequencies to help eliminate the measurement uncertainty associated with beacon



Table III. Percentage cloud amounts for several selected sites; Cb*cumulo-nimbus, C*cumulus, Ns*nimbostratus, St*stratus.

Station Cb C Ns St Total

New York, NY 2.3 3.0 13.5 34.5 70.5Singapore 9.5 8.8 5.5 17.0 76.0Darmstadt, Germany 2.0 4.0 12.0 37.3 63.3Clarksburg, MD 0.8 4.5 7.3 23.5 57.3

Figure 4. Measured and predicted cloud attenuation at Darmstadt, Germany: (a) 20 GHz, (b) 30 GHz.

derived path attenuation. Figure 5 shows the comparison of both the DAH model (this paper)and Salonen's model prediction [5] with the measured data. It is seen that both of the modelstends to underpredict the measurements, especially for percentage times below about 10 per cent;above 10 per cent, while both models provide reasonable agreement with the measurements, theDAH model is generally more accurate.

6. CONCLUSIONS

The above comparisons suggest that the proposed model is capable of reproducing some of themeasured results and even when the prediction fails to agree with the measured, the discrepancy isnot overly large. This is encouraging considering the fact that the model was solely based oncloud observations and gross features of a few cloud types. None of the cloud parameters used inthe model were tuned to obtain a better agreement with the measured data. However, the paucityof measured data does not allow either a complete validation of the model or applying anyre"nements to the model. Performance of the model at elevation angles below about 103 mayneed careful examination. At lower elevation angles, not only are the attenuation levels higher,but the inhomogeneity of clouds in the horizontal direction plays an important role. Both thesefactors may contribute to signi"cant prediction errors. However, in the absence of valid data tosubstantiate or refute the model in a meaningful manner, good performance demonstrated athigher elevation angles is expected to hold for lower elevation angles as well.



Figure 5. (a) Measured and predicted cloud attenuation at Clarksburg, MD at a frequency of (a) 20.2 GHz,(b) 27.5 GHz. The predictions are based upon the DAH model, developed by the authors of this paper, and

the model due to Dr Salonen that forms part of the ITU Recommendation.

ACKNOWLEDGEMENTS

The work presented here was carried out under INTELSAT contract INTEL-869. Any views expressed inthis paper are not necessarily those of COMSAT, INTELSAT, or the Virginia Polytechnic Institute andState University. This paper is a revised version of a submission to the COST 255 Management meeting inOctober 1998.

REFERENCES

1. Dissanayake AW, Allnutt JE, Haidara F. A prediction model that combines rain attenuation and other propagationimpairments along earth}satellite paths. IEEE ¹ransactions on Antennas and Propagations 1997; 45(10):1546}1558.

2. Warren SG et al. Global distribution of total cloud cover and cloud type amounts over land. National Center forAtmospheric Research (NCAR) Technical Notes, NCAR/¹N-273, October 1986.

3. Altshuler EA, Marr RA. Cloud attenuation at millimeter wavelengths. IEEE ¹ransactions on Antennas and Propaga-tion 1989; 37:1473}1479.



4. Dintelmann F, Ortgies G. A semi-empirical model for cloud attenuation prediction. Electronic ¸etters 1989;25:1487}1488.

5. Salonen E. Prediction models of atmospheric gases and clouds for slant path attenuation. Olympus ;tilizationConference, Sevilla, 1993; 615}622.

6. Stephens GL. Radiation pro"les in extended water clouds. Journal of the Atmospheric Sciences 1978; 35:2111}2122.7. Van de Hulst HC. ¸ight Scattering by Small Particles. Dover: New York, 1957.8. Manabe T, Liebe HJ, Hu!ord GA. Complex permittivity of water between 0 and 30 THz. 12th International

Conference on Infrared and Millimeter=aves, 1987; 229}230.9. Ortgies G, Rucker F, Dintelmann F. Statistics of clear-air attenuation on satellite links at 20 and 30 GHz. Electronic

¸etters 1990; 26:358}360.10. Golshan N (ed.). Proceedings of Ninth AC¹S Propagation =orkshop, Jet Propulsion Labs Publication 97-3,

November 1996.

AUTHORS' BIOGRAPHIES

Asoka Dissanayake received the BSc in Electronic Engineering from the University ofSri Lanka, Moratuwa, in 1972, and in 1975 received the MSc in Digital Electronicsfrom Loughborough University in the U.K. He received the PhD from the Universityof Bradford, U.K., in 1978.During 1981}1987 he worked as a systems engineer at the European Space and

Technology Center (ESTEC) in Noordwijk, Netherlands. In 1989 he joined INTEL-SAT in Washington D.C. and spent two years as an RF engineer. He joined COM-SAT laboratories in 1991 where he is currently the manager of the MicrowaveSystems Department. His areas of interest include radiowave propagation and wire-less communication.

Jeremy Allnutt earned his BSc and PhD in Electrical Engineering from the University of Salford, U.K., in1966 and 1970, respectively. From 1970 to 1977 he was at the Appleton Laboratory in Slough, U.K., wherehe ran propagation experiments with the US satellite ATS-6 and the European satellites SIRIO and OTS. In1977 he moved to BNR, nowNortel, in Ottawa, Canada, and worked on satellite and rural communicationsprojects before joining the International Telecommunications Satellite Organization (INTELSAT) inWashington, DC, U.S.A., in 1979. Jeremy Allnutt spent 15 years at INTELSAT in various departments.During this period he ran experimental programs in Europe, Asia, Africa, North and South America,Australia, and New Zealand, "nishing as Chief, Communications Research Section. Jeremy Allnutt spentone year as Professor of Telecommunications Systems at the University of York, U.K., and then joined theNorthern Virginia Center of Virginia Tech in 1986, where he later ran the masters program in ECE as well asbeing on the team that designed and set up the Masters in Information Technology program. In August of2000 he moved to George Mason University with dual appointments: Director of the new Masters inTelecommunications program and professor in the ECE department. Jeremy Allnutt has published 100papers in conferences and journals and written one book, most in his special "eld: radiowave propagation.He is a Fellow of the UK IEE and a Senior Member of the US IEEE.

Fatim Haidara received the Diplome D'IngeH nieur from l'Ecole SpeH ciale deMeH chanique et D'ElectriciteH , Paris, France, in 1985 and received the MS and PhDdegrees in Electrical Engineering from the Virginia Polytechnic Institute and StateUniversity in 1988 and 1993 respectively.From 1988 to 1993 she was a research assistant at the Virginia Polytechnic Institute

and State University where she was involved in the OLYMPUS propagation experi-ment. In 1993, she joined INTELSAT as an Assignee in the R&D department workingwith spacecraft antenna R&D and is now a Senior Engineer responsible for radiowavepropagation and ground terminals for broadband satellite system.



cloud attenuation modelling for shf and ehf applications

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