Dept. of ECE, K L University, Vaddeswaram, Guntur, Andhra Pradesh, India. 3. Consultant, NOTACHI EleKtronic Technologies, Andhra Pradesh, India 1

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1 Volume 115 No. 7 17, ISSN: (printed version); ISSN: (on-line version) url: ESTIMATION OF REFLECTIVITY AND CLOUD ATTENUATION IN TROPICAL REGIONS ijpam.eu Govardhani.Immadi 1, M.Venkata Narayana 2, Sarat K Kotamraju 3, K.C.H.Kavya 4, I.Amulya Sree 5, K.Chanukya Manoj Kumar 6, P.P.L.Sarvani 7, G.Sai Karthik 8 1,2,4,5,6,7,8 Dept. of ECE, K L University, Vaddeswaram, Guntur, Andhra Pradesh, India. 3 Consultant, NOTACHI EleKtronic Technologies, Andhra Pradesh, India 1 govardhanee_ec@kluniversity.in Abstract: Satellite communication systems are developed for long distance communication. Satellite communication use Ka band which operate at 26.5 GHz to GHz frequency and has higher bandwidth communication. It operates with a smaller satellite dish which is cheaper and easier for installation. There may be Propagation Impairments while transmitting in satellite communication. Rain attenuation is the propagation impairment which occurs above GHz frequency. Estimation of rain attenuation includes rain rate calculation. In this paper, we concentrate on the calculation of rain rate (mm/hr) and reflectivity (dbz) using Disdrometer. And we also calculate Cloud attenuation using Micro rain radar (MRR) data. Estimation of rain rate can be done using reflectivity through empirical relations like,,,, l etc. Here we mainly focus on and relations as they are most suitable for our tropical region and this calculation is done for the event at K L University, Vijayawada on and the cloud attenuation calculation is done for the event on Keywords: Rain rate, Reflectivity, Cloud attenuation.. 1. Introduction Satellite communication system acts as a channel for transmitter and receiver at different locations. There are different types of bands of frequencies L band, C band, K band, Ku band, Ka band which fulfill the requirements of satellite communication. Propagation is defined as transfer of signal from one place to another place without any losses. Impairment means mismatch between transmitter and receiver. So totally it is defined as propagation mismatch between transmitter and receiver evolving some losses in transmission. Here we are transmitting the signal using Ka-band, because it is the highest frequency band ranging from 26.4 GHz- GHz. The Ka-band is part of the K-band of the microwave frequencies of the electromagnetic spectrum. Propagation Impairment involves Rain attenuation, Cloud attenuation and Scintillation effects. The factors that cause attenuation include signal attenuation, fading, increase in sky noise temperature, crosstalk, variation in signal angle of arrival, inter-system interference. We are mainly focusing on the impairments which are caused by rain. Most of the long distance propagation failed due to impairments interconnected with rain. Rain on a satellite radio path causes fading [8]. Rain causes depletion in electromagnetic waves through the process of absorption and scattering. Rain also depolarizes satellite signals, converting the energy from one polarization to another, and causing interference between the channels [8]. The severity of rain depletion and depolarization depends on how hard it rains. The attenuation caused by the rain depends on parameters such as Raindrop Size, Rain temperature, Velocity of Drop, Polarization, Rain rate, Drop orientation, Frequency of Transmission [8]. To know the attenuation we require rain rate. In this paper, we are making use of device called as disdrometer which can calculate different parameters like rain intensity (rain rate), precipitation since start, radar reflectivity, different types of weather codes, MOR visibility, signal amplitude of laser band, heating current, sensor voltage, number of detected particles, temperature in sensor, spectrum with the help of ASDO software. Out of all the available parameters we use the Reflectivity and Rain Rate with respect to time. Whereas, the Micro Rain Radar (MRR) data uses METEK software and the different parameters that are available from the MRR are Rainrate, Reflectivity, Liquid Water Content, Height, Spectral Reflectivities, Drop size, Path attenuation, Fall Velocity [11]. Here we consider the Liquid water content for the calculation of specific attenuation within the cloud. 2. Rain Rate (R) - Reflectivity (Z) In this Rainrate-Reflectivity, we consider the disdrometer data on , where this disdrometer setup is available at the C block, 2nd floor of our KLUniversity. Disdrometer gives different parameters, out of which we make use of Rainrate and the reflectivity. This Rainrate can also be obtained not only from disdrometer but also from micro rain radar (MRR), beacon receiver etc. There are several types of disdrometers like Jossvoldvogel Disdrometer, 2-D video Disdrometer, OTT Parsivel Disdrometer. Here we make use of OTT Parsivel Disdrometer. It is a laser 471

2 based optical disdrometer which is used for the measurement of liquid and solid precipitation [1]. General relationship between Rainrate and the Reflectivity is, are frequency dependent values and they differ from one region to another region. Here we predict different values for, i.e., different empirical relations are considered and the best empirical relation is considered for the calculation of rain attenuation. Different empirical relations in the sense, the basic formula is same but, values change in each of the empirical relation. If we go by the formulas for the calculation of, values ; it takes more time for the calculation of the rain attenuation. So, in our paper, we compare different empirical relations and predict the best suitable relation for our tropical region. Table1. Different Z-R relations S.no Name Empirical relation Fundacao et al 7. Jones From the above table, different empirical relationships for the Reflectivity - Rainrate in various regions are shown [5]. These relations are compared using Matlab. The Rainrate is obtained from the disdrometer data and we apply these empirical relations and then compare these relations with the recorded Reflectivity. First, Reflectivity and Rainrate plots with respect to time using the complete day disdrometer data are shown in fig.1 and fig.2 respectively on Fig.3. shows the plot for Rainrate and the Reflectivity and reflectivity is increasing linearly with the rainrate. From eq(1), (M-P) and relations are calculated and are shown in fig.4,5,6. Whenever there isn't any rain, reflectivity content is absent. When both the M-P and relations are considered, is most suitable between them. Time Vs Reflectivity disdrometer Time in sec Figure 1.Time vs Reflectivity on Time Vs Rainrate disdrometer 1 1 Time in sec Figure 2.Time vs Rainrate on Rainrate Vs Reflectivity disdrometer - Figure 3.Rainrate vs Reflectivity on Time Vs Reflectivity for empirical relations recorded z m-p battens time in sec Figure 4.Time vs Reflectivity plot for M-P & 472

3 1 9 Time Vs Reflectivity for empirical relations recorded z m-p battens time in sec Figure 5.Time vs Reflectivity plot for M-P & 9 Rainrate Vs Reflectivity for empirical relations recorded z m-p battens Figure 6.Rainrate vs Reflectivity plot for M-P & Recorded z time in sec Figure 7.Time vs Reflectivity plot comparison for different empirical relations with the recorded Z. 1 9 Recorded z Figure 8.Rainrate vs Reflectivity plot comparison for different empirical relations with the recorded Z. 1 1 Time in sec Figure 9.Time vs Reflectivity plot for different empirical relations Figure.Rainrate vs Reflectivity plot for different empirical relations Different empirical relations are compared with the recorded reflectivity. And the empirical relation which is nearer among all the relations is treated as the best suitable one for our tropical region. Here fig.7. Shows the Time vs Reflectivity plot comparison for different empirical relations with the recorded Z and the fig.8. shows the Rainrate vs Reflectivity plot comparison for different empirical relations with the recorded Z. Fig.9 & Fig. are the plots for different empirical relations comparison among themselves. The above plots are all done for the event on Cloud Attenuation In the transmission of electromagnetic signals, cloud attenuation is the attenuation because of the scattering and absorption by water or ice particles in the cloud. Factors responsible for cloud attenuation are: 1.size of the particle, density and unsteady movement of the cloud. 2.length of transmission path in the clouds. In cloud attenuation, we consider the Micro Rain Radar (MRR) data on This set up is available at library block, 7th floor, KLUniversity located at 29.8m above the sea level with a latitude of N and a longitude of. It is placed with an elevation angle of [8]. For the calculation of specific attenuation within cloud, the following formula can be used where = Specific attenuation 473

4 = Specific attenuation co-efficient = Liquid water content Liquid water content is directly available from the Micro Rain Radar (MRR) data and we can have this data from the MRR. But Specific attenuation coefficient is not available directly. So we need to calculate the value of in order to get the specific attenuation, within the cloud. For the calculation of specific attenuation co-efficient, the following steps are required: Total attenuation in db Cloud attenuation Time percentage exceedence Figure12.Cloud attenuation in terms of percentage exceedence. As shown in fig.11., we considered the complete day data of MRR liquid water content on and calculated the specific attenuation. The plot shows that with the increase in the liquid water content, specific attenuation also increases. We also calculated the total attenuation for different total columnar content of liquid water. This plot is shown in fig.12. As percentage exceedance increases, there is a decrease in the total attenuation content with in the cloud. Temperatures as a function of frequency Here T : Temperature (in Kelvin (K)) and f : Operating frequency (GHz) Cloud attenuation (A) is calculated using : Where = Total columnar content of liquid water = Elevation angle = specific attenuation coefficient can be considered from the above specific attenuation calculation and the Total columnar content of liquid water is considered from the [2] for different percentage exceedence year probabilities. Here, we considered exceedence probability values and they are as follows: 4, 3, 3, 2, 1,.2 (according to our region). These total columnar content of liquid water values change from region to region. specific attenuation in db/km LWC Vs Specific attenuation liquid water content in g/m 3 Figure 11.Liquid water content vs Specific attenuation on S p e c i f i c a t t e n u a t i o n c o e f f i c i e n t i n ( d B / ( K m ) / ( g / m 3 ) ) -1 1 Frequency in GHz -8 C C C C Figure 13.Frequeny vs Specific attenuation in terms of temperature. For different Temperatures (K) as a function of Frequency (GHz), we have calculated the specific attenuation coefficients. This plot is shown in fig.13. With the increase in frequency, specific attenuation coefficient also increases as shown in the figure. 4. Conclusion Out of all the empirical formulas is best suitable for our tropical region. As battens plot reflectivity is nearer to the actual recorded disdrometer reflectivity data. Marshall Parmer is next suitable to our tropical region. With the increase in frequency with respect to temperature, specific attenuation co-efficient within the cloud also increases. 5. Acknowledgments 474

5 The authors especially thank the support given from SERB, Department of Science and Technology (DST), Government of India through the funded project with F.No: EMR/15/. The authors also thank the management of KL University for supporting and encouraging this work by providing the facilities in Centre for Applied Research in Electromagnetics (CARE) of ECE. COMPUTER SCIENCE, vol. 92, pp: 4-417, August 16. References [1] Rain Rate-Radar Reflectivity Relationship for Drop Size Distribution and Rain Attenuation calculation of Ku Band Signals by Govardhani.Immadi. [2] Attenuation due to clouds and fog, Recommendation ITU-R P.8-4. [3] Battan, L.J., Radar observation of the atmosphere. The University of Chicago Press, Chicago, 324 pp. [4] Radar Measured Rain Attenuation with Proposed Z- R Relationship at a Tropical Location J. X. Yeo, Y. H. Lee, Senior member, IEEE, and J. T. Ong. [5] Investigation of Radio wave Propagation Impairment at Super High Frequency due to Rain in Akure by Oluwadare E. J, Tomiwa A. C. [6] Propagation Impairment on Ka-Band SATCOM Links in Tropical and Equatorial Regions by Harry E. Green. [7] Comparison of Ionospheric scintillation models with experimental data for satellite navigati -on applications. [8] Estimation of Rain Attenuation based on ITU-R Model in Guntur (A.P), India by M. Sridhar, K. Padma Raju and Ch. Srinivasa Rao. [9] Marshall, J.S. and Palmer, W.M., The distribution of raindrops with size. J. Meteorol., 5, [] Rain Attenuation Model Comparison and Validation Charles E. Mayer, Bradley E. Jaeger. [11] MRR physics basics and User manual. [12] T. Padmapriya and V.Saminadan, Handoff Decision for Multi-user Multiclass Traffic in MIMO- LTE-A Networks, 2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC-16) Elsevier - PROCEDIA OF 475

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