and Animesh Maitra

Bio: and Animesh Maitra is an academic researcher. The author has contributed to research in topics: Attenuation & Ku band. The author has an hindex of 1, co-authored 1 publications receiving 34 citations.

Improving Rain Attenuation Estimation: Modelling of Effective Path Length Using Ku-Band Measurements at a Tropical Location

Abstract: Rain attenuation is an important aspect of signal propagation above 10GHz frequency. The attenuation time series generation from point rain rate measurement is crucial due to unavailability of actual signal measurements. In this paper, a simple and realistic approach has been demonstrated for better estimation of rain attenuation using Ku-band signal propagation data and ground rain rate measurements at Kolkata, India. The ITU-R model of rain attenuation has been modifled by incorporating an efiective slant path model. The efiective slant path has been estimated and modelled in terms of a power-law relationship of rain rate data of 2007{2008. The methodology has been validated with the measured data of 2006. Comparison with ITU-R and SAM clearly demonstrates the improved predictability of the proposed model at the present tropical location.

Rainfall measurement from the opportunistic use of an Earth–space link in the Ku band

Abstract: . The present study deals with the development of a low-cost microwave device devoted to the measurement of average rain rates observed along Earth–satellite links, the latter being characterized by a tropospheric path length of a few kilometres. The ground-based power measurements, which are made using the Ku-band television transmissions from several different geostationary satellites, are based on the principle that the atmospheric attenuation produced by rain encountered along each transmission path can be used to determine the path-averaged rain rate. This kind of device could be very useful in hilly areas where radar data are not available or in urban areas where such devices could be directly placed in homes by using residential TV antenna. The major difficulty encountered with this technique is that of retrieving rainfall characteristics in the presence of many other causes of received signal fluctuation, produced by atmospheric scintillation, variations in atmospheric composition (water vapour concentration, cloud water content) or satellite transmission parameters (variations in emitted power, satellite pointing). In order to conduct a feasibility study with such a device, a measurement campaign was carried out over a period of five months close to Paris. The present paper proposes an algorithm based on an artificial neural network, used to identify dry and rainy periods and to model received signal variability resulting from effects not related to rain. When the altitude of the rain layer is taken into account, the rain attenuation can be inverted to obtain the path-averaged rain rate. The rainfall rates obtained from this process are compared with co-located rain gauges and radar measurements taken throughout the full duration of the campaign, and the most significant rainfall events are analysed.

Rainfall measurement from opportunistic use of Earth-space link in Ku band

Abstract: The present study deals with the development of a low cost microwave device devoted to measure average rain rate observed along earth - satellite links. The principle is to use rain atmospheric attenuation along Earth - space links in Ku-band to deduce the path averaged rain rate. These links are characterized by a path length of a few km through the troposphere. Ground based power measurements are carried out by receiving TV channels from different geostationary satellites in Ku-band. The major difficulty in this study is to retrieve rain characteristics among many fluctuations of the received signal which are due to atmospheric scintillations, changes in the composition of the atmosphere (water vapour concentration, cloud water content) or satellite features (variation of the emitted power, satellite motions). In order to perform a feasibility study of such a device, a measurement campaign has been performed for five months near Paris. This paper proposes an algorithm based on an artificial neural network to identify drought and rainy periods and to suppress the variability of the received signal due to no-rain effects. Taking into account the height of the rain layer, rain attenuation is then inverted to obtain path averaged rain rate. Obtained rainfall rates are compared with co-located rain gauges and radar measurements on the whole experiment period, then the most significant rainy events are analyzed.