Modeling High-Resolution 3-D Cloud Fields for Earth-Space Communication Systems

Abstract

HF CARRIERS are these days becoming very attractive to satellite-communication (SatCom) system operators because they offer wide bandwidth for the provision of advanced multimedia and interactive services. Above 10 GHz, the atmosphere has a definite impact on Earth-space links and, while rainfall always represents the prevalent impairment affecting radio waves [1], the contribution of suspended liquid water becomes significant at frequencies above 20 GHz and in low elevation links, not only in terms of specific attenuation, but also for the high occurrence probability of clouds (40%-80% of the yearly time in Europe). Even more, at optical wavelengths, which, in principle, would enable Earth-space communication systems with extremely high data rates, the presence of clouds along the path is the limiting factor because Manuscript

Medium earth orbit optical satellite communication networks: Ground terminals selection optimization based on the cloud‐free line‐of‐sight statistics

“…To synthesize ILWC ( L ) time series correlated in temporal and spatial domains, the statistical parameters of ln ( L ), ie, the mean value ( m ), the standard deviation ( σ ), and the probability of cloud coverage ( P clw ) for each place of interest (OGS), are required . In this analysis, the statistical parameters are derived from the ERA‐Interim database using the same procedure as in Luini and Capsoni . In ITU‐R Recommendation P, a methodology for the generation of temporally correlated point values of ILWC is reported.…”

“…The elements of C depend on distance ( d ) and are computed according to Equation . To this end, all elements of L total_field are temporally and spatially correlated.…”

“…Cloud data are products from satellites like the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) system, Meteosat, the NASA's Terra and Aqua satellites, and the Geostationary Operational Environmental Satellite (GOES) . In addition, in Luini and Capsoni, a methodology for the generation of 3‐D spatially correlated cloud fields in accordance with the expressions for spatial correlation of clouds and the vertical extent of clouds is reported. In Lyras et al, an analytical method for the derivation of single and joint CFLOS statistics using a multidimensional integrated liquid water content (ILWC) synthesizer is proposed.…”

“…These algorithms guarantee a minimum availability per month and take advantage of using stations in different hemisphere. In the last four previously described papers, the use of the well‐defined expression for the vertical extent of clouds reported in Luini and Capsoni is exploited. ILWC and cloud statistics can be derived from meteorological databases like ERA‐Interim, ERA‐40 of ECMWF.…”

Pushing for higher rates and efficiency in Satcom: The different perspectives within SatNExIV

“…The probability that a number of stations is under no clouds can be calculated through two different methods: either through data from ECMWF ERA-40 or ERA-Interim considering the total cloud coverage, or through the definition of the joint probability that the integrated Liquid water content (L) is above 0 mm. The spatial correlation coefficient proposed in [5] will be used. For the channel model of optical turbulence, SDEs will also be employed and plane, spherical and Gaussian beam waves will be considered [6], [7].…”