Marianna Biscarini

In this work, we present an enhanced Sun-tracking microwave radiometry approach for atmospheric and solar characterization at millimeter-wave frequencies, specifically at W-band. Sun-tracking radiometry is a ground-based technique that uses the Sun as a stable natural source to simultaneously estimate the Sun's brightness temperature and the atmospheric optical thickness. We propose a dynamic modeling of the Sun–Earth geometry to account for seasonal variations, achieving a 50% reduction in the standard deviation of the Sun brightness temperature compared to previous studies. For the first time, we quantify the impact of Sun brightness temperature estimation errors on the retrieval of atmospheric attenuation, addressing an open issue previously unassessed in the literature. The analysis relies on a long-term dataset collected over three years in Rome, NY (USA), representing one of the few ground-based databases capable of statistically characterizing atmospheric attenuation in all-weather conditions at K-, Ka-, V-, and W-bands. Results confirm that improvements in Sun brightness temperature estimation do not compromise the precision of atmospheric optical thickness retrievals, validating the robustness and reliability of the enhanced Sun-tracking technique. These findings offer new insights for microwave remote sensing and propagation studies at millimeter wavelengths, supporting next-generation satellite communication and remote sensing systems.

Adaptive fade mitigation techniques, such as smart gateway diversity (SGD), are essential in addressing atmospheric fading affecting the operation of high throughput and very high throughput satellite systems at Ka‐band and beyond. Specifically, the Q/V‐band, offering up to 5 GHz of available spectrum, is very attractive for future high data rate services, but it is especially susceptible to atmospheric attenuation, with rain posing a significant challenge. This paper presents a simulation model for the design and performance assessment of a Q/V‐band SATCOM (satellite communication) system. The model consists of two primary modules: (1) a physical model of the propagation channel, which integrates numerical weather predictions (NWPs) with a radiopropagation simulator and a temporal downscaling module; (2) a system simulator that operates SGD alongside traditional fade mitigation techniques, that is, uplink power control (ULPC) for the uplink and adaptive coding and modulation (ACM) for the downlink. The latency of SGD emerges as a critical parameter that constrains system performance (e.g., carrier to noise plus interference ratio and data rate) when compared to an ideal system capable of real‐time traffic switching. This study explores the impact of varying SGD latency times on the performance of a Q/V‐band SATCOM system, providing insights into system behavior under realistic conditions.

To minimize the impact of propagation impairments occurring at Ka-band and beyond, satellite communication (SatCom) systems operating at such high frequencies require the implementation of adaptive fade mitigation techniques (FMTs). As beacon measurements are rarely available (especially with the spatial distribution and the time resolution required by FMTs), time-series generators represent a key tool for the design stage of such systems. This article proposes a novel three-module model chain to generate a time series of rain attenuation. The first module is a numerical weather prediction (NWP) model that forecasts meteorological parameters across a continental grid with $9\times 9$ km and 15-min resolution in space and time, respectively. The NWP model outputs feed a radio propagation model, which produces time series of the attenuation components (gas, clouds, and rain). Finally, the rain attenuation is generated ex-novo at a 1-s sampling rate, by taking advantage of the multisite time-series synthesizer (MTS). Each step of the process is validated against measurements covering a 28-day period.

Ka-band experimental validations of a radiometeorological forecast model chain are reported. Measurements from BepiColombo ESA mission to Mercury are used. An optimization of the satellite link exploiting daily weather-forecast statistics of the atmospheric channel is implemented, which defines a linkbudget optimization technique. Different global-scale data are used for the model initialization, whilst three ensemble methods for the computation of the daily statistics are used. 54 statistics were tested over 42 sample passes characterized by different meteorological conditions. The results demonstrate superiority of the model chain with respect to other conventional techniques.

A mixed physically based/machine learning (ML) approach to measure tropospheric attenuation A in all-weather conditions by means of microwave radiometers (MWRs) is proposed. The key idea is to combine the advantages originating from the accurate radiometric A retrievals, provided by the well-established Cosmic background (CB) approach in clear-sky conditions, with the benefits coming from ML techniques. The latter aim at estimating A in rainy situations through a simplified approach able to overcome the issues posed by more complex techniques such as the standard solution of the radiative transfer equation or the Sun tracking (ST) microwave technique. To this aim, an artificial neural network (ANN) is devised to turn the antenna noise temperatures measured by a four-channel MWR (from Ka- to W-band) into tropospheric attenuation at the frequencies of the radiometric channels, namely 23.8, 31.4, 72.5, and 82.5 GHz. The network is properly trained and tested by taking advantage of the concurrent CB and ST measurements collected by the RpG radiometer deployed at Politecnico di Milano, Milan, Italy, under the ESA-funded WRAD project. The proposed approach to retrieve the tropospheric attenuation is intended to overcome the limits associated both with the ST technique (only measurements during the day, link elevation strictly bound to the Sun ecliptic) and to the CB one (unreliable measurements in rainy conditions).

In this work we propose a model for the Probability Density Function (PDF) of the elevation angle and the PDF of the attenuation conditioned to the elevation angle at W-band and in all-weather conditions. The proposed models are suitable to retrieve the PDF of the total attenuation at variable elevation for application to non-geostationary satellite communication links at W-band. The models, based on the Generalized Extreme Value (GEV) distribution, were developed and tested exploiting W-band Sun-tracking microwave radiometer measurements available from two independent measurements campaigns in Milano (Italy) and Rome (New York, USA), the only two datasets available in the world to date. The obtained results are satisfactory with a good agreement between models and measurements and highlighting a potential relationship between the GEV parameters and the local climatology.

The weather-forecast based Radio Meteorological Operation Planner (RMOP) model for the dynamical link-budget design in satellite communications at Ka-band is described and validated. For the first time, actual received Ka-band data from a deep-space satellite mission (Hayabusa2 mission from JAXA and supported by ESA) were available for operational tests. RMOP-predicted link-budget parameters were delivered in real-time before each scheduled satellite transmission period. After each transmission, received data measured by the ground stations were exploited for the RMOP validation. The data-volume actually received (transmitted and lost) by Hayabusa2 was compared with the one that would have been obtained if the transmission had been configured according to RMOP predictions. The results prove that RMOP model is capable of receiving more than 100% of extra data-volume with respect to the classical link-budget design techniques while keeping data losses under control. A specific approach usable in case of rainy events is described, too. These outstanding results will pave the way to an operative use of the weather-forecast based RMOP model for future satellite missions.

Ground-based microwave radiometer (MWR) observations of downwelling brightness temperature (T_B) are commonly used to estimate atmospheric attenuation at relative transparent channels for radio propagation and telecommunication purposes. The atmospheric attenuation is derived from T_B by inverting the radiative transfer equation with a priori knowledge of the mean radiating temperature (T_MR). T_MR is usually estimated by either time-variant site climatology (e.g., monthly average computed from atmospheric thermodynamical profiles) or condition-variant estimation from surface meteorological sensors. However, information on T_MR may also be extracted directly from MWR measurements at channels other than those used to estimate atmospheric attenuation. This paper proposes a novel approach to estimate T_MR in clear and cloudy sky from independent MWR profiler measurements. A linear regression algorithm is trained with a simulated dataset obtained by processing 1 year of radiosonde observations of atmospheric thermodynamic profiles. The algorithm is trained to estimate T_MR at K- and V–W-band frequencies (22–31 and 72–82 GHz, respectively) from independent MWR observations at the V band (54–58 GHz). The retrieval coefficients are then applied to a 1-year dataset of real V-band observations, and the estimated T_MR at the K and V–W band is compared with estimates from nearly colocated and simultaneous radiosondes. The proposed method provides T_MR estimates in better agreement with radiosondes than a traditional method, with 32 %–38 % improvement depending on frequency. This maps into an expected improvement in atmospheric attenuation of 10 %–20 % for K-band channels and ∼30 % for V–W-band channels.

The effective isotropic radiated power (EIRP) is a crucial parameter characterizing the transmitting antennas of a radio frequency satellite link. During the satellite commissioning phase, the requirements compliance of communication subsystems is tested. One of the required tests concerns the EIRP of the satellite transmitting antenna. Ground-based power measurements of the satellite-emitted signal are collected to measure EIRP, provided that an estimate of the atmospheric losses is available from independent ancillary measurements or model data. This article demonstrates the applicability of the so-called Langley method to infer EIRP and atmospheric attenuation simultaneously from ground-based power measurements, with no need for ancillary measurements. It is shown that the proposed method gives results similar to more traditional methods, without prior information on atmospheric attenuation. Thus, the proposed method can be applied to monitor EIRP throughout the satellite lifetime from ground-based power measurements alone.