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Radiometeorological Forecast Model: a New Tool for Deep-Space Link Budget Optimization at Ka-band
... RadioMe-tOP is currently ingested in the ESA weather-forecast-based Ka-band operation system of the BepiColombo mission [11]. The satellite is in its cruise phase to Mercury and RadioMetOP is under validation as the first weather-forecast tool for deepspace transmissions at Ka-band [12], [13]. ...
... The final outputs of the RadioMetOP chain are the optimized operational parameters to setup the transmission during each satellite-Earth downlink: data-rate R bopt (bit/s) and minimum elevation angle θ mopt (deg) of the ground-station pointing at the spacecraft antenna (i.e., the elevation at which the transmission is started or stopped) [12], [13]. ...
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 characterization of the antenna noise temperature due to precipitating clouds at Ku band and above is described by deriving a closed-form solution of the scalar radiative transfer equation. Following the so called Eddington approximation, the analytical model is based on the truncated expansion of unpolarized brightness temperature angular spectrum in terms of Legendre polynomials. The accuracy of the sky-noise Eddington model (SNEM) is evaluated by comparing it with an accurate numerical solution, taking into consideration a wide variability of medium optical parameters as well as a typical rain slab model. The effect of the antenna pattern for ground-based antennas is also quantified. Physically-based radiative cloud models, characterized by a vertically-inhomogeneous geometry, are also introduced. Hydrometeor optical parameters are calculated and modeled for a large set of beacon channel frequencies. Nimbostratus and cumulonimbus models are finally applied to SNEM for simulating slant-path attenuation and antenna noise temperatures for ground-based antennas. Results are compared with ITALSAT satellite receiver measurements and co-located radiometric data between 13.0 and 49.5 GHz for various rain events during 1998.
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.
An improved weather-forecast-based link-budget design technique for space-to-Earth links is described. The aim is the stochastic optimization of both transmission symbol rate and received signal-to-noise ratio. The proposed radiometeorological operations prediction (RadioMetOP) model takes into account the forecast uncertainty by a space–time ensemble method exploiting the temporal evolution of the predicted radiometeorological variables over the weather-forecast spatial grid. The unique possibility of testing and validating the RadioMetOP model is presented, thanks to the
Ka
-band downlink measurements available from the support of the European Space Agency’s antenna tracking network to deep-space Hayabusa-2 (HB2) mission, operated by the Japan Aerospace Exploration Agency. First, the RadioMetOP model accuracy is tested by comparing the signal-to-noise ratio, measured during the transmission periods, with the simulated one, properly scaled to the symbol rate operated by HB2, finding correlation values of 0.9 that confirm the effectiveness of the proposed approach. Second, the
a
posteriori
analysis of the optimization process is accomplished, showing that depending on the considered criteria for the link-budget optimization, the use of the RadioMetOP model would have allowed a transmitted data volume more than doubled and an average signal-to-noise ratio gain between 2.1 and 3.8 dB.
The goal of this work is to demonstrate how the use of short-term radio-meteorological forecasts can aid the optimization of transferred data volumes from deep-space (DS) satellite payloads to Earth receiving stations. To this aim, a weather forecast (WF) numerical model is coupled with a microphysically oriented radiopropagation scheme in order to predict the atmospheric effects on Ka-band signals in DS links. A regional WFs model is exploited to obtain short-term predictions of the atmospheric state. The microphysically oriented radiopropagation scheme consists in a 3-D radiative transfer model which is used to compute the slant path attenuation and the antenna noise temperature at Ka-band in order to predict the signal-to-noise ratio at the receiving station. As a baseline, the BepiColombo mission to Mercury is chosen. Two prediction methods, statistical and maximization, are introduced and tested in two scenarios: 1) full-numerical scenario, where simulated data are used for evaluating the performances of the prediction techniques; 2) semiempirical scenario, where measured meteorological data are exploited to simulate beacon measurements in clear and rainy conditions. The results are shown in terms of received and lost data volumes and compared with benchmark scenarios. Using short-term radio-meteorological forecasts, yearly data volume return can be increased more than 20% if daily WFs, rather than monthly climatological statistics, are exploited.
Earth System modeling has become more complex, and its evaluation using satellite data has also become more difficult due to model and data diversity. Therefore, the fundamental methodology of using satellite direct measurements with instrumental simulators should be addressed especially for modeling community members lacking a solid background of radiative transfer and scattering theory. This manuscript introduces principles of multi-satellite, multi-sensor radiance-based evaluation methods for a fully coupled regional Earth System model: NASA-Unified Weather Research and Forecasting (NU-WRF) model. We use a NU-WRF case study simulation over West Africa as an example of evaluating aerosol-cloud-precipitation-land processes with various satellite observations. NU-WRF simulated geophysical parameters are converted to the satellite-observable raw radiance and backscatter under nearly consistent physics assumptions via the multi-sensor satellite simulator, the Goddard Satellite Data Simulator Unit (G-SDSU). We present varied examples of simple yet robust methods that characterize forecast errors and model physics biases through the spatial and statistical interpretation of various satellite raw signals: infrared (IR) brightness temperature (Tb) for surface skin temperature and cloud-top temperature, microwave Tb for precipitation ice and surface flooding, and radar and lidar backscatter for aerosol-cloud profiling simultaneously. Because raw satellite signals integrate many sources of geophysical information, we demonstrate user-defined thresholds and a simple statistical process to facilitate evaluations, including the infrared-microwave-based cloud types and lidar/radar-based profile classifications.
Due to spectrum limitations at lower frequencies, NASA's Deep Space Network is currently implementing Ka-band (32 GHz) tracking capabilities at all of its deep space communication complexes (DSCC's). Since weather effects and increases in the atmospheric noise temperature associated with them are the biggest uncontrollable factors in the performance of a Ka-band deep space telecommunications link, use of algorithms to forecast the atmospheric noise temperature for a pass is desirable. In this paper, an analytical method for comparing the performance of an ideal forecasting algorithm to the best statistical methods in terms of average data return is derived. This methodology is applied to two different cases. In the first case, the spacecraft cannot change its data rate during the pass. In the second case, the spacecraft can continuously vary its data rate. This methodology is applied to four different elevation profiles whose maximum elevation varies from less than 30 degrees to greater than 80 degrees for Goldstone, Madrid and Canberra DSCC's. This analysis shows that for the fixed data rate case, while the forecasting does not significantly increase the average data return on the link (between 0.2 dB and 0.4 dB, depending on the DSCC and the elevation profile) it does improve the reliability of the link significantly (in ideal case to 100%). For the continuously variable data rate case, forecasting improves both the average data return (by between 1 dB and 1.9 dB depending on the elevation profile and the DSCC) and the reliability of the link (in ideal case to 100%).
A description of the advanced research WRF version 3
- Shamarock
Maximization of data return at Ka-band for interplanetary missions
- M Montagna
Atmospheric Modelling and Millimetre Wave Propagation
- G Brussaard
- P A Watson