Preliminary results of a Mesoscale Model for MARS

The Earth atmospheric circulation has been studied for long time using both GCM (General Circulation Models) and Mesoscale Models or LAM (Limited Area Models). The latter have been widely applied to study local circulation at high resolution and for weather forecasting. In the last years, the Martian atmosphere arouse the interest of the scientific community, both for supporting the landing of Beagle 2 lander and for studying and assessing similarities/differences with the Earth atmosphere. To this aim, GCM have been successfully used. Recently, also Earth LAMs have been changed to simulate the Mars atmosphere, showing good results. The scarce availability of observations did not allowed for validating these models. In this work an attempt is made to validate the newly developed MARS-MM5 against GCM. The model simulation produced using a data base on the basis of output from multi-annual integration of two CGM (see Lewis S. R. et al., J. Geophys. Res., 104 (E10) (1999) 177) is used for statistically evaluates MARS-MM5. The preliminary results suggest that MARS-MM5 is able to correctly reproduce the Mars atmosphere, indeed either the horizontal and the vertical structure of temperature produced by MARS-MM5 is in good agreement with the ones produced by GCM. A few discrepancies are found in the PBL, probably produced by a different parameterization

Optimizing data volume return for Ka-band deep space links exploiting short-term radiometeorological model forecast

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 pay- loads 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 con- sists 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 receiv- ing 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 fore- casts, yearly data volume return can be increased more than 20% if daily WFs, rather than monthly climatological statistics, are exploited

EXPLOITING SUN-TRACKING MICROWAVE RADIOMETERS FOR TESTING RADIATIVE TRANSFER MODELS OF PRECIPITATING CLOUDS

The effects of the scattering troposphere on propagating signals are important for several microwave applications such as remote sensing and telecommunication. In particular, passive remote sensing exploits ground-based radiometers to retrieve profile information of the atmosphere. On the other hand, telecommunication applications (e.g., satellite communications) require an accurate estimation of the atmospheric effects to minimize the outage probability of the link. Within this context, atmospheric effects can be described through the joint knowledge of the radiopropagation parameters: atmospheric brightness temperature TB and total path attenuation At (also referred to as atmospheric extinction). These two quantities can be described by the radiative transfer theory that formalizes the spatial evolution of the atmospheric radiance and is implemented trough radiative transfer models (RTM). For successfully testing and validating RTM, measurements of both TB and At are needed. A typical approach to get these two quantities is to exploit combined measurements of satellite-beacon receivers (which provide measurements of At) and ground-based radiometers (which measures the TB). The disadvantage of this approach is that At and TB would be affected by different errors because they are derived using two distinct measuring instruments, each of them with different calibration and accuracy. Moreover, radiometers and beacon receivers typically work at different frequencies. This would imply that a frequency scaling approach would be required before using At and TB pairs for quantitative analysis. Actually, most of microwave radiometers are able to provide attenuation products as result of retrievals approaches based on forward models. However, such retrievals can suffer of large uncertainties in rainy conditions due to poor modeling of scattering. The only instruments able to provide simultaneous measurements of At and TB in all-weather conditions are ground based Sun-tracking radiometers (STR) that exploit the Sun as a stable radiance source. STR performs the retrieval of At exploiting two nearly simultaneous measurements of TB at the same elevation. This is accomplished by alternatively pointing the receiving antenna toward-the-Sun and off-the-Sun during the Sun tracking. The aim of this work is to exploit STR measurements to test and validate RTM simulations in cloudy and rainy conditions. We have considered two different models. First, a Sky Noise Eddington Model (SNEM): a 1D-model that gives an analytical approximation of the solution of the radiative transfer equation. SNEM simulations provide a synthetic clouds dataset through random generation of seasonal-dependent and time-decorrelated meteorological variables with statistics driven by radiosounding profiles. Second: a pseudo-3D radiative transfer model based on the Goddard Satellite Data Simulator Unit (G-SDSU, Matsui et al. JGR 2014) that is able to produce synthetic radiances and path attenuation as measured by ground-based microwave radiometers at several elevation angles and frequencies. In this work we use G-SDSU to convert 3D temporal profiles of meteorological variables, produced by Numerical Weather Forecasts, into predicted TB and At. RTMs are tested exploiting measurements from a STR installed at the Air Force Research Laboratory in Rome, NY, USA. The STR has four receiving channels at K (23.8 GHz), Ka (31.4 GHz), V (72.5 GHz) and W (82.5 GHz) band providing measurements for the years 2015-2016 at elevation angles varying between 20° and 70° (due to the antenna pointing-switching for the Sun tracking). The agreement between measurements and models highlights the reliability of the produced radiation database that can be exploited to develop and update parametric prediction models of attenuation. The use of RTM simulations driven by numerical weather forecasts paves the way to new approaches based on the prediction of radio-propagation parameters for specific target areas and temporal periods (as opposed to common prediction schemes based on stationary path attenuation models statistics)

Investigating ground-based radar and spaceborne infrared radiometer synergy for lightning areal prediction in complex orography

A new multi-sensor approach, named PoLCast (Probability of Lightning foreCast), for predicting the lightning activity in a complex orography geographical area is proposed and discussed. The PoLCast input information are the ground-based weather radar horizontally polarized reflectivity factor and the atmospheric instability indexes, derived from the Spin Enhanced Visible Infrared Imager (SEVIRI) radiometer onboard the Meteosat Second Generation (MSG) satellite. The weather radar data are used to calculate the probability of lightning following a direct relation between the maximum values of the reflectivity factor and the lightning occurrence, whereas the atmospheric instability indexes from SEVIRI are used to constrain the probability of lighting, derived from weather radar data, and enhance such probability in cases of more unstable troposphere. To test the PoLCast methodology, the output of the Blitzortung and the “Sistema Italiano Rilevamento Fulmini” (SIRF) ground-based lightning sensor networks are used together with the C-band Mt. Midia weather radar within its 180 km diameter coverage over the Central Italy area. Both satellite and radar data are pre-processed into PoLCast to obtain a single time series in terms of the areal probability of lightning (PoL). PoLCast performances are evaluated in terms of statistical scores using 12 heterogeneous case studies over Central Italy. Even though the number of available cases is relatively limited, quantitative results show high areal PoL (from 0 to 100%) with a case-by-case variability of false alarm rate from 3 to 72%. The advantage of a multi-sensor technique, such as PoLCast, with respect to an approach using weather radar data only, becomes more evident when lightning activity is not present and the leading time of lightning forecast exceeds 2.5 h

Validating weather-forecast-driven propagation models at millimeter waves using multisource ground-based radiometric data

In this work, several sources are used to characterize, in both deterministic and statistical ways, the atmospheric propagation channel in terms of brightness temperature and path attenuation at high frequency bands (such as K- Ka-, V- and W-band). We have used two different models: a weather-forecast-driven 3-dimensional radiative transfer model (RTM) and a stochastic 1-dimensional model (SNEM) with synthetic clouds dataset provided as inputs. We have compared the outputs of such radiative transfer simulations with actual measurements of two co-located microwave radiometers: a humidity and temperature profiler and a Sun-tracking radiometer. The comparisons show satisfactory results and a good agreement among all sources, with some small inaccuracies to be investigated in future works. RTM successfully reproduced correlations between brightness temperature and path attenuation at several frequency bands, confirming the advantage of using weather forecast models combined with physically-based radiative transfer models. Also, the SNEM model showed to be able to reproduce the atmospheric channel but a proper fine tuning is needed to better represent the climatological conditions of the area of interest