Avionic Weather Radar

5/20/2018 Avionic Weather Radar

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Alberto Lupidi1, Christian Moscardini

2, Andrea

Garzelli3, Fabrizio Berizzi

4, Fabrizio Cuccoli

5

CNIT-RaSS (National Interuniversitary Consortium for

Telecommunication-national laboratory of radar and

Surveillance Systems)

Italy{1a.lupidi,2c.moscardini,4f.berizzi}@ iet.unipi.it

3andrea.garzelli @ dii.unisi.it5fabrizio.cuccoli @ cnit.it

Marcello Bernab

SELEXGalileo S.p.A.Campi Bisenzio-Italy

marcello.bernabo @ selexgalileo.com

AbstractAvionic Weather Radar is an essential equipment in

aircraft. Polarimetry can improve the detection and the

classification of hydrometeors and thus the safety and the

efficiency of the flight. Here a 3D polarimetric radar simulator

for the feasibility study on avionic weather polarimetric radar is

presented.

I. INTRODUCTION

In current avionic systems is impossible to distinguish the typeof precipitation, water, snow, hail. Of course, assumptions canbe done, i.e., high reflectivity in a zone where temperature is15-20 degrees below zero is likely to indicate an hailstorm, butwe can have no precise information on type of precipitation

near and below the melting height (which also depend onseason and geographic region). About 70% of the high-reflectivity echoes that pilots see on their radar is non-hazardous (other than causing a decrease in visibility andmaking runways wet). To determine whether or not a particu-lar red echo is hazardous in terms of turbulence and hail andother dangers, the pilot must first know if the atmosphere inwhich he is flying is conducive to of hail and high turbulence.It is worth noting to recall that heavy rain without turbulence isnot an issue for the safety of the flight. But even withatmospheric knowledge, a pilot cannot say whether a particularhigh-reflectivity area is hazardous. Usually, the pilot evadesthat area, with an increase of costs, time and pollutingemissions due to the detour. The use of polarimetry can helpgiving us more precise details on hydrometeor types [1].

For example, rain tends to have an elliptical form withminor axis oriented vertically, resulting in HH signal to behigher than VV signal thus having a positive high DifferentialReflectivity. On the contrary, hail, due to its tumbling motion,appears as spherical, thus having a nearly zero DifferentialReflectivity, even at higher reflectivity (and higher hazard)level. Classification algorithms which utilize the polarimetricinformation on the three channels (HH, VV, HV/VH) can be

developed with the knowledge of Total (Z) and DifferentialReflectivity (ZDR) and Linear Depolarization Ratio LDRdefined as

DR HH dBZ VV dBZZ Z Z (1)

VH dBZ HH dBZ LDR Z Z (2)

In this work we assume X-Band based system (around 9-10 GHz) that are preferred because they have an antennawhose dimensions are compact and compatible also forbusiness aircrafts. Polarimetric classification algorithms for

ground based S-C bands systems already exist and in generalthere is no great difference between ground based and airborneoperation in the application of these algorithms [2], [3].Differences arise from the technical limitations of the airbornesystem, like antenna size, transmitted power and scanningspeed. Main issues for avionic weather radars in conjunctionwith the use of X-band are:

1. heavy beam path attenuation and Mie scattering effects

2. ground clutter

3. wider beam width

4. data availability

In this paper we did not deal with path attenuation and groundclutter. These problems will be addressed in future works. We

solved the problem of data availability simulating real radardata with a physical based approach described later. Section II

describes the scenario and the mentioned approach, while in

section III and IV we show some results and conclusion

respectively.

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II. ATMOSPHERICSCENARIO AND RADAR MODEL

One of the problems in weather radar engineering is theavailability of data. Moreover, in radar meteorology, data areavailable mainly in S-band (around 3 GHz) because this is the

band chosen for ground based weather radar. To simulaterealistic polarimetric radar data in X-Band, the two mostimportant things we need to know to compute the radarreflectivity are:

1. the Drop Diameter Distribution (DSD) of hydrometeors

N(D)measured in m-4

2. their polarimetric Radar Cross Section (RCS) H,VDmeasured in m2.

Polarimetric reflectivity is finally computed as

4

, ,50

( ) ( )0.93H V H V

Z D N D dD

, (3)

Total reflectivity is the result from summing the contributes ofhail and rain calculated separately.

For DSD calculation we adopted the Weather Research andForecast Model (WRF), a state-of the art NWP developed by aconsortium of research institutes including NOAA and NCAR[4]. The WRF can also provide the temporal evolution ofparameters based on a real scenario. This NWP gives usimportant parameters needed for the definition of an analytical,physical based Drop Size Distribution (DSD):

1) Hydrometeor mixing ratio [Kg/Kg]

2) Pressure [Pa]

3) Potential temperature [K]

4) Particle Number Concentration [particles/m3].

Additionally WRF provides the wind field used to computeDoppler shifts.

The DSD that we used in our computation is a Gammaprobability density function.

To compute the polarimetric RCS, we utilized a T-Matrixmethod. The T-Matrix method is the fastest exact technique forthe computation of non-spherical scattering based on a directsolution of Maxwell equations [5],[6]. Dielectric constants,particle orientation, diameter and the relationship betweendiameter and axial ratio are set as parameters to calculate the

electromagnetic scattering. Details on the generation of the 3Dreflectivity maps for every polarimetric channel can be foundin our previous work [7].

The received radar signal is then generated using a custo-

mized version of Airborne Windshear Doppler Radar

Simulation (ADWRS), extensively used by NASA in various

campaigns [8].

The simulation input values include the radar systems

parameters, the cinematic characteristics of the airborneplatform, the antenna parameters and the scanning anglestrategy. Other inputs specify the phenomenon characteristics

in term of wind field and radar reflectivity. Last two variable isrepresented by a 3D data cube, described before. From both the

initial aircraft position and the initial antenna scan direction,

the simulation consists of the generation of the instantaneousreceived signal. For each range bin, the amplitude and phase of

the received signal can be seen as the coherent sum of a

number of contributions that came from volumetric scatteringmechanism.

III. 3DSIMULATION RESULTS

A. Description of the simulated scenario

Simulations were performed with the transpondercharacteristics summarized in Table 1. It is worth nothing that

the radar simulator can perform a full 360 scanning, but foravionic uses we can reduce this range to 180 or less. Theaircraft is positioned in the center (0,0), heading south at 150knots. The relatively low transmitted power is meant tosimulate the latest state-of-the-art solid-state GaAs radartransmitters equipping civil avionic weather radars, designed towork with such low peak power.

TABLE I. TRANSCEIVER CHARACTERISTICS

Transmitted frequency 9.353 GHz

Pulse length 1s

PRF 6.5 kHz

Range resolution 150 m

Beam width 3

Transmitted power 195 Watt

Antenna Gain 33 dB

Noise figure 4 dB

An area of about 1800 km2

in the Mediterranean Sea, closeto Barcelona, Spain, was selected, with a maximum height of8000 m. Figure 1 shows the profiles of hydrometeor mixingratios obtained from WRF at altitudes of 450 m, 1000 m and2000 m with a RGB mapping. Red indicates hail/graupel, blueindicates rain and mixed precipitation zones are in purple.

B. Results

Figures 2 to 4 show some simulation results regarding ZHH,ZDRand LDR, which accounts for the more or less pronouncedoscillations of hydrometeors. All these parameters are usefulfor classification between liquid and solid dangerous particles.

Figure 2 shows results for the lowest altitude level, wellunder the melting layer, dominated by rain. We can notice thepresence of a heavy storm characterized by strong reflectivity

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echoes up to 53 dBZ, however we cannot distinguish if theseechoes are due to hail or rain. Analyzing ZDR, the radarretrieves values from 1 up to 3.6 dB in the storm core. Asexplained in section I, this behavior indicates a rain dominatedzone, as. Moreover, LDR level do not surpass -25 dB level,indicating small oscillations of particles during fall, which isanother characteristics of rain. Over -15 dB values appearusually where both Zhh and Zvh are very low, so the ratio issimilar.

Figure 3 represents an intermediate altitude where rain andhail are heavily mixed. As expected, total reflectivity levelremains the same as before, but we can appreciate variations inthe values of ZDR and LDR. ZDRbegins to decrease steadilyreaching his top at 2.8 dB, while LDR rises up to a value of -21.2 dB. This behavior is typical of a mixed precipitation zone,but we can still detect rain presence in near the borders of thescanned area at (0,-20) and (-15,10) coordinates.

Where polarimetry shows its potential in detectingdangerous area is well shown in Figure 4. Once more, total

reflectivity level remains in the 55 dBZ range, but observationof ZDR and LDR supports the evidence of a hail dominatedzone. Maximum value of ZDRdo not exceed 0.3 dB, and it evenhas negative value, -0.1 dB, which can be caused, other thanthe tumbling motion of hail, by the Mie scattering effects forlarger stones. LDR rise up to a value of -18/-17 dB, indicatinga very high signal power in the VH channel (see Eq. 2).

IV. CONCLUSIONS

It is clear that even in an uniform reflectivity phenomenon,in both its horizontal and vertical structure, polarimetric dataprocessing can provide useful information for featurediscrimination and thus to reduce risk due to solid particles

impact. Even if the beamwidth is three degrees, combining thesignal received from partially overlapping azimuthal sectors itis possible to have information which permit to make a gooddiscrimination and resolve different scattering behaviour.Further studies will be conducted to evaluate returns from verylong distances. Long ranges suffer also from heavy attenuationwhich can be compensated using an additional polarimetricvariable, the Specific Differential Phase (KDP), that is also agood estimator for rainfall rate. This accurate risk assessmentis not possible with single-polarization avionic radar, so theonly action that is taken is making long detours, even if thephenomenon would pose no threats.

REFERENCES

[1] F. J. Yanovsky, Evolution and Prospects of Airborne Weather RadarFunctionality and Technology, 18th International Conference onApplied Electromagnetics and Communications, 2005.

[2] V.N. Bringi, and V. Chandrasekar, Polarimetric Doppler WeatherRadar, Cambridge University Press, 2004.

[3] Classification and Quantification Using Polarimetric Radar Data:Synthesis of Relations, J. Appl. Meteor. 39, 2000, pp. 13411372.

[4] S.E. Koch, The Use of Simulated Radar Reflectivity Fields in theDiagnosis of Mesoscale Phenomena from High-Resolution WRF ModelForecasts, 32nd Conference on Radar Meteorology, 2005.

[5] P.C. Waterman, Scattering by Dielectric Obstacles, Alta Frequenza(Speciale), 1969, pp. 348352., 1969.

[6] M. Mishchenko, L.D. Travis, and A.A. Lacis, Scattering, Absorptionand Emission of Light by Small Particles, Cambridge University Press,2nd ed., 2005.

[7] A. Lupidi, C. Moscardini, F. Berizzi, M. Martorella, "Simulation of X-Band Polarimetric Weather Radar Returns based on the WeatherResearch and Forecast Model", 2011 IEEE Radar Conference, KansasCity, 2011.

[8] Britt, C., L., Kelly, C., W., Users Guide for an Airborne DopplerWeather Radar simulation (ADWRS), Center for AerospaceTechnology, Tech. Rep. 7473/029-05S NASA, 2002.



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Figure 1: Mixing Ratio: (a) 450 m (b) 1000 m (c) 2000 m altitude

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Figure 2: 450 m altitude: (a) Total Reflectivity, (b) ZDR, (c) LDR

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Avionic Weather Radar