444 IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, VOL. 53, NO. 2, FEBRUARY 2005
Retrieval of Information About Turbulence in Rainby Using Doppler-Polarimetric Radar
Felix J. Yanovsky, Senior Member, IEEE, Herman W. J. Russchenberg, and Christine M. H. Unal
Abstract—This paper considers new possibilities of turbulenceintensity retrieval by using Doppler and Doppler-polarimetricradar sounding. Peculiarities of microwave scattering on movingdroplets of different size and shape underlie new methods that areintroduced, discussed, and checked by using radar data.
Index Terms—Atmosphere turbulence, Doppler-polarimetricradar, Doppler polarimetry, Doppler spectrum, drop velocitydistribution, microwave radar, remote sensing of rain, spectraldifferential reflectivity.
I. INTRODUCTION
RADAR RETURNS from weather objects (clouds, pre-cipitation, etc.) are usually considered as clutter. Clutter
echoes are random and have noise-like characteristics becausethe individual clutter components (scatterers) give randomphases and amplitudes. In many cases, the clutter signal levelis much higher than the receiver noise level. Thus, the radar’sability to detect targets embedded in high clutter backgrounddepends frequently on the signal-to-clutter ratio rather than thesignal-to-noise ratio.
At the same time, in many cases, weather formations are ob-jects of radar detection, measurement, and recognition. Dopplerradars are used to obtain necessary information for weatherforecasts and aviation safety. One of the most important isinformation about the turbulence intensity in clouds and pre-cipitation. This information is contained in the spectra ofecho-signal. Echo-signal is formed during the interaction ofreturns from the scatterers, which are located in the radarresolution volume. These scatterers participate in several mo-tions, and one of them is caused by turbulence. Sometimesit is possible to distinguish the turbulent component of themotion among other ones by using a special mode of sounding,particularly at horizontal sounding.
However, the retrieval of information about the turbulenceintensity is a complicated problem in the general case [1]. Somenew possibilities of retrieving turbulence intensity in rain usingDoppler radar with an arbitrarily directed antenna beam wereconsidered in [2].
Manuscript received May 13, 2004. This work was supported by the DutchScientific Fund STW.
F. J. Yanovsky is with the Institute of the Information and DiagnosticSystems, National Aviation University, Kiev 03058, Ukraine (e-mail:[email protected]).
H. W. J. Russchenberg and C. M. H. Unal are with the Remote SensingSector, International Research Centre for Telecommunications and Radar,Delft 2600, The Netherlands (e-mail: [email protected];[email protected]).
Digital Object Identifier 10.1109/TMTT.2004.840772
In this paper, Doppler methods are expanded by Doppler-polarimetric consideration. Experimental results were obtainedwith the Transportable Atmospheric Radar (TARA), which isa full-polarization -band FM continuous wave (CW) Dopplerradar system [3].
II. DROP VELOCITY DISTRIBUTION AND DOPPLER SPECTRUM
Raindrops take part simultaneously in several motions causedby different reasons. This consideration takes into account onlytwo main reasons, which are: 1) gravity and 2) turbulence. Bothfall drop radial velocity distribution and turbulence dropvelocity distribution were described in [4]. Taking intoaccount that velocity of a raindrop is an algebraic sum of twovelocities , the combined distribution ,caused by both gravitational falling and turbulence influence,can be determined by the convolution
(1)
with as the maximal possible drop velocity caused by turbu-lence. The probability distribution of drop velocity causedby both reasons is
(2)
Substituting the expressions of and into (1)and (2), the drop velocity distributioncan be calculated at a different eddy dissipation rate , antennaelevation , parameters of gamma model of dropsize distribu-tion and , and turbulence maximal scale . An exampleof calculation is shown in Fig. 1 in the Marshall–Palmer case
for light ( cm s , dashed line) and heavy (cm s , solid line) turbulence at two modes of sounding,
i.e., the antenna is pointed toward the zenith andthe antenna elevation equals . The rest of the param-eters are constant ( mm, m). The curvesat are located to the right-hand side of the curves at
because the radial drop fall velocity is maximum whenthe antenna is pointed toward the zenith. The curves that cor-respond to heavy turbulence are significantly broader than theones for light turbulence. The value of the broadening due toturbulence is more apparent at small elevation anglesthan at large elevation angles . More positive veloci-ties (toward the radar) are seen at the sounding into the zenith,
YANOVSKY et al.: RETRIEVAL OF INFORMATION ABOUT TURBULENCE IN RAIN BY USING DOPPLER-POLARIMETRIC RADAR 445
Fig. 1. Radial drop velocity probability distribution at different values ofturbulence intensity " and antenna elevation �.
and more negative (away from the radar) velocities can occur atheavy turbulence.
Doppler spectrum is the radial drop velocity distributioncaused by both gravity and turbulence weighted by radar crosssection (RCS) , i.e.,
(3)
It can be measured by Doppler radar and used for the retrievalof important information on microstructure and dynamics ofrain. In particular, Doppler spectrum width increases when tur-bulence intensity increases. No suppositions concerning particleshape were made when (3) was derived. Hence, polarizationcannot be taken into account at such consideration.
III. SPECTRAL DIFFERENTIAL REFLECTIVITY
In recent years, a new Doppler-polarimetric technique wasdeveloped [5], [6], i.e., the spectral differential reflectivity
. With this technique, polarization radar measurementsare combined with Doppler measurements in such a way that,for a volume of radar scatterers, the specific polarization prop-erties for targets with different velocities can be determined.The technique comprises the following two steps.
Step 1) The Doppler-velocity spectrum of a signal comingfrom the radar target is calculated at differentpolarizations.
Step 2) For each velocity class in the spectrum, the ratioof the signal power at two different polarizations iscalculated.
Usually, the ratio of horizontally and verticallypolarized returns is measured as
(4)
where denotes the estimate of the value in angle brackets;and are Doppler spectra at polarization in-
dicated by indexes (the first index denotes polarization of areceived signal component and the second one denotes polar-ization of a transmitted wave).
Fig. 2. Spectral differential reflectivity at different turbulence intensity ".
In contrast to well-known differential reflectivity [7], thespectral differential reflectivity is a function of velocity.
Doppler spectrum for polarization, where ,; , can be determined as
(5)
where is drop velocity distribution due to both gravity andturbulence. In case , RCS can be calculated ac-cording to
(6)
with as the radar wavelength, as the equivolumetric diam-eter of a sphere, as the relative permittivity, and asthe term that represents the shape of the particle (quantified with
); takes into account the orientation ( , ) of theparticle and the radar elevation angle . The functionsand were explained in [8]. The upper limit of in-tegration in (5) should be chosen as if
, where is a spatial scale below whichturbulence does not affect raindrops of a certain size [4].Otherwise it should be calculated as an inverse function from
at . This helps to take into account the in-ertia of drops. is the biggest raindrop diameter (usually
mm).Spectral differential reflectivity curves calculated by substi-
tuting (5) into areshown in Fig. 2 as functions of Doppler velocity at different tur-bulence intensity and invariably other parameters of the model,particularly , mm, , and cm.Raindrops can be different by size. Bigger droplets are moreoblate, and they also fall faster than smaller ones. In general,one can say that if scatterers become more oblate, willincrease as well.
446 IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, VOL. 53, NO. 2, FEBRUARY 2005
Fig. 2 shows this behavior clearly, especially at negligibleturbulence cm s . The behavior changes in case ofturbulence intensity increases: raindrops that would otherwisefall with a different velocity may get the same speed because ofthe turbulence-induced random mixing.
This also implies that particles with different shapes aremixed now and may get the same fall velocity, thereby effec-tively changing . At small fall velocities,goes up, and at large velocities, it goes down. Thecurve will flatten with the increased intensity of turbulence.
IV. DOPPLER APPROACHES TO RETRIEVE
TURBULENCE INTENSITY
Several possibilities exist to measure turbulence by Dopplerradar.
The first way is the retrieval of turbulence contribution fromthe Doppler spectrum width . At ground-based radar, remotesensing of rain with a rather narrow antenna beam at compara-tively short (less than 10 km) distances, the main contributionsto the Doppler spectrum variance are the variance of dropfall velocities and turbulence . That is why the pro-posed simple algorithm provides the following key steps.
Step 1) Estimate median drop diameter as a param-eter of dropsize distribution (for each resolutionvolume) by using radar measurements of, e.g., radarreflectivity or/and differential reflectivity[1], [7].
Step 2) Make some assumption about spread parameter ofdropsize distribution , retrieve raindrop fall ve-locity distribution using thedeveloped models [4], [5].
Step 3) Calculate the variance of (denoted ) as thesecond central moment of .
Step 4) Estimate the velocity variance due to turbulence, assuming , where
is the estimated variance of the measured Dopplerspectrum.
Step 5) Calculate eddy dissipation rate as a function of(taking into account the scale of turbulence).
In this case, we use information only about small-scale tur-bulence (less than radar resolution). However, assuming homo-geneous isotropic turbulence, one can calculate from such datathe rms of turbulent motion of air at any maximum spatial scalein the limits of the turbulence inertial interval.
The second way is the retrieval of turbulence informationfrom spatial mean Doppler velocity changes. We can tryto detect turbulent zones from the same data by comparingin contiguous resolution volumes if the radar data are obtainedwith good resolution. The profiles of being calculated fromnonaveraged Doppler spectra can probably indicate the places ofspatial mean velocity changes since the behavior of differencesbetween and can indicate the intensity of turbulenceof the appropriate spatial scale.
In this case, we get information about turbulence, the scale ofwhich is more than radar spatial resolution since the minimumscale is defined by the distance between the resolution volumes
Fig. 3. Normalized drop fall velocity distribution at different: (a) D and(b) heights.
with mean Doppler velocity and . In fact, large gra-dient of indicates turbulence.
The third way comprises the retrieval of turbulence informa-tion from temporal mean Doppler velocity changes. For thispurpose, a large number of profiles of the same path is neces-sary. In this case, the scale of spatial averaging can be limitedby radar resolution.
Let us consider the retrieval of turbulence contribution fromthe Doppler spectrum width employing radar reflectivity .Such an estimation can use the drop fall velocity distribution,which is derived from gamma dropsize distribution. The vari-ance can be calculated more accurately taking into accountthe drop fall velocity dependence on height . We approximateit as , where is the drop fallvelocity at sea level. Under these conditions, the normalizeddrop fall velocity distribution is shown in Fig. 3 at different(left-hand side) and heights (right-hand side).
The median drop diameter is difficult to measure. How-ever, a rough estimate can be easily achieved using radarreflectivity measurements. We suppose that the common
YANOVSKY et al.: RETRIEVAL OF INFORMATION ABOUT TURBULENCE IN RAIN BY USING DOPPLER-POLARIMETRIC RADAR 447
Fig. 4. Procedure for extracting turbulence contribution � from measuredDoppler spectrum width [W = (Doppler spectrum width) ].
empirical relation between and rain rate is true:with and . The rain rate is
(7)
where is the volume of the drop of diameter ,is the steady drop fall velocity, and is thedropsize distribution. In the Marshall–Palmer case ,
. General formula (7) after the unit transfor-mation and analytical integration using Atlas’ approxi-mation [1] is reduced to
(8)
where is a function of and . Equation (8) is valid forthe integer only.
Due to dependence on being rather weak, it can be simpli-fied by substitution of .
Numerical solving shows that, finally, simple empirical de-pendence between and may be used.
Now we have all necessary formulas to extract turbulencecontribution from measured Doppler spectrum width. Themethodology is clearly demonstrated in Fig. 4 as a calculationprocedure, which uses measured reflectivity , height , andDoppler velocity variance .
In accordance with this procedure, having estimates of ,, and , we can calculate drop fall velocity distribution ,
variance of drop fall velocity, and then the contribution ofturbulence to the measured variance of Doppler velocity
as a difference . Finally, the estimate ofthe Doppler spectrum width caused by turbulence is
.
V. DOPPLER-POLARIMETRIC APPROACH TO RETRIEVE
TURBULENCE INTENSITY
The developed forward model calculates Doppler spectraat different polarizations and curves at given input
Fig. 5. Modeled relationship between turbulence intensity given by " andSlope Z .
parameters like rain intensity and the eddy dissipation rateof turbulence (Fig. 2). This gives the possibility relatingDoppler-polarimetric parameters with parameters of turbulence.Specifically, the slope of the curve (or its other pa-rameters) contains information about turbulence. Fig. 5 showsan example of the relationship between turbulence eddy dis-sipation rate and the Slope calculated by using themodeled data [accordingly to (5) and (6)] with linear inter-polation of the intermediate points.
The described behavior of leads to the interestingoption to remotely measure the intensity of turbulence withDoppler-polarimetric weather radar. However, the researchbased on experimental data should first be done to confirm thetheory.
VI. MEASUREMENTS
Here, both Doppler and Doppler-polarimetric approaches arechecked by using data. The data of rain observation were ac-quired September 19, 2001 by the FM CW TARA radar system[3], Cabauw, The Netherlands. Basic specifications of TARAare: 1) carrier frequency 3.315 GHz; 2) frequency sweep canbe changed from 2- to 50-MHz computer controlled (that cor-responds to range resolution from 75 to 3 m); 3) dynamic range90 dB; 4) receiver noise figure is equal 1 dB; and 5) antenna pa-rameters: beamwidth 2.2 , cross polarization 30 dB, firstsidelobe level 25 dB. During the measurements of rain, therange resolution was set to 15 m and the Doppler velocity reso-lution was set to 1.8 cm/s.
Radar data were processed using MATLAB. Fig. 6 shows 13profiles of Doppler spectrum width components obtained at theslant sounding of widespread rain.
In the upper panel of Fig. 6, the measured Doppler velocityspectrum width (in meters/second) versus height (in me-ters) is presented, the middle panel of Fig. 6 shows the retrievedcontribution of the drop fall velocity , and the lower panelof Fig. 6 demonstrates the result of the retrieval of turbulencecontribution in accordance with the algorithm given inFig. 4.
The developed approach allows experimentally estimatingthe turbulent contribution to the Doppler spectrum width
. This is an estimate of rms turbulent velocity inside theresolution volume, i.e., turbulent velocities of spatial scales lessthan .
448 IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, VOL. 53, NO. 2, FEBRUARY 2005
Fig. 6. Measured profiles of the Doppler spectrum width (DS width), drop fallvelocity distribution width (DFVD width), and retrieved turbulent drop velocitydistribution width (TDVD width).
Another estimation of turbulence was obtained from the timefluctuations of mean Doppler velocity . In this case, the res-olution volume is considered as a point, and only turbulent ed-dies whose size is bigger than radar resolution affect the meanDoppler velocity. This means that the rms of the mean Dopplervelocity is an estimate of turbulence of spatial scales more than
m.The universal parameter of turbulence intensity is eddy dis-
sipation rate . Theoretically, in the inertial sub-range of tur-bulence, the value should be constant. In that case, it can beunambiguously determined from the rms of turbulence velocityat the given spatial scale. Different approaches for retrievalfrom value can be applied. We used a simple formula asfollows, which was given in [1]:
(9)
where is a dimensionless factor of the order of one in theKolmogorov–Obukhov law of isotropic turbulence (minus 5 3law), is a range from radar to the resolution volume, andis a beamwidth.
From (9), the value can be calculated using retrieved turbu-lence contribution to the Doppler spectrum width . Equa-tion (9) does not contain a range resolution term , though,in the strict sense, it should affect. Evidently, (9) was obtained[1] for the operational Doppler weather radar system where nor-mally . For a flexible radar system like TARA, whenradar range resolution is very high m , the tangen-tial and radial sizes of the resolution volume become equal onlywhen the distance between the radar antenna and a target is
m. Thus, (9) is valid for . For the cases when, a more complicated expression should be used.
Doppler-polarimetric observable variables are shown inFig. 7 as grayscale spectrograms in “height–time” coordinates.
Note that Slope is bigger in upper heights, whilevalues of are the smallest there. Evident layer structure ofthe space draws attention, especially the line at approximately800 m with increased turbulence and decreased Slope .
Generally, one can see that behavior of slope ofcurves is opposite to the behavior of .
Fig. 7. Slope of Z (v) (upper panel) and Doppler spectrum width �
(lower panel) versus time and space. Note their negative correlation.
Fig. 8. Comparison of different measures of turbulence.
The same conclusion can be done when considering the mea-surements of Slope fulfilled simultaneously with esti-mates of the eddy dissipation rate , , rms mean Dopplervelocity, or spatial gradient mean Doppler velocity . It is in-teresting to note that behaviors of Doppler parameters that weremeasured at different polarizations ( and ;and ) are very similar, and it is questionable to expectthat joint estimates of them can significantly increase the reli-ability of turbulence detection or measuring intensity of turbu-lence. At the same time, the new Doppler-polarimetric variable
, and particularly parameter Slope , is verypromising because it provides an independent estimate of tur-bulence-related variable.
Comparison of different measures of turbulence is given inFig. 8. The solid curve is the rms of mean Doppler velocitythat is a measure of turbulence, the scales of which are morethan . The dotted curve represents Doppler spectrum width
. The dashed curve shows retrieved contribution of turbu-lence as a component of measured .
Value is a measure of turbulence the scales of which areless than . Both values and were measured at horizontal
YANOVSKY et al.: RETRIEVAL OF INFORMATION ABOUT TURBULENCE IN RAIN BY USING DOPPLER-POLARIMETRIC RADAR 449
Fig. 9. Vertical profiles of the spatial gradient of mean Doppler velocity(dashed line), rms of the time variations of mean Doppler velocity (solid line),and retrieved eddy dissipation rate of turbulence.
polarization, but vertically polarized waves lead to very similarresults.
The behaviors of rms and are similar, and it is re-markable because they represent the results of independent mea-sures of turbulence (inside and outside radar resolution volume).
The last two curves in Fig. 8, marked by dots and crosses,represent measured Slope and retrieved eddy dissipationrate correspondingly.
The changes of the Doppler-polarimetric variable Slopeon average are opposite to other characteristics, as was
predicted theoretically (Fig. 5).The values of parameter that characterizes turbulence in-
tensity and was retrieved by the algorithm, given in Fig. 4, weredivided by ten in order to be plotted in a convenient scale. Thevalues of indicate light turbulence in the measured rain, andturbulence is stronger in the surface layer. It has a maximum atapproximately 800 m.
All considered parameters , rms , Slope , ,and were measured or calculated by using the time variationsin each radar resolution volume. They can be referred to the firstor third way of turbulence measuring (see above). The possi-bility to retrieve turbulence information from changes of spatialmean Doppler velocity was indicated above as the secondway, and the results of corresponding measurements are shownin Fig. 9.
The dashed line represents the absolute values of spatialgradient calculated over all range bins in the rainzone. rms (solid line) and (dotted line) lines are shown forcomparison. One can see very nice correspondence betweenthe measurements in the time and spatial domains.
VII. CONCLUSION
New techniques for turbulence information retrieval from themeasurements of Doppler and Doppler-polarimetric radars havebeen developed.
The developed procedure for the retrieval of turbulencecomponent from the total Doppler spectrum width can be
successfully used for measuring the intensity of turbulence inrain by Doppler radar. Further improvement of this methodcan be achieved by increasing the accuracy of median dropdiameter estimate . This can be done by using polarimetricmeasurements additionally to Doppler, differential reflectivity
instead of or together with reflectivity can particularlybe used.
However, a real Doppler-polarimetric approach is not asimple combination of Doppler and polarimetric parameters.New Doppler-polarimetric variables can be introduced pro-viding new possibilities. One of them is spectral differentialreflectivity and its parameter Slope .
Theoretical investigation, modeling, and data processing haveclearly demonstrated that is correlated to turbulence inrain. This leads to the interesting option to remotely measurethe intensity of turbulence with Doppler-polarimetric radar.
The fulfilled processing of real data by using differentDoppler approaches and the new Doppler-polarimetric ap-proach has demonstrated well-correlated results.
Application of independent turbulence-related radar variablescan improve the reliability and accuracy of radar measurements.
Measurements of microstructure and dynamic parametersof weather objects by Doppler-polarimetric radar can be veryuseful to solve wave propagation problems for the tasks ofmicrowave communications and the radar’s ability to detecttargets embedded in a high-clutter background.
ACKNOWLEDGMENT
The main volume of data for this paper was processed duringthe Doppler-Polarimetric Radar Observations of Turbulence inRain (09.2002–03.2003) project in the framework of long-termcooperation between the International Research Centre forTelecommunications and Radar (IRCTR), Delft, The Nether-lands, the Technical University of Delft, Delft, The Netherlands,and the National Aviation University (NAU), Kiev, Ukraine.
REFERENCES
[1] R. J. Doviak and D. S. Zrnic, Doppler Radar and Weather Observations,2nd ed. San Diego, CA: Academic, 1993.
[2] F. J. Yanovsky, “Doppler radar: Retrieval of information about turbu-lence in rain,” in 15th Int. Microwaves, Radar and Wireless Communi-cations Conf., Warsaw, Poland, May 2004, pp. 86–91.
[3] S. H. Heijnen and L. P. Ligthart, “TARA: Development of a new Trans-portable Atmospheric Radar,” in 5th Int. Radar Systems Conf., Brest,France, May 1999, pp. 223–226.
[4] F. J. Yanovsky, H. W. J. Russchenberg, and L. P. Ligthart, “Doppler-po-larimetric models of microwave remote sensing of rain,” in 11th Mi-crowave Technique Conf., Pardubice, Czech Republic, Sep. 2001, pp.47–62.
[5] C. M. H. Unal, D. N. Moisseev, F. J. Yanovsky, and H. W. J. Russ-chenberg, “Radar Doppler polarimetry applied to precipitation measure-ments: Introduction of the spectral differential reflectivity,” in 30th Int.American Meteorological Soc. Radar Meteorology Conf., Munich, Ger-many, Jul. 2001, pp. 316–318.
[6] F. J. Yanovsky, “Phenomenological models of Doppler-polarimetric mi-crowave remote sensing of clouds and precipitation,” in IEEE Int. Geo-science and Remote Sensing Symp., vol. 3, Toronto, ON, Canada, Jun.2002, pp. 1905–1907.
[7] V. N. Bringi and V. Chandrasekar, Polarimetric Doppler WeatherRadar. Cambridge, U.K.: Cambridge Univ. Press, 2002.
[8] D. A. De Wolf, H. W. J. Russchenberg, and L. P. Ligthart, “Effectivepermittivity of and scattering from wet snow and ice droplets at weatherradar wavelengths,” IEEE Trans. Antennas Propag., vol. 38, no. 9, pp.1317–1325, Sep. 1990.
450 IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, VOL. 53, NO. 2, FEBRUARY 2005
Felix J. Yanovsky (M’94–SM’96) received theEngineer (M.S.) and D.Sc. degrees from the NationalAviation University (NAU), Kiev, Ukraine, in 1968and 1992, respectively, and the Ph.D. and D.Sc.degrees from the Moscow State Technical University(MSTUCA), Moscow, Russia, in 1979 and 1993,respectively.
He is currently a Full Professor with the Institute ofthe Information and Diagnostic Systems, NAU, anda Guest Top Scientist with the Delft University ofTechnology, Delft, The Netherlands. His research in-
terests are radar and remote sensing, Doppler polarimetry, signal processing,math modeling, and multiparametric and adaptive methods. He introduced sig-nificantly to airborne weather radar theory and practice and was one of the ini-tiators of air-traffic collision-avoidance system development in the Ukraine. Hehas authored or coauthored over 300 scientific papers and six books. He holds38 patents. He appears in Who’s Who in the World.
Dr. Yanovsky is a member of the General Assembly (GA) of the European Mi-crowave Association. He served as a section organizer, chairman, and TechnicalProgram Committee (TPC) member of numerous International Conferences. Hewas elected as an Academician of the Transport Academy of Ukraine, Interna-tional Academy of Navigation and Traffic Control, St. Petersburg, Russia, andElectromagnetics Academy, Cambridge, MA. He was the recipient of the StateAward of the Ukraine in the field of science and engineering in 1996.
Herman W. J. Russchenberg is Head of theRemote Sensing Sector, International ResearchCentre for Telecommunications and Radar, Delft,The Netherlands. He possesses extensive experiencein remote sensing of clouds and precipitation withground-based radar, lidar, and microwave radiom-etry, and is one of the initiators of this work in TheNetherlands. He is experienced in theoretical, as wellexperimental research of the scattering process andthe retrieval of geophysical parameters from radarand lidar measurements. He has performed several
studies for the European Space Agency (ESA), dealing with radar observationsof clouds and precipitation. He is the initiator of the Cabauw Experimental Sitefor Atmospheric Research.
Christine M. H. Unal received the D.E.A. degreein physics for remote sensing from the University ofParis, Paris, France, in 1987.
In 1988, she joined the Delft University ofTechnology, Delft, The Netherlands, where sheis currently a Research Scientist. She possessesexperience with radar polarimetric calibration andradar Doppler polarimetry (quasi-simultaneousDoppler spectra of polarimetric measurements, theirprocessing, and their interpretation). Since 2003, herresearch has focused on radar Doppler polarimetry
