ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

Measurements of a network of mobile radars

during the experimental campaign of

the HydroRad project

Kalogiros J. 1, Anagnostou M.

1,2, Anagnostou E.

3, Marzano F.S.

2,4, Picciotti E.

5, Cinque G.

5,

Montopoli M. 4, Bernardini L.

5, Volpi A.

6, Telleschi A.

6

1NOA National Observatory of Athens, Lofos Nymfon, Athens, Greece, e-mail: jkalog@ noa.gr

2Department of Information Engineering, Sapienza University of Rome, Rome, Italy

3University of Connecticut, Storrs, Connecticut, USA, e-mail: manos@engr.uconn.edu

4CETEMPS, University of L’Aquila, L’Aquila, Italy, e-mail: marzano@die.uniroma1.it 5HIMET, Strada Statale 17 Ovest 36, L'Aquila, Italy, e-mail: errico.picciotti@himet.it

6ELDES, Via di Porto 2/b, Firenze, Italy, e-mail: a.volpi@eldes.it

John Kalogiros

1. Introduction

Small size X-band polarimetric radars (hereafter called mini-radars) constitute a low-cost solution to the problem of

hydrologic and flood forecast in urban, small-scale basins and coastal areas where operational weather radars cannot provide

adequate coverage. Short-wavelength radar systems are more attractive also for research purposes because of their small size.

Their limitations are the smaller range due to low power and the significant signal attenuation at X-band in heavy rain. In the

framework of HydroRad project (Picciotti et al. 2012) an innovative dual-polarization X-band mini-radar system was

developed and facilitated by a suite of software tools including rain estimation, nowcasting, precipitation classification and

integration with hydrological and meteorological models for applications in weather and flood forecasting. The integrated

system is described in detail by Picciotti et al. (2012), while this work presents validation of the mini-radar rainfall estimates

against in situ observations and evaluates differences against a benchmark high-resolution dual-polarization X-band radar

(XPol). These mini-radars are easy to deploy and, thus, ideal for the setup of radar networks to cover areas with complex

terrain. The overall system was tested in an experimental campaign in Moldova, where mini-radar data were collected

synergistically with XPol and in-situ rain measurements from raingauges and a disdrometer (Fig. 1).

Fig. 1 One of the mini-radars (left) and the XPol mobile radar with the nearby disdrometer and raingauges (right) in the

Moldova field campaign.

Fig. 2 The basin of the Bic river in central Moldova. The locations of the radars are shown.

ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

The Moldova Operational Field campaign (MOF) took place in the time period September to October 2011. The

experimental area was the basin of the river Bic with its tributaries around the Moldovan capital Chisinau (Fig. 2). The

terrain is characterized by low hills with elevation up to 300 m. The three mini-radars were installed in locations around

Chisinau in order to cover the basin, while XPol and the 2D-video disdrometer were installed at Chisianu suburbs. Six pairs

of tipping raingauges were installed at different positions along the river. The range of the radars was 60 km with a resolution

of 120 m. Radar observations included the horizontal reflectivity Zh, the differential reflectivity Zdr, co-polar correlation

coefficient ρhv and the differential phase shift Φdp. The specific differential phase shift Kdp is estimated as half the gradient of

Φdp along the radar ray. XPol was scanning in the azimuth sector from 319o to 125

o due to blockage by nearby buildings. The

mini-radars had also small azimuth sectors of partial beam blockage due to terrain. PPI scans at low elevation angles (up to

3.5o) of the radar antenna were performed as well as RHI scans in selected azimuth angles in order to estimate the vertical

structure of the rain field. The time period for a full volume scan was about 3 minutes. The disdrometer data was used for the

analysis of droplet size distribution, shape (axis ratio) and orientation of rain droplets, and the theoretical estimation

(scattering calculations) of polarimetric radar products. In the following sections, radar data processing and rainfall

estimation validation results for the mini-radar system are presented. Comparisons against the XPol rainfall estimates provide

a basis to evaluate performance differences due to differences in the radar characteristics.

2. Radar data processing

At X-band frequencies attenuation of radar signal by rain can be quite significant with values greater than 10 dB in heavy

rain. There are many dual-polarization rain attenuation correction algorithms like the ZPHI algorithm (Testud et al. 2000),

which is based on Φdp measurements, and its extension with the additions of a Φdp - Zdr constraint (Bringi et al. 2001). In this

study a new attenuation correction algorithm (Kalogiros et al. 2012b) was used which is based on the new rain microphysics

parameterizations described in Kalogiros et al. (2012a) with minimum parameterization error (up to 5%). These

parameterizations use the theoretical Rayleigh scattering limits corrected by a multiplicative rational polynomial function of

reflectivity-weighted raindrop diameter to approximate the Mie character of scattering. The rain attenuation algorithm was

applied to the three mini-radars and the XPol radar data from MOF. Calibration of the reflectivity measurements from the

mini-radars was carried out through comparison with the radar observables estimated from the disdrometer data (not shown

here). XPol was already calibrated from a large dataset collected during the past five years in Athens, Greece. XPol

reflectivity was also used to further check the calibration of the mini-radars.

Due to the use of high elevation angles of the radar antennas to avoid beam blockage by terrain features melting layer

effects (bright band) were observed at these elevation angles during widespread (stratiform) rain. For MOF data a low

elevation angle (1.5o) was used for rainfall estimation in order to avoid melting layer effects and to minimize effects due to

ground clutter. Figure 3 presents a high elevation angle (3o) PPI from XPol. The bright band is seen as a circular zone of high

reflectivities values in the range from 40 to 50 km corresponding to altitudes of about 2000 and 2500 m, respectively. The

white line shown is the base of the melting layer which was detected using ρhv as the rain-to-mixed phase classification

criterion. After detection a correction algorithm for the average apparent vertical profile of reflectivity in the PPI scan was

applied (Kalogiros et al. 2012c). The reflectivity corrected for the vertical profile of reflectivity (VPR) is indicated with Zhc.

It can be seen that the bright band has been removed in the corrected reflectivity. The corrected reflectivity at 3o elevation

angle is quite similar to the reflectivity field at 1.5o elevation angle (not shown here) with differences at some azimuth angles

due to partial blockage of XPol beam at the lower elevation angle by nearby terrain.

Fig. 3 Example of VPR correction of XPol PPI scan at an antenna elevation of 3.0o on 9/10/2011 showing the melting layer

(bright band).

ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

Fig. 4 PPI scans from R3 mini-radar at the same time period as in Fig.3 at different elevation angles of the antenna.

Fig. 5 Statistical difference between R3 mini-radar and XPol measurements versus range of the mini-radar during the 8-

9/10/2011 rain event for two different elevation angles of the antennas.

Even though the highest elevation of the mini-radars scanning was 3.5o a melting layer signature was not detected in these

data. Figure 4 presents PPI scans at 0.5o and 3.5

o from R3 mini-radar at about the same time as XPol data in Fig. 3. Both

PPIs show the same horizontal structure of reflectivities but with lower average value at the higher elevation. The reason for

this difference from XPol is the wide beam of the mini-radars (half power width 3o compared to 1

o of XPol). When the mini-

radar beam crosses the melting layer at a range of about 35 km it is 1800 m wide. In the vertical direction the beam width

corresponds to an integration (filtering) of the vertical profile of reflectivity within this width according to the beam pattern.

The horizontal beam width is the same but it simply results in reduced horizontal resolution, which can be improved with

angular oversampling in PPIs and inverse filtering. In the vertical direction the beam is essentially covering most of the rain

layer and the melting layer above it. This filter in the vertical direction results in hiding the bright band at this elevation

angle. The bright band could probably be observed in mini-radar data if its altitude was lower and the elevation angle was

higher. Thus, in MOF data no VPR correction could be applied to the mini-radar data.

The wide beam of mini-radars has also additional effects in their measurements. Figure 5 shows the effect of wide beam on

reflectivity, differential reflectivity, correlation coefficient and specific differential phase shift by comparing the

measurements from R3 mini-radar with XPol measurements versus the range of the mini-radar for all the data of the

widespread rain event on 8-9 October 2011. Only data with correlation coefficient ρhv higher than 0.8 (i.e., good rain signal)

ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

were included in the statistical comparison. The wide beam results in partial beam blockage by the terrain at elevation angles

below 3.5o. The average difference of R3 mini-radar reflectivity from XPol data is about 4 dB at the elevation angle of 3.5

o.

The near zero difference at 1.5o is due to the calibration of the mini-radar using the data from this elevation. On average it

was found that due to this partial beam blockage the R3 mini-radar reflectivities at 0.5o, 1.5

o and 2.5

o elevations were lower

from the reflectivity at 3.5o elevation angle by 10.5 dB, 4 dB and 1dB, respectively. These difference values depend also on

the terrain around the mini-radar because different terrain will cause different partial blockage. In addition to reflectivity the

rest of radar measurements were also affected by the wide beam effect. The differential reflectivity was showing a significant

difference by XPol data at the highest elevation and ranges longer than 45 km and it is probably due to the extension of the

beam well above the rain layer at these distances. The correlation coefficient shows a significant reduction with range at low

elevations and less at the highest elevation probably due to signal de-correlation by the reflection from the ground, too. The

specific differential phase shift is about 30% of XPol measurements at the low elevation angle and very small at the higher

elevation. However, as there was no trend in the differences with XPol versus range the low specific differential phase shift

may be also due to noise problems in the mini-radars.

3. Rainfall estimation

Rainfall estimates based on classical weather radar observations have quantitative limitations mainly due to the lack of

uniqueness in the relationship of the single radar measurable (reflectivity) to the associated rainfall intensity. The polarization

diversity capability of modern weather radars is expected to moderate this effect using polarimetric relations for estimation of

the rainfall rate, which combine Zh, Zdr and Kdp (Matrosov et al. 2002, Anagnostou et al. 2004, Park et al. 2005). In this study

three rainfall estimators were evaluated. The first one is a classic Z-R estimator with steady coefficients which were evaluated

from historic XPol data:

R=3.36x10-2

Zh0.58

,

(1)

where R is rainfall rate in mm h-1

units and Zh is in linear units instead of dBZ. The second estimator is a polarimetric one,

which is based the Nw normalization approach with constants estimated from electromagnetic scattering simulations

(Kalogiros et al. 2006). Nw is the intercept parameter (units mm-1

m-3

) of rain droplet size distribution (DSD), which is

approximated with a normalized Gamma distribution (Bringi and Chandrasekar 2001). Nw is obtained also from polarimetric

relations found from simulations. The first polarimetric rainfall estimator is:

Rp1=1.305x10-3

Nw(Zh/Nw)0.58

. (2)

The third rainfall estimator is a new polarimetric estimator which minimizes the approximation error using the theoretical

Rayleigh scattering limit with the addition of a rational polynomial function of reflectivity-weighted droplet diameter to

approximate the Mie character of scattering (Kalogiros et al. 2012a, Anagnostou et al. 2012):

Rp2=0.8106FR(µ)ΝwD04.67

fR(D0) . (3)

D0 and µ are the median volume diameter and the shape parameter of the DSD, respectively, FR is a function of µ, which is

included in the Gamma approximation of the DSD, and fR is a third degree rational polynomial of D0 with constants

evaluated by the simulations. For the estimation of rainfall rate by the radars the 1.5o elevation angle was used. Data from

two rain events were used. Figure 6 shows an example of reflectivity maps from R3 mini-radar and XPol during the first rain

event (8-9 September 2011) which was of convective type with isolated strong rain cells. The positions of the three mini-

radars and the six raingauge sites relative to XPol are also shown in the figure. The mini-radar reflectivity field agrees well

with the near concurrent field from XPol, which shows the capability of the mini-radar to capture well the spatial variability

of rain. The second rain event (8-9 October 2011) was of stratiform rain type with widespread moderate rain.

Fig. 6 PPI scans of horizontal reflectivity Zh measured from the XPol and the R3 mini-radar at an elevation angle of 1.5

o.

The locations of the three mini-radars and the six pairs of raingauges relative to XPol are also shown.

ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

Fig. 7 Time series of mini-radars rainfall estimates (accumulation in 30 minutes) from the three algorithms of Eqs. (1)-(3).

Fig. 8 Histograms of difference between mini-radars and XPol estimates of total rainfall accumulated in the rain event using

the three algorithms of Eqs. (1)-(3).

Fig. 9 Statistical difference between mini-radars and XPol rain estimates (accumulation in 30 minutes time periods) versus

range of the mini-radars during the 8-9/10/2011 rain event for the 1.5o elevation angle of the antennas.

ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

Figure 7 presents time-series comparison between the mini-radar and XPol rainfall estimates (accumulation in 30 minutes)

from the three rainfall algorithms Z-R, Rp1 and Rp2 and the raingauge measurements. It was not possible to compare the mini-

radars to all the raingauges because of partial beam blockage by the terrain in the direction of some raingauges. The time

series comparison shows that the rainfall estimates by the mini-radars follow well the temporal evolution of rain and that the

classic estimator Z-R generally underestimates the rainfall rate. It should be noted that the constants in Z-R relation were

estimated from historic XPol data in Athens, Greece. Histograms of the difference between mini-radar and XPol rainfall

estimates are shown in Fig. 8. This comparison was made after interpolation of the rainfall maps for each mini-radar and

XPol on a common grid of 90 km radius with 1 km spatial resolution and XPol position at the center of the grid. It can be

seen that the bias of the mini-radar rainfall estimates is small. Tails in the histograms are probably due to altitude differences

of the measurement volume of the mini-radars and XPol and beam blockage effects on the radar data by the terrain in some

azimuth sectors. The differences of the rainfall estimates from the polarimetric algorithm show more spread compared to the

Z-R algorithm, which is mainly due to noise in the Kdp observations by the mini-radars.

Figure 9 shows the statistics of differences in rainfall estimates (accumulation in 30 minutes time periods) against range for

the three algorithms between the mini-radars and XPol for an elevation angle of 1.5o during the stratiform rain event of 8-9

October 2011. In general, the mini-radars underestimate rainfall rate at ranges longer than 40 km, which probably has to do

with the wide beam effect of mini-radars described in section 2. The underestimation by R3 at short ranges is probably due to

the underestimation of Kdp by the mini-radar (Fig. 5). The Rp2 polarimetric algorithm performs better than the other two

algorithms, but it is probably more affected by the noise in mini-radar data as it was seen in the histogram comparison in Fig.

8. It should be noted that even though the estimates Z-R algorithm between mini-radars and XPol are similar this algorithm

underestimates rain as concluded by the comparison with raingauge data in Fig. 7.

4. Conclusions A preliminary comparison of polarimetric mini-radars, which were developed in the framework of HydroRad project,

against a benchmark polarimetric X-band radar showed promising results. The mini-radars measure accurately the spatial

variability of rain, but there are problems with partial beam blockage and significant vertical averaging especially at long

ranges due to their wide beam. New polarimetric algorithms for attenuation correction, rainfall and rain microphysics

estimation were applied satisfactorily to the mini-radar data, but further improvement in noise level which affected the

differential phase measurements is needed. Despite these limitations at longer ranges these X-band systems provide a reliable

low cost solution for weather and flood monitoring in small scales. Networks of mini-radars can cover broader areas in

complex terrain where high-power operational systems cannot achieve this.

Acknowledgment

The research leading to these results has received funding from the European Union’s Seventh Framework Programme

(FP7/2007-2013) under grant agreement n 232156 within FP7-SME-2008-1.

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