Measurements of a network of mobile radars during the experimental campaign of the HydroRad project

Transcription

1 ERAD 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 1 NOA National Observatory of Athens, Lofos Nymfon, Athens, Greece, jkalog@ noa.gr 2 Department of Information Engineering, Sapienza University of Rome, Rome, Italy 3 University of Connecticut, Storrs, Connecticut, USA, manos@engr.uconn.edu 4 CETEMPS, University of L Aquila, L Aquila, Italy, marzano@die.uniroma1.it 5 HIMET, Strada Statale 17 Ovest 36, L'Aquila, Italy, errico.picciotti@himet.it 6 ELDES, Via di Porto 2/b, Firenze, Italy, 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.

2 The Moldova Operational Field campaign (MOF) took place in the time period September to October 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 Z h, the differential reflectivity Z dr, co-polar correlation coefficient ρ hv and the differential phase shift Φ dp. The specific differential phase shift K dp is estimated as half the gradient of Φ dp along the radar ray. XPol was scanning in the azimuth sector from 319 o 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 3.5 o ) 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 Φ dp measurements, and its extension with the additions of a Φ dp - Z dr 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 (1.5 o ) 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 (3 o ) 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 ρ hv 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 Z hc. It can be seen that the bright band has been removed in the corrected reflectivity. The corrected reflectivity at 3 o elevation angle is quite similar to the reflectivity field at 1.5 o 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 3.0 o on 9/10/2011 showing the melting layer (bright band).

3 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 3.5 o a melting layer signature was not detected in these data. Figure 4 presents PPI scans at 0.5 o 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 3 o compared to 1 o of XPol). When the miniradar 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 Only data with correlation coefficient ρ hv higher than 0.8 (i.e., good rain signal)

4 were included in the statistical comparison. The wide beam results in partial beam blockage by the terrain at elevation angles below 3.5 o. 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 1.5 o 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 0.5 o, 1.5 o and 2.5 o elevations were lower from the reflectivity at 3.5 o 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 Z h, Z dr and K dp (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 Z h 0.58, (1) where R is rainfall rate in mm h -1 units and Z h is in linear units instead of dbz. The second estimator is a polarimetric one, which is based the N w normalization approach with constants estimated from electromagnetic scattering simulations (Kalogiros et al. 2006). N w 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). N w is obtained also from polarimetric relations found from simulations. The first polarimetric rainfall estimator is: R p1 =1.305x10-3 N w (Z h /N w ) (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): R p2 =0.8106F R (µ)ν w D f R (D 0 ). (3) D 0 and µ are the median volume diameter and the shape parameter of the DSD, respectively, F R is a function of µ, which is included in the Gamma approximation of the DSD, and f R is a third degree rational polynomial of D 0 with constants evaluated by the simulations. For the estimation of rainfall rate by the radars the 1.5 o 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 miniradars 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 Z h 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.

5 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 1.5 o elevation angle of the antennas.

6 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, R p1 and R p2 and the raingauge measurements. It was not possible to compare the miniradars 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 K dp 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 1.5 o during the stratiform rain event of 8-9 October 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 K dp by the mini-radar (Fig. 5). The R p2 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 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/ ) under grant agreement n within FP7-SME References Anagnostou, E., Anagnostou M., Krajewski W., Kruger A., and Miriovsky B., 2004: High-resolution rainfall estimation from X-Band polarimetric radar measurements. J. Hydrometeor., 5, Anagnostou, M. N., J. Kalogiros, F. S. Marzano, E. N. Anagnostou, M. Montopoli, and E. Picciotti, 2012: Performance evaluation of a new dual-polarization microphysical algorithm based on long-term X-band radar and disdrometer observations. Submitted to the J. Hydrometeor. Bringi, V., and Chandrasekar V., 2001: Polarimetric Doppler weather radar. Cambridge University Press, Cambridge, UK. Bringi, V., Keenan T., and Chandrasekar V., 2001: Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: a self consistent method with constraints. IEEE Trans. Geosci. Remote Sens., 39, Kalogiros, J., M. N. Anagnostou, and E. N. Anagnostou, 2006: Rainfall retrieval from polarimetric X-band radar measurements. Proceedings of the 4 th European Conference on Radar in Meteorology and Hydrology (ERAD), Barcelona, Spain, pp Kalogiros, J., M. N. Anagnostou, E. N. Anagnostou, M. Montopoli, E. Picciotti, and F. S. Marzano, 2012a: Optimum estimation of rain microphysical parameters using X-band dual-polarization radar observables. Submitted to the IEEE Trans. Geosci. Remote Sens.. Kalogiros, J., M. N. Anagnostou, E. N. Anagnostou, M. Montopoli, E. Picciotti, and F. S. Marzano, 2012b: Evaluation of a new polarimetric algorithm for rain path attenuation correction of X-band radar observations against disdrometer data. Submitted to the IEEE Geosci. Remote Sens. Lett.. Kalogiros, J., M. N. Anagnostou, E. N. Anagnostou, M. Montopoli, E. Picciotti, and F. S. Marzano, 2012c: Correction of polarimetric radar reflectivity measurements and rainfall estimates for apparent vertical profile in stratiform rain. Submitted to the J. Appl. Meteorol. Clim.. Matrosov, S., Clark K., Martner B., Tokay A., 2002: X-band polarimetric radar measurements of rainfall. J. Appl. Meteor., 41, Park, S., Maki M., Iwanami K., Bringi V., and Chandrasekar V., 2005: Correction of radar reflectivity and differential reflectivity for rain attenuation at X-band, Part II: Evaluation and application. J. Atmos. Ocean. Technol., 22, Picciotti, E., Marzano F.S., Cinque G., Montopoli M., Bernardini L., De Sanctis K., Anagnostou E., Kalogiros J., Anagnostou M., Fessas Y., Volpi A., Telleschi A., Cazac V., and Pace R, 2012: Exploiting X-band dual-polarization mini-radar network and hydrometeorological forecast models in Moldova territory during the field campaign of HYDRORAD project. Proceeding of the 7 th European Conference on Radar in Meteorology and Hydrology (ERAD), Toulouse, France, June Testud, J., Le Bouar E., Obligis E., and Ali-Mehenni M., 2000: The rain profiling algorithm applied to polarimetric weather radar. J. Atmos. Ocean. Technol., 17,