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ИСПОЛЬЗОВАНИЕ ПОЛЯРИЗАЦИОНННЫХ РАДАРОВ В МЕТЕОРОЛОГИИ
Александр Рыжков
Университет Оклахомы E-mail: Alexander.Ryzhkov@noaa.gov. Адрес: Норман, Оклахома, США
Аннотация: В статье содержится обзор метеорологических приложений Доплеровских поляризационных радаров, которые уже внедрены в оперативную практику в США и находятся на различных стадиях разработки и внедрения на сетях метеорологического оповещения в ряде стран мира, включая Россию. Использование поляризационных радаров приводит к существенному улучшению качества радарных данных, измерения осадков и распознавания различных типов осадков и опасных явлений погоды, включая ливни, приводящие к паводкам, торнадо, сильный град и обледенение. Полномасштабное оперативное использование 160 сетевых поляризационных радаров в США начатое 2 - 3 года назад уже продемонстрировало их исключительные возможности, среди которых своевременное обнаружение торнадо и разделение осадков различной фазы (включая границу между снегом и дождем) в совокупности с эффективной фильтрацией неметеорологических эхо получили признание профессиональных метеорологов и широкой публики.
Ключевые слова: Доплеровский поляризационный радар, измерение осадков, неметеорологические эхо.
Introduction
World-scale modernization of existing weather radar networks by adding polarimetric capabilities is underway. Massive deployment of 160polarimetrically retrofitted NEXRAD radars in USA has been completed in 2013. Similar polari-metric upgrade is being carried out in many countries of the world including Russia where the deployment of extensive network of C-band polarimetric weather radars has begun recently. The benefits of the dual-polarization radars for improvement of radar data quality, discrimination between different types of radar echo and hydro-meteor species as well as for more accurate rainfall estimation has been successfully demonstrated in numerous research and validation studies [1- 4].
A scheme with simultaneously transmitted and received waves of horizontal and vertical polarizations is selected for implementing on the operational radars [5]. According to this scheme, three polarimetric variables: differential reflectivity ZDR, differential phase Odp, and cross-correlation coefficient phv between orthogonally polarized radar
returns are measured in addition to the reflectivity factor Z, mean Doppler velocity V, and Doppler spectrum width ov available from conventional single-polarization radars. An important polarimetric variable, specific differential phase KDP is computed as a radial derivative of total differential phase Odp.
A brief summary of major meteorological applications of polarimetric radars is presented herein.
Proven benefits of polarimetric weather radars
A. Improvement in data quality. Differential phase Odp and its radial derivative KDP are immune to radar miscalibration, attenuation caused by precipitation and wet radome, and partial beam blockage (PBB). Hence, these variables can be efficiently used for absolute calibration of radar reflectivity factor and its correction for attenuation and PBB using weather radar data. Polarimetric attenuation correction is especially important for the radars operating at shorter wavelengths. The concept of self-consistency of Z, ZDR, and KDP in rain is utilized for the data-based absolute calibration of
Radar reflectivity (dBZ) Differential reflectivity (dB) Cross-correlation coefficient
X (km) X (km) X (km)
Fig. 1. Example of the fields of Z, ZDR, and phv at antenna elevation 0.5° for mesoscale convective system on
05/13/2005
radar reflectivity with the accuracy of 1 - 1.5 dB. Nonmeteorological radar echo (ground clutter, insects, birds, chaff, forest / grass fires) can be easily recognized and filtered out using polarimet-ric capability. Most of nonmeteorological targets (excluding man-made structures) are characterized by much lower cross-correlation coefficient phv than weather scatterers and simple thresholding of radar data based on phv effectively filters out non-weather echoes.
B. Classification. Polarimetric radars provide true multiparameter measurements which can be utilized for discrimination between different classes of hydrometeors. The operational algorithm for hydrometeor classification implemented onpo-larimetric NEXRAD distinguishes between light / moderate rain, heavy rain, rain dominated by big drops (often associated with convective updrafts), mixture of rain and hail, dry snow, wet snow, graupel, and ice crystals. The hydrometeor classification algorithms (HCA) commonly utilize principles of fuzzy logic, decision-tree logic, and neural networks [6-7]. An example of the fields of Z, ZDR, and phv measured in the mesocale convective system (MCS) by the prototype of the polarimetric S-band WSR-88D radar and the corresponding results of radar echo classification are illustrated in Figs.1 and 2.
Biological scatterers and ground clutter are recognized ahead of a squall line. A leading edge of the squall line is marked by a thin line of "big drops" associated with size sorting caused by up-drafts and wind shear followed by heavy rain and occasional hail. Gradual transition from rain to wet snow, dry snow, and crystals is observed in
the stratiform part of MCS once the ray approaches melting level and overshoots it.
Classification
200
-100
-200
Fig. 2. Results of classification using the fields of polarimetric variables displayed in Fig. 1. GC -ground clutter/AP, BS - biological scatterers, DS -dry snow, WS - wet snow, CR - crystals, GR -graupel, BD - big drops, RA - rain, HR - heavy rain, RH - rain/hail
C. Rainfall estimation. Improvement in rainfall estimation is one of the greatest benefits of polar-imetric radars. Such an improvement is a result of combined use of Z, ZDR, and KDP which helps to reduce the uncertainty caused by variability of drop size distributions inherent for conventional R- Z relations. The improvement in radar data quality, identification and suppression of non-weatherechoes, and utilization of the results of hydrometeor classification indirectly contribute to reducing the errors in radar rainfall estimates. The operational NEXRAD algorithm for polarimetric rainfall estimation is contingent on the output of HCA [8].
Fig. 3. The dependencies of the biases (panel a) and rms errors (panel b) of hourly rain accumulation obtained from the conventional (black curve) and polarimetric (blue curve) estimators as functions of the distance from
the radar. 43 events, 179 hours of observations
Fig. 3 summarizes the results of extensive validation study performed in Oklahoma using the micronet and mesonet rain gage networks [8]. In this study, 43 rain events and 179 hours of radar observations have been examined to quantify the biases and rms errors of hourly rainfall estimates as functions of the distance from the radar. Black curves correspond to the estimates from the conventional R(Z) algorithm, whereas blue curves depict results obtained using the best polarimetric algorithm which utilizes Z, ZDR, and KDP as well as the results of hydrometeor classification.
The bias and rms errors of the hourly rain totals are reduced at the distances up to 200 km from the radar. The improvement is particularly significant at close distances where the rms error is reduced roughly by a factor of 2. Combining hydrometeor classification and rainfall estimation helps to improve the accuracy of rain measurements at longer distances from the radar where the radar resolution volume is more likely to be filled with frozen or mixed-phase hydrometeors.
Recently, a principally novel technique for po-larimetric rainfall estimation based on the use of specific attenuation A has been suggested by Ryzhkov et al. [9]. Radial profile of specific attenuation can be obtained from radial profile of Z and total span of differential phase Odp along propagation path. The R(A) estimate of rain rate is immune to radar miscalibration, partial beam blockage, attenuation, and wet radome impact is
almost insensitive to the variability of raindrop size distribution. Fig. 4 shows an example of how the effect of partial beam blockage is completely eliminated in the map of 6-hour rain total if the R(A) algorithm is used instead of R(Z) The method proved to be efficient at S, C, and X bands in several validation campaigns and emerges as a leading candidate for rainfall estimation on the NEXRAD radar network.
D. Tornado detection. Tornadic debris produces discernible polarimetric signature which allows to reliablydetect tornado on the ground [10]. In addition, vigorous size sorting in supercell storms leads to the pronounced increase of ZDR at the southern flank of the storms. The corresponding signature, the "ZDR arc", can be utilized for estimation of storm-relative helicity which can be utilized for tornado forecast [11 - 12].
The method for tornado detection with dual-polarization radar is illustrated in Fig. 5 where the fields of Z, ZDR, and phv are displayed at the moment of tornado touchdown during the storm on 05/10/2003 in the Oklahoma City metropolitan area. A well-pronounced hook echo usually associated with potential development of tornado is clearly visible in the SW flank of the storm but identification of tornado on the ground can be reliably made only using the signatures in the ZDR and phv fields. Dramatic drop in ZDR and phv in the hook area signifies tornadic debris up in the air.
R(Z) KVNX WSR-88D, 05/20/2011, 08 - 14 UTC R(A)
Fig. 4. Maps of 6-hour rain total obtained from the KVNX WSR-88D radar on 20 May 2011 (08 - 14 UTC) using the R(Z) and R(A) algorithms. Gauge accumulations (in mm) are displayed in white squares. Negative bias in rain total due to PBB in SW quadrant is eliminated in the R(A) map (right panel)
It is important to document tornado occur- hailstones start melting, they acquire a coat of
rence. If the storm has a history of producing tornado, then it is likely that it may hit again. Using polarimetric radar is the only way to detect tornado in real time (not after the fact), especially in the dark or when tornado is wrapped in rain and is not visually observable. Tornado detection has been recognized as a very important benefit of polarimetric radars in the US where violent tornadoes claim tens of human lives every year.
E. Hail detection and determination of its size. As opposed to rain for which ZDR increases with increasing Z, dry hail is usually characterized by high Z (which is not always the case) and low ZDR caused by tumbling behavior of falling hailstones and low dielectric constant of ice. However, once
melted water which stabilizes their orientation and increases ZDR. Most recent techniques for hail detection and determination of its size take into account this fact and make use of the height of the radar resolution volume with respect to the melting layer. There is strong evidence that discrimination between small hail (D < 2.5 cm), large hail (2.5 cm < D < 5.0 cm), and giant hail (D > 5.0 cm) is possible using polarimetric radar [13,14].
It was shown in recent studies that giant hail is associated with noticeable depression of phv above t he melting level where hailstones are usually dry or have thin film of water on their surface if they grow in the "wet growth" regime. The corresponding ZDR can be slightly negative due to the effects
Radar reflectivity
Differential reflectivity
Correlation coefficient
Fig. 5. Fields of Z, ZDR, and phv at elevation 0.5° measured at the moment of tornado touchdown during the storm on 05/10/2003 in Oklahoma City. Tornado touchdown in the hook echo is marked with significant reduction of ZDR and phv
Reflectivity [dBZ] at 0.50 deg. elevation
Differential Reflectivity fdB] at 0.50 dea. elevation
Correlation Coefficient at 0.50 deg. elevation
Final Surface HCA
Fig. 6. Composite PPI of Z, ZDR, phv, and classification results for a typical winter storm with transition from rain to snow and mixed-phase precipitation. Discrimination between 15 classes of precipitation is performed (bottom right panel). In the classification plot, RA stands for rain, FR - for freezing rain, FR/IP - for the mixture of freezing rain and ice pellets, IP - for ice pellets, WS - for wet snow, DS - for dry snow, and CR - for ice
crystals
of resonance scattering although hail may have larger horizontal dimension. This means that in order to predict very large hail at the surface, one may look aloft in the storm where giant hail is formed.
The algorithm for hail detection initially developed at S band should be modified before applied at shorter wavelength, particularly at C band where large raindrops originated from melting hail may have anomalously high ZDR which can overwhelm low intrinsic ZDR of hail mixed with rain below the freezing level. Hence, melting hail mixed with rain usually has much higher ZDR at C band compared to the measurements at S band.
The study of Ryzhkov et al. [13] shows also that radar reflectivity factor Z at C and X bands is much less sensitive to the size of hail compared to S band.
F. Discrimination between rain and snow. Ice particles with the same shape and orientation as raindrops have lower ZDR and KDP due to lower refractive index. These polarimetric parameters decrease with decreasing density of dry snow caused by aggregation. On the other hand, wet snow may be characterized by high ZDR and low phv. Pristine ice crystals with very nonspherical shapes and the density of solid ice may produce high ZDR and KDP. These differences between po-larimetric properties of rain and snow/ice of dif-
ferent types make possible a reliable discrimination not only between rain and snow in general but also between various habits of snow and ice. The most advanced algorithm for classification of winter precipitation types combines polarimetric radar data and vertical profiles of temperature and humidity retrieved from the regional numerical weather prediction models [15].
An example of discrimination between rain, freezing rain, ice pellets, wet snow, and dry snow among other classes of winter precipitation near the surface in the Pittsburgh, PA area is presented in Fig. 6. The radar reflectivity does not provide any clue about relative locations of rain, mixed-phase precipitation, and snow, whereas ZDR and phv delineates the transition between rain and snow with remarkable accuracy which is reflected in the classification map in the bottom right panel in Fig. 6.
Apart from heavy snowfall, freezing rain represents one of the most dangerous types of winter precipitation because of potential accumulation of ice on the roads, trees, power lines, and bodies of airplanes. The classification algorithm is capable to identify the regions of freezing rain at the surface and aloft which is very important for aviation. The appearance of the so-called "refreezing signature" in ZDR in the subfreezing layer below the temperature inversion aloft is a clear indication that freezing rain transforms into a less dangerous category of "ice pellets" [16].
New Promising applications
Additional polarimetric radar applications listed in this section are in their exploratory stage but there is enough observational evidence that they may offer significant benefits for meteorologists.
A. Convective initiation. It was shown in [17] that convective initiation and development may be monitored by examining "ZDR columns" indicating localization and strength of convective updrafts. These columns containing supercooled raindrops and graupel / hail undergoing dry or wet growth can stretch vertically well above the environmental freezing level depending on the strength of the updraft. The ZDR columns can be utilized for prediction of subsequent development of heavy rain or hail as was demonstrated in [17]. It turns out that the volume of the ZDR column above the freezing level may characterize the intensity and type of the precipitation developed afterwards.
B. Quantification of snow. The benefits of po-larimetric measurements for better quantification
of snowfall have not been demonstrated yet. However, the ability of the dual-polarization radar to distinguish between snow of different density and to detect the formation of dendrites aloft as a major source of snow at the surface will eventually materialize in the improvement in snow measurements. More polarimetric radar observations accompanied with snow gage measurements in colder climates are needed to address this issue.
C.Improvement in microphysical parametriza-tion of NWP models. Inadequate microphysical parametrization (MP) of numerical weather prediction (NWP) models is one of the major factors restricting their capability for a more accurate short-term storm forecast. Dual-polarization measurements coupled with cloud models offer unique chance to improve MP via better para-metrization of size distributions of different hy-drometeor species and via optimization of various parameters in equations characterizing rates of different microphysical processes. This is a new frontier of research which will potentially benefit NWP models and storm forecasting.
A series of theoretical and observational investigations of polarimetric signatures associated with different microphysical processes leading to the formation of precipitation (particle size sorting, evaporation, melting, refreezing) has been performed at the University of Oklahoma and National Severe Storms Laboratory in recent years. It was clearly shown that storm models with singlemoment microphysical parametrization widely utilized for weather forecast can not adequately reproduce the observed polarimetric signatures in critical parts of the storm so that polarimetric radars are capable to provide crucial information for further modification of NWP models.
E. Assimilation of polarimetric radar data into NWP models. Another great opportunity is assimilation of polarimetric radar data into NWP models. The models, however, should be adequate to digest polarimetric data and successful assimilation is contingent on the improvement of MP in NWP. To test the adequacy of the NWP models, their output has to be converted into the fields of polarimetric radar variables using a forward radar observation operator [18]and these fields should be compared with the observed ones. This is a big challenge for future studies.
References
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Поступила 15 мая 2014 г.
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English
Weather applications of dual-polarization radars
Alexander Ryzhkov - University of Oklahoma, National Severe Storms Laboratory, Advanced Radar Research Center.
Address: Norman, Oklahoma, USA.
Abstract: The paper contains a summary of weather applications of Doppler polarimetric radars which have already been deployed at the operational network in the US and are under development for national weather warning networks in a number of countries including Russia. The benefits of polarimetric radars include significant improvement in the quality of radar data, quantitative precipitation estimation, and identification of different precipitation types and hazardous weather phenomena including flash floods, tornado, large hail, and icing. A large-scale operational utilization of 160 operational polarimetric weather radars which started 2 - 3 years ago in the US has already demonstrated their remarkable capabilities. Among those receiving recognition of professional meteorologists and general public are timely tornado detection, discrimination between precipitation with various phase composition (such as rain / snow delineation), and effective suppressing and filtering of nonmeteorological radar echo.
Key words: Doppler polarimetric radar, quantitative precipitation estimation, nonmeteorological radar echo.
