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Hydrometeor Classification System Using Dual-Polarization Radar Measurements: Model Improvements and In Situ Verification S. Lim, V. Chandrasekar, and V. N. Bringi
Abstract—A hydrometeor classification system based on a fuzzy logic technique using dual-polarization radar measurements of precipitation is presented. In this study, five dual-polarization radar measurements (namely horizontal reflectivity, differential reflectivity, specific differential phase, correlation coefficient, and linear depolarization ratio) and altitude relating to environmental melting layer are used as input variables of the system. The hydrometeor classification system chooses one of nine different hydrometeor categories as output. The system presented in this paper is a further development of an existing hydrometeor classification system model developed at Colorado State University (CSU). The hydrometeor classification system is evaluated by comparing inferred results from the CSU CHILL Facility dual-polarization radar measurements with the in situ sample data collected by the T-28 aircraft during the Severe Thunderstorm Electrification and Precipitation Study. Index Terms—Colorado State University CHILL (CSU-CHILL) radar, dual-polarization radar measurements, fuzzy logic, hydrometeor classification, Severe Thunderstorm Electrification and Precipitation Study (STEPS).
I. INTRODUCTION
D
UAL-POLARIZATION radar measurements of precipitation are sensitive to the hydrometeor properties such as shape, orientation, size, phase state, and fall behavior. Therefore, dual-polarization radar measurements of precipitation can be used effectively to identify hydrometeor types in precipitation. Bringi et al. [1] used reflectivity at horizontal polarization and differential reflectivity to detect hail and discriminate between hail and rain regions in convective storms. and to Hall et al. [2] studied the vertical structure of identify the transition from ice to rain. Bringi et al. [3], [4] , , and linear depolarization showed that the profiles of ratio were useful for classifying graupel and hail regions in the core of convective storms. Aydin et al. [5] studied , and dual-wavelength measurethe vertical profiles of , ments in terms of the size, shape, and fall behavior of hailstones to identify hail. Zrnic´ et al. [6] examined specific differential phase ( ), backscatter differential phase, correlation coeffi, and to detect hail and identify mixed-phase precient cipitation over severe hailstorms. Manuscript received May 9, 2004; revised November 19, 2004. This research was supported by the National Science Foundation (NSF) under Grants ATM0121546, 9982030, and 0313881. The Colorado State University CHILL National Radar Facility is supported by the NSF under Grant ATM-9500108. The authors are with the Colorado State University, Fort Collins, CO 80523 USA (e-mail:
[email protected]). Digital Object Identifier 10.1109/TGRS.2004.843077
A Boolean decision tree method using predefined boundaries of radar measurements has been used in the past to classify hydrometeor types [7], [8]. This method can lead to inadequate classification results, because the radar observations for different precipitation types are not mutually exclusive and measurement fluctuations can also lead to errors. The fuzzy logic technique is well suited for classifying hydrometeors as argued by Liu and Chandrasekar [9], because it is easier to implement than other techniques such as statistical decision method using the prior probability as well as the probability density function. The fuzzy logic classification also has the ability to identify hydrometeor types with overlapping and noise-contaminated measurements. There are several articles in the literature over the last few years describing various aspects of fuzzy logic hydrometeor classification [9]–[12]. A fuzzy logic classification system has three principal components, namely: 1) fuzzification; 2) inference; and 3) defuzzification. The literature referred here [9]–[12] builds on two independently developed models for fuzzy logic hydrometeor classification, namely: 1) the Colorado State University (CSU) model and 2) the National Center for Atmospheric Research (NCAR)/National Severe Storms Laboratory (NSSL) model. This paper presents further development of the CSU model proposed by Liu and Chandrasekar [9] with application to the data from the Severe Thunderstorm Electrification and Precipitation Study (STEPS) experiment. Modifications to the existing CSU model are made in the all three areas of fuzzy logic hydrometeor classification, namely fuzzification, inference, and defuzzification. The original CSU model used the product method in the inference stage, whereas the NCAR/NSSL model used the additive method. This paper proposes a hybrid model, which combines the additive method and the product method in the inference stage. This hybrid model strikes a compromise between the control properties of the additive method and the product method, which essentially balances the metrics of probability of error and false positive classification. The fuzzy logic hydrometeor classification algorithm proposed here uses the weighting factors extensively. The importance of radar measurements can be different according to hydrometeor types. For and may play example, in the identification of rain, more important role than the other measurements. We can build a more effective hydrometeor classification system by applying suitable weights to the radar measurements according to hyis of poor quality due drometeor types. In addition, when to low signal-to-noise ratio of the cross-polar channel [13], the is developed. classifier using measurement sets without
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Fig. 1. Simple block diagram of HCS1.
Another significant addition to the hydrometeor classification process is the information about melting layer (ML). Melting in the stratiform region was layer detected by minimum of can identify used by Zrnic´ et al. [12]. The minimum of the melting layer in the stratiform region well; however, it may not be sufficient in the convective region. In this paper, the and melting layer is detected by the vertical profiles of instead of , because the vertical profiles of can indicate properly melting layer in the storm core. The detected melting layers are used for adjusting the various thresholds of membership function through a precipitation cell. The paper is organized as follows. Section II provides a brief overview of the existing CSU model, whereas Section III describes the various improvements of the proposed model in the three stages of fuzzification, inference, and defuzzification in detail. Section IV presents the application of the hydrometeor classification system to the data collected during STEPS. The important results of this paper are summarized in Section V.
Fig. 2. Detailed architecture of HCS1 (adopted from [9]). TABLE I OUTPUT CATEGORIES OF THE FUZZY CLASSIFICATION SYSTEM
II. CSU-MODEL FOR HYDROMETEOR CLASSIFICATION SYSTEM (HCS1) The current CSU model uses five polarimetric radar measurements as input variables, namely reflectivity at horizontal polar, differential reflectivity , specific differential ization , linear depolarization ratio , and correlation phase coefficient and as corresponding environment factor. The output of the system is one of the ten possible hydrometeor types, namely: 1) drizzle; 2) rain; 3) dry and low-density snow; 4) dry and high-density crystals; 5) wet and melting snow; 6) dry graupel; 7) wet graupel; 8) small hail; 9) large hail; and 10) a mixture of rain and hail. The hydrometeor classification is implemented using a fuzzy logic system. The details of this system are described in Liu and Chandrasekar [9]. Fig. 1 shows the block diagram overview of HCS1, whereas Fig. 2 shows the detailed architecture of HCS1. The main characteristics of the HCS1 lie in the definition of membership functions, construction of rule strength, and defuzzification. The system uses a Beta function model for membership function, which is defined as (1)
where is center point, is the half-width at inflection point, is conand indicates slope of the curve. Rule strength as structed by the product of the individual propositions
(2)
where is the index of the measurement variables and is the index of hydrometeor types. The defuzzification is performed by maximum rule strength. III. IMPROVEMENTS TO THE FUZZY LOGIC HYDROMETEOR CLASSIFICATION SYSTEM (HCS2) as input The HCS2 uses five radar measurements and and produces a hydrometeor class of outputs listed in Table I. The reflectivity is proportional to the received power, which is related to the volumetric radar cross section of the precipitation in the radar resolution volume. The classification problem may not be solved from the reflectivity by itself. However, reflectivity can be play important role to identify hydrometeor types in combination with other radar measurements. Differential reflectivity is sensitive to the shape and orientation of precipitation particles. Therefore, is a good discriminator between rain and hail. is independent of absolute calibration and
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Fig. 3. Configuration of HCS2.
attenuation [14], [15]. can be used to isolate anisotropic hydrometeors such as rain from isotropic hydrometeors such as tumbling hail [16]. depends on nonsphericity, orientation, and canting of precipitation particles. Tumbling wet nonspherical particles such as melting graupel can be identified with large values, whereas drizzle and dry ice particles are values. Values of are close to associated with low unity for rain and ice crystals. In the case of melting ice and mixed-phase conditions, is smaller than unity due to a variety of shapes and sizes compared to uniform precipitation type. Low values of are typically associated with melting snow and mixed-phase precipitation. is the environmental “melting height” variable that can be used to discriminate the vertical transition from ice to rain. HCS2 has two simple changes and three significant modification compared to HCS1. In this paper, nine hydrometeor classes are used as listed in Table I, whereas Liu and Chandrasekar [9] used ten hydrometeor types. Some of the output categories are further divided whereas others are combined. According to the change of hydrometeor types the membership functions are also changed. As another modification, two sets of classifiers are introduced with and without the use of . depends on the measurement of cross-polar signal which on the average is about 20–25 dB below the copolar signal returns. It is well known that deteriorates rapidly with poor signal-to-noise ratio in the cross-polar channel [13]. In CSU-CHILL radar, the radar system lower limit of measurement is about 34 dB observations made under low signal-to-noise ratio [17]. conditions may not be reliable. Therefore, the usage can be included based on the signal-to-noise ratio of the cross-polar signals , which is restricted to of 10 dB or higher. Fig. 3 shows the block diagram implementing the two classification schemes, including and excluding based on , whereas Fig. 4 shows the detailed configuration of HCS2 scheme with . Fig. 3 also shows the general blocks of fuzzy hydrometeor classification, namely fuzzification, inference, and defuzzification. There are three additional important modifications compared to HCS1, namely melting layer detection, application of the hybrid method with weight factors, and preliminary output control, which are described further in the following. A. Melting Layer Detection Melting layer of convective storms can vary according to the environment and the storm evolution. Recognition of the varying melting layer is needed for detecting raindrops above
freezing level such as supercooled rain. Automatic recognition of melting layers has been attempted extensively in spaceborne radar environment such as the Tropical Rainfall Measuring Mission (TRMM) [18]. In dual-polarization ground radars, differential reflectivity, correlation coefficient, and linear depolarization ratio have been extensively used to detect melting layers. Bringi and Chandrasekar [13] provide the physical basis for such an can be effectively identification. In the stratiform region, used to detect melting layers [19]. The in the stratiform region of the storm cell was used in [12]. However, in a convective storm core, can be more useful for detecting melting layers than . In this paper, the vertical profiles of with its gradient and are used to recognize the varying melting layers throughout a storm cell. The melting layer is decided at the height which the gradient of is above 0.25 dB/km whenever reflectivity is above 30 dBZ. Fig. 5 shows an example of the and at melting layer detected and the vertical profile of the resolution of 0.5 km 0.5 km. The echoes below 30 dBZ can be handled two ways. In convective storms, the neighboring averaged melting levels are extended to regions where reflectivity is below 30 dBZ. In stratiform events, where there is horizontal homogeneity, spatial averaging is done to get an averaged verand to infer the melting level. In addition, tical profile of can also be used in stratiform region to detect a melting layer. This procedure is repeated throughout the storm cell to detect the varying melting layer. The melting layers detected in the vertical structure of storms are shown as dotted lines in field of Figs. 7, 11, and 15. The detected melting layers are used to describe the thresholds of the membership function. B. Fuzzification The purpose of fuzzification is to convert the precise input measurements to fuzzy sets with corresponding membership degree. The specification of membership functions is critical to the classification performance. Several functional forms can provide adequate representation of membership functions, such as triangular, trapezoidal, Gaussian, or Beta functions. In this paper, the input variables are fuzzified by six Beta membership functions of 11 preliminary output categories. The Beta function has a flat region of unity for a preferred region with degree 1, tapers off outside the preferred region, and has an adjustable tail. , , , and are fuzzified by one-dimensional membership functions, while and are fuzzified by two-dimensional (2-D) membership functions because their thresholds strongly depend on . The details of the membership functions are given in Liu and Chandrasekar [9]. The parameters of the Beta membership function for five radar measurements are fixed according to the thresholds of hydrometer types, which are adopted from Liu and Chandrasekar [9]. The parameters of the membership function are adjusted according to the varying melting layer at the resolution of 0.5 km 0.5 km throughout the precipitation cell, whereas the HCS1 used the constant parameters of membership function that applied average meting layer according to region and season. The varying membership function can be effectively used to identify melting ice cores or supercooled liquids above freezing level. The probability of existence for hydrometeor types along height is controlled by detected melting layers through storm.
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Fig. 4. Detailed architecture of HCS2 with
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LDR. TABLE II WEIGHT FACTORS OF THE INFERENCE TO VARIOUS POLARIMETRIC RADAR MEASUREMENTS
Fig. 5.
Example of melting layer detection.
C. Inference The inference process is another area where changes are made to the current CSU model. The inference procedure of the existing CSU model uses product of the individual proposition strength as (2). In any inference or classification problem there are two performance measures, namely degree of correct
classification and degree of error. The product method for rule strength proposed by Liu and Chandrasekar [9] minimizes the chance of absurd classification. For example, if one measurement is considerably out of range, then it will be sure that it is not classified in an absurd manner. On the other hand, the rule strength can be constructed from the individual proposition strengths by the additive weighting method as described by Zrnic´ et al. [12]. This is another way to define the rule strength, where the probability of “detection” of the right type of hydrometeors is maximized. The scheme presented here develops a hybrid rule strength combining the advantage of both the additive and the product method of proposition strength to yield the rule strength. Measurement error in conjunction with dynamic range of a radar parameter determines the error in the classification process. For example, has a typical measurement error of 0.2 dB and a nominal dynamic range of 6 dB (at S-band). Due to measurement error, , , , and
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Fig. 6. CAPPI of reflectivity (Z ) and hydrometeor classification result corresponding to the case of June 11, 2000. Black line in Z field is the track of T28 aircraft (: start time, +: end time). DZ: Drizzle, R: Rain, WS: Wet Snow, DS: Dry Snow, G/SH: Graupel and/or Small Hail, SH: Small Hail, LH: Large Hail, SRH: Rain and Small Hail mixture, LRH: Rain and Large Hail mixture.
Fig. 8. (a) Radar measurements and the classification result along the T-28 aircraft track (alphabets in H-Type field correspond to 2DC image data in Fig. 9). (b) Hail total counts and hail average diameter from hail spectrometer corresponding to the case of June 11, 2000.
Fig. 7. Vertical structure of radar measurements (Z , Z ), hydrometeor classification result corresponding to the case of June 11, 2000. Black line in Z field—T28 aircraft track along time and altitude (: start time, +: end time), dotted line in Z field—Melting layer detected, alphabets in H-Type field correspond to 2DC image data in Fig. 9.
can potentially move across hydrometeor class boundaries [20] and can inhibit the correct classification. Though reflectivity by itself does not completely contribute to the forward problem of classification, reflectivity value of 55 dBZ automatically eliminates drizzle and ice crystals. Therefore, a hybrid procedure using the strength of both the additive and the product rule
Fig. 9.
2DC image data corresponding to the case of June 11, 2000.
strength construction is developed in this paper. The additive rule strength construction is used for , , , and , whereas the product rule strength mechanism is used for and . The resulting rule strength can be written as
(3)
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Fig. 10. CAPPI of reflectivity (Z ) and the hydrometeor classification result corresponding to the case of June 22, 2000. Black line in Z field is the track of T28 aircraft (: start time, +: end time). DZ: Drizzle, R: Rain, WS: Wet Snow, DS: Dry Snow, G/SH: Graupel and/or Small Hail, SH: Small Hail, LH: Large Hail, SRH: Rain and Small Hail mixture, LRH: Rain and Large Hail mixture.
Fig. 11. Vertical structure of radar measurements (Z , Z ) and the hydrometeor classification result corresponding to the case of June 22, 2000. Black line in Z field—T28 aircraft track along time and altitude (: start time, field—melting layer detected, alphabets in +: end time), dotted line in Z H-Type field correspond to 2DC image data (A–G) and HVPS data (H–J) in Fig. 13.
where is the proposition strength for hydrometeor type and is the weight factor. A discussion regarding the weight is ap-
Fig. 12. (a) Radar measurements and the classification result along the T-28 aircraft track (alphabets in H-Type field correspond to 2DC image data (A–G) and HVPS data (H–J) in Fig. 13). (b) Hail total counts and hail average diameter from hail spectrometer corresponding to the case of June 22, 2000.
propriate here. The weight factors used in this work are based on a different set of principles compared to those described in [12]. The weight factors presented in this paper change with the hydrometeor types and the radar measurements, whereas the results of [12] suggested to match the weights to the radar
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for these inputs are the same as for the good rule strength can be expressed as
case. The
(4) case, the weight factors are set For the noise-contaminated to 1.0, 0.6, and 0.4, for , , and , respectively. There is no change in defuzzification. IV. APPLICATION OF THE NEW HYDROMETEOR CLASSIFICATION AND IN SITU VALIDATION The performance of the improved hydrometeor classifier using dual-polarization radar measurements is evaluated by comparing the results of classifier presented here with in situ airborne observations with the T-28 aircraft using instruments such as the 2-D cloud particle measurement probe (2DC), high volume particle sampler (HVPS), and hail spectrometer data. Details about T-28 aircraft and the instrumentation can be found at http://www.ias.sdsmt.edu/institute/t28. The radar data used in this study were collected by CSU-CHILL radar deployed near the Burlington Airport (latitude 39.234 95, longitude 102.278 77) during the Severe Thunderstorm Electrification and Precipitation Study (http://lab.chill.colostate.edu/STEPS). The following describes three different case studies of dual-polarization radar and simultaneous in situ measurements of T-28 instrumentation data, on June 11, 22, and 29, 2000, respectively. Fig. 13. (a) 2DC image data. (b) HVPS image data corresponding to the case of June 22, 2000.
measurements. Some measurements may have a higher impact in specific hydrometeors. The choice of these weighting factors is determined from prior knowledge in the literature and based on a combination of the importance of the measurement variable for a specific hydrometeor class and the accuracy. In general, is given the lowest weight because of the coexistence with . Table II shows the weight factor matrix as a function of hydrometeors and radar measurements. Note that the weight factors do not add up to 1 (sum of the weights is 2), but can be normalized for decision purpose. is a fairly accurate parameter and mostly takes the largest weight. D. Defuzzification and Postprocess The rule strengths computed in fuzzy inference stage are aggregated and finally converted into one number by a defuzzifier. The defuzzifier selects the hydrometeor corresponding to the one that has maximum . After that, the preliminary classification result is categorized into one of nine classes of precipitation (see Table I) by the postprocess. This process combines potentially overlapping ice categories, compared to HCS1. E. Classification System for Noisy (
In the case of noisy , , , ,
, the classifier uses five variables ) for inputs. Membership functions
A. Data Sources and Instrumentation The CSU-CHILL radar is an S-band, fully polarimetric Doppler radar with polarization agility and diversity [13]. The CSU-CHILL system has a gain of 43 dB including waveguide loss, 1.1 of 3-dB beamwidth, and 800–2500 of pulse repetition time. The characteristics of CSU-CHILL radar can be found at http://lab.chill.colostate.edu/chill-technical.html. T-28 aircraft is an armored aircraft operated by South Dakota School of Mines and Technology. Various sensors equipped at the T-28 aircraft cover different portions of the size range in an overlapping fashion. Cloud ice and small precipitation particles are imaged by a 2-D cloud probe, with a maximum vertical window of 0.8 mm. Larger precipitation particles are imaged and counted by the custom-built optical array hail spectrometer, sensitive to particles between 0.9 mm and 12 cm in diameter. The automated counting and sizing circuitry includes only particles in the size range 4.5 mm to 4.5 cm. The HVPS can measure particles size up to 4.5 cm by taking 2-D images of hydrometeors that pass through a 4.5 cm 20 cm plane that is normal to the direction of aircraft flight. B. In Situ Validation 1) June 11, 2000: A mesoscale convective system was observed by the CHILL radar on June 11, 2000. During the storm event, T-28 aircraft flew through the storm at average altitude of 4.5 km above ground and experienced heavy icing. The Constant Altitude Plan Position Indicator (CAPPI) of reflectivity
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Fig. 14. CAPPI of reflectivity (Z ) and the hydrometeor classification result corresponding to the case of June 29, 2000. Black line in Z field is the track of T28 aircraft (: start time, +: end time), DZ: Drizzle, R: Rain, WS: Wet Snow, DS: Dry Snow, G/SH: Graupel and/or Small Hail, SH: Small Hail, LH: Large Hail, SRH: Rain and Small Hail mixture, LRH: Rain and Large Hail mixture.
and the hydrometeor classification result are shown in Fig. 6, where the black solid line in the reflectivity field indicates the track of the T-28 aircraft. Fig. 7 shows the vertical structure of , , and the classification result corresponding to the airfield indicates the T-28 craft track. The black solid line in aircraft track according to T-28 flight time and altitude, whereas field is the detected melting layer. The the dotted line in radar measurements and classification result along the T-28 aircraft track (220 400–221 200 UTC), and the hail total counts and hail average diameter from hail spectrometer are shown in Fig. 8. 2DC images are shown in Fig. 9 where the markings (A–F) correspond to the regions marked in Figs. 7 and 8(a). We can see that the graupel/small hail and small hail region of hydrometeor classification results match well with hail total counts from hail spectrometer and 2DC image as shown in Fig. 8(b) and Fig. 9, respectively. 2) June 22, 2000: During the storm event, T-28 aircraft flew through the storm at average altitude of 4.5 km above ground and encountered significant turbulence, icing, hail, and frequent lightning. The CAPPI of reflectivity and the hydrometeor classification result are shown in Fig. 10, whereas vertical profile of , , and the corresponding classification result are shown in Fig. 11. Radar measurements and classification results along the T-28 aircraft track (001 500–002 059 UTC) and the hail total counts and hail average diameter from the hail spectrometer are shown in Fig. 12. In situ 2DC image and HVPS images along the aircraft track are shown in Fig. 13. By comparing the classification results with in situ T-28 aircraft data, we can see fairly good agreement between HCS2 results and T-28 aircraft data. We can see that the dry snow region and small hail region of classification results are in accord with aggregate snow in 2DC image data [A, B, and C in Fig. 13(a)] and small hail in HVPS data [H, I, and J in Fig. 13(b)]. The graupel/small hail region of classification result also matches well with hail total counts and hail average diameter from the hail spectrometer as shown in Fig. 12. 3) June 29, 2000: This was a supercell storm in the STEPS eastern Doppler lobe for over 2 h. Radar reflectivity of 60 dBZ reached to altitude exceeding 10 km. The storm moved only
Fig. 15. Vertical structure of radar measurements (Z , Z , K , LDR, and ) and the hydrometeor classification result corresponding to the case of June 29, 2000. Black line in Z field—T28 aircraft track along time and altitude (: start time, +: end time), dotted line in Z field—melting layer detected, alphabets in H-Type field correspond to 2DC image data in Fig. 17.
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wet hail particles at 234 230 UTC (D in Fig. 17), where the region is classified to small rain hail mixture regions as shown in the hydrometeor field of Fig. 16(a). The classification result agrees well with the observation data of T-28 aircraft except at the edge region of the storm. V. SUMMARY AND CONCLUSION
Fig. 16. (a) Radar measurements and classification result along the T-28 aircraft track (alphabets in H-Type field correspond to 2DC image data in Fig. 17). (b) Hail total counts and hail average diameter from hail spectrometer corresponding to the case of June 29, 2000.
The main goal of this research is to further develop the fuzzy logic hydrometeor classification system introduced by Liu and Chandrasekar [9] and evaluate by comparing the results of the hydrometeor classification applying to CSU-CHILL radar data with in situ data from T-28 aircraft. One difference between the previous system and the proposed system is the membership function. A previous system used the constant level that applied the average melting layer according to region and season, whereas the hydrometeor classification system proposed memhere uses a varying melting layer detected for the bership function, which makes it possible to identify raindrops or rain hail mixture above freezing level. From the June 29, 2003 case, we can see that the rain or rain hail mixture in high altitude can be classified effectively by using a varying melting layer instead of a fixed freezing level. Next, the previous system used a product method to infer the rule strength, whereas the fuzzy logic hydrometeor classification system has been developed by adopting a hybrid model, which combines the additive model and the product model to compromise between the probability of error and false positive classification. This method is very effective to reduce misclassification by contaminated radar measurements. The system also uses the weight factor extensively according to hydrometeor types and radar variables. By applying the weight factors for radar variables, we can use the observations more effectively to identify precipitation types taking their error structure into consideration. In addition, the classifier is separated in two schemes according to the quality . In case of noisy , it is not used for the input of of the classifier. The results of fuzzy hydrometeor classification obtained from CSU-CHILL radar data compare very well with T-28 aircraft instrumentation data. The classification results show good agreement with in situ observations. ACKNOWLEDGMENT
Fig. 17.
2DC image data corresponding to the case of June 29, 2000.
slowly at first, then turned rightward as it became a full-fledged supercell. Hail up to golf ball size was reported on the ground. The flight through the storm was at altitude of around 4.5 km above ground. The CAPPI of reflectivity and the hydrometeor classification result are shown in Fig. 14. Fig. 15 shows the ver, , and the corresponding classification tical profile of result. Radar measurements and classification result along the T-28 aircraft track (233 900–234 330 UTC), and the hail total counts and hail average diameter from hail spectrometer are shown in Fig. 16. 2DC images are shown in Fig. 17. The hail average diameter of the hail spectrometer shows above 1 cm around 234 200 UTC in Fig. 16(b) where the hydrometeor classification system identifies hail. 2DC image data show rain or
The authors acknowledge the collaborators in the STEPS program as well as the T-28 facility personnel. REFERENCES [1] V. N. Bringi, T. A. Seliga, and W. A. Cooper, “Analysis of aircraft hydrometeor spectra and differential reflectivity (ZDR) radar measurements during the cooperative convective precipitation experiment,” Radio Sci., vol. 19, pp. 157–167, 1984. [2] M. P. M. Hall, J. W. F. Goddard, and S. M. Cherry, “Identification of hydrometeors and other targets by dual-polarization radar,” Radio Sci., vol. 19, pp. 132–140, 1984. [3] V. N. Bringi, R. M. Rasmussen, and J. Vivekanandan, “Multiparameter radar measurements in Colorado convective storms. Part I: Graupel melting studies,” J. Atmos. Sci., vol. 43, pp. 2545–2563, 1986. [4] V. N. Bringi, J. Vivekanandan, and J. D. Tuttle, “Multiparameter radar measurements in Colorado convective storms. Part II: hail detection studies,” J. Atmos. Sci., vol. 43, pp. 2564–2577, 1986.
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[5] K. Aydin, Y. Zhao, and T. A. Seliga, “A differential reflectivity radar hail measurement technique: Observations during the Denver hailstorm of 13 June 1984,” J. Atmos. Oceanic Technol., vol. 7, pp. 104–113, 1990. [6] D. S. Zrnic´ , V. N. Bringi, N. Balakrishnan, K. Aydin, V. Chandrasekar, and J. Hubbert, “Polarimetric measurements in a severe hailstorm,” Mon. Weather Rev., vol. 121, pp. 2223–2238, 1993. [7] H. Höller, “Radar-derived mass-concentrations of hydrometeors for cloud model retrievals,” in Preprints, 27th Conf. Radar Meteorology, Vail, CO, 1995, pp. 453–454. [8] H. Abou-El-Magd, V. Chandrasekar, V. N. Bringi, and W. Strapp, “Multiparameter radar and in situ aircraft observation of graupel and hail,” IEEE Trans. Geosci. Remote Sens., vol. 38, no. 1, pp. 570–578, Jan. 2000. [9] H. Liu and V. Chandrasekar, “Classification of hydrometeor based on polarimetric radar measurements: Dvelopment of fuzzy logic and neurofuzzy systems and in situ verification,” J. Atmos. Oceanic Technol, vol. 17, pp. 140–164, 2000. [10] J. M. Straka and D. S. Zrnic´ , “An algorithm to deduce hydrometeor types and contents from multi-parameter radar data,” in Preprints, 26th Conf. Meteorology, Boston, MA, 1993, pp. 513–515. [11] J. Vivekanandan, D. S. Zrnic´ , S. M. Ellis, R. Oye, A. Ryzhkov, and J. M. Straka, “Cloud microphysics retrieval using S-band dual-polarization radar measurements,” Bull. Amer. Meteorol. Soc., vol. 80, pp. 381–388, 1999. [12] D. S. Zrnic´ , A. V. Ryzhkov, J. Straka, Y. Liu, and J. Vivekanandan, “Testing a procedure for automatic classification of hydrometeor types,” J. Atmos. Oceanic Technol., vol. 18, pp. 892–913, 2001. [13] V. N. Bringi and V. Chandrasekar, Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge, U.K.: Cambridge Univ. Press, 2001. [14] V. Chandrasekar, V. N. Bringi, N. Balakrishnan, and D. S. Zrnic´ , “Error structure of multiparameter radar and surface measurements of rainfall. Part iii: Specific differential phase,” J. Atmos. Oceanic Technol., vol. 7, pp. 621–629, 1990.
801
[15] D. S. Zrnic´ and A. V. Ryzhkov, “Advantages of rain measurements using specific differential phase,” J. Atmos. Oceanic Technol., vol. 13, pp. 454–464, 1996. [16] K. Aydin, V. N. Bringi, and L. Liu, “Rain-rate estimation in the presence of hail using S-band specific differential phase and other radar parameters,” J. Appl. Meteorol., vol. 34, pp. 404–410, 1995. [17] J. Hubbert, V. N. Bringi, and L. D. Carey, “CSU-CHILL polarimetric radar measurements from a severe hail storm in eastern Colorado,” J. Appl. Meteorol., vol. 37, pp. 749–775, 1998. [18] V. Chandrasekar and B. Zafar, “Precipitation type determination from spaceborne radar observations,” IEEE Trans. Geosci. Remote Sens., vol. 42, no. 10, pp. 2248–2253, Oct. 2004. [19] D. S. Zrnic´ , N. Balakrishnan, C. L. Ziegler, V. N. Bringi, K. Aydin, and T. Matejka, “Polarimetric signatures in the stratiform region of a measoscale convective system,” J. Appl. Meteorol., vol. 32, pp. 678–693, 1993. [20] J. M. Straka, D. S. Zrnic´ , and A. V. Ryzhkov, “Bulk hydrometeor classification and quantification using polarimetric radar data: synthesis of relations,” J. Appl. Meteorol., vol. 39, pp. 1341–1372, 2000.
S. Lim, photograph and biography not available at the time of publication.
V. Chandrasekar, photograph and biography not available at the time of publication.
V. N. Bringi, photograph and biography not available at the time of publication.
