Atmospheric Research

Transcription

1 ATMOS-02508; No of Pages 15 Atmospheric Research xxx (2011) xxx xxx Contents lists available at SciVerse ScienceDirect Atmospheric Research journal homepage: Recent advances in classification of observations from dual polarization weather radars V. Chandrasekar a,, R. Keränen b, S. Lim a, D. Moisseev c a Colorado State University, Fort Collins, CO, United States b Vaisala Oyj, Helsinki, Finland c University of Helsinki, Finland article info abstract Article history: Received 24 December 2010 Received in revised form 12 July 2011 Accepted 26 August 2011 Available online xxxx Keywords: Weather radar Classification Hydrometeor Data quality Echo It has been a decade since some of the early papers on dual-polarization radar based hydrometeor classification were published. Subsequently this topic has seen rapid advances especially due to adaptation of this technology for operational applications. Since the early papers on hydrometeor classification, additional characterization in terms of echo classification, data quality and hydrometeor identification have been researched, and products produced some distinctly, some with all three procedures combined. This paper provides a summary of the advances since the early papers in this area, especially with an emphasis on how the successful operational applications have influenced this field. Principles of the various types of classification and examples of characterizations such as data quality, echo and hydrometeor classification are also described Elsevier B.V. All rights reserved. 1. Introduction Dual polarization radar measurements of precipitation are sensitive to the hydrometeor properties such as shape, size, orientation, phase state, and fall behavior. By integrating information from the various dual polarization measurements, hydrometeor types in precipitation can be identified effectively. Various studies of characteristics of polarimetric signals from bulk hydrometeors and other scatterer types have been reported in the literature (Aydin et al., 1990; Zrnić et al., 1993a, 1993b; Straka et al., 2000; Bringi and Chandrasekar, 2001). There are a number of classification methods based on dual polarization radar measurements such as Boolean logic decision tree or fuzzy logic. Initially, contours of Boolean logic equations were used to depict distinct populations of empirically identified hydrometeor classes in the parameter space (Straka and Zrnić, 1993; Höller et al., 1994; Zeng et Corresponding author at: Colorado State University, 1373 Campus delivery, Fort Collins, CO , United States. address: chandra@engr.colostate.edu (V. Chandrasekar). al., 2001). Liu and Chandrasekar (1998, 2000) comprehensively studied the various methodologies to combine the information from dual polarization measurements and presented arguments for using the fuzzy logic system of hydrometeor classification. The fuzzy logic technique is well suited for classifying hydrometeors as argued by Liu and Chandrasekar (1998, 2000) and Straka et al. (2000) because it is easier to implement and has the ability to identify hydrometeor types with overlapping and noise-contaminated measurements. In the last decade, the fuzzy logic algorithms have proven successful, validated directly or indirectly (Vivekanandan et al., 1999; Liu and Chandrasekar, 2000; Zrnić et al., 2001; Lim et al., 2005; Baldini et al., 2005; Ryzhkov et al., 2005; Keränen et al., 2007; Park et al., 2009). As with any decision process, there is a balance between probability of correct classification and false alarm. For example, the hydrometeor classification system proposed by Lim et al. (2005) balances the metrics of probability of error and false positive classification by combining additive and product inference method. This system also introduced the use of varying melting level information and of weighting factors for each radar observations to the hydrometeor /$ see front matter 2011 Elsevier B.V. All rights reserved. doi: /j.atmosres

2 2 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx classification process. Though originally the hydrometeor classification was introduced for S band radars the success of the hydrometeor classification system encouraged extension to radar observations at attenuating frequencies such as C and X band frequency radars. The initial success was in extension to C band radar systems. For example, the hydrometeor classification methodology of Lim et al. (2005) was evaluated with the C-band University of Huntsville ARMOR radar data (Baldini et al., 2005) and the C-band University of Helsinki research radar data (Keränen et al., 2007). The success of the use of vector of dual-polarization radar observations for hydrometeor classification was extended to areal classification by usage of texture information (Gourley et al., 2007). Park et al. (2009) presented a hydrometeor classification system adopting new features such as confidence vector, a matrix weights, melting layer and vertical continuity checks. Dual polarization weather radar data has also been used extensively for identifying data quality issues. However quality control systems have not found their way into scientific literature. One such documentation for the procedure was provided in Wang and Chandrasekar (2009) where polarimetric radar observations have been used in generating data masks for quality control. A consensus of small standard deviation of ϕ dp and high ρ hv is used to ensure confidence in the detection of weather echoes. In addition dual polarization radar observations have been used for performing echo classification. This capability is well suited to data quality considerations such as mitigation of non-meteorological echoes. The principle is based on using the microphysical characteristics of precipitation with relatively high co-polar correlation and relatively smooth radial behavior of differential phase and of echo power, accompanied with specific relations between reflectivity (Z h )anddifferentialreflectivity(z dr ). A fuzzy classification scheme for meteorological vs. non-meteorological targets has developed in the Joint Polarization Experiment (JPOLE), which uses texture parameters of Z h and differential phase (ϕ dp )(Ryzhkov et al., 2005). Recent hydrometeor classification systems provide identification for precipitation types as well as non-meteorological targets such as clutter or insects and birds by integrating various classification systems such as data quality, echo and hydrometeor classification. This paper describes the principles of these classifications with data examples and the paper is organized as follows: Section 2 introduces briefly dual polarization radar measurements used for various classifications. Section 3 describes data quality classification whereas Section 4 presents echo classification. In Section 5 hydrometeor classification is summarized and Section 6 describes classification for winter precipitation. In Section 7 various examples of different classifications are presented and the important conclusions are summarized in Section Dual polarization radar measurements and microphysical properties This section describes briefly dual-polarization weather radar parameters that are used for various classifications. Each parameter has advantage for specific target discrimination. Typically by combining these parameters, a classification system can be established. Reflectivity factor at a given polarization (say horizontal, Z h ) is proportional to the received power at the horizontal (h) port and related to the power of a horizontally polarized backscattered electric field from a radar resolution volume for a horizontally polarized transmitted wave. For a particle of given size, ice produces lower Z h than does liquid because of difference in dielectric constant. Differential reflectivity (Z dr ) is obtained from the ratio of horizontal reflectivity and vertical reflectivity, which is related to mean shape of particles. The combination of Z h and Z dr is a good discriminator between rain, hail and non-meteorological targets such as birds. In a Z dr profile the sharp change in Z dr occurs near the 0 C isotherm and marks the transition between ice particles and water (Bringi and Chandrasekar, 2001). The differential phase (ϕ dp ) is the measurement proportional to the propagation or forward scatter properties of hydrometeors that is the difference between horizontal and vertical phase. In horizontally oriented hydrometeors such as rain, horizontal propagation phase is larger than a vertically polarized wave. In addition in regions of non-meteorological echoes, due to the poor correlation between the horizontal and vertical polarizations, ϕ dp fluctuations are significantly higher than precipitation. Specific differential phase (K dp ) is the range derivative of the differential propagation phase between horizontally and vertically polarized signals. K dp is independent of absolute calibration and it is not affected by attenuation. K dp can be used to isolate the presence of rain from isotropic hydrometeors such as tumbling hail. The ratio of the received cross-polar power to the transmitted co-polar power defines the linear depolarization ratio (LDR). The hydrometeor characteristics associated with generation of cross polar returns include hydrometeor shape, shape irregularity, thermodynamic phase, dialectic constant, and canting in the plane of polarization (Herzegh and Jameson, 1992). Tumbling, wet nonspherical particles such as hail, melting aggregates, and wet graupel can be identified with large LDR values, whereas drizzle and dry ice particles are associated with low LDR values. The correlation coefficient between horizontally and vertically polarized echoes (ρ hv ) is affected by the variability in the ratio of the vertical to horizontal size of individual hydrometeors. Values of ρ hv are close to unity for rain and pure ice crystals. In the case of melting and mixed phase conditions, ρ hv is smaller than unity. Low values of ρ hv can be used for detecting hail and mixed phase precipitation and non-meteorological targets such as ground clutter. The normalized coherent power (NCP) is the ratio of the power calculated at lag one to the total received power and is very efficient at removing noise. The lower the NCP value the more likely that given radar return is noise. This parameter can be used for classifying non-meteorological targets. The spectral width (SW) is a measure of velocity dispersion within the radar sample volume and has ability to estimate turbulence associated with mesocyclones and boundaries. By combination with reflectivity, the spectral width can be used to identify some of the noise that the NCP field misses. Higher spectral width value at low reflectivity can be identified as noise. In addition to these weather observations, spatial variability variables (called as texture) can be used to discriminate precipitation and non-precipitation echoes. Texture of Z h,z dr and ϕ dp are

3 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx 3 used widely for classification. It can be obtained by root mean square difference of radar observations at azimuth and range direction (Gourley et al., 2007). 3. Data quality classification Data quality classification can be defined as a classification index that is generated to indicate primarily good data from precipitation or bad data. The definition of bad data could include clutter, or precipitation data contaminated by extensive clutter, or noise etc. The good data/bad data index is also sometimes refereed as data mask. It is called as data mask or polarimetric meteorological index (PMI). This classification is critically important in operational systems, where subsequent computations are done such as precipitation rates, downstream of the data flow. Typically the principle of this classification is based on the fact that echo from precipitation must have specific characteristics, and the most commonly used fields are ρ hv and smoother spatial variation of ϕ dp,z h and Z dr. Wang and Chandrasekar (2009) documented a procedure that used the standard deviation of differential phase and the ρ hv to identify precipitation data regions only. Small standard deviation of ϕ dp and high ρ hv value indicate good data. This process is simple and robust for precipitation echo detection. Another method is the fuzzy logic based identification. The PMI is calculated by combining each fuzzy-rule-strength (RS) for each classification category as expressed as (Chanthavong et al., 2010) PMI ¼ RS ð meteorological signal Þ= frs i g ð1þ where i indicates a class in the hypothesis set. If PMI is above a threshold, the data can be flagged as good data. The primary objective of data quality classification is for selecting precipitation from radar data. 4. Echo classification Echo classification has been pursued for a long time with weather radars, primarily based on echo features. For example convective/stratiform classification of radar echoes has been studied (Steiner et al., 1995; Zafar and Chandrasekar, 2004). The convective/stratiform classification has been used extensively in the literature to understand the percentage of precipitation with different latent heat profiles. Most of the convective/stratiform classification is based on the principle of echo variability that is somewhat similar to the texture analysis, but more of spatial scale and variability analysis. Similarly, the term echo classification has also been used to refer to the type of precipitation cells, such as line storms, isolated cells and super cells etc. (Schiesser et al., 1995; Alexiuk et al., 1999; Baldwin et al., 2005; Guillot et al., 2008; Gagne et al., 2009). Since the introduction of hydrometeor classification, the echo classification has assumed a broader scope, where classification is done, to classify precipitating and non-precipitating echoes such as, chaff, birds, insects and clutter. This means the echoes that are not precipitation are also further identified (Giuli et al., 1991; Silveira and Holt, 2001; Gourley et al., 2007; Rico-Ramirez and Cluckie, 2008). This has blurred the boundaries between data quality and echo classification. Fig. 1. General block diagram of echo classification. Precipitation exhibits higher ρ hv, smaller LDR value and smaller spatial fluctuations in ϕ dp and Z h,z dr than ground clutter. Textures of ϕ dp and Z dr can play critical roles to discriminate between precipitation and nonprecipitating echoes. Birds and insects are identified by higher Z dr, and lower ρ hv with lower Z h (typically below 30 dbz for bird). By combination of radar variables such as Z h,z dr, ρ hv and textures of radar variables (σ(z h ), σ(z dr ), σ(ϕ dp )), echoes can be classified effectively. Fig. 1 shows the general block diagram of an echo classifier. 5. Hydrometeor classification The fuzzy logic technique is well suited for hydrometeor classification due to its ability to identify hydrometeor types with overlapping and noisy measurements. There are several studies in the literature over the last decade describing various aspects of fuzzy logic hydrometeor classification applied for S- and C-band radar observations (example Liu and Chandrasekar, 1998, 2000; Zrnić et al., 2001; Lim et al., 2005; Vivekanandan et al., 1999) and for C-band (Baldini et al., 2005; Keränen et al., 2007). A fuzzy logic based hydrometeor classification system typically consists of three principal aspects, namely: 1) fuzzification, 2) inference, and 3) defuzzification. The general concept of the fuzzy logic based hydrometeor classification shown in Fig. 2. Fuzzification is the process used to convert the precise input measurements to fuzzy sets with corresponding membership degree. Membership functions play an important role in the classification performance. Trapezoidal and Beta functions are used widely. Inference is a rule-based procedure to obtain the strength of individual propositions. Typically the rule can be described as an if then statement. The most commonly used inference methods in weather radar classification are additive and productive method. Defuzzification is an aggregation of rule strength and a selection of the best representative. A recent hydrometeor classification procedure proposed by Lim et al. (2005), balances the metrics of probability error and false positive classification by using both additive and product rules in inference. This hydrometeor classification system uses five polarimetric radar measurements as input variables, namely Z h, Z dr, K dp, LDR, and ρ hv and Height (or Temperature) as corresponding environment factor. The system also uses the weight factor extensively according to hydrometeor types and radar variables. By Fig. 2. General architecture of fuzzy logic based hydrometeor classification.

4 4 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx Fig. 3. The detailed architecture of fuzzy logic based hydrometeor classification (Lim et al., 2005). applying the weight factors for radar variables, one can use the observations more effectively to identify precipitation types, taking their error structure into consideration. The detailed architecture of the hydrometeor classification proposed by Lim et al. (2005) is shown in Fig. 3. The system has been evaluated extensively with S-band CSU-CHILL radar, the C-band University of Huntsville ARMOR radar (Baldini et al., 2005), and the C-band University of Helsinki research radar (Keränen et al., 2007). The system has also developed to guide rainfall application with the hydrometeor classes, namely rain, mixed precipitation and ice (Cifelli et al., 2011). Most of the hydrometeor classification procedures were point wise classifications and the areal coherence of the classification was primarily guaranteed due to the continuity of the inherent measurements. Any fluctuations or discontinuities in hydrometeor classification inferences were removed by simple processing techniques such as speckle filter. However with the introduction of texture into the hydrometeor classification was another improvement brought into the processing by about The concept of difference in the texture of reflectivity and differential reflectivity has been used for a long time to identify clutter regions (Giuli et al., 1991). The texture field formalizes the use of this information similar to how the transition was done for simple Z h,z dr, and LDR classification. In machine learning applications, texture is defined as a pattern or structure that is recognized but difficult to define. Often texture is defined as a local statistical property of an image that is constant or slowly Fig. 4. Example of data mask from classification used by Wang and Chandrasekar (2009). (a) Z h before, (b) ρ hv, (c) ϕ dp, (d) Z h after filtered by data mask. Data was collected by CHILL radar on UT.

5 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx 5 varying. Texture in weather radar means spatial variability of the radar variable. There are several techniques to calculate texture of radar measurements. Giuli et al. (1991) and Schuur et al. (2003) used absolute value of difference between running average and original values of radar measurements. It can be defined as SDðZ h Þ ¼ Z h Zh. Zh is the running average with a 1 km window size along radial. For texture of ϕ dp, 2 km window is used. Gourley et al. (2007), calculated texture as the root mean square difference between one particular gate and adjacent gates in radial and azimuthal direction. It can be expressed as TZ ð Þ ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi. ð Z i a;j b Z a;bþ 2 M i¼1 N j¼1 M N where a and b indicate the azimuth and range of the gate and M and N represent number of gates at azimuth and range, respectively. The textures of Z h and Z dr have been shown to have the ability to discriminate meteorological targets and ground clutter, but it has not been successfully used to classify precipitation and bio-scatter. The texture of ϕ dp ð2þ may be most efficient for discriminating between precipitation, ground clutter/ap and bio-scatter. Texture is now a common input in modern hydrometeor classification systems, though the actual texture definition may vary between them. 6. Classification for snowfall Weather radar interpretation in snowfall is notoriously difficult. Following the successful application of fuzzy logic hydrometeor classification algorithms in summer time precipitation, the research community is turning its attention to winter precipitation. Radar observations depend on phase, size, shape, and density of precipitating particles. Variability in these physical properties is one of the major error sources in quantitative snowfall estimation with radar (Mitchell et al., 1990). One approach, to limit this variability, is to divide winter precipitation according to hydrometeor classes, such as aggregates, crystals, rimed particles, as inferred from dual-polarization radar observations. Furthermore, winter precipitation often consists of a mixture of different types of hydrometeors, for example Fig. 5. Example of data quality classification from C-band Kerava radar in Finland ( UT). (a) Z h, (b) ρ hv, (c) data mask (green: meteorological target). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

6 6 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx ice particles and supercooled water droplets, crystals and aggregates, etc. The physical properties of ice precipitation are governed by growth mechanisms, i.e. water vapor deposition, aggregation, riming and ice multiplication processes. Those processes modify the particles as well as the precipitation microphysical properties. As an alternative to hydrometeor classification, therefore, one could consider using radar Fig. 6. Example of echo classification from C-band Kerava radar in Finland ( UT). (a) Z h, (b) Z dr, (c) ϕ dp, (d) ρ hv, (e) classification result (green: precipitation, dark orange: bio-scatter and saddle brown: GC/AP). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

7 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx 7 observations to identify the microphysical processes. The growth process identification requires not only analysis of dual-polarization signatures for a given radar observation volume, but also analysis of vertical profiles and spatial behavior of polarimetric radar measurements. Doppler spectra have been used to identify altitudes where spectra either exhibited bimodality or a rapid change in a fall velocity. The bimodality can be used to identify cases where riming and/or secondary ice production was present. Thus in summary, a combination of polarimetric radar measurements, vertical profiles and often Doppler Spectra can be used to infer snow precipitation processes. 7. Overview of various implementation and examples This section presents a comprehensive summary of various hydrometeor classification schemes. This is an operational product offered by several manufacturers and therefore has enjoyed the benefit of extensive testing and validation, and also maturing over time. The following shows a selected set of examples that demonstrate the various aspects of echo classification, data quality classification and precipitation classification Implementation of data quality classifier Data quality classifier is mainly for extracting good data from radar observations contaminated by noise or non-meteorological targets. Fig. 4 shows an example of data quality classifier documented by Wang and Chandrasekar (2009). This type of data quality filtering has been extensively used in processing software, used by Colorado State University. Good data is primarily indicated by threshold of ρ hv and standard deviation of ϕ dp, in addition to other obvious factors such as signal to noise ratio. Fig. 5 shows an example of data quality classifier used by Chanthavong et al. (2010). The good data is classified by PMI N0.5. Fig. 7. Example of hydrometeor classification from the S-band CSU-CHILL radar ( UT): (a) Z h, (b) Z dr, (c) ρ hv (d) texture of ϕ dp and (e) classified hydrometeor types (DZ: drizzle, R: rain, WS: wet snow, DS: dry snow, G/S.H: graupel and/or small, H: hail, R+H: rain/hail mixture).

8 8 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx 7.2. Implementation of echo classifier and examples Echo classifier does not have a standard definition such as hydrometeor classification or data quality index. Within the polarimetric radar context some commonly used echo classification schemes include three categories such as precipitation, bio-scatter (birds and insects), as well as ground (and sea) clutter/anomalous echo. The examples of echo classification are shown in Fig. 6. The radar observations (Z h, Z dr, ϕ dp, ρ hv ) from Kerava radar in Finland are shown in Fig. 6(a) (d) and the result of echo classification is in Fig. 6(e). From the result of Fig. 6(e), we can see that precipitation, ground clutter, and bio-scatter are identified well Implementation of hydrometeor classifier and examples Hydrometeor classifier is mostly for identifying precipitation types. Examples in this section are classified by the fuzzy hydrometeor classification system. Fig. 7(a) (d) shows the radar observations (Z h,z dr, ρ hv )andtextureof ϕ dp,andtheclassificationresultsareshowninfig. 7(e). The radar data are collected on by CSU-CHILL radar. This case is the mixed structure of both stratiform and convective sections. Other case is for C-band Helsinki research radar on The radar variables (Z h,z dr, ρ hv ) are shown in Fig. 8(a) (c) and classification results in Fig. 8(d). The third case is for X-band CASA IP1 radar data. The data was collected on by the Chickasha radar located in southwestern Oklahoma. Fig. 9(a) (d) shows radar variables (measured Z h,attenuationcorrected Z h, ρ hv )andtextureofϕ dp whereas the classification results are shown in Fig. 9(e). The three examples here on the first instance may appear as examples from S, C and X bands, but they are also implementation on three different types of radars with different types of transmitters, namely Klystrons and magnetrons. In addition, at attenuating frequencies the hydrometeor classification is done slightly differently, especially post the attenuation correction process. Other examples of hydrometeor classification at X band can be found in Dolan and Rutledge (2009) as well as Snyder et al. (2010). The X band radar hydrometeor classification shown in Fig. 9 is similar to the results of Dolan and Rutledge (2009), except the system of Fig. 9 uses texture information also. At X band, texture is very useful to discriminated rain or ice regions from mixtures such wet snow or rain/hail mixture. Apart from that, the Kumpula radar is a quasi-operational system using a standardized operational commercial product from the operational community namely HydroClass. Thus the simultaneous presentation of the three examples shows the performance of the hydrometeor classification system over the past decade. Fig. 8. Example of hydrometeor classification from the C-band Kumpular radar ( UT): (a) Z h, (b) Z dr, (c) ρ hv and (d) classified hydrometeor types (R: rain, DS: Dry snow, WS: wet snow, G/SH: graupel and/or small hail, H: hail, R+H: rain/hail mixture).

9 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx 9 Fig. 9. Example of hydrometeor classification from the X-band CASA IP1 radar (KSAO: UT): (a) observed Z h, (b) corrected Z h, (c) ρ hv, (d) texture of ϕ dp and (d) classified hydrometeor types (DZ: drizzle, R: rain, WS: wet snow, DS: dry snow, G/S.H: graupel and/or small hail, H: hail, R+H: rain/hail mixture) Classification of snow fall and winter precipitation processes In this section three different types of winter precipitation are described. The event on March 3rd, 2009 in Helsinki is a typical example of a winter precipitation event where precipitation particles were mainly formed by aggregation. The Z h and Z dr were collected by Kumpula radar, whereas the vertical Doppler observations were collected by the C band vertical looking Doppler radar. There were a number of ground observations of large aggregates. Simultaneous Doppler and dual-polarization radar observations, for this event, are presented in Fig. 10. These observations can be divided into three regions starting from the echo top at about 4 km altitude and continuing to the ground. The analysis, presented here, refers to the observations taken above the Doppler radar, namely 32 km from Kumpula radar. The first region, located between 4 and 3.5 km above the ground, is characterized by a rapid growth of both Z dr and Z h with decreasing height. Differential reflectivity and reflectivity grow at rates of roughly 2 db/km and 20 dbz/km respectively. At the same time the mean Doppler velocity remains approximately constant and equal to 0.5 m/s. The second region, located between 3.5 and 3 km above the ground, is characterized by a continuing rapid growth of reflectivity while differential reflectivity is rapidly decreasing. The differential reflectivity reached its maximum at the altitude of 3.5 km. At the same altitude the mean vertical velocity begin to increase, at a rate of more than 1ms 1 /km, and reaches a value of about 1 m/s at 3 km above the ground. The rapid growth of reflectivity and mean velocity ceases at about 3 km above the ground.from this altitude to the

10 10 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx ground, reflectivity increases at a rate of about 2.5 db/km, mean velocity changes at a rate of 0.15 m s 1 /km and differential reflectivity stays more or less constant at a value of 0.5 db. The presented observations can be explained by a combination of two snow growth processes. Hogan et al. (2002) have observed that the high Z dr bands correspond to areas where a small amount of supercooled water is present. In those layers, ice crystals grow rapidly by vapor condensation. The growth of these particles explains the increase in Z dr and the initial increase in the reflectivity. The second region is the region where vapor deposition is being replaced by aggregation as the dominating growth process. This change explains further increase in the reflectivity and the significant increase in the observed fall velocity. At the last stage, aggregation dominates the growth process. Changes in the number concentration of aggregates, explains both changes in reflectivity and velocity as a function of height. The decreasing Z dr can also be explained by aggregation process. The observations from Fig. 10 show a very similar snow growth pattern to a classic aggregation snow growth pattern deduced by Lo and Passarelli (1982) from aircraft measurements of height evolution of snow size-spectra. In Fig. 11 observations taken on 1020 UTC on March 9th, 2010, at one of the instances where Doppler radar observations indicate presence of riming, are shown. As can be seen in Fig. 11, the measured mean Doppler velocity is close to 2 m/s for all altitudes below 1.5 km, except for the lowest 500 m where observations are not available due to system limitations. As was discussed by Zawadzki et al. (2001) and Mosimann (1995), this vertical velocity is a clear signal that riming is present. Given the observed mean Doppler velocity one can expect presence of rimed aggregates and/or lump graupel at the surface (Locatelli and Hobbes, 1974). There is also an indication of a second spectrum mode with a mean velocity around 1 m/s that can be observed between 1 and 2 km in height in Fig. 11. This mode is either caused by secondary ice or supercooled water droplets and supports our inferences about presence of riming. By comparing radar reflectivity measurements taken during the above-discussed aggregation case and this riming case, as shown in Figs. 10 and 11 respectively, one can observe that even though maximum reflectivity values are similar, structures of the reflectivity fields are different. The reflectivity field observed during the riming case exhibits more spatial variability. The differential reflectivity fields, on the other hand, exhibit Fig. 10. Radar observations of an aggregation case on March 3rd, 2009 (AZ: 11.8 ). Top two panels show reflectivity and differential reflectivity (RHI) observed by the Kumpular radar. The bottom panels show Doppler spectrograph (left) recorded by the Jarvenpaa radar and temperature profiles (right: dotted line dew point temperature), respectively. The vertical dashed lines on RHI plots indicate the location of the Jarvenpaa radar. Slanting lines show trajectories of ice particles that fall with velocities that correspond to the right edge of the Doppler spectrum (dash dotted line), mean velocity (solid line) and left edge of the spectrum (dashed line).

11 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx 11 Fig. 11. Same as Fig. 10, only for the riming case that took place on March 9th, somewhat similar patterns. There are well-defined high Z dr bands in both cases. During the snowfall event that took place on March 17th, 2005, precipitation phase changed from intense snowfall with low-density particles to supercooled drizzle. At 0807 UTC the Jarvenpaa radar has recorded bimodal Doppler spectra as shown in Fig. 12. Zawadzki et al. (2001) have shown that bimodal Doppler spectra are often observed during riming events and can be attributed either to mixed phase conditions or secondary ice production. The coinciding dual-polarization observations are confirming that the bimodality is caused by a mixture of supercooled drizzle, liquid droplets as large as 0.15 mm, and ice particles. As can be observed, the differential reflectivity is higher in the areas where bimodality is observed. It is around 1 db for the heights where bimodality is observed between 0.5 and 1 km. In this case, reflectivity and differential reflectivity values are positively correlated Implementation and examples for an integrated classification system Examples in this section depict the classification system integrated by various classifiers such as data quality, echo, Fig. 12. Vertically pointing Doppler and dual-polarization radar measurements by University of Helsinki Jarvenpaa and Kumpula radars. The observations took place during a snowfall event of March 17th, The right figure shows observed Doppler spectra. Left two figures show reflectivity and differential reflectivity measurements. Dashed lines on those figures indicate measurements that coincide with Doppler observations.

12 12 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx and hydrometeor classifier. The observations were collected on October 28th, 2008 by the Helsinki University research radar. Fig. 13(a) (c) show radar variables (Z h, ρ hv, ϕ dp ). The classification results are shown in Fig. 13(d). From the classification results, we can see the anomalous echoes (or second trip echo) and sea clutter regions correctly identified as Nomet (non-meteorological target) successfully. Fig. 14 is another example of integrated classification system. Fig. 14(a) (c) show radar variables (Z h, ρ hv, ϕ dp ). The classification results are shown in Fig. 14(d). From the classification results, we can see the bio-scatter (birds in this case) is classified correctly as Nomet. 8. Summary and potential future directions The hydrometeor classification systems have progressed substantially over the past decade since the early days of introducing fuzzy logic based precipitation classification systems. The implementation procedures have also evolved significantly, to expand from point wise classifications to areal decisions, introducing texture fields. This is an area that is likely to develop significantly in the near future as more systems get deployed and more users evaluate them. Texture is an intuitive field that captures human observation and discrimination capabilities. Apart from that the scope of the hydrometeor classification systems has expanded significantly since the early days of pure precipitation type classification. It has been well known that dual-polarization radars also brought in advanced and very useful ways to identify data quality issues, resulting in products such as data mask and PMI. The modern Integrated Weather Radar Classification Systems such as HydroClass include all the three aspects of classification such as data quality, echo classification and hydrometeor identification. Since this and other such hydrometeor classification products are operationally used they have had the benefit of user feedback and improvement in performance. The application of hydrometeor classification to X band radars is another significant milestone. Hydrometeor classification has been implemented at X band CASA systems and they have had to implement them after attenuation correction. Thus there in an extra layer of uncertainty and the potential for performance degradation. In spite Fig. 13. Implementation and example from Kumpala radar in Finland ( UT). (a) Z h, (b) ρ hv, (c) ϕ dp, and (d) classification result.

13 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx 13 Fig. 14. Implementation and example from Kumpala radar in Finland ( UT). (a) Z h, (b) ρ hv, (c) ϕ dp, and (d) classification result. of this, the X band hydrometeor classification systems have been fairly successful. Application of hydrometeor classification in pure snow storms or high latitude winter storms has been challenging. In this case the precipitation classification part had to simplify to high density or low density ice particles as well as super-cooled water regions. However the classification systems were more useful in providing guidance for identifying precipitation processes. This has brought a new application where the time evolution of this process identification is important. Thus, the hydrometeor classification topic presents exciting future research opportunities, and is likely to remain active for a long time. Acknowledgments The authors acknowledge various research agencies including the US National Science Foundation, NASA, the Finnish agencies including Tekes and Finnish Science Foundation, as well as Vaisala, for supporting the research reported in this paper over a period of time. The award from Finnish agency Tekes, stimulated the collaboration between the US and Finnish authors. References Alexiuk, M., Pizzi, N., Pedrycz, W., Classification of volumetric stormcell patterns. Proc. IEEE Canadian Conf. on Electrical and Computer Engineering, Vol. 2. IEEE, Edmonton, AB,Canada, pp Aydin, K., Zhao, Y., Seliga, T.A., A differential reflectivity radar hail measurement technique: observations during the Denver hailstorm of 13 June J. Atmos. Oceanic Technol. 7, Baldini, L., Gorgucci, E., Chandrasekar, V., Peterson, W., Implementations of CSU hydrometeor classification scheme for C-band polarimetric radars. 32nd AMS Conf. on Radar Meteorology, Amer. Meteor. Soc., Albuquerque, N.M. Baldwin, M., Kain, J., Lakshmivarahan, S., Development of an automated classification procedure for rainfall systems. Mon. Weather. Rev. 133, Bringi, V.N., Chandrasekar, V., Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge University Press. Chanthavong, V., Holmes, J., Keränen, R., Paris, D., Selzler, J., Siggia, A., Stordell, T., Mitigation of sea clutter and other nonstationary echoes based on general purpose polarimetric echo identification. Sixth European Conf. on Radar in Meteorology and Hydrology, ERAD, Sibiu, Romania. Cifelli, R., Chandrasekar, V., Lim, S., Kennedy, P.C., Wang, Y., Rutledge, S.A., A new dual-polarization radar rainfall algorithm: application in Colorado precipitation events. J. Atmos. Oceanic Technol. doi: / 2010JTECHA Dolan, B., Rutledge, S.A., A theory-based hydrometeor identification algorithm for X-band polarimetric radars. J. Atmos. Oceanic Technol. 26,

14 14 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx Gagne II, D.J., McGovern, A., Brotzge, J., Classification of convective areas using decision trees. J. Atmos. Oceanic Technol. 26, Giuli, D., Gherardelli, M., Freni, A., Seliga, T.A., Aydin, K., Rainfall and clutter discrimination by means of dual-linear polarization radar measurements. J. Atmos. Oceanic Technol. 8, Gourley, J.J., Tabary, P., du Chatelet, J.P., A fuzzy logic algorithm for the separation of precipitating from nonprecipitating echoes using polarimetric radar observations. J. Atmos. Oceanic Technol. 24, Guillot, E.M., Smith, T.M., Lakshmanan, V., Elmore, K.L., Burgess, D.W., Stumpf, G.J., Tornado and severe thunderstorm warning forecast skill and its relationship to storm type. Preprints. 24th Conf. on Interactive Information Processing Systems for Meteorology, Oceanography, and Hydrology, New Orleans, LA, Amer. Meteor. Soc. Herzegh, P.H., Jameson, A.R., Observing precipitation through dualpolarization radar measurements. Bull. Am. Meteorol. Soc. 73, Hogan, R.J., Field, P.R., Illingworth, A.J., Cotton, R.J., Choularton, T.W., Properties of embedded convection in warm-frontal mixed-phase cloud from aircraft and polarimetric radar. Q. J. R. Meteorolog. Soc. 128, Höller, H., Bringi, V.N., Hubbert, J., Hagen, M., Meischner, P.F., Life cycle and precipitation formation in a hybrid-type hailstorm revealed by polarimetric and Doppler radar measurements. J. Atmos. Sci. 51, Keränen, R., Saltikoff, E., Chandrasekar, V., Lim, S., Holmes, J., Selzler, J., Real-time hydrometeor classification for the operational forecasting environment. 33th Radar Meteorology Conference, Cairns, Australia. Lim, S., Chandrasekar, V., Bringi, V.N., Hydrometeor classification system using dual-polarization radar measurements: model improvements and in-situ verification. IEEE Trans. Geosci. Remote. Sens. 43, Liu, H., Chandrasekar, V., An adaptive neural network scheme for precipitation estimation from radar observations. IGARSS '98 4, Liu, H., Chandrasekar, V., Classification of hydrometeors based on polarimetric radar measurements: development of fuzzy logic and neurofuzzy systems, and in situ verification. J. Atmos. Oceanic Technol. 17, Lo, K.K., Passarelli Jr., R.E., The growth of snow in winter storms: an airborne observational study. J. Atmos. Sci. 39, Locatelli, J.D., Hobbes, P.V., Fall Speeds and Masses of Solid Precipitation Particles. J. Geophys. Res. 79, Mitchell, D.L., Zhang, R., Pitter, R.L., Mass-dimensional relationships for ice particles and the influence of riming on snowfall rates. J. Appl. Meteorol. 29, Mosimann, L., An improved method for determining the degree of snow crystal riming by vertical Doppler radar. Atmos. Res. 37, Park, H., Ryzhkov, A.V., Zrnić, D.S., Kyung-Eak, K., The hydrometeor classification algorithm for the polarimetric WSR-88D: description and application to an MCS. Weather. Forecast. 24, Rico-Ramirez, M.A., Cluckie, I.D., Classification of ground clutter and anomalous propagation using dual-polarization weather radar. IEEE Trans. Geosci. Remote. Sens. 46, Ryzhkov, A.V., Schuur, T.J., Burgess, B.W., Heinselman, P.L., Giangrande, S., Zrnić, D.S., The joint polarization experiment polarimetric rainfall measurements and hydrometeor classification. Bull. Am. Meteorol. Soc 86, Schiesser, H.H., Houze Jr., R.A., Huntrieser, H., The mesoscale structure of severe precipitation systems in Switzerland. Mon. Weather. Rev. 123, Schuur, T., Ryzhkov, A., Heinselman, P., Zrnić, D.,Burgess,D.,Scharfenberg, K., Observations and Classification of Echoes with Polarimetric WSR-88D Radar, NOAA, Norman, Oklahoma, University of Oklahoma. Silveira, R.B.Da, Holt, A.R., An automatic identification of clutter and anomalous propagation in polarization-diversity weather radar data using neural networks. IEEE Trans. Geosci. Remote. Sens. 39, Snyder, J.C., Bluestein, H.B., Zhang, G., Frasier, S.J., Attenuation Correction and Hydrometeor Classification of High-Resolution, X-band. Dual- Polarized Mobile Radar Measurements in Severe Convective Storms. J. Atmos. Oceanic Technol. 27, Steiner, M., Houze Jr., R.A., Yuter, S.E., Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data. J. Appl. Meteorol. 34, Straka, J.M., Zrnić, D.S., An algorithm to deduce hydrometeor types and contents from multi-parameter radar data. Preprints, 26th AMS Conf. on Radar Meteorology, Amer. Meteor. Soc., Boston, MA. Straka, J.M., Zrnić, D.S., Ryzhkov, A.V., Bulk hydrometeor classification and quantification using polarimetric radar data: synthesis of relations. J. Appl. Meteorol. 39, Vivekanandan, J., Zrnić, D.S., Ellis, S.M., Oye, R., Ryzhkov, A., Straka, J.M., Cloudmicrophysics retrieval using S-band dual-polarization radar measurements. Bull. Am. Meteorol. Soc. 80, Wang, Y., Chandrasekar, V., Algorithm for estimation of the specific differential phase. J. Atmos. Oceanic Technol. 26, Zafar, B.J., Chandrasekar, V., SOM of space borne precipitation radar rain profiles on global scale. Proceedings. IGARSS '04, 2, pp Zawadzki, I., Fabry, F., Szyrmer, W., Observations of supercooled water and secondary ice generation by a vertically pointing X-band Doppler radar. Atmos. Res , Zeng, Z., Yuter, S.E., Houze Jr., R.A., Kingsmill, D.E., Microphysics of the rapid development of heavy convective precipitation. Monthly Weather Rev. 129, Zrnić, D.S., Balakrishnan, N., Ziegler, C.L., Bringi, V.N., Aydin, K., Matejka, T., 1993a. Polarimetric signatures in the stratiform region of a measoscale convective system. J. Appl. Meteorol. 32, Zrnić, D.S., Bringi, V.N., Balakrishnan, N., Aydin, K., Chandrasekar, V., Hubbert, J., 1993b. Polarimetric measurements in a severe hailstorm. Mon. Weather. Rev. 121, Zrnić, D.S., Ryzhkov, A.V., Straka, J., Liu, Y., Vivekanandan, J., Testing a procedure for automatic classification of hydrometeor types. J. Atmos. Oceanic Technol. 18, V. Chandrasekar is currently a Professor at Colorado State University (CSU), Fort Collins. He has been actively involved with research and development of weather radar systems for about 30 years. He has played a key role in developing the CSU- CHILL National Radar Facility as one of the most advanced meteorological radar systems available for research, and continues to work actively with the CSU-CHILL radar supporting its research and education mission and is a Co-PI and the engineering leader of the facility. He serves as the Deputy Director and research leader of the NSF-ERC, Center for Collaborative Adaptive Sensing of the Atmosphere. He is an avid experimentalist conducting special experiments to collect in situ observations to verify the new techniques and technologies. He serves as the College leader for promoting International Research Collaboration. He is a co-author of two test books and five general books. He has served as academic advisor for over 50 graduate students. Dr. Chandrasekar has served as a member of the National Academy of Sciences Committee that wrote the books, Weather Radar Technology beyond NEXRAD and Flash Flood Forecasting in Complex Terrain. He served as the General Co-Chair for the IGARSS '06 Symposium and serves as the chief Editor of the Journal of Atmospheric and Oceanic technology. He has been a visiting professor of National Research Council of Italy, currently serves as the Distinguished Professor of Finland and Indian Institute of Tropical Meteorology, and an affiliate scientist of the National Center for Atmospheric Research. He has received numerous awards including, Halliburton Foundation Research award, the Abell Foundation Outstanding Researcher Award, NASA Technical Contribution Award, University Outstanding Advisor Award, the Abell Foundational Award for International Contributions, Preston Davis Award for Instructional Innovation, the IEEE Education Award, and the NOAA/NWS Director's Medal of Excellence. He is a Fellow of the IEEE, American Meteorological Society and NOAA/CIRA. Reino Keränen received the degree of Doctor in Technology in high energy physics at the Helsinki University of Technology, Finland, in His doctorate research focused on the development and data analysis of the DELPHI particle experiment, at the European Center of Particle Physics. In 1990's, he continued with instrumentation and physics analysis of DELPHI, delivering more than 200 peer reviewed publications in the DELPHI collaboration. In other teams at public and private institutions, he has conducted published research in low temperature physics, future particle accelerator experiments, and pursued instrumentation in electronics and optics. In 2000, he joined Vaisala Oyj, global in environmental and industrial measurement. He has contributed to various technologies such as gas sensing with tunable diode lasers, and the polarimetric weather radar. He is an inventor in two Vaisala patents, an author of several conference contributions in radar meteorology, and a member of European Physical Society and American Meteorological Society.

15 V. Chandrasekar et al. / Atmospheric Research xxx (2011) xxx xxx 15 Sanghun Lim received the Ph.D. degree in Electrical Engineering from the Colorado State University. He is currently a research scientist at Colorado State University. He was awarded Vaisala/SNOW V-10 Cooperative Postdoctoral Research Fellowship. His research interests include the rainfall/snowfall estimation and hydrometeor classification using dual-polarization radar measurements and attenuation correction. Dmitri Moisseev has received PhD degree from Delft University of Technology, The Netherlands in Since then he has gained an extensive experience carrying out research with various types of dual-polarization radars both in Europe and the US. Currently, he is a research scientist at University of Helsinki in-charge of coordinating the University of Helsinki Kumpula radar and its operations. He is currently pursuing advancement of dual-polarization radar applications for winter applications and for characterization of high latitude precipitation systems.