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Class centroid profiles of the R model. Profile counts per class are shown at the bottom omitting the count for low-reflectivity class R0. Between the panes, each class has been assigned a color code.
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Vertical profiles of polarimetric radar variables can be used to identify fingerprints of snow growth processes. In order to systematically study such manifestations of precipitation processes, we have developed an unsupervised classification method. The method is based on k-means clustering of vertical profiles of polarimetric radar variables, nam...
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Context 1
... centroids of rain and snow profile classes are shown in Figs. 2 and 3, respectively. The centroid profiles of dualpolarization radar variables are inverse transformed from corresponding centroids in PCA space. Classes are numbered in the ascending order by the value of the first principal component in the class centroids. By definition, the first component has the largest variance and therefore has the ...
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... a general pattern in Figs. 2 and 3 we see that the highest values of Z DR are associated with low echo tops while the highest K dp values occur in deeper clouds. This is in line with the previously reported findings ( Kennedy and Rut- ledge, 2011;Bechini et al., 2013;Moisseev et al., 2015;Schrom et al., 2015;Griffin et al., 2018) that echo tops in the DGL are associated ...
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... difference in K dp intensity: consistently lower values are present in snow events. There are four rain profile classes in contrast to only two snow profile classes with peak cluster centroid K dp exceeding 0.1 • km −1 . They represent total fractions of 13 % and 4 % of rain and snow profiles, respectively. Corresponding to this difference, in Figs. 2 and 3, as well as in Figs. 7 and 8 introduced later, K dp is visualized in different ranges in relation to rain and snow profiles. The seasonal differences in Z DR and Z e intensities are less prominent. High K dp in the summer may be linked to higher water content during the season. Additionally, the seasonal variability of vertical motion ...
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... frequencies are presented in the bottom panels of Figs. 2 and 3. Classes S0 and R0 represent very low values of Z e throughout the column, i.e., profiles with very weak or no echoes. Therefore their frequencies depend merely on the subjective selection of observation period boundaries and are thus omitted in the figures. Boundaries of the precipitation events are partly based on these two 0 ...
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... Figs. 2 and 3, each class is assigned a color code (between the panels). This color coding is used in Figs. 7 and 8 to mark classification results in a rain and a snow case, respectively. Note that the same set of colors is used for denoting rain and snow profile classification, but they should not be confused with each ...
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Cite as:
Gaztelumendi S. , Egaña J., Gelpi I.R., Otxoa de Alda K., Maruri M., Hernandez R. (2008) Use of Kapildui radar for analysis and surveillance in a storm case. In Proceedings of 5th European Conference on Radar in Meteorology and Hydrology. (ERAD 2008 Helsinki, Finland. 30 June - 4 July 2008). doi:10.13140/RG.2.2.24679.73125
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Citations
... Weather radar is an indispensable active remote sensing observation equipment in the meteorological field and plays an important role in precipitation estimation [1,2], hydrometeor classification [3,4], and microphysical retrieval [5,6]. The principle of weather radar can be summarized in the following three steps (as shown in Figure 1): (1) weather radar emits electromagnetic waves into the atmosphere; (2) when the electromagnetic waves "touch" targets (e.g., rain, snow, hail, and other non-meteorological targets that are not the focus of this study) along their propagation path, scattering occurs in all directions and a back-scattering signal is received by the radar; (3) valuable information about the scattering targets (e.g., their size, phase, shape, and orientation) can be extracted by properly processing the received signal. ...
Parameter estimation is important in weather radar signal processing. Time-domain processing (TDP) and frequency-domain processing (FDP) are two basic parameter estimation methods used in the weather radar field. TDP is widely used in operational weather radars because of its high efficiency and robustness; however, it must be assumed that the received signal has a symmetrical or Gaussian power spectrum, which limits its performance. FDP does not require assumptions about its power spectrum model and has a seamless connection to spectrum analysis; however, its application performance has not been fully validated to ensure its robustness in an operational environment. In this study, we introduce several technical details in FDP, including window function selection, aliasing correction, and noise correction. Additionally, we evaluate the performance of FDP and compare the performance of FDP and TDP based on simulated and measured weather in-phase/quadrature (I/Q) data. The results show that FDP has potential for operational applications; however, further improvements are required, e.g., windowing processing for signals mixed with severe clutter.
... In addition, to process rate retrievals, classification techniques such as those used for HCAs are now being applied to vertical profiles of the polarimetric radar variables to characterize microphysical fingerprints. For example, Ref. [127] used an unsupervised classification method based on principal component analysis and k-means clustering to categorize fingerprints. The technique was able to identify some of the "textbook examples" of snow microphysics fingerprints, including those described above. ...
This article reviews how precipitation microphysics processes are observed in dual-polarization radar observations. These so-called “fingerprints” of precipitation processes are observed as vertical gradients in radar observables. Fingerprints of rain processes are first reviewed, followed by processes involving snow and ice. Then, emerging research is introduced, which includes more quantitative analysis of these dual-polarization radar fingerprints to obtain microphysics model parameters and microphysical process rates. New results based on a detailed rain shaft bin microphysical model are presented, and we conclude with an outlook of potentially fruitful future research directions.
... The changes in the S(Z) multiplier for a given N 0 are also associated with the changes in a liquid water path, a proxy for the degree of riming. In another study which partially used BAECC data in conjunction with the data from the University of Helsinki Hyytiälä station, Tiira and Moisseev [83] developed the unsupervised method for classification of vertical profiles of polarimetric radar variables using k-means clustering. They showed that the profiles of radar variables can be grouped into 16 snow classes which capture the most important snow growth and ice cloud processes. ...
... Radar polarimetry opens new horizons for snow habit classification and quantification (e.g., [83]). Since the advance of polarimetry, only a handful of studies have explored polarimetric quantitative snow retrievals, mostly focused on ice water content (IWC) quantification. ...
... This may limit the efficiency of polarimetric radar estimates in "warm" snow with temperatures just slightly below 0 • C. One possibility to address this issue is to resort to the polarimetric estimates of snow rate at higher altitudes and lower temperatures where K DP (or Z DR ) are sufficiently high and project these estimates down to the surface assuming either conservation of the snow flux or predicting its downward evolution using simple cloud models as discussed in [112]. An alternative option is to switch to the S(Z) relation optimized for a certain snow type in areas of unreliable K DP and Z DR [83]. Note that the polarimetric snow relations are expected to perform better at shorter radar wavelengths (e.g., at C and X bands) for which K DP is higher and can be measured more reliably compared with S band. ...
Radar quantitative precipitation estimation (QPE) is one of the primary tasks of weather radars. The QPE quality was substantially improved after polarimetric upgrade of the radars. This study provides an overview of existing polarimetric methodologies for rain and snow estimation and their operational implementation. The variability of drop size distributions (DSDs) is a primary factor affecting the quality of rainfall estimation and its impact on the performance of various radar rainfall relations at S, C, and X microwave frequency bands is one of the focuses of this review. The radar rainfall estimation algorithms based on the use of specific attenuation A and specific differential phase KDP are the most efficient. Their brief description is presented and possible ways for their further optimization are discussed. Polarimetric techniques for the vertical profile of reflectivity (VPR) correction at longer distances from the radar are also summarized. Radar quantification of snow is particularly challenging and it is demonstrated that polarimetric methods for snow measurements show good promise. Finally, the article presents a summary of the latest operational radar QPE products available in the US by integration of the information from the WSR-88D radars via the Multi-Radar Multi-Sensor (MRMS) platform.
... Other studies suggested to use polarimetry to 100 investigate growth processes of snow and their signatures on dual-polarization and Doppler velocity radar observations . Tiira and Moisseev (2020) developed an unsupervised classification of snow and ice crystals particles exploiting profiles of polarimetric signatures on radar variables. The most important growth processes of ice particles were studied using several years of the Ikaalinen C-band radar data, in Hyytiälä forestry station in Juupajoki, Finland. ...
Ice growth processes within clouds affect the type as well as the amount of precipitation. Hence, the importance of an accurate representation of ice microphysics in numerical weather and numerical climate models has been confirmed by several studies. To better constrain ice processes in models, we need to study ice cloud regions before and during monitored precipitation events. For this purpose, two radar instruments facing each other were used to collect complementary measurements. The C-band POLDIRAD weather radar from the German Aerospace Center (DLR), Oberpfaffenhofen and the Ka-band MIRA-35 cloud radar from the Ludwig Maximilians University of Munich (LMU) were used to monitor stratiform precipitation in the vertical cross-section area between both instruments. The logarithmic difference of radar reflectivities at two different wavelengths (54.5 and 8.5 mm), known as dual-wavelength ratio, was exploited to provide information about the size of the detected ice hydrometeors, taking advantage of the different scattering behavior in the Rayleigh and Mie regime. Along with the dual-wavelength ratio, differential radar reflectivity measurements from POLDIRAD provided information about the apparent shape of the detected ice hydrometeors. Scattering simulations using the T-matrix method were performed for oblate and horizontally aligned prolate ice spheroids of varying shape and size using a realistic particle size distribution and a well-established mass-size relationship. The combination of dual-wavelength ratio, radar reflectivity and differential radar reflectivity measurements as well as scattering simulations was used for the development of a novel retrieval for ice cloud microphysics. The development of the retrieval scheme also comprised a method to estimate the hydrometeor attenuation in both radar bands. To demonstrate this approach, a feasibility study was conducted on three stratiform snow events which were monitored over Munich in January 2019. The ice retrieval can obtain ice particle shape, size and mass which are in line with differential radar reflectivity, dual-wavelength ratio and radar reflectivity observations when a suitable mass-size relation is used and when ice hydrometeors are assumed to be represented by oblate ice spheroids. A furthermore finding was the importance of the differential radar reflectivity for the particle size retrieval directly above the MIRA-35 cloud radar. Especially for that observation geometry, the simultaneous slantwise observation from the polarimetric weather radar POLDIRAD could reduce ambiguities in retrieval of the ice particle size by constraining the ice particle shape.
... The added value of radar polarimetry (e.g., Kumjian, 2012;Tiira and Moisseev, 2020) is of particular relevance for characterizing salient features in radar profiles, like the melting layer (Griffin et al., 2020), but also for deciphering the microphysical structure of snowfall. Bader et al. (1987), Andrić et al. (2013), Schneebeli et al. (2013), Moisseev et al. (2015), Grazioli et al. (2015), Besic et al. (2018) and Gehring et al. (2020), among others, show the potential of dual-polarization variables for studying some characteristics of snowflakes (e.g., type, shape, orientation, size and phase) and the underlying microphysical processes that drive their evolution. ...
... Range height indicator (RHI) scans can also be used to extract vertical profiles at a given azimuth (e.g., Andrić et al., 2013;Moisseev et al., 2015;Gehring et al., 2020). The analysis of vertical profiles of dual-polarization variables from RHI scans has been further developed by Tiira and Moisseev (2020) with a multivariate unsupervised classification of vertical profiles of Z H , Z DR and K dp . The mean profiles of the resulting classes have been a posteriori interpreted in terms of specific meteorological conditions and microphysical processes. ...
Polarimetric radar systems are commonly used to study the microphysics of precipitation. While they offer continuous measurements with a large spatial coverage, retrieving information about the microphysical processes that govern the evolution of snowfall from the polarimet-ric signal is challenging. The present study develops a new method, called process identification based on vertical gradient signs (PIVSs), to spatially identify the occurrence of the main microphysical processes (aggregation and riming, crystal growth by vapor deposition and sublimation) in snowfall from dual-polarization Doppler radar scans. We first derive an analytical framework to assess in which meteorological conditions the local vertical gradients of radar variables reliably inform about microphysical processes. In such conditions , we then identify regions dominated by (i) vapor depo-sition, (ii) aggregation and riming and (iii) snowflake subli-mation and possibly snowflake breakup, based on the sign of the local vertical gradients of the reflectivity Z H and the differential reflectivity Z DR. The method is then applied to data from two frontal snowfall events, namely one in coastal Adélie Land, Antarctica, and one in the Taebaek Mountains in South Korea. The validity of the method is assessed by comparing its outcome with snowflake observations, using a multi-angle snowflake camera, and with the output of a hydrometeor classification, based on polarimetric radar signal. The application of the method further makes it possible to better characterize and understand how snowfall forms, grows and decays in two different geographical and meteorological contexts. In particular, we are able to automatically derive and discuss the altitude and thickness of the layers where each process prevails for both case studies. We infer some microphysical characteristics in terms of radar variables from statistical analysis of the method output (e.g., Z H and Z DR distribution for each process). We, finally, highlight the potential for extensive application to cold precipitation events in different meteorological contexts.
... The existing data-driven unsupervised (Grazioli et al., 2015;Ribaud et al., 2019;Tiira and Moisseev, 2020) and semi-supervised approaches (Bechini and Chandrasekar, 2015;Besic et al., 2016;Wang et al., 2017;Roberto et al., 2017;Besic et al., 2018) only partially provide an answer to the first question (Grazioli et al., 2015) and do not consider the temporal evolution or dependencies between the identified classes. The approach described here performs an unsupervised clustering of QVPs. ...
Correct, timely and meaningful interpretation of polarimetric weather radar observations requires an accurate understanding of hydrometeors and their associated microphysical processes along with well-developed techniques that automatize their recognition in both the spatial and temporal dimensions of the data. This study presents a novel technique for identifying different types of hydrometeors from quasi-vertical profiles (QVPs). In this new technique, the hydrometeor types are identified as clusters belonging to a hierarchical structure. The number of different hydrometeor types in the data is not predefined, and the method obtains the optimal number of clusters through a recursive process. The optimal clustering is then used to label the original data. Initial results using observations from the National Centre for Atmospheric Science (NCAS) X-band dual-polarization Doppler weather radar (NXPol) show that the technique provides stable and consistent results. Comparison with available airborne in situ measurements also indicates the value of this novel method for providing a physical delineation of radar observations. Although this demonstration uses NXPol data, the technique is generally applicable to similar multivariate data from other radar observations.
... Mason et al. (2018) have incorporated the Doppler velocity and radar reflectivity observations from vertically pointing Ka-and W-band radars into an optimal estimation scheme to infer the riming fraction, among other parameters. In addition to multifrequency radar observations, dual-polarization radar measurements show promise in improving our understanding of ice precipitation processes (e.g., Bechini et al., 2013;Giangrande et al., 2016;Kumjian et al., 2016;Ryzhkov et al., 2016;Moisseev et al., 2015Moisseev et al., , 2017Li et al., 2018;Oue et al., 2018;Vogel and Fabry, 2018;Moisseev et al., 2019;Tiira and Moisseev, 2020). Therefore, the utilization of collocated multifrequency and dual-polarization radar observations may pave the way for a better understanding of the connection between dry and melting snow microphysics. ...
... The detailed properties of ice particles are complex as manifested by the extraordinary variety in their habit, size, mass and concentration (Korolev et al., 2000(Korolev et al., , 2003Bailey and Hallett, 2009). This complexity is exacerbated by the diversity of ice growth processes that take place in ice clouds (Li et al., 2018;Oue et al., 2018;Barrett et al., 2019;Moisseev et al., 2015Moisseev et al., , 2017Moisseev et al., , 2019Tiira and Moisseev, 2020). Despite the recent attempts to resolve the ice microphysics (e.g., Mason et al., 2018Mason et al., , 2019Barrett et al., 2019), direct characterization of ice particles and their growth processes is still challenging. ...
... Recent studies have demonstrated the potential of polarimetric measurements in revealing cloud microphysics and improving precipitation forecasts (Tiira and Moisseev, 2020;Trömel et al., 2019). Given the importance of precipitation intensity to the ML, it is necessary to address how the dualpolarization observations are dependent on PR. ...
In stratiform rainfall, the melting layer is often visible in radar observations as an enhanced reflectivity band, the so-called bright band. Despite the ongoing debate on the exact microphysical processes taking place in the melting layer and on how they translate into radar measurements, both model simulations and observations indicate that the radar-measured melting layer properties are influenced by snow microphysical processes that take place above it. There is still, however, a lack of comprehensive observations to link the two. To advance our knowledge of precipitation formation in ice clouds and provide an 5 additional constraint on the retrieval of ice cloud microphysical properties, we have investigated this link. This study is divided into two parts. Firstly, surface-based snowfall measurements are used to devise a method for classifying rimed and unrimed snow from X-and Ka-band Doppler radar observations. In the second part, this classification is used in combination with multi-frequency and dual-polarization radar observations to investigate the impact of precipitation intensity, aggregation, riming, and dendritic growth on melting layer properties. The radar-observed melting layer characteristics show strong dependence on 10 precipitation intensity as well as detectable differences between unrimed and rimed snow. This study is based on data collected during the Biogenic Aerosols-Effects on Clouds and Climate (BAECC) experiment, which took place in 2014 in Hyytiälä, Finland.
Modelled geospatial Lagrangian trajectories are widely used in Earth Science, including in oceanography, atmospheric science and marine biology. The typically large size
of these datasets makes them arduous to analyze, and their underlying pathways challenging to identify. Here, we show that a Machine Learning unsupervised k-means++ clustering method can successfully identify the pathways of the Labrador Current from a large set of modelled Lagrangian trajectories. The presented method requires simple pre-processing of the data, including a Cartesian correction on longitudes and a PCA reduction. The clustering is performed in a kernalized space and uses a larger number of clusters than the number of expected pathways. During post-processing, similar clusters are grouped into pathway categories by experts in the circulation of the region of interest. We find that the Labrador Current mainly follows a westward-flowing and an eastward retroflecting pathway (20% and 50% of the flow, respectively) that compensate each other through time in a see-saw behaviour. These pathways experience a strong variability of up to 96%. We find that two thirds of the retroflection occurs at the tip of the Grand Banks, and one quarter at Flemish Cap. The westward pathway is mostly fed by the on-shelf branch of the Labrador Current, and the eastward pathway by the shelf-break branch. Pathways of secondary importance feed the Labrador Sea, the Gulf of St. Lawrence through the Belle Isle Strait, and the subtropics across the Gulf Stream.
Ice growth processes within clouds affect the type and amount of precipitation. Hence, the importance of an accurate
representation of ice microphysics in numerical weather and numerical
climate models has been confirmed by several studies. To better constrain
ice processes in models, we need to study ice cloud regions before and
during monitored precipitation events. For this purpose, two radar
instruments facing each other were used to collect complementary
measurements. The C-band POLDIRAD weather radar from the German Aerospace
Center (DLR) in Oberpfaffenhofen and the Ka-band MIRA-35 cloud radar from the
Ludwig Maximilians University of Munich (LMU) were used to monitor
stratiform precipitation in the vertical cross-sectional area between the two
instruments. The logarithmic difference of radar reflectivities at two
different wavelengths (54.5 and 8.5 mm), known as the dual-wavelength ratio, was
exploited to provide information about the size of the detected ice
hydrometeors, taking advantage of the different scattering behavior in the
Rayleigh and Mie regime. Along with the dual-wavelength ratio, differential
radar reflectivity measurements from POLDIRAD provided information about the
apparent shape of the detected ice hydrometeors. Scattering simulations
using the T-matrix method were performed for oblate and horizontally aligned
prolate ice spheroids of varying shape and size using a realistic particle
size distribution and a well-established mass–size relationship. The
combination of dual-wavelength ratio, radar reflectivity, and differential
radar reflectivity measurements as well as scattering simulations was used
for the development of a novel retrieval for ice cloud microphysics. The
development of the retrieval scheme also comprised a method to estimate the
hydrometeor attenuation in both radar bands. To demonstrate this approach, a
feasibility study was conducted on three stratiform snow events which were
monitored over Munich in January 2019. The ice retrieval can provide ice
particle shape, size, and mass information which is in line with differential
radar reflectivity, dual-wavelength ratio, and radar reflectivity
observations, respectively, when the ice spheroids are assumed to be oblates
and to follow the mass–size relation of aggregates. When combining two
spatially separated radars to retrieve ice microphysics, the beam width
mismatch can locally lead to significant uncertainties. However, the
calibration uncertainty is found to cause the largest bias for the averaged
retrieved size and mass. Moreover, the shape assumption is found to be
equally important to the calibration uncertainty for the retrieved size,
while it is less important than the calibration uncertainty for the
retrieval of ice mass. A further finding is the importance of the
differential radar reflectivity for the particle size retrieval directly
above the MIRA-35 cloud radar. Especially for that observation geometry, the
simultaneous slantwise observation from the polarimetric weather radar
POLDIRAD can reduce ambiguities in retrieval of the ice particle size by
constraining the ice particle shape.
