A Semisupervised Robust Hydrometeor Classification Method for Dual-Polarization Radar Applications

Bechini, R.; Chandrasekar, V.

doi:10.1175/jtech-d-14-00097.1

Cited by 85 publications

(55 citation statements)

References 45 publications

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“…Radar-based hydrometeor classification, that is the proper identification of different types of hydrometeors from radar observations, is important for an improved understanding of atmospheric dynamics, an improved quantitative precipitation estimation (QPE), an improved verification and assimilation in numerical weather prediction models and operational nowcasting applications like aircraft or road safety (Bringi et al, 2007). The spread of polarimetry for weather radar has significantly changed the capability of radar systems to identify meteorological and non-meteorological echoes, as well as to identify different hydrometeor types under the radar umbrella (Bringi et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach

et al. 2016

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Abstract. Polarimetric radar-based hydrometeor classification is the procedure of identifying different types of hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are then employed in operational labelling of different hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the operational potential of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach

et al. 2016

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“…Polarimetric data have been used to identify areas of intense growth of dendritic and planar crystals (Kennedy and Rutledge, 2011;Bechini et al, 2013;Schrom et al, 2015) and to formulate hypotheses on the dominant microphysical processes occurring at various altitude levels during snowfall Andric et al, 2013;Kumjian et al, 2014). The combination of numerical modeling, radar observations and in situ data led to the development of classification methods aiming to estimate the dominant type of hydrometeors that populate the radar resolution volumes (e.g., Straka et al, 2000;Dolan and Rutledge, 2009;Bechini and Chandrasekar, 2015;Grazioli et al, 2015). However, the complex microphysics of ice-phase precipitation cannot be fully captured by polarimetric radars alone, nor simply described by a single hydrometeor type.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric radar and in situ observations of riming and snowfall microphysics during CLACE 2014

et al. 2015

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Abstract. This study investigates the microphysics of winter alpine snowfall occurring in mixed-phase clouds in an inner-Alpine valley during January and February 2014. The available observations include high-resolution polarimetric radar and in situ measurements of the ice-phase and liquidphase components of clouds and precipitation. Radar-based hydrometeor classification suggests that riming is an important factor to favor an efficient growth of the precipitating mass and correlates with snow accumulation rates at ground level. The time steps during which rimed precipitation is dominant are analyzed in terms of temporal evolution and vertical structure. Snowfall identified as rimed often appears after a short time period during which the atmospheric conditions favor wind gusts and updrafts and supercooled liquid water (SLW) is available. When a turbulent atmospheric layer persists for several hours and ensures continuous SLW generation, riming can be sustained longer and large accumulations of snow at ground level can be generated. The microphysical interpretation and the meteorological situation associated with one such event are detailed in the paper. The vertical structure of polarimetric radar observations during intense snowfall classified as rimed shows a peculiar maximum of specific differential phase shift K dp , associated with large number concentrations and riming of anisotropic crystals. Below this K dp peak there is usually an enhancement in radar reflectivity Z H , proportional to the K dp enhancement and interpreted as aggregation of ice crystals. These signatures seem to be recurring during intense snowfall.

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“…After K dp estimation, a hydrometeor classification is performed on the dual-polarization observations (Bechini and Chandrasekar, 2015). The output of the classification is used to select the data for the different calibration procedures.…”

Section: Arpa Piemonte C-band Weather Radarsmentioning

confidence: 99%

“…-observations in liquid phase, as inferred from the application of the hydrometeor classification (Bechini and Chandrasekar, 2015);…”

Section: Self-consistencymentioning

confidence: 99%

“…This is intended to minimize the possible contamination by meteorological echoes (Silberstein et al, 2008 andWolff et al, 2015). The radar volumes are processed by the hydrometeor classification algorithm (Bechini and Chandrasekar, 2015) to identify clutter and meteorological echoes. Subsequently, empirical thresholds are applied to the volumes in order to be used for the clutter statistics: the percentage of meteorological echoes should be less than 1 % and the percentage of clutter echoes greater than 12 % of the total echoes inside the volume.…”

Section: Ground Clutter Calibrationmentioning

confidence: 99%

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An integrated approach to monitoring the calibration stability of operational dual-polarization radars

et al. 2016

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Abstract. The stability of weather radar calibration is a mandatory aspect for quantitative applications, such as rainfall estimation, short-term weather prediction and initialization of numerical atmospheric and hydrological models. Over the years, calibration monitoring techniques based on external sources have been developed, specifically calibration using the Sun and calibration based on ground clutter returns. In this paper, these two techniques are integrated and complemented with a self-consistency procedure and an intercalibration technique. The aim of the integrated approach is to implement a robust method for online monitoring, able to detect significant changes in the radar calibration. The physical consistency of polarimetric radar observables is exploited using the self-consistency approach, based on the expected correspondence between dual-polarization power and phase measurements in rain. This technique allows a reference absolute value to be provided for the radar calibration, from which eventual deviations may be detected using the other procedures. In particular, the ground clutter calibration is implemented on both polarization channels (horizontal and vertical) for each radar scan, allowing the polarimetric variables to be monitored and hardware failures to promptly be recognized. The Sun calibration allows monitoring the calibration and sensitivity of the radar receiver, in addition to the antenna pointing accuracy. It is applied using observations collected during the standard operational scans but requires long integration times (several days) in order to accumulate a sufficient amount of useful data. Finally, an intercalibration technique is developed and performed to compare colocated measurements collected in rain by two radars in overlapping regions. The integrated approach is performed on the C-band weather radar network in northwestern Italy, during July-October 2014. The set of methods considered appears suitable to establish an online tool to monitor the stability of the radar calibration with an accuracy of about 2 dB. This is considered adequate to automatically detect any unexpected change in the radar system requiring further data analysis or on-site measurements.

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A Semisupervised Robust Hydrometeor Classification Method for Dual-Polarization Radar Applications

Cited by 85 publications

References 45 publications

Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach

Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach

Polarimetric radar and in situ observations of riming and snowfall microphysics during CLACE 2014

An integrated approach to monitoring the calibration stability of operational dual-polarization radars

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