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

Bešič, Nikola; Ventura, Jordi Figueras i; Grazioli, Jacopo; Gabella, Marco; Germann, Urs; Berne, Alexis

doi:10.5194/amt-9-4425-2016

Cited by 84 publications

(97 citation statements)

References 46 publications

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“…Details of the measurement system are given in Schneebeli et al (2013) and . Note also that from polarimetric variables, it is possible to classify the scattering hydrometeors into distinct classes following the semisupervised statistical clustering method developed by Besic et al (2016). Furthermore, the application of the demixing method from Besic et al (2018) makes it possible to infer the proportions of the different hydrometeor types in a given radar sampling volume.…”

Section: Mxpol Radar Hydrometeor Types and Mixturesmentioning

confidence: 99%

Microphysics of Snowfall Over Coastal East Antarctica Simulated by Polar WRF and Observed by Radar

et al. 2019

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The current assessment of the Antarctic surface mass balance mostly relies on reanalysis products or climate model simulations. However, little is known about the ability of models to reliably represent the microphysical processes governing the precipitation. This study makes use of recent ground‐based precipitation measurements at Dumont d'Urville station in Adélie Land to evaluate the representation of the precipitation microphysics in the Polar version of the Weather Research Forecast (Polar WRF) atmospheric model. During two summertime snowfall events, high‐resolution simulations are compared to measurements from an X‐band polarimetric radar and from a Multi‐Angle Snowflake Camera (MASC). A radar simulator and a “MASC” simulator in Polar WRF make it possible to compare similar observed and simulated variables. Radiosoundings and surface‐meteorological observations were used to assess the representation of the regional dynamics in the model. Five different microphysical parameterizations are tested. The simulated temperature, wind, and humidity fields are in good agreement with the observations. However, the amount of simulated surface precipitation shows large discrepancies with respect to observations, and it strongly differs between the simulations themselves, evidencing the critical role of the microphysics. The inspection of vertical profiles of reflectivity and mixing ratios revealed that the representation of the sublimation process by the low‐level dry katabatic winds strongly influences the actual amount of precipitation at the ground surface. By comparing the simulated radar signal as well as MASC and model particle size distributions, it is also possible to identify the microphysical processes involved and to pinpoint shortcomings within the tested parameterizations.

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Section: Mxpol Radar Hydrometeor Types and Mixturesmentioning

confidence: 99%

Microphysics of Snowfall Over Coastal East Antarctica Simulated by Polar WRF and Observed by Radar

et al. 2019

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“…Polarimetric weather radar can provide relevant information to discriminate particles regarding to their size, shape, phase state and orientation, and various hydrometeor classification algorithms have been proposed (e.g., Chandrasekar et al, 2013;Besic et al, 2016). These products are particularly powerful because they enable the sampling of a large spatial domain at a high temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

“…Another more accessible alternative to assess the performance and reliability of remote sensing retrievals is to compare the output with the hydrometeor types observed at ground level by in situ measurement devices (e.g., Colle et al, 2014;Grazioli et al, 2015;Besic et al, 2016). For this purpose, ground-based snowflake imagers like the twodimensional video disdrometer (2DVD; Kruger and Krajewski 2002), the Hydrometeor Velocity and Shape Detector (HVSD; Barthazy et al, 2004), the Snowflake Video Imager (SVI or PIP in its newest version; Newman et al, 2009) and the Multi-Angle Snowflake Camera (MASC; Garrett et al, 2012) provide relevant information in the form of twodimensional binary or grayscale particle images and in some cases the associated fall speed measurements.…”

Section: Introductionmentioning

confidence: 99%

Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera

2017

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Abstract.A new method to automatically classify solid hydrometeors based on Multi-Angle Snowflake Camera (MASC) images is presented. For each individual image, the method relies on the calculation of a set of geometric and texture-based descriptors to simultaneously identify the hydrometeor type (among six predefined classes), estimate the degree of riming and detect melting snow. The classification tasks are achieved by means of a regularized multinomial logistic regression (MLR) model trained over more than 3000 MASC images manually labeled by visual inspection. In a second step, the probabilistic information provided by the MLR is weighed on the three stereoscopic views of the MASC in order to assign a unique label to each hydrometeor. The accuracy and robustness of the proposed algorithm is evaluated on data collected in the Swiss Alps and in Antarctica. The algorithm achieves high performance, with a hydrometeor-type classification accuracy and Heidke skill score of 95 % and 0.93, respectively. The degree of riming is evaluated by introducing a riming index ranging between zero (no riming) and one (graupel) and characterized by a probable error of 5.5 %. A validation study is conducted through a comparison with an existing classification method based on two-dimensional video disdrometer (2DVD) data and shows that the two methods are consistent.

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“…The dual polarization and the new receiver-over-elevation design provide increased sensitivity, better removal of non-meteorological echoes and better precipitation estimation. Also, the new generation of radars motivates innovation and development of new products, as for example the semi-supervised polarimetric hydrometeor classification (Besic et al, 2016). An overview of the upgrade of the Swiss weather radar network can be found in Germann et al (2015;.…”

Section: Radar Datamentioning

confidence: 99%

A 10‐year radar‐based analysis of orographic precipitation growth and decay patterns over the Swiss Alpine region

Foresti

Sideris

Panziera

et al. 2018

Quart J Royal Meteoro Soc

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The dependence of precipitation growth and decay on the orientation of orographic features, mesoscale flow and freezing‐level height is quantified using a 10‐year archive of composite weather radar images over the Swiss Alpine region. The mesoscale flow is described by the motion of radar precipitation echoes, computed through variational echo tracking, while the freezing‐level height is extracted from the analyses of the numerical weather prediction model COSMO. On the northern side of the Alps, the areas of growth are generally observed on the upwind slopes of the Alpine chain, while on the southern side their location also depends on factors other than the motion of precipitation, such as the convergence of low‐level flows. On the other hand, the decay of precipitation is generally found in the inner Alpine valleys and on the downwind slopes of the Alpine chain. Compared to situations characterized by low freezing level, the areas of precipitation growth penetrate more into the mountain range with high‐freezing‐level conditions. When considering a time lag of 1 h and specific flow conditions, the systematic growth and decay of precipitation can explain up to 30–40% of its total variability over the orography. The relative contribution to the total variability is lower with high freezing level because the non‐systematic variability is larger. The statistical analysis of precipitation growth and decay using large archives of composite radar images highlights the mesoscale flow conditions and geographical locations most prone to orographic precipitation enhancement, but also has potential to improve existing precipitation nowcasting systems in mountainous regions and to provide a basis for the verification of systematic biases of numerical weather prediction models.

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

Cited by 84 publications

References 46 publications

Microphysics of Snowfall Over Coastal East Antarctica Simulated by Polar WRF and Observed by Radar

Microphysics of Snowfall Over Coastal East Antarctica Simulated by Polar WRF and Observed by Radar

Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera

A 10‐year radar‐based analysis of orographic precipitation growth and decay patterns over the Swiss Alpine region

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