Hydrometeor classification system using dual-polarization radar measurements: model improvements and in situ verification

Lim, Sanghun; Chandrasekar, V.; Bringi, V. N.

doi:10.1109/tgrs.2004.843077

Cited by 119 publications

(92 citation statements)

References 17 publications

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“…The results of T-matrix simulations were used to define proper beta membership functions of a fuzzy logic (FL) HCA (Liu and Chandrasekar, 2000;Lim et al, 2005) tailored for C-band and graupel detection. Inputs of the FL classificator are the Polar 55C radar measurements of Z h , Z dr , and K dp , the standard deviation of the differential propagation phase shift (φ dp ) (calculated in a 375 m range moving window, i.e., five 75 m range bins), and the height of the top of the melting layer (ML).…”

Section: Hca Optimization For Graupel Identificationmentioning

confidence: 99%

Multi-sensor analysis of convective activity in central Italy during the HyMeX SOP 1.1

et al. 2016

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Abstract.A multi-sensor analysis of convective precipitation events that occurred in central Italy in autumn 2012 during the HyMeX (Hydrological cycle in the Mediterranean experiment) Special Observation Period (SOP) 1.1 is presented. Various microphysical properties of liquid and solid hydrometeors are examined to assess their relationship with lightning activity. The instrumentation used consisted of a C-band dual-polarization weather radar, a 2-D video disdrometer, and the LINET lightning network. Results of Tmatrix simulation for graupel were used to (i) tune a fuzzy logic hydrometeor classification algorithm based on Liu and Chandrasekar (2000) for the detection of graupel from Cband dual-polarization radar measurements and (ii) to retrieve graupel ice water content. Graupel mass from radar measurements was related to lightning activity. Three significant case studies were analyzed and linear relations between the total mass of graupel and number of LINET strokes were found with different slopes depending on the nature of the convective event (such as updraft strength and freezing level height) and the radar observational geometry. A high coefficient of determination (R 2 = 0.856) and a slope in agreement with satellite measurements and model results for one of the case studies (15 October 2012) were found. Results confirm that one of the key features in the electrical charging of convective clouds is the ice content, although it is not the only one. Parameters of the gamma raindrop size distribution measured by a 2-D video disdrometer revealed the transition from a convective to a stratiform regime. The raindrop size spectra measured by a 2-D video disdrometer were used to partition rain into stratiform and convective classes. These results are further analyzed in relation to radar measurements and to the number of strokes. Lightning activity was not always recorded when the precipitation regime was classified as convective rain. High statistical scores were found for relationships relating lightning activity to graupel aloft.

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Section: Hca Optimization For Graupel Identificationmentioning

confidence: 99%

Multi-sensor analysis of convective activity in central Italy during the HyMeX SOP 1.1

et al. 2016

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“…Commonly, fuzzy-logic classification schemes are employed to determine the most probable HM type (Straka et al, 2000;Keenan, 2003;Lim et al, 2005;Park et al, 2009;Al-Sakka et al, 2013). The verification of a classification scheme is typically done with test data sets, where hydrometeor types are known (Al-Sakka et al, 2013).…”

Section: Hydrometeor Classificationmentioning

confidence: 99%

Polarimetric radar observations during an orographic rain event

Frech

Steinert

2015

Hydrol. Earth Syst. Sci.

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Abstract. An intense orographic precipitation event on 5 January 2013 is analyzed using a polarimetric C-band radar situated north of the Alps. The radar is operated at the meteorological observatory Hohenpeißenberg (MHP, 1006 m a.s.l.-above sea level) of the German Meteorological Service (DWD). The event lasted about 1.5 days and in total 44 mm precipitation was measured at Hohenpeißenberg. Detailed high resolution observation on the vertical structure of this event is obtained through a birdbath scan at 90 • elevation which is part of the operational scanning. This scan is acquired every 5 min and provides meteorological profiles at high spatial resolution which are often not available in other radar networks. In the course of this event, the melting layer (ML) descends until the transition from rain into snow is observed at ground level. This transition from rain into snow is well documented by local weather observers and a presentweather sensor. The orographic precipitation event reveals mesoscale variability above the melting layer which can be attributed to a warm front. This variability manifests itself through substantially increased hydrometeor fall velocities. Radiosounding data indicate a layered structure in the thermodynamic field with increased moisture availability in relation to warm air advection. Rimed snowflakes and aggregation in a relatively warm environment lead to a signature in the radar data which is attributed to wet snow. The passage of the warm front leads to a substantial increase in rain rate at the surface. We use the newly implemented hydrometeor classification scheme "Hymec" to illustrate issues when relating radar products to local observations. For this, we employ data from the radar near Memmingen (MEM, 65 km west of MHP, 600 m a.s.l.) which is part of DWD's operational radar network. The detection, in location and timing, of the ML agrees well with the Hohenpeißenberg radar data.Considering the size of the Memmingen radar sensing volume, the detected hydrometeor (HM) types are consistent for measurements at or in a ML, even though surface observations indicate for example rain whereas the predominant HM is classified as wet snow. To better link the HM classification with the surface observation, either better thermodynamic input for Hymec or a statistical correction of the HM classification similar to a model output statistics (MOS) approach may be needed.

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“…Classification of snowfall from weather radar measurements is difficult. Despite the successful application of fuzzylogic-based hydrometeor classification algorithms (Vivekanandan et al 1999;Liu and Chandrasekar 2000;Straka et al 2000;Lim et al 2005;Ryzhkov et al 2005;Keränen et al 2007;Park et al 2009;Chandrasekar et al 2013) to summer precipitation, limited progress has been made in winter precipitation. As shown by experience, polarimetric radar signatures are not very different for many types of ice particles, i.e., aggregates and rimed ice particles.…”

Section: Introductionmentioning

confidence: 99%

Classification and Quantification of Snow Based on Spatial Variability of Radar Reflectivity

Lim

Moisseev

Chandrasekar

et al. 2013

Journal of the Meteorological Society of Japan

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In this study, a classification methodology of snow particle types, i.e., crystals, aggregates, rimed snow, and graupel, by using spatial variability of the equivalent radar reflectivity factor is proposed. The methodology is formulated on the basis of the analysis of vertically pointing Doppler radar, scanning dual-polarization weather radar, and supporting surface observations. It is argued that by using the proposed snow-type identification methodology, it is possible to guide the choice of the particular parameters of power law relations of equivalent radar reflectivity factor̶liquid equivalent snowfall rate. The validity of the classification results are demonstrated by comparing the classification output to Vaisala WXT observations, which can be used to detect presence of high-density particles in snow. The performance of the proposed quantitative snowfall estimation algorithm is illustrated using an example of the data collected from the C-band operational Helsinki Vantaa radar and ground instruments (Vaisala PWD-11, Pluvio).

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Hydrometeor classification system using dual-polarization radar measurements: model improvements and in situ verification

Cited by 119 publications

References 17 publications

Multi-sensor analysis of convective activity in central Italy during the HyMeX SOP 1.1

Multi-sensor analysis of convective activity in central Italy during the HyMeX SOP 1.1

Polarimetric radar observations during an orographic rain event

Classification and Quantification of Snow Based on Spatial Variability of Radar Reflectivity

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