Recent advances in classification of observations from dual polarization weather radars

Chandrasekar, V.; Keränen, R.; Lim, Sanghun; Moisseev, Dmitri

doi:10.1016/j.atmosres.2011.08.014

Cited by 145 publications

(130 citation statements)

References 30 publications

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“…The DT method has been successfully used in data classification and data forecasting in radar data analysis [47]. Thus, DT is a very suitable statistical technique to select the right direction to reach the best result in primary radar data.…”

Section: B Decision Tree (Dt)mentioning

confidence: 99%

“…Moreover, it is able to recognize hydrometeor forms with overlapping and noisy capacities (Chandrasekar et al, 2011).Using fuzzy rule base systems (RBS) in predicting meteorological conditions events, researchers managed to implement a fuzzy rule (design eight-step procedure) for solving the problem of predicting weather happenings based on different weather limitations in Lahore. Here rule base used by the inference engine was developed by three sources: a) expert opinion, b) automated rule generation and c) literature survey.…”

Section: G Fuzzy Logicmentioning

confidence: 99%

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Advanced of Mathematics-Statistics Methods to Radar Calibration for Rainfall Estimation; A Review

Nikahd¹

2015

IJRITCC

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Abstract-Ground-based radar is known as one of the most important systems for precipitation measurement at high spatial and temporal resolutions. Radar data are recorded in digital manner and readily ingested to any statistical analyses. These measurements are subjected to specific calibration to eliminate systematic errors as well as minimizing the random errors, respectively. Since statistical methods are based on mathematics, they offer more precise results and easy interpretation with lower data detail. Although they have challenge to interpret due to their mathematical structure, but the accuracy of the conclusions and the interpretation of the output are appropriate. This article reviews the advanced methods in using the calibration of ground-based radar for forecasting meteorological events include two aspects: statistical techniques and data mining. Statistical techniques refer to empirical analyses such as regression, while data mining includes the Artificial Neural Network (ANN), data Kriging, Nearest Neighbour (NN), Decision Tree (DT) and fuzzy logic. The results show that Kriging is more applicable for interpolation. Regression methods are simple to use and data mining based on Artificial Intelligence is very precise. Thus, this review explores the characteristics of the statistical parameters in the field of radar applications and shows which parameters give the best results for undefined cases.

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Section: B Decision Tree (Dt)mentioning

confidence: 99%

Section: G Fuzzy Logicmentioning

confidence: 99%

Advanced of Mathematics-Statistics Methods to Radar Calibration for Rainfall Estimation; A Review

Nikahd¹

2015

IJRITCC

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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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“…Dual polarization also offers a wide variety of methods (e.g. Gourley et al, 2007;Hurtado and Nehorai, 2008;Unal, 2009;Chandrasekar et al, 2013). Fuzzy logic classification techniques for the identification and removal of spurious echoes from radar are also in use (e.g.…”

Section: Introductionmentioning

confidence: 99%

A simple biota removal algorithm for 35 GHz cloud radar measurements

et al. 2018

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Abstract. Cloud radar reflectivity profiles can be an important measurement for the investigation of cloud vertical structure (CVS). However, extracting intended meteorological cloud content from the measurement often demands an effective technique or algorithm that can reduce error and observational uncertainties in the recorded data. In this work, a technique is proposed to identify and separate cloud and nonhydrometeor echoes using the radar Doppler spectral moments profile measurements. The point and volume targetbased theoretical radar sensitivity curves are used for removing the receiver noise floor and identified radar echoes are scrutinized according to the signal decorrelation period. Here, it is hypothesized that cloud echoes are observed to be temporally more coherent and homogenous and have a longer correlation period than biota. That can be checked statistically using ∼ 4 s sliding mean and standard deviation value of reflectivity profiles. The above step helps in screen out clouds critically by filtering out the biota. The final important step strives for the retrieval of cloud height. The proposed algorithm potentially identifies cloud height solely through the systematic characterization of Z variability using the local atmospheric vertical structure knowledge besides to the theoretical, statistical and echo tracing tools. Thus, characterization of high-resolution cloud radar reflectivity profile measurements has been done with the theoretical echo sensitivity curves and observed echo statistics for the true cloud height tracking (TEST). TEST showed superior performance in screening out clouds and filtering out isolated insects. TEST constrained with polarimetric measurements was found to be more promising under high-density biota whereas TEST combined with linear depolarization ratio and spectral width perform potentially to filter out biota within the highly turbulent shallow cumulus clouds in the convective boundary layer (CBL). This TEST technique is promisingly simple in realization but powerful in performance due to the flexibility in constraining, identifying and filtering out the biota and screening out the true cloud content, especially the CBL clouds. Therefore, the TEST algorithm is superior for screening out the low-level clouds that are strongly linked to the rainmaking mechanism associated with the Indian Summer Monsoon region's CVS.

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Recent advances in classification of observations from dual polarization weather radars

Cited by 145 publications

References 30 publications

Advanced of Mathematics-Statistics Methods to Radar Calibration for Rainfall Estimation; A Review

Advanced of Mathematics-Statistics Methods to Radar Calibration for Rainfall Estimation; A Review

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

A simple biota removal algorithm for 35 GHz cloud radar measurements

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