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Algorithms for QPE, 3D mosaic and hydrometeor classiﬁcation Deliverable De5.1 L. Liu1 , H. Wang1 , Y. Xiao1 , Z. Hu1 1 Chinese Academy of Meteorological Sciences, CAS, P.R. China Dissemintation level: Programme Participants Lead beneﬁciary ID: CAMS
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ISSN/ISBN: c 2010 Edited by the CEOP-AEGIS Project Oﬃce LSIIT/TRIO, University of Strasbourg BP10413, F-67412 ILLKIRCH Cedex, France Phone: +33 368 854 528; Fax: +33 368 854 531 e-mail: management@ceop-aegis.orgNo part of this publication may be reproduced or published in any formor by any means, or stored in a database or retrieval system, without thewritten permission of the CEOP-AEGIS Project Oﬃce.
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CEOP-AEGIS Report De 5.1 Table of content1.! Introduction ................................................................................................................................................... 2! 1.1.! Identification .......................................................................................................................................... 2! 1.2.! Overview ................................................................................................................................................ 2!2.! 2. Algorithm Description I (QPE and 3D mosaic)........................................................................................ 2! 2.1.! Introduction ............................................................................................................................................ 2! 2.2.! Targets to be observed ........................................................................................................................... 2! 2.3.! Observation radar system....................................................................................................................... 2! 2.4.! Coverage of radar network..................................................................................................................... 4! 2.5.! QC of radar data ..................................................................................................................................... 5! 2.6.! Remap of raw data ................................................................................................................................. 8! 2.7.! Mosaic .................................................................................................................................................. 10! 2.8.! Qualitative Precipitation estimation (QPE).......................................................................................... 11!3.! Algorithm Prototyping ................................................................................................................................ 13! 3.1.! Mountain blockage and radar coverage ............................................................................................... 13! 3.2.! Radar data Quality................................................................................................................................ 14! 3.3.! Remap of reflectivity............................................................................................................................ 15!4.! Validation Plan............................................................................................................................................ 15! 4.1.! Introduction .......................................................................................................................................... 15! 4.2.! Approach .............................................................................................................................................. 16! 4.3.! Validation Sites .................................................................................................................................... 16!5.! Ancillary Data ............................................................................................................................................. 16!6.! 6. Algorithm Description II (Hydrometeor classification).......................................................................... 16! 6.1.! Introduction .......................................................................................................................................... 16! 6.2.! Targets to be observed ......................................................................................................................... 16! 6.3.! Input and output ................................................................................................................................... 17! 6.4.! Inference............................................................................................................................................... 20! 6.5.! Aggregation.......................................................................................................................................... 20!7.! Algorithm Prototyping ................................................................................................................................ 20!8.! Validation Plan............................................................................................................................................ 21! 8.1.! Introduction .......................................................................................................................................... 21! 8.2.! Approach .............................................................................................................................................. 22! 8.3.! Validation Sites .................................................................................................................................... 22!9.! Ancillary Data ............................................................................................................................................. 22! 1
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CEOP-AEGIS Report De 5.1!" #$%&()*%+$,, 1.1. Identification The Algorithm for Qualitative Precipitation Estimation (QPE), 3 Dimensional (3D)mosaic. Algorithm for hydrometeor type classification with polarimetric radar. 1.2. Overview In this report, the radar scan strategies and the associated beam geometry in Tibetanand Qinghai Province is first introduced. The following section (2 to 5) provides the radarcoverage in mountain regions, radar data quality, transformation of radar data fromspherical coordinate to Cartesian grid, mosaic of reflectivity. The fuzzy logic method for classification of hydrometeor type based on polarimetricradar measurements is described in sections 6 to 9.-" -",./0&+%12,345*&+6%+$,#,789:,;$(,<3,25;+*=, 2.1. Introduction The Algorithm for Quantitative Precipitation Estimation (QPE), 3 Dimensional (3D)mosaic include: (1) radar data quality control; (2) transformation of radar data fromspherical coordinate to Cartesian grid; (3) mosaic of reflectivity; (4) Z-R relationship andprecipitation estimation. 2.2. Targets to be observed The Algorithm is used to construct 3D reflectivity and precipitation estimation productswith high temporal and spatial resolutions. 2.3. Observation radar system In Tibetan Plateau, there are four C band Doppler radars, located in Lasa, Naqu, Rikezeand Linzhi, respectively. There are also 2 C band Doppler radars in Qinghai Province,located in Xining and. There radars do continuously 24h observation one day in summerwith volume scan 6 minute. The C band Doppler radar works on 5.5 cm wavelength withbeam width of 1.0°, and gate width of 250m. There radars performed volume scan with 14or 9 elevation angles once every 5 min which are similar with WSR-88D (Fig.2.1 ). 2
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CEOP-AEGIS Report De 5.1 19.5 16.7 14 12 10 8.7 7.5 6.2 5.25 4.3 3.35 2.4 1.45 0.5 19.5 14.6 9.9 6.0 4.3 3.35 2.4 1.45 0.5 Fig.2.1 The volume scans of VCP11 top and VCP21 bottom) The radar data analyses should provide end users with high-resolution radar reflectivitydata fields that are comparable to the raw data with the advantage of a Cartesian coordinatesystem (longitude, latitude, altitude (above mean sea level (MSL) ). A Cartesian coordinatesystem provides a common framework in which other observational datasets, such asprecipitation data, can be merged and cross-correlated. 3
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CEOP-AEGIS Report De 5.1 2.4. Coverage of radar network The mountain blockages to radar network were analyze with ground surface altitude data,and the radar data for precipitation cases were used in examining the mountain blockage forradar beams. The beam blockage by mountain was calculated using an algorithm that usedhigh resolution DEM digital elevation model data, radar beam pattern (Gaussian beamapproximation), and radar beam propagation path (assuming radar beams propagate understandard atmospheric refraction conditions). Assuming the ground target position is defined , the radar position is . The distance along the earth surface, azimuth and elevation of groundtarget with radar are calculated by: 2.1 2.2 2.3Where is earth radius is equivalent radius is linear distance fromradar to ground target, which is got by: 2.4 The altitude of ground target is got with DEM data. According to the calculation results, we can get the hybrid scan for each scan. The hybridscan mean that for each radar beam and gate, it defines as the lowest elevation angle with nomountain blockage for echo azimuth, using hybrid scan data for each radar, we can definethe radar coverage and reflectivity for QPE. 4
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CEOP-AEGIS Report De 5.1 2.5. QC of radar data The fuzzy logical based algorithm is used to detect the anomalous propagation groundclutter. Briefly, a fuzzy logic classifier uses various derived fields (formally known as"features") as input, scales them to a common reference frame by use of a "membershipfunction," and then computes a weighted sum of the resultant "interest" fields. Afterapplication of a threshold, the final output product of the detection algorithm is obtained andcontains the locations of the AP ground clutter. The general schematic of AP clutterdetection algorithms is shown in Fig. 2.2. The reflectivity, radial velocity and spectrumwidth in polar coordinate are input into the “feature generator” for calculation of thefeatures. The features used in the software includes: texture of the reflectivity (TdBZ), thevertical difference of the reflectivity (GDBZ) between two elevation angles, the medianradial velocity (MVE) and spectrum width (MSW), the standard deviation of radial velocity(SDEV), Percent of all of possible differences, that exceed the minimum differencethreshold (SPIN) and mean sign of reflectivity change along range (SIGN). The TDBZ,SDEV and RGDZ are defined as: (2.5) (2.6)where the Ngates and Nbeams are range and azimuth interval to defined a regional area. Forfeatures derived from reflectivity, Ngates=5 and Nbeams=5, for radial velocity andspectrum width, the Ngates=9 and Nbeams=5. (2.7)where Rangewet defined in Fig.2 (H) is range weighting function. (2.8) where the ALL_counts is toll number of radar data in specified area . SPINchang_countsand All_counts is calculated as: 5
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CEOP-AEGIS Report De 5.1 (2.9) The value of Zthresh=2dBZ for WSR-88D is suitable to separate the AP clutter fromprecipitation echo (Sterner and Smith, 2002). (2.10) where: (2.11) The MDVE and MDSW are calculated by median filtering along the range for 7 gates.Three features from reflectivity, two features from velocity, one from spectrum width andone from vertical structure of reflectivity are used in the software to detect AP clutter. Theseven features calculated gate by gate are input to the memberships to calculate the interests,respectively. Fig.2.3 shows the memberships for the seven features. The echo with lowradial velocity and spectrum width, high horizontal and vertical spatial gradient, has highlikelihood to be AP clutter. The difference between this algorithm described in this paperand method proposed by Kessinge are that the median velocity and spectrum are used in thismodel, instead of mean velocity and spectrum, considering the velocity folding and noise ofthe data. All of memberships are given a equal weight when calculating the interest field of APclutter, and 0.5 is the threshold to detect the AP clutter. Fig. 2.2 The general schematics of AP clutter detection algorithms. 6
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CEOP-AEGIS Report De 5.1 a b c d e f Fig 2.3 The membership functions of MDVE a SDVE b SW c TDBZ d GDBZ e SPIN f for ground clutter detection. 7
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CEOP-AEGIS Report De 5.1 2.6. Remap of raw data The transformation of radar data from a spherical coordinate to a Cartesian gridprovides a more direct approach of combining multiple radars onto a common grid. Thisprovides the ability to integrate the full-resolution base-level data from multiple radars ontoa common 3D framework. The 3D mosaic grid can benefit forecasters, meteorologists, andresearchers with a wide variety of products and displays, including flexible horizontal orvertical cross sections in addition to regional rainfall maps. High-resolution reflectivityanalyses can also serve as an important source in data assimilations for convective-scalenumerical weather modeling over large domains and for merging conventional datasets withthe radar data. Four interpolation approaches were investigated to remap raw radar reflectivity fieldsonto a 3D Cartesian grid with high resolution. (1)Nearest-neighbor mapping The nearest neighbor mapping (NNM) uses the value of the closest radar bin to grid cell,where distance is evaluated using the location of the centers of the radar bins. (2)nearest neighbor on range-azimuth planes combined with a linear interpolation invertical direction NVI The Fig. 2.4 shows the schedule of the approach. For each grid ( )in smallelevation angles (<20°), Find two observations of radar raw data points( , ) on the two adjacent tilts below and above the grid cell, respectively,and at the same range and azimuth as the grid cell. 2.12Here are the interpolation weights given to the he reflectivity observations belowand above the grid cell, respectively. The weights are determined by : 2.13 8
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CEOP-AEGIS Report De 5.1 2.14where !i, !o1, and !o2 represent elevation angles of the grid cell and the radar bins below andabove, respectively. 3 Linear interpolation in vertical direction plus a horizontal interpolation VHI Fig.5 shows the VHI approach. For the grid cell , the four reflectivityobservations are ,the analysisformula for the vertical and horizontal interpolation (VHI) scheme is 2.15Here are nterpolation weights given to the he reflectivity observations 2.16 2.17 (4) Dual linear interpolation As shown in Fig. are 8 reflectivityobservations near the grid cell , the analysis values is : (2.18) 2.19 2.20 In this study, the third approach is used to remap the raw radar data to 3D grid data. Figure 2.5 dual linear interpolation 9
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CEOP-AEGIS Report De 5.1 2.7. Mosaic In addition to remapping single radar reflectivity fields, one must consider a multipleradar scenario where more than one radar collects reflectivity data for the same point inspace. The 4DDG provides a methodology that takes advantage of multiple observationswhile handling those observations that do not agree with each other. Some grid cells are sampled by more than one radar bin. The mosaic approach is tocalculated a final reflectivity value for grid cells oversampled by radar bins. Threeapproaches of combining multiple-radar reflectivity fields were investigated. They aremaximum reflectivity, Nearest-neighbor reflectivity and distance weighted means. Results have shown the distance- exponential-weighted mean scheme provided a spatiallyconsistent reflectivity mosaics while retaining the magnitude of the observations from theclose radar. Mosaics could mitigate various problems that caused by the geometry of radarbeam such as data voids with the cone of silence above the radar and in regions below thelowest beam. (1) Nearest-neighbor mosaic approach In this approach, the analysis value from the closest radar is assigned to the grid cell.The nearest-neighbor method does not impose any smoothing when creating a mosaic frommultiple radars. However, discontinuities may appear at the equidistant lines between radarsin the mosaicked field. 2 Maximum reflectivity This mosaicking method simply uses the maximum reflectivity value among themultiple observations that cover the same grid cell. This method does not involve smoothingand it retains the highest reflectivity intensities in the data fields. 3) Distance weighted means The mosaic method considered is a weighted mean, whereby the weight is based on thedistance between an individual grid cell and the radar location. Two weighting functionswere tested; both monotonically decrease with range, that is exponential weighting functionand Cressman weight function are used in this study. These distance weighted means takeadvantage of multiple observations while handling those observations that do not agree witheach other. The shape of an exponential weighting function is easily adjustable to achieve arapid decrease with range while retaining a positive weight value. Thus, an exponentially 10
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CEOP-AEGIS Report De 5.1decaying function is especially useful for radar analysis and is employed in the algorithm.The two functions are expressed as following: 2.21 Here r is the distance of the observation from its respective radar and R is an adaptablelength scale (i.e., 100 km). 2.22Where is effective radius (i.e. 300 km) and r is the distance of the observation from itsrespective radar. The shape of an exponential weighting function is easily adjustable to achieve a rapiddecrease with range while retaining a positive weight value. Thus, an exponentiallydecaying function is especially useful for radar analysis and is employed in the algorithm. 2.8. Qualitative Precipitation estimation (QPE) The QPE Algorithm in this study is based on 3D mosaic. It composed of five mainscientific processing components and one external support functions. The five scientificsubalgorithms are identified as follows: 1) preprocessing 2) rain rate estimation, 3)accumulation , 4) adjustment , and 5) products ). The two support functions, precipitationdetection and rain gauge data acquisition.(1) Rain gauge data acquisition The external support function within the QPE is called the rain gauge data acquisitionfunction. It receives the rain gauge reports. In order to deal with all of kinds of rain gaugedata, the precipitation rate is transfer into unit structures, which include the position ofraingauge, beginning and end of time, precipitation amount.(2) Reflectivity preprocessing The CAPPI of 3D mosaic reflectivity between 2-5 km (MSL) is chosen to calculate theprecipitation. The altitude of CAPPI depend on the domain for QPE, the blockage of radarbeam, the precipitation system (deeply or shallow developed ) . The horizontal and verticalresolution of 3D grid data are 2km and 0.5km, respectively. Because the blockage of radarbeam in Tibetan is very serious, the compositive reflectivity will be used. This quality control step removes reflectivity data that are abnormally large inmagnitude but small in area, such as those associated with nonmeteorological targets 11
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CEOP-AEGIS Report De 5.1(airplanes, anomalous propagation returns, or residual ground clutter). Two reflectivitythresholds are used here. Isolated sample bins are defined as grids with reflectivities thatexceed a certain threshold (an adaptable parameter currently set at 15 dBZ) and for which nomore than one of the eight surrounding neighbors is also above that same threshold. Isolatedbins are replaced with a valid reflectivity. A second maximum reflectivity threshold parameter (currently set at 65 dBZ) is used toquality control the extremely large point outlier reflectivity bins typically associated withresidual ground clutter or anomalous propagation that have not previously been removed byAP detection . If a grid has a reflectivity that exceeds this threshold, it is replaced with eitheran average of surrounding values if none of them are above the same threshold, or otherwiseit is assigned a very small value (7 dBZ; an adaptable parameter). This step will not removeall occurrences of residual clutter or anomalous propagation, and hence additional qualitycontrol steps are necessary.(3) Rain rate conversion from reflectivity It converts reflectivity factor data from the CAPPI of reflectivity in 3D mosaic into rainrates using a standard Z–R power law relationship derived from the empirical relationshipbetween the two variables. The current default equation is Z = 300R1.4 2.23where Z has units of mm6 m"3 and R in mm h"1. The Z–R parameter settings are designed tobe adjustable at each site. In this work, we design method to form Z-R relationship every volume scan. The rain rateand respective reflectivity are matched to fitting the Z-R relationship under the assumptionof Z–R power law relationship. The reflectivity stronger than 55dBZ are considered as hail cores of thunderstorms, it isnecessary to cap them at a maximum value expected to be associated with rain only. The“hail cap” threshold, an adaptable parameter representing the maximum expectedinstantaneous, rain rate, has typical values ranging from about 75 mm h"1 (3 in. h"1) to about150 mm h"1 (6 in. h"1) except in highly unusual events. (4) Gauge–radar adjustment A temporally fixed Z–R relationship, as is the current design of the PPS, will not beappropriate for all rainfall events. Because it is not currently feasible to manually adjust theZ–R parameters in real time due to the lack of proven, robust, and objective criteria valid 12
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CEOP-AEGIS Report De 5.1over a broad range of rainfall types, it is useful to make automated adjustments to therainfall estimates by comparing them with real-time rain gauge data on an hourly time step.Real-time rain gauge data can be used to adjust the radar rainfall estimates in the algorithm.Optimal interpolation (OI) analysis is used in this QPE.<" ./0&+%12,9&%%>6+$0, 3.1. Mountain blockage and radar coverage The mountain blockages for radars in Tibetan and Qinghai are calculated. Figure 3.1shows the example of blockage of radar. The heavy rainfall case is used to examine thecoverage of the radar (Fig. 3. 2) Fig. 3.1 the blockage of Xining radar 13
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CEOP-AEGIS Report De 5.1 Fi.g 3.2 the CAPPI of reflectivity at 3.0km 3.2. Radar data Quality The ground clutter and radio noise distinguish with the radar QC algorithm are shown inFig. 3.3 and 3.4 Fig. 3.3 The raw data and after QC data at 11:43, July 15, 2008 in Henan Province 14
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CEOP-AEGIS Report De 5.1 Fig. 3.4 The raw data and after QC data at 14:46, July 15, 2008 in Jiangsu Province 3.3. Remap of reflectivity (a) (b) Fig. 3.5 the CAPPI of reflectivity in Xining Fig. 3.5 shows the CAPPI of reflectivity at 3km.?" @;/+(;%+$,9/;$,, 4.1. Introduction In mountain areas, radar observations are often contaminated by echoes from groundclutter, high-speed moving vehicles and by point-wise ground clutter under either normalpropagation (NP) or anomalous propagation (AP) conditions. Many factors can introducedthe bias of reflectivity measurement, such as transmitter power, noise figure variations.Transformation of radar data from spherical coordinate to Cartesian grid can also producehorizontal and vertical structures variations of reflectivity. The reflectivity bias betweendifferent radars can affect the 3-D mosaic. 15
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CEOP-AEGIS Report De 5.1 4.2. Approach In order to validate the algorithm, the ground data, raw data display are compared withthe products from the algorithm. The experts can distinguish the ground clutter and radionoise pixels of radar each, this data is used to examine the radar data QC. 4.3. Validation Sites Chinese Academy of Meteor. Sciences.A" .$*+//;&>,3;%;,, The raw radar data from Qinghai Province and Tibetan; Raingauge data rom Qinghai Province and Tibetan; Processing radar data and provide 3D reflectivity data to WP5. Grid Reflectivity inQinghai from 18 July 2008 -21 July 2008 were product with spatial and temporalresolution (0.01°#0.01°#0.5km#5min), the radar data in Tibetan from 18 June 2008 to19June 2008, 18 July 2008 to 20 July 2008 were provide.B" B",./0&+%12,345*&+6%+$,##,7C>(&24%4&,*/;55+D+*;%+$=, 6.1. Introduction There are several methods that can be potentially used for hydrometeor identification,such as classification using differential reflectivity (ZDR) classification using lineardepolarization ratio (LDR), classification using specific differential propagation phase (KDP)and coefficient ($HV), classic statistical decision theory, neural networks, and fuzzy logic.Fuzzy logic is used in this study for classification because it has many inherent advantagesover other methods. Many polarimetric radar measurements lie in a limited measurementspace for each hydrometeor type. The fuzzy logic system possesses the ability to reachdistinct decisions based on overlapping and “noise contaminated” data. The fuzzy logicmethod for classification of hydrometeor type based on polarimetric radar is developed byChinese Academy of Meteorological Sciences. 6.2. Targets to be observed Algorithm for hydrological classification with polarimetric radar can generate thehydrological classification products (rain, snow, crystal, hail and graupel) from polarimetricradar raw data. A fuzzy logic method for classification of hydrometeor type based onpolarimetric radar measurements is described in this report. 16
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CEOP-AEGIS Report De 5.1 First, the four radar measurements and altitude are fuzzified by using membershipfunctions. There are 10 membership functions for each of the input variables in the system.After fuzzification, then the membership functions are aggregated from each hydrometeortype. The last step is defuzzification, which can convert aggregation result to a singlehydrometeor type. The following provides detailed description of the steps used in theclassification process. 6.3. Input and output Four radar measurements, namely, horizontal reflectivity (ZH), differential reflectivity(ZDR), differential propagation phase shift (KDP) and correlation coefficient ["HV] have beenused as input variables to the fuzzy logical hydrometeors classification. ZH for horizontally and vertically polarized waves are proportional to the hydrometeor’scross section integrated over a volume. ZDR can be related to the axis ratio and size ofhydrometeors. The differential phase KDP is the difference between propagation constantsfor horizontally and vertically polarized waves .In the region filled with horizontallyoriented hydrometeors such as rain or ice crystals, a horizontally polarized wave has largerphase shifts (per unit length) and propagates more slowly than a vertically polarized wavedoes; the opposite holds for vertically oriented hydrometeors. In theory, KDP allowsdiscrimination between statistically isotropic and anisotropic hydrometeors; isotropichydrometeors produce similar phase shifts for horizontally and vertically polarized waves. The degree of decorrelation as measured using the correlation coefficient at zero lag $HVbetween horizontally and vertically polarized echoes results. Decorrelation physicallyoccurs if the horizontal and vertical backscatter fields do not vary similarly. $HV decreaseswith increasing diversity of hydrometeor orientations and shapes. When particles are wet orwhen they are large and irregular in shape. Moreover, $HV is lower when there are mixturesof hydrometeor types rather than when just one type is present. The output of the neuro-fuzzy system is one of the many possible hydrometeor types:1) drizzle, 2) rain, 3) dry and low density snow, 4) dry and high-density crystals, 5) wet andmelting snow, 6) dry graupel, 7) wet graupel, 8) small hail, 9) large hail, and 10) a mixtureof rain and hail. 17
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CEOP-AEGIS Report De 5.1 Table 6.1 Output of the Algorithm for hydrological classification Hydrometer type Classify output Hydrometer type Classify output DRizzle(DR) 1 Dry Graupel(DG) 6 RAin(RA) 2 Wet Graupel 7 (WG) Dry Snow(DS) 3 Small Hail(SH) 8 Dry Cristal(DC) 4 Large Hail(LH) 9 Wet Snow (WS) 5 Hail Rain(HR) 10 The function of the “fuzzification” is to convert the crisp inputs (or precisemeasurements) to the fuzzy sets ( 0-1 ) with a corresponding membership degree. A specificcrisp input can be to different fuzzy sets but with different membership degrees (or truthvalue). The most important component in fuzzification is the membership function, which isused to describe the relationship of the crisp input and the fuzzy sets in the input domain.The definition of membership function is as follows: T(x) is called membership function offuzzy set A (for a fuzzy variable x), whose value is the degree to which x is a member offuzzy set A. (6.1) We can define the functions MBFi-j for each polarization variables i and hydrometeortypes. Fig. 6.1 shows the member function for polarization variables and hydrometeorclassifications. 18
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CEOP-AEGIS Report De 5.1 6.4. Inference In a fuzzy logic system, rules are used to describe linguistically the complex relationshipbetween the input and output fuzzy variables in the form of IF–THEN statements. Typically,the rule is composed of several antecedents in the IF statement and one or severalconsequents in the THEN statement. The process of deducing the “strength” of theseconsequents from the strength of the antecedents is called rule inference. The mostcommonly used inference methods are correlation minimum, correlation product, and MIN–MAX. The total contributions of polarization variables to the hydrometeor type are definedas: (6.2)Here Ai is coefficient weight for ith polarization variables. AZH=AZDR=1 AKDP=0.8A$HV=0.5. 6.5. Aggregation We can use the inference methods to derive the strength of each rule, then theaggregation method can be used to determine an overall fuzzy region. Two commonly usedaggregation methods are additive aggregation and MAX Aggregation, which mean that themaximum values of RSj is the hydrometeor type.E" ./0&+%12,9&%%>6+$0, The Koun radar in USA for hail storm is used to examine the algorithm. Figure 7.1 showsthe radar measurement. Figure 7. 2 shows the hydrometric classification results. 20
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CEOP-AEGIS Report De 5.1 (a) (b) (c) (d) Figure 7.1 Four PPI image at 0.5 of the Oklahoma City tornado on 10 May2003 1903 UTC.Radar is located at the central point of half circle. (a) ZH (dBZ) (b)ZDR(dB) (c) DP(deg Km-1) (d) HV (a) (b) Figure 7.2 Two kinds of hydrometeor type classification results at 19:03 UTC by different inference rules: (a) PSi-j multiply (b) PSi-j plusF" @;/+(;%+$,9/;$,, 8.1. Introduction Knowing what precipitation type is reaching the ground is a fundamental prerequisite foraccurate determination of amount. Thus, for quantitative precipitation estimation (QPE),first a correct classification needs to be made so that appropriate semiempirical relations can 21
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CEOP-AEGIS Report De 5.1be chosen to estimate the corresponding rates and/or accumulations. Because of sensitivityto hydrometeor concentration, shape, orientation, dielectric constant, and size, polarimetricvariables have emerged as leading discriminators of precipitation type. The situ observationwith aircraft is simpler means to develop and evaluate the algorithm, we also rely on spatialcontinuity, height above ground, and comparison with conceptual models to qualify thealgorithms performance. 8.2. Approach The radar observation will be compared with model output, the results are analyze toexamine the algorithm performance. 8.3. Validation Sites Chinese Academy of Meteor. Sciences.G" .$*+//;&>,3;%;,, The raw radar data from C band polarimetric radar; 22
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CEOP-AEGIS Report De 5.1ReferencesBrandes, E. A., A. V. Ryzhkov, and D. S. Zrnic, 2001: An evaluation of radar rainfall estimates from specific differential phase. J. Atmos. Oceanic Technol., 18, 363–375.Doviak, R., V. N., Bringi, A. Ryzhkov, A. Zahrai and D. Zrnic, 2000: Consideration for polarimetric upgrades to WSR-88D radar. J. Atmos. Oceanic Technol. 17, 257-278.Doviak., R. J., and D. S. Zrinc, 1993: Doppler radar and weather observation. Academic Press, 562 pp.Fulton, R. A., J. P. Breidenbach, D. J. Seo, D. A. Miller, and T. OBannon, 1998: The WSR- 88D rainfall algorithm. Weather Forecasting, 13, 377–395.Gorgucci, E., V. Chandrasekar, V. N. Bringi, and G. Scarchilli, 2002: Estimation of raindrop size distribution parameters from polarimetric radar measurements. J. Atmos. Sci., 59, 2373–2384.Joss, J., and R. W. Lee, 1995: The application of radar–gauge comparisons to operational precipitation profile corrections. J. Appl. Meteorol., 34, 2612–2630.Kessinger, C., S. Ellis, J. Vanandel, and J. Yee, 2003: The AP clutter mitigation scheme for the WSR-88D. Preprints, 31st conference on radar meteorology, Seattle WA, Amer. Meteor. Soc, 526-529.Ryzhkov, A. V., and D. S. Zrnic, 1996: Assessment of rainfall measurement that uses specific differential phase. J. Appl. Meteor. 35, 2080–2090Schuur, T. A. Ryzhkov , P. Heinselman, D. S. Zrnic, D. Burgess and K. Scharfenberg, 2003: Observations and classification of echoes with polarimetric WSR-88D radar, Report of National Severe Storm Laboratory, Norman, OK. 46pp.Vivekanandan J., S. M. Ellis, R. Oye, D. S. Zrnic, A. V. Ryzhkov, and J. Straka, 1999: Cloud microphysics retrieval using S-band dual-polarization radar measurements. Bull. Amer. Meteor. Soc., 80, 381–388 23
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AcknowledgmentsThe work described in this publication has been supported by the EuropeanCommission (Call FP7-ENV-2007-1 Grant nr. 212921) as part of the CEOP-AEGIS project (http://www.ceop-aegis.org) coordinated by the Universityof Strasbourg, France.
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