《TheWhistlerRadarKNMI惠斯勒雷达KNMI》由会员分享，可在线阅读，更多相关《TheWhistlerRadarKNMI惠斯勒雷达KNMI（47页珍藏版）》请在金锄头文库上搜索。
1、Radar Quality Control and Quantitative Precipitation Estimation Intercomparison ProjectStatusPaul JoeEnvironment CanadaCommission of Instruments, Methods and Observations (CIMO)Upper Air and Remote Sensing Technologies (UA&RST)OutlineProject ConceptThe Problem Overview of Data Quality TechniquesPre-
2、RQQI ResultsStatusExternal FactorsSegmenting the DQ Process for Quantitative Precipitation EstimationRemove Artifacts- Cleaned Up 3D volumeEstimating Surface/3D ReflectivityEstimating Surface/3DPrecipitationMosaicingSpace-Time EstimationFocus on ReflectivityNowcastingClear Air Echo as InformationSeg
3、menting the DQ ProcessRemove Artifacts- Cleaned Up 3D volumeEstimating Surface/3D Radar MomentsEstimating Surface3D Precipitation (Classification)MosaicingSpace-Time Estimation in 3DReflectivityRadial VelocityDual-PolarizationEvery radar has clutter due to environment!Sea Clutter and DuctingElectrom
4、agnetic InterferenceTechniquesCAPPI is a classic technique to overcome ground clutterVVO5o43210Lines are elevation angles at 1o spacing, orange is every 5o.Canada AustraliaU.S./China VCP21 Whistler Valley Radar3.0 CAPPI1.5 CAPPIThere are a variety of Scan Strategies(CAPPI Profiles)Make better or dro
5、pThe elevation angles but nature of weather important for CAPPI2.5o1.5o0.5oPPIs1.5km CAPPIDoppler Zero Velocity Notch1.Doppler Velocity SpectrumPulse pair (time domain)FFT (frequency domain)2. Reflectivity statisticsBeforeAfterDoppler FilteringSNOWRAINToo much echo removed! However, better than with
6、out filtering?Data Processing plus Signal ProcessingTexture + Fuzzy Logic + SpectralDixon, Kessinger, HubbertData Processing plus Signal ProcessingFUZZY LOGICRemoval of Anomalous PropagationNONQC QCLiping Liu, CMAThe Metric of SuccessIso-range “Variance” as an intercomparison MetricDaniel Michelson,
7、 SMHIAccumulation a winter season log (Raingauge-Radar Difference)No blockageRings of decreasing valueDifference increases range!almostConvectionStratiformSnowVertical Profile of Reflectivity is smoothed as the beam spreads in rangeDue to Earth curvature and beam propagating above the weather.Varian
8、ce MetricSimilar to before except area of partial blockage contributes to lots of scatterAlgorithms that are able to infill data should reduce the variance in the scatter!MichelsonProposed MetricAlternate MetricsAccumulation of Radial Velocity should produce the mean wind for the site.Both look beli
9、evable, maybe difference is due to different data set lengthnonQC QCModalityNeed a variety of techniquesNeed a variety of scan strategiesNeed a variety of data sets that integrate to a uniform patternNeed weather with a variety of artifactsPilot StudyPurpose is to test the assumptions of the project
10、 modality-Short data sets for uniformity-Check the interpretation of the metric-Variety of scan strategies, algorithms, etc-Evaluate feasibilityUniform FieldsSample CasesUniform with local clutter (XLA)Uniform with partial blocking (WVY)Urban Clutter/Niagara Escarpment (WKR)Strong Anomalous Propagat
11、ion Echo (TJ 2006)Strong AP with Weather (TJ 2007)Sea Clutter (Sydney AU, Kurnell)Sea Clutter / Multi-path AP (Saudi 2002)Convective Weather with Airplane Tracks - One season (TJ Radar 2007)XLAThe data accumulates to uniform pattern. Widespread snow. A baseline case. IRIS formatted data. 24 elevatio
12、n angles. Doppler (dBZT, dBZc, Vr, SPW) at low levels. Range res = 1km or 0.5 km. Az res = 1 or 0.5 degrees.WVYThe data accumulates to uniform pattern with an area of blockage. Widespread snow. A baseline case. IRIS formatted data. 24 elevation angles. Doppler (dBZT, dBZc, Vr, SPW) at low levels. Ra
13、nge res = 1km or 0.5 km. Az res = 1 or 0.5 degrees.WKRThe data accumulates to uniform pattern with an area of blockage. Widespread snow. Urban (skyscrapers) and small terrain clutter. IRIS formatted data. 24 elevation angles. Doppler (dBZT, dBZc, Vr, SPW) at low levels. Range res = 1km or 0.5 km. Az
14、 res = 1 or 0.5 degrees.BSCAN of Z accumulation with no filtering, Doppler and CAPPICAPPIDopplerNo FilteringAzimuthRange km0 100Probability Density Function of Reflectivity as a function of rangeRaw Doppler CAPPIWhat length of data sets are needed?Highly Variable More uniform, smoother, more continu
15、ousThe TechniquesDoppler NotchingCAPPI 1.5kmCAPPI 3.0kmMixed of Doppler Notching and CAPPIRadar Echo Classifier (REC)Anomalous PropagationSea ClutterREC-CMAThe StatisticSpread of PDF (at constant range) for various cases and techniquesStatusStatus and AcknowledgementsKimata, JapanLiu, ChinaSeed, Aus
16、traliaMichelson, SwedenSempere-Torres, SpainHoward, USAHubbert, USACalhieros, BrazilLevizzani, Italy/IPWGGaussiat, UK/OPERA HUBDonaldson, CanadaData ProvidersAlgorithm ProvidersEvaluation TeamReviewersSummaryOn-goingData Providers, Processors identifiedODIM_H5 format identifiedBOM will host and convert data for Data ProcessorsInitial Metric identifiedReviewVariety of techniquesVariety of scan strategiesVariety of data sets Weather (e.g. convective, snow) with a variety of artifactsAlternate radial velocity metric
