6/29/2005 Q2 Workshop, Norman, OK
3-D Radar Mosaic and 3-D Radar Mosaic and
Initial Q2 Development Initial Q2 Development
PlansPlans
Jian ZhangJian Zhang11, Ken Howard, Ken Howard22, and Steve Vasiloff, and Steve Vasiloff22
11University of Oklahoma, Norman, OKUniversity of Oklahoma, Norman, OK22National Severe Storms Lab, Norman, OKNational Severe Storms Lab, Norman, OK
OutlineOutline NMQ Components OverviewNMQ Components Overview
Single Radar ProcessSingle Radar Process
2-D Radar Mosaic2-D Radar Mosaic
3-D Radar Mosaic3-D Radar Mosaic
Initial Q2 Development PlansInitial Q2 Development Plans
OutlookOutlook
OutlineOutline NMQ Components OverviewNMQ Components Overview
Single Radar ProcessSingle Radar Process
2-D Radar Mosaic2-D Radar Mosaic
3-D Radar Mosaic3-D Radar Mosaic
Initial Q2 Development PlansInitial Q2 Development Plans
OutlookOutlook
Radar
Satellite
Sfc Obs & Sounding
Lightning
Model
QPEQPE
IngestIngest & QC& QC
Rain Gauge HydroHydro
Model*Model*
NMQ Overview FlowchartNMQ Overview Flowchart
PrecipPrecipProductsProducts
HydroHydroProductsProducts
UsersUsers
QPFQPF2D/3D 2D/3D Radar Radar MosaicMosaic
MosaicMosaicProductsProducts
VerificationVerification
Radar
Satellite
Sfc Obs & Sounding
Lightning
Model
QPEQPE
IngestIngest & QC& QC
Rain Gauge HydroHydro
Model*Model*
NMQ Overview FlowchartNMQ Overview Flowchart
PrecipPrecipProductsProducts
HydroHydroProductsProducts
UsersUsers
QPFQPF2D/3D 2D/3D Radar Radar MosaicMosaic
MosaicMosaicProductsProducts
VerificationVerification
NMQ PhilosophyNMQ Philosophy An open R&D systemAn open R&D system
Dynamic enhancements/improvements to Dynamic enhancements/improvements to scientific componentsscientific components
Real-time 24/7 testing and evaluation on CONUS Real-time 24/7 testing and evaluation on CONUS domain to address real-world problemsdomain to address real-world problems
A real-time verification systemA real-time verification system
Cost-effective algorithms for operational benefitsCost-effective algorithms for operational benefits
Incorporation of new data as they become Incorporation of new data as they become
availableavailable
A common framework for joint scientific research A common framework for joint scientific research and developmentand development
Data IngestData Ingest
RadarRadar WSR-88D, level-II and level-III (140+radars)WSR-88D, level-II and level-III (140+radars)
Canadian radar network (~35 radars, efforts Canadian radar network (~35 radars, efforts
undergoing) undergoing)
TDWR (ongoing, limited data availability)TDWR (ongoing, limited data availability)
CASA/gap-filling radars (future)CASA/gap-filling radars (future)
Dual-pol radar data (future)Dual-pol radar data (future)
Data Ingest (Cont.)Data Ingest (Cont.)
SatelliteSatellite GOES IR imagery data (Tb)GOES IR imagery data (Tb)
For QC and radar-satellite QPEFor QC and radar-satellite QPE
GOES sounder data (ECA)GOES sounder data (ECA) For QCFor QC
Other (GOES multi-spectral, exploring)Other (GOES multi-spectral, exploring)
Auto Estimator (efforts undergoing)Auto Estimator (efforts undergoing)
GMSRA (future) GMSRA (future) GOES Multi-Spectral Rainfall Algorithm
SCaMPR (future) SCaMPR (future) Self-Calibrating Multivariate Precipitation Retrieval
Data Ingest (cont.)Data Ingest (cont.) Rain GaugeRain Gauge
NCEP/USGS hourly gage dataNCEP/USGS hourly gage data
OK mesonetOK mesonet
Additional gage networks (mesowest, LCRA, Additional gage networks (mesowest, LCRA,
prism)prism)
Other?Other?
Data Ingest (cont.)Data Ingest (cont.)
Model (RUC 20km, hourly analysis)Model (RUC 20km, hourly analysis)
Upper Air SoundingUpper Air Sounding
LightningLightning
Surface Observations (ASOS) (future)Surface Observations (ASOS) (future)
Other?Other?
OutlineOutline NMQ Components OverviewNMQ Components Overview
Single Radar ProcessSingle Radar Process
2-D Radar Mosaic2-D Radar Mosaic
3-D Radar Mosaic3-D Radar Mosaic
Initial Q2 Development PlansInitial Q2 Development Plans
OutlookOutlook
Single Radar ProcessSingle Radar Process Reflectivity QC (dynamically evolving effort!)Reflectivity QC (dynamically evolving effort!)
Noise filterNoise filter
Sun beam filterSun beam filter
Terrain based QC (hybrid scan)Terrain based QC (hybrid scan)
Horizontal texture and vertical structure based Horizontal texture and vertical structure based
QCQC
Temporal continuity based QC Satellite based QCTemporal continuity based QC Satellite based QC
Satellite based QCSatellite based QC
Dual-pol data (future)Dual-pol data (future)
Velocity DealiasingVelocity Dealiasing
Noise FilterNoise Filter
Sunbeam FilterSunbeam Filter
Horizontal and Vertical Structure Horizontal and Vertical Structure Based QCBased QC
To remove the hardware testing pattern:
Check sudden increase in echo coverage between consecutive volume scans
Temporal Continuity QCTemporal Continuity QC
Effective Cloud Amount
Single Radar Process Single Radar Process (cont.)(cont.)
Reflectivity climatologyReflectivity climatology
Brightband IdentificationBrightband Identification
Precipitation typingPrecipitation typing (1-good strat rain; 2- bad strat rain; 3-good strat snow; 4- bad (1-good strat rain; 2- bad strat rain; 3-good strat snow; 4- bad
strat snow; 5-mixed phase; 6-convective).strat snow; 5-mixed phase; 6-convective).
Hybrid scan reflectivity and the associated heightHybrid scan reflectivity and the associated height
Composite reflectivity (QC and UnQC) and the Composite reflectivity (QC and UnQC) and the
associated heightassociated height
Vertical Profile of Reflectivity (VPR)Vertical Profile of Reflectivity (VPR)
VPR-adjusted hybrid scan reflectivityVPR-adjusted hybrid scan reflectivity
22
Convective/Stratiform Convective/Stratiform SegregationSegregation
dBZ > 50 in any bin or,dBZ > 50 in any bin or, dBZ > 30 at temperatures < -10 C or,dBZ > 30 at temperatures < -10 C or, 1 lightning flash1 lightning flash
Composite Reflectivity Precip Flags
Convective
Bright Band Identification Bright Band Identification (BBID)(BBID)
(Gourley and Calvert, 2003, WAF)(Gourley and Calvert, 2003, WAF) 3-D Reflectivity Field3-D Reflectivity Field Find Layer of Higher ReflectivityFind Layer of Higher Reflectivity Vertical Reflectivity GradientVertical Reflectivity Gradient Spatial/Temporal SmoothingSpatial/Temporal Smoothing
Precipitation type Precipitation type classificationclassification
Stratiform rain/snowStratiform rain/snow Precip. typePrecip. type Composite refl.Composite refl.
Single Radar Process Single Radar Process (cont.)(cont.)
3-D Single Radar Cartesian (SRC) Grid reflectivity 3-D Single Radar Cartesian (SRC) Grid reflectivity
(QC’d and UnQC’d)(QC’d and UnQC’d)
3-D SRC reflectivity (QC’d with VPR gap-filling)3-D SRC reflectivity (QC’d with VPR gap-filling)
Multi-scale storm trackingMulti-scale storm tracking
3-D SRC grid with synchronization3-D SRC grid with synchronization
X
Single Radar Cartesian Single Radar Cartesian GridGrid
R
R = 460km for coastal radars and 300km for other radars.
Horizontal grid(~1km x 1km)
Vertical grid (31 levels)
3-D Spherical to Cartesian 3-D Spherical to Cartesian TransformationTransformation (Zhang et al. 2005, JTECH)(Zhang et al. 2005, JTECH)
o oo
o
+
No BB:Vertical linear interpolation
BB exists:Vertical and horizontal linear interpolation
BB
o
o
+No BB
Convective Case1: RHI, Convective Case1: RHI, 263°263°
Raw Interpolated
Stratiform Case 2: RHI, Stratiform Case 2: RHI, 0°0°
Raw Interpolated
OutlineOutline NMQ Components OverviewNMQ Components Overview
Single Radar ProcessSingle Radar Process
2-D Radar Mosaic2-D Radar Mosaic
3-D Radar Mosaic3-D Radar Mosaic
Initial Q2 Development PlansInitial Q2 Development Plans
OutlookOutlook
2-D Radar Mosaic2-D Radar Mosaic Composite reflectivity (QC’d and UnQC’d) Composite reflectivity (QC’d and UnQC’d)
and associated heightand associated height
Hybrid scan reflectivity (QC’d, with and Hybrid scan reflectivity (QC’d, with and
without VPR-adjustment)without VPR-adjustment)
Precipitation typePrecipitation type
Radar coverage maps (spatial and Radar coverage maps (spatial and
temporal)temporal)
Layered composite reflectivity (e.g., the Layered composite reflectivity (e.g., the
lowest 4 tilts)lowest 4 tilts)
2D Hybrid Scan Refl 2D Hybrid Scan Refl MosaicMosaic
2D HYBREF height AGL2D HYBREF height AGL
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Strat Rain (good)Convective (good)Bright Band (bad)Frozen (bad)
2D Precipitation Type 2D Precipitation Type MosaicMosaic
OutlineOutline NMQ Components OverviewNMQ Components Overview
Single Radar ProcessSingle Radar Process
2-D Radar Mosaic2-D Radar Mosaic
3-D Radar Mosaic3-D Radar Mosaic
Initial Q2 Development PlansInitial Q2 Development Plans
OutlookOutlook
3-D Radar Mosaic3-D Radar Mosaic 3-D multi-radar mosaic grid3-D multi-radar mosaic grid
QC’dQC’d
UnQC’dUnQC’d
QC’d with VPR gap-fillingQC’d with VPR gap-filling
2-D derived products:2-D derived products: Composite reflectivity and the associated heightComposite reflectivity and the associated height
Hybrid scan reflectivity and associated heightHybrid scan reflectivity and associated height
Hail products (SHI, POSH, MEHS)Hail products (SHI, POSH, MEHS)
VIL and VILDVIL and VILD
ETOPETOP
Layered composite reflectivityLayered composite reflectivity
€
wiradar = exp −diradar2
R2
⎛
⎝ ⎜
⎞
⎠ ⎟
€
R = 50km
Computational TilesComputational Tiles
Cross Sections from 3-D Cross Sections from 3-D MosaicMosaic
Dallas Hail Storm, 5/5/1995
Vertical Cross Section Loop Vertical Cross Section Loop (W-E)(W-E)
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OutlineOutline NMQ Components OverviewNMQ Components Overview
Single Radar ProcessSingle Radar Process
2-D Radar Mosaic2-D Radar Mosaic
3-D Radar Mosaic3-D Radar Mosaic
Initial Q2 Development PlansInitial Q2 Development Plans
OutlookOutlook
Q2 ComponentsQ2 Components
Radar QPERadar QPE
Satellite QPESatellite QPE
Rain gage QPERain gage QPE
Multi-sensor QPEsMulti-sensor QPEs Radar+satellite (& model and sounding)Radar+satellite (& model and sounding)
Radar+gageRadar+gage
Radar+satellite+gageRadar+satellite+gage
Radar QPERadar QPE Rain rateRain rate
Derived from:Derived from: Hybrid scan reflectivity from 3-D radar mosaic Hybrid scan reflectivity from 3-D radar mosaic
(QC’d, with and without VPR gap-filling)(QC’d, with and without VPR gap-filling)
Layer composite reflectivity of the lowest 4 Layer composite reflectivity of the lowest 4 tilts (from 2D radar mosaic)tilts (from 2D radar mosaic)
Different Z-R relationships based on 2D Different Z-R relationships based on 2D mosaic precip type fieldmosaic precip type field
1km x 1km, update every 5 min1km x 1km, update every 5 min
Accumulations (1- to 72-h or longer)Accumulations (1- to 72-h or longer)
Z-R relationshipsZ-R relationships
Taiwan
Oklahoma Convective
Oklahoma Stratiform
Satellite QPESatellite QPE
Products from existing algorithms:Products from existing algorithms: Hydro (Auto) EstimatorHydro (Auto) Estimator
GMSRAGMSRA
SCaMPRSCaMPR
Rain Gauge QPERain Gauge QPE
Individual stationsIndividual stations Objective analysis -- gridded gage products Objective analysis -- gridded gage products
(e.g., ADAS)(e.g., ADAS)
Issues:Issues:
Bad dataBad data
Spatial representativeness of gage obsSpatial representativeness of gage obs Non-uniform and sparse gage distributionsNon-uniform and sparse gage distributions
Terrain effectsTerrain effects
Real-time latencyReal-time latency
Radar-satellite QPERadar-satellite QPE Radar rain rate - satellite Tb regressionsRadar rain rate - satellite Tb regressions
Multiple regressions -- one for each weather regimesMultiple regressions -- one for each weather regimes
Initial weather regimes are defined by:Initial weather regimes are defined by:
Surface temperature zones (hourly RUC surface analysis)Surface temperature zones (hourly RUC surface analysis)
Regression using data pairs within a running hourly Regression using data pairs within a running hourly windowwindow
Rain rate averaged for each 1 deg Tb binRain rate averaged for each 1 deg Tb bin
Derive a dynamic exponential regression to the data Derive a dynamic exponential regression to the data in a least square fit sensein a least square fit sense
Various rules to prevent an ill-conditioned regressionVarious rules to prevent an ill-conditioned regression
Radar-satellite QPE Radar-satellite QPE (Contd.)(Contd.)
Satellite rain rateSatellite rain rate
Apply regression curves to the Tb field in each Apply regression curves to the Tb field in each
weather regimes and obtain rain rateweather regimes and obtain rain rate
Distance weighted mean across boundaries Distance weighted mean across boundaries
between different weather regimesbetween different weather regimes
Use rain/no-rain mask (defined by radar obs Use rain/no-rain mask (defined by radar obs
and satellite)and satellite)
Accumulations (1-72h)Accumulations (1-72h)
Satellite/Radar Satellite/Radar RegressionRegression
Regression Equation
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Rad
ar R
ainr
ate
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Sat
elli
te C
TT
Regresses co-located satellite Tb with stratiform R from radar.One for each weather regimes.
Updates regression curves hourly and purges old data
Sur
face
Tem
p
Generating Multi-sensor Generating Multi-sensor RateRate
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Q2 Rainfall Rate
Reg
r. E
qn Regression parameters are usedto calibrate cloud-top temperature field by supplying precipitation rates
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Sat
elli
te C
TT
Sur
face
Tem
p
Radar-gage QPERadar-gage QPE Pre-defined bias regions (radar umbrella? basins? Pre-defined bias regions (radar umbrella? basins?
weather regimes?)weather regimes?)
Regional radar/gage bias adjustmentRegional radar/gage bias adjustment Compute mean radar/gage bias for each bias regionCompute mean radar/gage bias for each bias region Adjust radar QPE using the biasAdjust radar QPE using the bias Smoothing over the boundaries between bias regions Smoothing over the boundaries between bias regions
Point radar/gage bias adjustmentPoint radar/gage bias adjustment Compute radar/gage bias at each gage stationCompute radar/gage bias at each gage station Objective analysis of the point biases Objective analysis of the point biases Adjust radar QPE using the gridded bias fieldAdjust radar QPE using the gridded bias field
Bias is based on hourly accumulationBias is based on hourly accumulation
Adjustment is performed in real-time dynamicallyAdjustment is performed in real-time dynamically
OutlineOutline NMQ Components OverviewNMQ Components Overview
Single Radar ProcessSingle Radar Process
2-D Radar Mosaic2-D Radar Mosaic
3-D Radar Mosaic3-D Radar Mosaic
Initial Q2 Development PlansInitial Q2 Development Plans
OutlookOutlook
OutlookOutlook Radar QPERadar QPE
Improve radar data QCImprove radar data QC
VPR/range correctionVPR/range correction
Additional data streamsAdditional data streams
Continue improving precip typing including Continue improving precip typing including identification of warm rain processidentification of warm rain process
More adaptive Z-R relationshipsMore adaptive Z-R relationships
Gage QPEGage QPE Improved gage QCImproved gage QC Adaptive influence of radius for objective analysisAdaptive influence of radius for objective analysis
Non-uniform spatial distributionsNon-uniform spatial distributions Terrain effects (mountain mapper?)Terrain effects (mountain mapper?)
Precipitation typingPrecipitation typing
Warm/cold rainWarm/cold rain Cold rain echo core (dbZ)Cold rain echo core (dbZ)
Warm rain echo core Warm rain echo core (dbZ)(dbZ)
-10°C-10°C
timetime
heig
ht
heig
ht
timetime
heig
ht
heig
ht
40
4050
60 6060
60
60
60
50
50
5050
New Data Streams (e.g. New Data Streams (e.g. TDWR)TDWR)
Better coverageat lower atmosphere
Higher spatialresolutionnear urban areas
Outlook (Outlook (ContdContd.).) Radar-satellite QPERadar-satellite QPE
Refine weather regimes for satellite-radar Refine weather regimes for satellite-radar regressionsregressions
Multi-variable regression using multi-spectral Multi-variable regression using multi-spectral satellite data (SCaMPR concepts)satellite data (SCaMPR concepts)
Systematic verificationSystematic verification Extensive case studies from different weather Extensive case studies from different weather
regimesregimes Real-time verification of all productsReal-time verification of all products
Quantification of uncertainties in different Quantification of uncertainties in different
QPEsQPEs