6/29/2005 q2 workshop, norman, ok 3-d radar mosaic and initial q2 development plans jian zhang 1,...

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


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

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?






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






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

QuickTime™ and aNone decompressor

are needed to see this picture.

Strat Rain (good)Convective (good)Bright Band (bad)Frozen (bad)

2D Precipitation Type 2D Precipitation Type MosaicMosaic






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)

QuickTime™ and aGraphics decompressor







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



Rad

ar R

ainr

ate



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



Q2 Rainfall Rate

Reg

r. E

qn Regression parameters are usedto calibrate cloud-top temperature field by supplying precipitation rates



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






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

6/29/2005 Q2 Workshop, Norman, OK

THANK YOU!THANK YOU!

[email protected]@noaa.gov

6/29/2005 q2 workshop, norman, ok 3-d radar mosaic and initial q2 development plans jian zhang 1,...

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