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A Doppler Radar Emulator and its Application to the Detection of Tornadic Signatures. Ryan M. May. Acknowledgements. Reading Committee Dr. Michael Biggerstaff Dr. Ming Xue Dr. Robert Palmer Dr. Tian-You Yu Curtis Alexander Gordon Carrie. Motivation. - PowerPoint PPT Presentation
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A Doppler Radar Emulator and its Application to the
Detection of Tornadic Signatures
Ryan M. MayRyan M. May
AcknowledgementsAcknowledgements
Reading CommitteeReading Committee Dr. Michael BiggerstaffDr. Michael Biggerstaff Dr. Ming XueDr. Ming Xue Dr. Robert PalmerDr. Robert Palmer
Dr. Tian-You YuDr. Tian-You Yu Curtis AlexanderCurtis Alexander Gordon CarrieGordon Carrie
MotivationMotivation
Create tool that generates radar moment Create tool that generates radar moment data for a given set of radar operating data for a given set of radar operating parametersparameters
Useful for:Useful for: Radar system designRadar system design Scanning strategy designScanning strategy design Algorithm developmentAlgorithm development Retrieval technique evaluationRetrieval technique evaluation
Previous WorkPrevious Work
Zrnic (1975) simulated time series radar data Zrnic (1975) simulated time series radar data using an assumed Gaussian distribution of using an assumed Gaussian distribution of velocities within a volumevelocities within a volume
Chandrasekar and Bringi (1987) simulated Chandrasekar and Bringi (1987) simulated reflectivity values as a function of raindrop size reflectivity values as a function of raindrop size distribution parametersdistribution parameters
Wood and Brown (1997) evaluated the effects of Wood and Brown (1997) evaluated the effects of WSR-88D scanning strategies on the sampling WSR-88D scanning strategies on the sampling of mesocyclones and tornadoesof mesocyclones and tornadoes
Capsoni and D’Amico (1998) simulated time Capsoni and D’Amico (1998) simulated time series radar data using returns from individual series radar data using returns from individual hydrometeors within a volumehydrometeors within a volume
Radar ConfigurationRadar Configuration
WavelengthWavelength LocationLocation Transmit PowerTransmit Power Antenna GainAntenna Gain Antenna BeamwidthAntenna Beamwidth Noise ThresholdNoise Threshold
Pulse LengthPulse Length PRFPRF Pulses per RadialPulses per Radial Rotation RateRotation Rate Gate LengthGate Length Scan AnglesScan Angles
CapabilitiesCapabilities
Azimuthal ResolutionAzimuthal Resolution Range ResolutionRange Resolution AttenuationAttenuation Range AliasingRange Aliasing Velocity AliasingVelocity Aliasing Anomalous PropagationAnomalous Propagation Antenna SidelobesAntenna Sidelobes
ScatteringScattering
Currently, the Rayleigh approximation is Currently, the Rayleigh approximation is used for scattering:used for scattering:
62
4
5
DKwb
Rain is assumed to have a Marshall-Palmer Rain is assumed to have a Marshall-Palmer distributiondistribution
Cloud droplets are assumed to be monodisperseCloud droplets are assumed to be monodisperse
)Im(32
wa KD
Emulator DesignEmulator Design
A “pulse” is propagated through the A “pulse” is propagated through the model’s numerical output grid along the model’s numerical output grid along the current pointing anglecurrent pointing angle
This pulse is subdivided into many small, This pulse is subdivided into many small, individual elementsindividual elements
Each element is assigned values for Each element is assigned values for reflectivity, radial velocity, and attenuation reflectivity, radial velocity, and attenuation factor from the model grid, using nearest factor from the model grid, using nearest neighbor samplingneighbor sampling
Emulator Design (cont.)Emulator Design (cont.)
Representation of segmented pulse being matched to model grid field
Emulator Design (cont.)Emulator Design (cont.)
At a given instant, two pulses are being At a given instant, two pulses are being used, allowing for the simulation of 2used, allowing for the simulation of 2ndnd trip trip echoesechoes
For every range gate along the beam, the For every range gate along the beam, the pulses are sampled to produce a value of pulses are sampled to produce a value of returned power, Doppler velocity, and returned power, Doppler velocity, and velocity variancevelocity variance
Emulator Design (cont.)Emulator Design (cont.)
Returned power is calculated as:Returned power is calculated as:
i ii
iiiit
rl
VWfgPrP
22
24
3
22
0 )4()(
Doppler velocity is the power-weighted average of all velocities for all pulse elementsDoppler velocity is the power-weighted average of all velocities for all pulse elements The velocity variance for the pulse is the power-weighted variance of velocities for all pulse elementsThe velocity variance for the pulse is the power-weighted variance of velocities for all pulse elements
Emulator Design (cont.)Emulator Design (cont.)
When the returns for the specified number When the returns for the specified number of pulses for a radial have been of pulses for a radial have been calculated, a radial of data is generatedcalculated, a radial of data is generated
Returned power is the average returned Returned power is the average returned power for all pulsespower for all pulses
Doppler velocity is the power-weighted Doppler velocity is the power-weighted average velocity for all pulsesaverage velocity for all pulses
Spectrum width is the power-weighted Spectrum width is the power-weighted variance for all pulsesvariance for all pulses
Emulator Design (cont.)Emulator Design (cont.)
At this point, the velocity is forced to a At this point, the velocity is forced to a value within the Nyquist co-interval, value within the Nyquist co-interval, simulating velocity aliasingsimulating velocity aliasing
Also, equivalent radar reflectivity factor is Also, equivalent radar reflectivity factor is calculated from the returned power as:calculated from the returned power as:
221
23
2210 2ln2
wt
re
KcgP
PrZ
Simulation CharacteristicsSimulation Characteristics
Simulation created using the Advanced Simulation created using the Advanced Regional Prediction System (ARPS)Regional Prediction System (ARPS)
Horizontal grid resolution: 50mHorizontal grid resolution: 50m Stretched vertical grid (~18m at surface)Stretched vertical grid (~18m at surface) Warm rain precipitation microphysicsWarm rain precipitation microphysics Produces a 200m diameter tornado with a Produces a 200m diameter tornado with a
160 m/s change in velocity across the 160 m/s change in velocity across the vortexvortex
Capabilities – Radar Capabilities – Radar CharacteristicsCharacteristics
Exp. λ (cm)
Beamwidth(deg)
PRF(Hz)
Pulse Length(μs)
Rot. Rate
(deg s-1)
Pulses Per Rad.
Gate Length(m)
control 10 1 1500 1.5 20 75 250
OS 10 1 1500 1.5 15 50 250
GS 10 1 1500 .75 20 75 125
NSL 10 1 1500 1.5 20 75 250
BW 10 2 1500 1.5 20 75 250
NY 10 1 1000 1.5 20 50 250
X 3 1 1500 1.5 20 75 250
ST 10 1 1500 1.5 20 75 250
Examples – 10cm, 1Examples – 10cm, 1o o BeamwidthBeamwidth
Returned Power Equivalent Reflectivity Factor
Doppler Velocity Spectrum Width
Examples – Azimuthal OversamplingExamples – Azimuthal Oversampling
CONTROL Equivalent
Reflectivity Factor
CONTROL Doppler Velocity
OVERSAMPLEDEquivalent
Reflectivity Factor
OVERSAMPLED Doppler Velocity
Examples – Azimuthal OversamplingExamples – Azimuthal Oversampling
Equivalent Reflectivity Factor Difference (Oversampled - Orginal)
Examples – 125m Gate SpacingExamples – 125m Gate Spacing
CONTROL Equivalent
Reflectivity Factor
CONTROL Doppler Velocity
125M GATE SPACING Equivalent
Reflectivity Factor
125M GATE SPACING
Doppler Velocity
Examples – No SidelobesExamples – No Sidelobes
NO SIDELOBES Equivalent
Reflectivity Factor
NO SIDELOBES Doppler Velocity
CONTROL Equivalent
Reflectivity Factor
CONTROL Doppler Velocity
Examples – 2Examples – 2oo Beamwidth Beamwidth
Equivalent Reflectivity Factor
(original)Doppler Velocity
(original)
Equivalent Reflectivity Factor
(2o Beamwidth)Doppler Velocity (2o Beamwidth)
Examples – Low PRFExamples – Low PRF
Returned Power Equivalent Reflectivity Factor
Doppler Velocity Spectrum Width
Examples – X-band (3cm)Examples – X-band (3cm)
Returned Power Equivalent Reflectivity Factor
Doppler Velocity Spectrum Width
Examples – 2Examples – 2ndnd Trip Echoes Trip Echoes
Returned Power Equivalent Reflectivity Factor
Doppler Velocity Spectrum Width
We’ve now seen examples of the We’ve now seen examples of the emulator’s capabilitiesemulator’s capabilities
Let’s move on to material that’s more…Let’s move on to material that’s more…practical: detecting tornadoespractical: detecting tornadoes
From Examples to ApplicationFrom Examples to Application
Example Application:Example Application:Tornado DetectionTornado Detection
Emulated data for prototype CASA radarsEmulated data for prototype CASA radars 4 Metrics for tornado intensity:4 Metrics for tornado intensity:
Maximum velocityMaximum velocity ΔΔVelocityVelocity DiameterDiameter Axisymmetric vorticity: 2Axisymmetric vorticity: 2ΔΔV / DV / D
4 Ranges : 3km, 10km, 30km, 50km4 Ranges : 3km, 10km, 30km, 50km Matched Sampling / OversamplingMatched Sampling / Oversampling
Tornado Detection – Radar Tornado Detection – Radar CharacteristicsCharacteristics
Parameter Matched Sampling Oversampled
λ (cm) 3 3
Beamwidth (deg) 2 2
PRF (Hz) 2000 2000
Rot. Rate (deg s-1) 40 40
Pulses Per Rad. 100 50
Pulse Length (μs) .5 .5
Gate Length (m) 100 100
Tornado – 3km, Matched SamplingTornado – 3km, Matched Sampling
Equivalent Reflectivity
Factor
Spectrum Width
Doppler Velocity Doppler Velocity
(no aliasing)
Tornado – 3km, OversamplingTornado – 3km, Oversampling
Equivalent Reflectivity
Factor
Spectrum Width
Doppler Velocity Doppler Velocity
(no aliasing)
Tornado – 10km, Matched SamplingTornado – 10km, Matched Sampling
Equivalent Reflectivity
Factor
Spectrum Width
Doppler Velocity Doppler Velocity
(no aliasing)
Tornado – 10km, OversamplingTornado – 10km, Oversampling
Equivalent Reflectivity
Factor
Spectrum Width
Doppler Velocity Doppler Velocity
(no aliasing)
Tornado – 30km, Matched SamplingTornado – 30km, Matched Sampling
Equivalent Reflectivity
Factor
Spectrum Width
Doppler Velocity Doppler Velocity
(no aliasing)
Tornado – 30km, OversamplingTornado – 30km, Oversampling
Equivalent Reflectivity
Factor
Spectrum Width
Doppler Velocity Doppler Velocity
(no aliasing)
Tornado – 50km, Matched SamplingTornado – 50km, Matched Sampling
Equivalent Reflectivity
Factor
Spectrum Width
Doppler Velocity Doppler Velocity
(no aliasing)
Tornado – 50km, OversamplingTornado – 50km, Oversampling
Equivalent Reflectivity
Factor
Spectrum Width
Doppler Velocity Doppler Velocity
(no aliasing)
Tornado Detection ResultsTornado Detection Results
Experiment Vmax (m s-1) ΔV (m s-1) D (m) 2 ΔV/D (s-1)
3km, Matched 49.1 93.3 216 0.864
3km, Oversampled 55.7 110.6 216 1.024
10km, Matched 35.2 57.6 705 0.163
10km, Oversampled 36.3 62.7 529 0.237
30km, Matched 31.8 33.4 1047 0.064
30km, Oversampled 32.3 42.7 1047 0.082
50km, Matched 27.5 29.5 1749 0.034
50km, Oversampled 28.5 38.6 1749 0.044
ConclusionsConclusions
The large beamwidth of the CASA radars The large beamwidth of the CASA radars will be significant hurdle to the detection of will be significant hurdle to the detection of tornadoestornadoes Oversampling does help mitigate Oversampling does help mitigate somesome of this of this
problemproblem These sampling issues will compound the These sampling issues will compound the
dealiasing problems due to the low dealiasing problems due to the low Nyquist velocity at X-bandNyquist velocity at X-band The quality of the dealiasing procedure for the The quality of the dealiasing procedure for the
data will be data will be extremelyextremely important important
Future StudiesFuture Studies
Continue examining the detectability of Continue examining the detectability of tornadoestornadoes Test detection using objective algorithmsTest detection using objective algorithms Examine impacts of attenuationExamine impacts of attenuation Examine data for times when storm is not Examine data for times when storm is not
tornadictornadic Examine vertical continuityExamine vertical continuity
Evaluate scanning impacts on quality of Evaluate scanning impacts on quality of dual Doppler analysisdual Doppler analysis
Future DevelopmentFuture Development
Mie ScatteringMie Scattering Phased Array AntennaPhased Array Antenna Time Evolution of Model FieldTime Evolution of Model Field Polarimetric VariablesPolarimetric Variables Ground Clutter TargetsGround Clutter Targets
Questions?Questions?
Capsoni, C., and M. D'Amico, 1998: A physically based radar simulator. J. Atmos. Oceanic Technol., 15, 593-598.
Chandrasekar, V., and V. N. Bringi, 1987: Simulation of radar reflectivity and surface measurements of rainfall. J.
Atmos. Oceanic Technol., 4, 464-478.Wood, V. T., and R. A. Brown, 1997: Effects of radar sampling on single-Doppler velocity signatures of mesocyclones and tornadoes. Wea. Forecasting, 12, 928-938.Zrnic, D. S., 1975: Simulation of weatherlike Doppler spectra
and signals. J. App. Meteor., 14, 619-620.
Examples – 10cm, 1Examples – 10cm, 1o o BeamwidthBeamwidth
Returned Power Equivalent Reflectivity Factor
Doppler Velocity Spectrum Width
Examples – 125m Gate SpacingExamples – 125m Gate Spacing
Returned Power (control)
Equivalent Reflectivity Factor
Doppler Velocity Spectrum Width
Examples – No SidelobesExamples – No Sidelobes
Returned Power Equivalent Reflectivity Factor
Doppler Velocity Spectrum Width