Surveillance Weather Radar 2000 AD

Weather Radar Technology- Merits in Chronological Order

WSR-57

WSR-88D

WSR-07PD

Technology Developments

• Digital receivers – Easy to achieve sampling rate higher than reciprocal

of pulse (oversampling)

• Versatile circuits for transmitter control– Easy to phase code, to interleave PRTs (staggered

and other), to compress pulse

• Signal processing on general purpose computers (PCs)– Easy to program algorithms and analyze Doppler

spectra

Capability of the NSSL’s R&D weather surveillance radar

• Doppler and Dual Polarization• Phase coding of transmitted pulses• Transmission of arbitrary non uniform pulse

sequence including staggered PRT • Oversampling

– by a factor of 5 in Dual Polarization Mode– By a factor of 10 in Single Polarization Mode

• Arbitrary scanning strategy (including RHI)• Recording of time series data

Oversampling

• To increase speed of volume coverage

• To decrease errors in estimates of reflectivity, velocity, spectrum width, and polarimetric variables

Z, Standard Processing, Aug 04

Z, from Decorrelated Samples

Mitigation of range velocity ambiguities

• Phase coding at lower elevations• Staggered PRT at higher elevations• Demonstration of clutter filtering for both

schemes• Integration into volume coverage patterns• Inclusion of oversampling• Adaptive automatic choice of PRTs based

on obscurations in immediately preceding scans

ReflectivityLong PRT

EL = 0.5 deg

10/08/02 15:11 GMT

Phase Coding

Doppler VelocityPhase coding, medium

PRT

EL = 0.5 deg

10/08/02 15:11 GMTDoppler Velocity

Processing as on WSR-88D

va = 23.7 m s-1, ra = 175 km va = 23.7 m s-1, ra = 175 km

Phase Coding

Staggered PRT

ReflectivityStaggered PRT

EL = 2.5 deg

04/06/03 4:42 GMT

Staggered PRT

va = 25.4 m s-1 va = 45.2 m s-1

148 km

184 km

KTLX Doppler VelocityVCP 11 – Batch Mode

KOUN Doppler Velocity

Staggered PRT (184 km/276 km)EL = 2.5 deg

04/06/03 4:42 GMT

Dual Polarization at NSSL

• 1983: Upgrade of Cimarron radar to dual polarization; switching between horizontal and vertical polarization

• 1984: Collection of first (anywhere) dual polarization time series data • 1985 to 1989: Definition of the complete set of polarimetric variables.

Development of schemes to obtain these variables together with spectral moments

• 1992: First (anywhere) collection of polarimetric variables at all range locations

• 1992 to present: Development of schemes to classify hydrometeor type. Improvement of rainfall estimation. Design of a system functionally compatible with the WSR-88D; simultaneous transmission and reception of horizontally and vertically polarized waves

• 2002: Upgrade of KOUN radar to dual polarization• 2002-2003: Joint POLarization Experiment (JPOLE)

Fields of polarimetric variables

Dual Polarization - Benefits

• Vastly superior data quality: calibration, mitigation of attenuation and beam blockage effects

• Discrimination between insects, birds, ground echoes, and precipitation

• Superior measurement of rainfall• Detection of hail• Classification of precipitation – rain vs freezing rain

vs snow• Determination of hail size • Measurement of snowfall• Icing detection

Stratiform Rain vs Snow

Imminent Goals

• Combining techniques to mitigate range and velocity ambiguities with optimum (pseudo whitening) procedure to increase speed of volume coverage and decrease errors of estimates

• Incorporating the above combined technique into dual polarization radar

• Developing adaptive scanning strategy for agile beam phased array radar

Three Challenges

• Direct estimation of wind transverse to the radar beam

• Determination of the alias interval of Doppler velocity from a single pulse

• Estimation of the forward propagation coefficient using returns from hydrometeors or biological scatterers

Major Endeavor

• Explaining bulk hydrometeor properties that cause distinct polarimetric signatures in convective storms

Major Endeavor

• Assimilation of radar data into local NWP (short term ~ 3 h, fine resolution ~ 1 km) model – coupled to distributed hydrological model for

use over small watersheds (~ 1000s km2)– capable of predicting tornadoes, strong winds,

hail, and other hazards

END

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