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Radar Requirements

2013 Warn-on-Forecast Workshop and Technical Guidance Meetings. Radar Requirements. David J. Stensrud NOAA/National Severe Storms Laboratory. Dual-Polarization Radar. Radar reflectivity ( Z ) – backscattered power related to the drop-size distribution

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Radar Requirements

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  1. 2013 Warn-on-Forecast Workshop and Technical Guidance Meetings Radar Requirements David J. Stensrud NOAA/National Severe Storms Laboratory

  2. Dual-Polarization Radar • Radar reflectivity (Z) – backscattered power related to the drop-size distribution • Radial velocity (VR) – component of wind velocity oriented along the radar beam • Differential reflectivity (ZDR) - ratio of backscattered power at orthogonal polarizations • For targets with isotropic scattering properties such as spherical or chaotically tumbling particles, ZDR is 0 dB. High ZDRis mostly associated with large, wet hydrometeors. • Good indicator of median particle shape and can be useful to detect hail, updraft location and melting layer.

  3. Specific differential phase (KDP) – range derivative of differential propagation phase. • KDPis a good indicator of the liquid water content within the radar sampling volume, as it is nearly zero for heavily aggregated snow or dry graupel/hail. • Strongly related to rain rate, so used for QPE. • Correlation coefficient (ρHV) - correlation coefficient between the backscattered returns at horizontal and vertical polarizations at zero lag time. • approaches unity in pure rain or pure dry hail at S band, but is decreased when a mixture of rain and hail is present. • Low values may indicate mixed precipitation types, non-meteorological scatters or large hail.

  4. Dual-Polarization Benefits Tornado debris signature from dual-pol NWS radar in Huntsville, Alabama 2 March 2012

  5. Hail signature (large Z, low ZDR) ZDR arc (high ZDR indicating large raindrops) Conceptual model of dual-pol signatures in thunderstorms being developed.

  6. Dual-Polarization Benefits • Tells us something about the characteristics of precipitation as seen by radar • May be used to classify hydrometeor types • Should help with radar data quality control • Benefits of direct assimilation of DP variables to Warn-on-Forecast are uncertain

  7. Functional Radar Requirements 2030 • NOAA/NWS Functional Weather Radar Requirements Integrated Working Team was formed in late 2012 • FAA has December 2014 decision point • Near simultaneous volume scans every 1 or 2 minutes • Sample variances as new radar variables • Staggered pulse repetition times to improve radial velocities • Improved quality control within signal processor or radar system

  8. Value of Phased Array Radar Observing System Simulation Experiments (Yussouf and Stensrud 2010 MWR)

  9. After 1 hour of Assimilation Truth Model Analysis Yussouf and Stensrud (2010 MWR)

  10. Benefits of PAR15 minutes of assimilation Truth PAR Analysis WSR88D Analysis Yussouf and Stensrud (2010)

  11. Real Data Case: 24 May 2011 Courtesy of Lou Wicker • PAR data with 1-min volume scans, 10 tilts • Degrade to 88D using 2/3 tilts per minute, so full volume scan in 5 minutes • Radar data thinned to 4 km grid

  12. Assimilation Details • 60 members • LETKF + adaptive inflation • Perturbations to u,v profiles • Bubble initialization • 1.5 km horizontal/200 m vertical • NCOMMAS with LFO and turbulent mixing • Assimilate from 2020 to 2100 UTC • Assimilate 0 dBZ away from storms • Reflectivity data only assimilated every 5 minutes for all experiments

  13. Main Points • Radar observations key to Warn-on-Forecast • Unique in-storm observations • Dual-pol should help with quality control, maybe with direct assimilation or through use of HCA • PAR “snapshots” seem to yield better results • Radar requirements for 2030 being discussed

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