Radar-Derived Precipitation Part 5

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Radar-Derived Precipitation Part 5

• I. Radar Climatology
• II. Radar Representation of Precipitation
• III. WSR-88D, PPS
• IV. PPS Adjustment, Limitations
• V. Effective Use

Hydrometeorology 99-2 Matt Kelsch Tuesday, 22
June 1999 kelsch_at_comet.ucar.edu
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V. Effective UseStage I PPS Strengths

• Numerous quality control steps to minimize
  limitations both in the radar estimate of
  precipitation, and the rain gauge representation
  of precipitation.
• Spatial and temporal resolution are excellent for
  the mesoscale detail of precipitation systems.
• Spatial detail over a large area
• Monitor evolution of events between gauge sites
• Real time information

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Stage 1 PPS Strengths (cont.)

• Opportunity for important rainfall information in
  remote, poorly instrumented areas.
• Adaptation parameters provide some flexibility
  for different locations and climate regimes.
• Has the versatility to evolve into a better
  algorithm that can effectively account for
  variability on a geographic, seasonal, and even
  hourly basis.
• Offers important input for a comprehensive,
  multi-sensor system.

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Radar-Derived PrecipWhen changing Z-R
coefficients is not the real solution

• Range degradation, overshooting low-levels
• Problem associated with propagation of beam, not
  Z-R.
• Snowfall
• More complexity than liquid hydrometeors.
• Phase changes and mixed phases exist over small
  space/time scales.
• Range degradation often co-exists.
• Phase change hail, melting snow
• Radical storm-scale changes in Z to R
  relationship.
• Minimal proof that hail correction can be done
  with Z-R.
• Inconsistent relationship between Z-R and hail
  occurrence.

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Radar-Derived PrecipWhen changing Z-R may help

• Consistently different average DSD (climate)
• Tropical versus mid-latitude (warm vs. cold
  process)
• Maritime versus continental
• Consistently different average DSD (season)
• Convective versus stratiform
• Precip System character
• Identify Convective versus Stratiform signature
• Identify warm versus cold rain signature
• Identify maritime versus continental

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Why cant the adaptation parameters and bias
adjustment procedure solve all the limitations?

• Radar bias adjustment is only one uniform
  adjustment. It depends on adequate
  representation of precip by the local gauge
  network.
• Adaptation parameters can greatly help the
  algorithm performance for a given site and/or
  season. The parameters tune the algorithm for
  the typical scenario. Atypical events, such as
  unusually high rainfall rates, may not be
  diagnosed well.
• The most effective use of PPS is to make it a
  function of meteorology, not the normal
  climatology.

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Can we account for the important atypical events
without degrading the guidance for the more
common typical events?

• Meteorological information from soundings,
  profilers, and surface reports are a few examples
  of data sources that can assist with real-time
  adjustment of adaptation parameters.
• Information from other NEXRAD algorithms, such as
  HAIL or VIL, may provide some guidance.
• The most effective use of PPS is to make it a
  function of meteorology, not the normal
  climatology.

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DATA Soundings and Rainfall RatesWhat are
reasonable maximum rainfall rates expected?
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Radar-derived PrecipitationA Summary Of Major
Points

• Radar provides one of several useful methods for
  sampling precipitation
• Quantitative reliability issues are related to
  the fact that radar is sampling some volume at
  some elevation to estimate precipitation at the
  ground
• Radar-derived precipitation is most reliably
  modeled for liquid hydrometeors hail and snow
  add complexity
• The above two points are not effectively
  corrected by changing Z-R coefficients Z-R
  changes should be related to Drop Size
  Distribution knowledge.
• Radars and rain gauges do not measure equal
  samples
• Rain gauges do not provide a good representation
  of precipitation distribution, especially
  convective precip.
• Radar provides excellent information about the
  spatial and temporal evolution of precipitation
  systems.