SOOSTRC WRF Environmental Modeling System

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SOO/STRC WRF Environmental Modeling System
Simulated Radar Reflectivity

• Robert Rozumalski
• National SOO Science and Training Resource
  CoordinatorNOAA/NWS/OCWWS/Training Division/FDTB

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Simulated Reflectivity
WRF Simulated Reflectivity
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Simulated Reflectivity
Cross Section of Simulated Reflectivity
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Simulated Reflectivity
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Simulated Reflectivity
Lake effect snowbands
Narrow Cold Frontal Rainband
Information provided by Koch et al.
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Simulated Reflectivity
Precipitation Bands
3-hourly Total precipitation
Information provided by Koch et al.
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Simulated Reflectivity
Gravity Waves Precipitation Bands
Information provided by Koch et al.
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Simulated Reflectivity
Precipitation Vs. Simulated Reflectivity
03 UTC 1 March 2005
Information provided by Koch et al.
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Simulated Reflectivity
ARW
NMM
Information provided by Koch et al.
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Simulated Reflectivity
ARW 4km
NMM 4km
ARW 2km
Note NMM reflectivity does not exceed gt 52 dBZ
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Simulated Reflectivity
Simulated reflectivity offers several advantages
over customary accumulated precipitation products

• Easier to visualize relationships between cloud
  and precipitation bands
• Many mesoscale phenomena are more readily
  revealed, especially during the cold season
• Convective storm structures, such as supercells,
  are more clearly identified
• Possibility of drawing comparisons to observed
  radar imagery for model verification

Information provided by Koch et al.
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Simulated Reflectivity
Simulated reflectivity Caveat Emptor!

• One can not draw conclusions about the relative
  performance of NWP models unless the reflectivity
  is computed such that it is consistent with model
  microphysics
• It is not possible to make strict comparisons
  between predicted vertical profiles of
  reflectivity and observed radar imagery because
• Radar resolution degrades with distance from
  transmitter
• Information is lost at low levels due to earth
  curvature
• Ground clutter, AP, and other issues
• Bright Banding

Information provided by Koch et al.
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SOO/STRC WRF Environmental Modeling System
Simulated Radar Reflectivity

• The End

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Simulated Reflectivity
The difference in the reflectivity product
appearance between the WRF-NMM and ARW models is
largely attributed to the differences in model
microphysics -primarily the snow intercept
parameter and the size distributions for snow
Information provided by Koch et al.
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Simulated Reflectivity
Both cores use single-moment microphysics schemes
that assume the Marshall-Palmer exponential
DSD Ns(Ds) Nosexp(-lsDs) Where Ns(Ds)
The number of snow particles per unit size range
per unit volume Nos Snow intercept
parameter Ds The diameter of the snow
particles ls The slope factor for
snow Equivalent reflectivity is computed using
the S(ND6) method assuming Rayleigh scattering
and spherical hydrometeors
Information provided by Koch et al.
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Simulated Reflectivity

• ARW Core Microphysics
• WSM5 5 class scheme
• WSM6 6 class scheme (WSM5 graupel)
• In WRF Postprocessor
• Cloud water and ice are combined
• Rain and snow are also combined
• Fixed intercept No used in computing reflectivity
  even though WSM5/6 uses a temperature dependent No

Information provided by Koch et al.
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Simulated Reflectivity

• NMM Core Microphysics (Ferrier) has 4 classes of
  hydrometeors
• Cloud liquid
• Cloud Ice
• Mixed Ice (Snow, graupel, sleet)
• Rain
• In WRF Postprocessor uses same temperature
  dependent No, which is consistent with Ferrier
  scheme

Information provided by Koch et al.
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Simulated Reflectivity
Method for computing simulated reflectivity

• Equivalent reflectivity factor for M-P
  distribution
• Ze G(7)Nol-7
• After accounting for non-solid ice particles and
  the larger dielectric constant for liquid
  compared to ice (Kl/Ki) 5.3
• Ze 0.224G(7)Nol-7(rs/ri)2
• The slope factor for snow (l) depends on the snow
  mixing ratio
• l ((pNors)/(raqs))1/4

Information provided by Koch et al.
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Simulated Reflectivity
Method for computing simulated reflectivity

• It can be shown that the equivalent reflectivity
  factor for snow can be written in terms of the
  snow mass content Ms and the snow number
  concentration ns instead of the slope factor and
  intercept parameter as follows
• Ze 1.634 x 104 (Ms2/ns)
• Thus, for the same snow mass content, differences
  in radar reflectivity will scale with differences
  in parameterized snow number concentrations
  e.g., for a value of ns 0.1 g m-3
• dBZeS31.117.5logMS 13.6 - Fixed intercept
  ARW
• dBZeS38.617.5logMS 21.1 - WSM-5
• dBZeS30.010logMS 20.0 - NMM Ferrier

Information provided by Koch et al.
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Simulated Reflectivity
Method for computing simulated reflectivity

• The Ferrier (NMM) method predicts higher
  reflectivity values than the Fixed-intercept
  (ARW) method for all values of snow number
  concentrations ns lt 0.713gm-3
• For convective storms, the NMM reflectivity
  values are restricted to a value of 52 dBZ, which
  is primarily the result of the following imposed
  restrictions
• The mean size of raindrops is assumed fixed at
  0.45mm for rain contents gt 1 gm-3
• The maximum number concentration of precipitating
  ice is fixed, and thus the intercept parameter is
  fixed

Information provided by Koch et al.