Latent Heating Evaluation

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Latent Heating Evaluation

• Yukari N. Takayabu
• CCSR/Univ. of Tokyo
• with
• S. Shige, Osaka Prefecture University,
• W.-K. Tao, C.-L. Shie, GSFC/NASA,
• and Y. Kodama, Hirosaki University

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TRMM Spectral Latent Heating (SLH) Estimate

• SLH utilizes precipitation-top-heights and
  precipitation rate at melting level and at
  surface obtained from TRMM PR for the LH
  estimates.
• With the aid of a 2D Cloud Resolving Model
  (GCEM) simulations forced by field experiment
  data, SLH constructs a set of precipitation-LH
  tables for convective rain and stratiform rain,
  separately. (Shige et al.
  2004, JAM)

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Spectral Latent Heating (SLH) Algorithm
Field Experiments
COARE
SCSMEX
GATE
2D CRM simulation (GCEM)
Lookup Tables Conv / Strat
Simulated rain
PR2a25 Rain
Retrieval
GV
Diagnosed Q1R
Simulated Q1R
Reconstructed Q1R
Estimated Q1R
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GV for LH
Apparent Heat Source Apparent Moisture Sink
diagnozed from upper-air network observations
(Yanai et al. 1973)
? Large-scale network observation, such as GATE,
COARE, SCSME, KWAJEX.
Q1RQ1-QR
? Large-scale network observation
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Field Experiments
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Q1R with COARE-TABLE-SLH vs. GCE-simulated
GATE Reconstructed Q1R is higher than simulated
(forced)
(b) SCSMEX 2-9 Jun 1998
SCSMEX Reconstructed Q1R is lower than
simulated (forced)
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For the same storm height, precipitation
profiles, therefore Q1R profiles, differ among
experiments.
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COARE LH look-up tables
Pf Additional usage of precipitation rate 1km
above the melting level for the convective
rain We also adjusted the melting level of the
table to the observed melting level.
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Heating from diagnostic calculations (Johnson and
Ciesielski 2002) and SLH algorithm for SCSMEX (15
May 20 June 1998).
Budget Q1
SLHQ1RGCE QR
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Possibility of utilizing wind profiler w-wind
data for evaluating Q1
Kodama (2005)
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GPM ERA
Precipitating Phenomena in the Midlatitude with a
significant role of LH

• Explosive developments of the extratropical
  cyclones (ex. QEII Storm in 1978)
• Mesoscale (meso-alpha, beta) systems in the
  Baiu front
• Winter disturbances over the Japan Sea

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GPM ERA
Precipitating Phenomena in the Midlatitude with a
significant role of LH

• Explosive developments of the extratropical
  cyclones (ex. QEII Storm in 1978)
• Mesoscale (meso-alpha, beta) systems in the
  Baiu front
• Winter disturbances over the Japan Sea

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GPM ERA
Precipitating Phenomena in the Midlatitude with a
significant role of LH

• Explosive developments of the extratropical
  cyclones (ex. QEII Storm in 1978)
• Mesoscale (meso-alpha, beta) systems in the
  Baiu front
• Winter disturbances over the Japan Sea

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The role of the Latent Heating on the explosive
development of Extratropical Cyclone (Queen
Elizabeth II Storm)
QEII
Omega for different LH peaks
Difference of Psfc and winds between with and
without LH
300hPa
500hPa
700hPa
Pressure drop
Anthes et al. 1983
Gyakum 1983
Time ?
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Mesoscale Precipitation Systems on the Baiu Front
X-BAIU-99
Moteki et al. 2004
Three-dimensional dynamical structure determines
the precipitation characteristics which feed back
to the dynamical fields.
Baiu front
continental moist air
oceanic moist air
Water vapor front
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Winter Disturbances over the Japan Sea (Yoshizaki
et al. 2004)
WMO-01
GMS
5km-NHM
JPCZ is well simulated
QuickSCAT
Sfc flux contributes largely to ltQ1gt ltQ1-QR-Q2gt
SLE
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www.ecmwf.int
From Dr. M. Ishihara (JMA)s presentation
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GPM ERA
Precipitating Phenomena in the Midlatitude with a
significant role of LH

• Interaction with dynamical three-dimensional
  structures of phenomena becomes more important.
• Various network observations and other satellite
  data (winds, clouds) will be available.
• Larger computer resources will be available.

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Latent Heating Estimates and GV in GPM ERA

• With these background,
• GPM precipitation More satellites to observe
  winds, and clouds
• Wind Profiler Networks
• Radar Networks (NEXRAD, R-AMeDAS)
• Non-hydrostatic Numerical Models with larger
  computer resources
• We should collaborate for ? 4DVar data
  assimilation
• (In collaboration with operational
  organizations)
• Field experiments to verify the analysis data
• Especially, validation for model cloud
  microphysics will be very important

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