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Remote sensing and modeling of cloud contents and precipitation efficiency

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Chung-Hsiung Sui Institute of Hydrological Sciences National Central University Xiaofan Li Joint Center for Satellite Data Assimilation and NOAA/NESDIS/Office of ... – PowerPoint PPT presentation

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Title: Remote sensing and modeling of cloud contents and precipitation efficiency


1
Remote sensing and modeling of cloud contents and
precipitation efficiency
Chung-Hsiung Sui Institute of Hydrological
Sciences National Central University Xiaofan
Li Joint Center for Satellite Data Assimilation
and NOAA/NESDIS/Office of Research and
Applications
On-going collaborations Ming-Dah Chou
(National Taiwan Univ.) Ming-Jen Yang (IHS,
NCU) Radar and regional modeling groups, IAP,
NCU Chang-Hoi Ho (Seoul National Univ.)
2
Link of cloud contents to microphysicsthrough
satellite measurements and cloud models
Cloud ratio IWP LWP Rate ratio (PDEPPSDEPPGDEP) PCND CMPE Ps (PDEPPSDEPPGDEPPCND)
QPE Climate feedback processes? A 2D
version of the Goddard Cumulus Ensemble Model
TOGA COARE experiments? 3D non-hydrostatic
regional models, MM5 and WRF Typhoon
simulations
3
Satellite measured and simulated cloud contents
  • global oceanic tropics on March 2003
  • Microwave Surface and Precipitation
    NCEP/Global Forecast System (GFS)
  • Products System (MSPPS)

4
MSPPS C
  • 6-hourly data from three satellites (NOAA-15,
    16, 17) are used in the MSPPS data whereas hourly
    data are analyzed in C.

5
Cloud and rate ratios
Cloud ratio IWP LWP Rate ratio (PDEPPSDEPPGDEP) PCND
A 2D version of the Goddard Cumulus Ensemble
Model is forced by the vertical velocity derived
from the Tropical Ocean Global Atmosphere Coupled
OceanAtmosphere Response Experiment (TOGA
COARE).
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7
rate ratio 1
S
M
rate ratio approaches 0
rate ratio 0.1
rate ratio gt1
C
S
M
cloud ratio gt1
cloud ratio 1
cloud ratio 0.4
cloud ratio approaches 0
C
8
  Cloud ratio IWP LWP Rate ratio (PDEPPSDEPPGDEP) PCND CMPE Ps (PDEPPSDEPPGDEPPCND)
Stratiform 2.10 2.10 0.48
Convective 0.13 0.03 0.72
Mixed 0.60 0.30 1.01
Cloud microphysical budgets in stratiform and
convective regimes
Stratiform (cloud ratio gt1 or rate ratiogt1)
Convective (cloud ratiolt0.4 or rate ratiolt0.1)
9
Cloud ratio and microphysics
10
rain rates lt 0.3 mm h-1 rain rates gt
0.3 mm h-1
melting of graupel and accretion of cloud water
by precipitation ice vapor deposition
and condensation Rainfall and evaporation of
rain Unit is h-1.
11
Column averaged water budgets
Precipitation efficiency





,
SIqv PCND PDEP PSDEP PGDEP
SOqv PREVP PMLTG PMLTS
LSPE
CMPE




1

?
12
Short-term averaged budgets(hourly)
What processes determine CMPE ?CONVc , vertical
velocity, wind shear
13
Long-term averaged budgets(longer than daily)
What factors determine CMPE ?Temperature,
humidity (land vs ocean surface),CCN
concentration, etc.
14
processes
Realistic simulations2D GCE COARE
experimentThe 3D cloud-resolving modeling is
based on the MM5 simulation of Typhoon Nari
(2001).
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16
Simulated Track
Simulated Intensity
17
(a) surface rainrate (mm h-1) (b) LSPE
18
(c) CMPE (d) SOqv/SIqv
19
(e) positive CONVc/SIqv (f) negative
CONVc/SIqv
20
118 km2
96 km
59 km2
48 km
30 km2
24 km
21
118 km2
96 km
59 km2
48 km
30 km2
24 km
22
118 km2
96 km
59 km2
48 km
30 km2
24 km
23
FactorsIdealized experiments
24
Summary
  • Cloud ratio is statistically related to rate
    ratio? Time rate of change of rate ratio is
    derived as a function of three groups of
    microphysics processes Precipitation
    efficiency (short-term average)? Es CONVqv
    SIqv regardless of the average area ? LSPE
    CMPE. ? Function of (CONVC , PS) (other
    processes ?) Precipitation efficiency (long-term
    average)? Function of Ts (other factors ?)

25
Further research TRMM/GPM application
? Statistic analysis (like probability
distributions) of LWP, IWP, CR, CR tendency, and
Ps in different large-scale disturbances and
environments using satellite data. ? Evaluation
of simulated LWP, IWP, CR, CR tendency, Ps , and
precipitation efficiency in different models
using explicit cloud micro-physics schemes
against satellite derived quantities. ?
Investigations of responses of water vapor and
clouds (LWP, IWP, CR, and CR tendency) to climate
forcing anomalies.
26
FDDA
MOTHER DOMAIN (90 km)
NESTED DOMAIN (30 km)
OBS(NRA1)
27
Next steps
  • 1. Further refinements better spectral
    constraints (Aref),
  • reduce model physical and computational
    errors
  • 2. Finer resolution in the cyclogenesis region

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Improve the prognostic cloud microphysics scheme
in the atmosphere regional model to predict the
formation and transport of hydrometeors.
Combine radar and satellite measurements with
the cloud (microphysics) model for cloud and
rainfall estimate in severe weather
30
Remote sensing and modeling of cloud contents and
precipitation efficiency
Effect on weather and climate Cloud microphysics
and cloud dynamics Mesoscale and large-scale
circulation Parameterization and
prediction Quantitative rainfall estimate ? rain
gauges (bucket, optical, acoustic) spatial
resolution ? radar Z-R relations
(microphysics), localized ? satellite (IR,
microwave) space and time sampling,
micro-physics, cloud optical properties,
radiative transfer ? validation
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