Transcript Diapositiva 1

Active and passive microwave
remote sensing of
precipitation at high latitudes
R. Bennartz - M. Kulie - C. O’Dell (1)
S. Pinori – A. Mugnai (2)
(1) University of Wisconsin – AOS – Madison,WI - USA
(2) Institute of Atmospheric Science and Climate, National Research Council, Rome, Italy
Outline
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Introduction
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Modeling Strategy
Light snow/rain validation database
Case study
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High latitudes and why study light rain snow
Light snowfall event from radar
Satellite-model comparison
UW-NMS mesoscale model comparison
Sensitivity of the MW frequencies to perturbation in the IWC
Outlook
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Towards GPM
IPWG
SNOW AT MID-TO-HIGH LATITUDES
(Figures from P. Yoe, J. Koistinen)
Snowfall Accumulation
Snow to Total Precipitation Ratio
At mid-to-high latitudes,
snowfall represents a substantial
portion of the precipitation.
From higher latitudes at least 90%
of the precipitation occurs at rates
less than 3 mm/hr and 60 % at less
than 1 mm/h
What we can observe
Radar reflectivity (vertically resolved)
Passive MW brightness temperatures (vertical integral)
What we can NOT observe:
Drop size distribution
Ice particle density
Index of refraction
.
.
.
What we can NOT observe:
Drop size distribution
Ice particle density
Index of refraction
.
.
.
We need models to relate the microphysics to
microwave optical properties
What we can NOT observe:
Drop size distribution
Ice particle density
Index of refraction
.
.
.
We need models to relate the microphysics to
microwave optical properties
And those models have to agree with all available
information
How can we trust our modeling assumptions?
How can we trust our modeling assumptions?
Radar
reflectivites
Environmental
data
Precip microphysics
model
Change
microphysics
Radiative transfer
model
Simulated TBs
Compare
Observed
TBs
One Microphysics Model (Bennartz & Petty 2001)
Adjustable parameters:
Ice density
Size of ice relative to liquid particles
Consistent description of Radar Refl/ Fall Speed/ Particle number concentration
X = 0.5
Frozen
Liquid
X=1
X=2
High latitude light snow/rain database (2002-ongoing)
Radar data
BALTRAD radar composites
BALTRAD gauge adjustments
Gotland radar volume scans
Satellite data
NOAA 15,16,17 AMSU-A/B
AQUA AMSR-E
SSMIS (if/when available)
Global/regional model data:
global NCEP/GFS data
UW-NMS model (for selected cases)
CASE STUDY
Light snowfall over the Baltic
Sea the 12-13 January, 2003.
Comparing different groundbased, satellite and modelling
data
MODIS 15 March 2003
2003-01-12
0130 UTC
Gotland radar reflectivity (lowest scan)
2003-01-12
0130 UTC
2003-01-12
0130 UTC
2003-01-12
0130 UTC
Radar composite (gauge adjusted surface rain rate)
2003-01-12
AMSU 89 GHz and 150 GHz
0130 UTC
NOAA-17 0107 UTC
2003-01-12
AMSU 89 - 150 GHz
0130 UTC
NOAA-17 0107 UTC
2003-01-12
0130 UTC
AMSR 89 GHz AQUA 01:31 UTC
RT : Reverse 3D Monte-Carlo with Henyey-Greenstein Phase Function, on a
2 km x 2 km x 1 km grid with 10 vertical levels. FASTEM-2 Ocean
emissivity model, everywhere.
89 GHz (a) channel, at
radar resolution
89 GHz (a) channel, at 36
GHz resolution
Model vs. Observation Comparison: Little bias, reasonably
good correlation. Only areas where there is precip
UW-NMS MODEL SETUP
3 two-way nested grids
18 hr simulation: from 12 UTC 11 January
to 06 UTC 12 January 2003
3rd grid: 6 hours from 00UTC 12 Jan
6 category bulk microphysics:
Cloud droplets, Rain, Pristine crystals,
Snow (rimed crystals/low density
graupel), Aggregated crystals, High
density graupel
Mixing ratios of total water and 5
hydrometeors categories are predicted:
rain, graupel, snow, pristine crystals, and
aggregates. Cloud water is diagnosed
[Tripoli 1992]
RADAR-MODEL COMPARISON
Selected two areas of similar
environmental parameters (LWP,WVP).
dBZ
Take into account the radar beam width
at ~100 km from the radar site
SCATTERING INDEX FOR PRECIPITATING AREA
Relation between scattering index and 89 GHz brightness temperature for
model (blue) and AMSR (red) for x=1;
Relation between scattering index and 89 GHz brightness temperature for radar
(red) and AMSR (black) for x=1.
Red: radar
Black:satellite
Radar and model datasets are in good agreement, with the scattering index
ranging from -5 and 20 K.
AMSU–MODEL COMPARISON
Relation between TB89-TB150 and the surface precipitation for different size ratio x
for observed AMSU-B data (red) and simulated data (blue).
X=1
Where are we?
Microphysics model agrees with radar observations
Microphysics model agrees with passive mw
observation at various scattering frequencies
Surface rain rates are comparable to gaugeadjusted radar
Channel definition for new sensors
The Jacobian is defined as
the partial derivative of a
function:
TB
TB
J

IWC  * IWC
The increase the IWC of ε
allow us to see the sensitivity
of TBs to perturbations in
hydrometeor contents.
K / (g/m3)
K / (g/m3)
150 GHz
89 GHz
150 GHz is more sensitive to the
IWC perturbation than the 89GHz
especially in the upper levels.

118±8.5 GHz
118±4.2 GHz
118±2.3 GHz
K / (g/m3)
Potential of the O2-sounding channels for
frozen precipitation detection
Conclusions/Outlook
• Use all observable Tb dBZ to ensure consistency of
microphysical assumptions in observation space
• Need for coordination of different groups working
towards snowfall/high lat precip. using different
microphysics schemes (intercomparison) -> IPWG
Conclusions/Outlook
• Use all observable Tb dBZ to ensure consistency of
microphysical assumptions in observation space
• Need for coordination of different groups working
towards snowfall/high lat precip. using different
microphysics schemes (intercomparison) -> IPWG
• Dedicated experiments necessary to better
understand cloud microphysics
Conclusions/Outlook
• Use all observable Tb dBZ to ensure consistency of
microphysical assumptions in observation space
• Need for coordination of different groups working
towards snowfall/high lat precip. using different
microphysics schemes (intercomparison) -> IPWG
• Dedicated experiments necessary to better
understand cloud microphysics
• BUT on a global scale we have to go with simple
solutions for retrieval algorithms etc…
Two more things for high latitudes
• We need channels that are surface blind
• We need GPM like radars
Thanks