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Vertical Structure of the Atmosphere within Clouds Revealed by COSMIC Data
Xiaolei Zou, Li Lin Florida State University
Rick Anthes, Bill Kuo, UCAR
Fourth FORMOSAT-3/COSMIC Data Users Workshop27-29 October 2009: Boulder, Colorado, U. S. A.
Outline
• Motivations
• A Brief Description of GPS RO & CloudSat Data
• Comparisons between GPS ROs and ECMWF&NCEP
Analyses at Cloud Top and within Clouds
• Development of a New Algorithm for GPS Cloudy-
Profile Retrieval & Comparison with Standard GPS
Retrieval
• Summary and Future Work
Motivations
GPS RO data are globally available, not affected by clouds,
and of high vertical resolution, making them ideally suitable
for studying the environment of clouds.
This study uses GPS RO data to examine the observed
vertical structures of the atmosphere within and outside
clouds and compare them with large-scale analyses.
CloudSat
Instrument: 94-GHz profiling radar
Launch time: April 28, 2006
One orbital time: ~1.5 hours
Along-track resolution: ~1.1 km
Track width: ~1.4 km
reflectivity
liquid/ice water content
Observed variables: cloud top height
cloud base height
cloud types
A CloudSat Orbital Track and a Collocated GPS RO
One granule of CloudSat orbital track:17:02:24 UTC June 5, 2007
A collocated GPS sounding: (72.98oW, 43.79oN)
Reflectivity (dBz) of a deep convection system
Data Selection
• Time periods of data search:
(1) June-September 2006, June 2007
(2) September 2007 to August 2008
• Collocation of CloudSat and COSMIC data:
Time difference < 0.5 hour
Spatial distance < 30 km
Cloud top >2 km
Collocated Cloudy and Clear-Sky Sounding Numbers
Four-month period:
Total cloudy profiles: 147
Total clear-sky profiles: 86
Mean/RMS of Fractional N Differences
clear-sky
cloudy
RMS
NCEP
ECMWFclear-sky
cloudy
Four-month period
mean
CloudSat Cloud Types
NGPSwet-NNCEPNGPSwet-NECMWF
Cloud-Top Temperature (data in June 2007)
NCEP analysis is warmerthan both GPS and ECMWF
ECMWF compares more favorably with GPS than NCEP
GPS dry retrieval is severaldegrees colder than otherdata for low cloud (z<5km)
O Cloud-top height
Th
ick
nes
s (k
m)
Profile Number
TECMWF– TGPSwet
TNCEP – TGPSwet
TGPSdry – TGPSwet
Cloud-Top Temperature (all data)C
lou
d T
op H
eigh
t (k
m)
Clo
ud
Top
Hei
ght
(km
)
Clo
ud
Top
Hei
ght
(km
)
Clo
ud
Top
Hei
ght
(km
)
Mean RMS
Refractivity at Cloud Top (all data)
Mean and RMS
NECMWF– NGPSwet NNCEP– NGPSwet
Clo
ud
Top
Hei
ght
(km
)
Clo
ud
Top
Hei
ght
(km
)
Clo
ud
Top
Hei
ght
(km
)
Temperature near the Cloud-Top (all data)
TECMWF– TGPSwet
TNCEP – TGPSwet
Cloud top:2-5 km
Cloud top:5-8 km
Cloud top:8-12 km
Sounding Number
GPS Cloudy Retrieval Algorithm
Assumption: Cloudy air is saturated.
Atmospheric refractivity for cloudy air
1
P
dP
dz=−
gRdT 1+ 0.61qs( )
Hydrostatic equation:
We have two equations for two unknown variables T and P.
In-cloud profiles of T and p can be uniquely determined from GPS ROs given initial conditions at the cloud top.
dry term wet term
GPS observation
),(45.1)(
1073.36.772
5 PTNWT
Te
T
PN s =+×+=
liquid water term
• TGPSsat-TGPSwet is small when the relative humidity is nearly 100%• TGPSsat-TGPSwet is mostly less than 4oC when the relative humidity >85% • TGPSsat-TGPSwet > 4oC appears when the relative humidity <85%
Dependence of TGPSsat-TGPSwet on Relative Humidity
TGPSsat-TGPSwet (oC)
EC
MW
F e
/es (%
)
GPS Refractivity within Cloud
Atmospheric refractivity for cloudy air
Cloud occupies only a fraction of an analysis grid box.
cloudclearobs NNN ⋅+⋅−= αα)1(
N sat =3.73×105 esT 2
Nclear=Ndry+Nwet
Ncloud=Ndry+Nsat and
relative humidity parameter
where
Mean Relative Humidity within Clouds
RMS
Mean
GPSwet ECMWF
NCEP
Clo
ud-m
iddl
e H
eigh
t
Clo
ud-m
iddl
e H
eigh
t
Clo
ud-m
iddl
e H
eigh
t
Clo
ud-m
iddl
e H
eigh
t
Relative Humidity (%) Relative Humidity (%)
Relative Humidity (%) Relative Humidity (%)
In-cloud Temperature Differences
Mean (oC) Standard Deviation (oC)
2
-2 -1 0 1 2
2
-2
-2
-4
-6
6
4
2
heig
ht (
km)
heig
ht (
km)
heig
ht (
km)
-2 -1 0 1 3 0.6 0.8 1 1.4
0.8 1 1.2 1.6
2
-2
-2
-4
-66
4
2
10-2 -1 2 0.9 1 1.20.8
0 0
0 01.2
1.4
1.100 TGPScloud - TGPSwet
Tα=0.85 - TGPSwet
Cloud middle
Cloud base
Cloud top
TGPScloud - TECMWF
TGPScloud - TNCEP
In-cloud Temperature Differences
-1 -0.5 0 0.5 1 1.5
-1 -0.5 0 0.5 1 1.5
0.2 0.4 0.6 0.8
0.2 0.4 0.6 0.8
2
-2
-2
-4
-66
4
2
2
-2
-2
-4
-66
4
2
0
0
0
heig
ht (
km)
heig
ht (
km)
heig
ht (
km)
0
0
0
Mean (oC) Standard Deviation (oC)-1 -0.5 0 0.5 1 1.5 0.2 0.4 0.6 0.8
Cloud middle
Cloud base
Cloud top
Lapse Rates within CloudCloud middle
Cloud base
Cloud top5 6 7 8 1.20.4 2
5 6 7 8 1.20.4 2
heig
ht (
km)
2
-2
-2
-4
-66
4
2
heig
ht (
km)
heig
ht (
km)
0
0
0
2
-2
-2
-4
-6
6
4
2
0
0
0
Mean (oC) Standard Deviation (oC)5 6 7 8 1.20.4 2
GPSwet
ECMWF
NCEP
GPScloud
α=0.85
Summary
1. A new cloudy retrieval algorithm is developed.
2. GPS ROs are compared with large-scale analysis separately in cloudy and clear-sky environment for the first time.
3. CloudSAT data are combined with GPS RO datafor studying clouds.
Summary
• ECMWF temperature compares more favorably with GPS wet than NCEP
• Positive N-bias are found for cloudy soundings
Negative N-bias for clear-sky conditions
Major Findings:
• Cloudy-algorithm retrieved temperature is warmer than GPS wet retrieval in the middle of cloud and slightly colder near cloud top and cloud base, resulting a lapse rate that increases with height above cloud middle
Future Work
2. Validation of Cloudy Retrieval
• In-cloud profile retrieval with dropsonde data (average)
1. Algorithm Adaptation
• Use cloud-top pressure (height) provided by IR
3. Extended Period
• Investigation global thermodynamic characteristics
based on cloud types
More details:
Lin, L., X. Zou, R. Anthes, and Y.-H. Kuo, 2009: COSMIC
GPS radio occultation temperature profiles in clouds, Mon.
Wea. Rev., (accepted for publication last week)
Future Work
2. Validation of Cloudy Retrieval
1. Algorithm Adaptation
• Use cloud-top pressure (height) provided by IR
3. Extended Period
• Investigation global thermodynamic characteristics
based on cloud types
More Ideas?
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