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?