Integrated Profiling at the AMF

Kerstin Ebell1, Ulrich Löhnert1, Susanne Crewell1, Dave Turner2

1Institute for Geophysics and Meteorology, University of Cologne

2SSEC, University of Madison – Madison, WI

6th COPS Workshop, University of Hohenheim 27 – 29 February 2008

2 6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

temporally highly resolved

Motivation

Only possible through integration of multiple measurement principles

Accurate information on the atmospheric state desireable for numerous applications

• evaluation of NWP models, i.e. assessment of radiation schemes

• development of parametrization schemes

• validation of satellite products• data assimilation• …

continuously and automatically

3 6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

HATPRODPR 90/150 Cloud radar

Radiometer

www.arm.govwww.arm.gov

Radiosondes

Integrated Profiling Technique (IPT) – Overview 1

Integration = physically consistent combination of all employed measurement information

Need knowledge on:• inversion methods• instrument characteristics (theory, error)• forward model, i.e. radiative transfer• Background information

4 6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

provides continuous vertical profiles of temperature, humidity and liquid water content (LWC)

needs information of different active and passive remote sensing instruments

Integrated Profiling Technique (IPT) – Overview 2

Application to measurements of ARM Mobile Facility + measurements of multi-spectral radiometers in the Black Forest (Supersite M) from April 1 to December 31, 2007

5 6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

Integrated Profiling Technique (IPT) – Overview 3

Microwave radiometers Cloud radar A priori information

(here radiosondes)

CloudNet Target Categorization: Each pixel is categorized in terms of

the presence of liquid droplets, ice, insects or aerosol.

Discrimination between precipitating and non-precipitating clouds.

Integration of measurements in the framework of optimal estimation

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LWC

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x=(T,q,log10(LWC))y=(TB,Z)xa: a priori profiles of T, q, log10(LWC) Sa: a priori covariance matrix Se: combined measurement and forward model error covariance matrix

6 6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

CloudNet Target Classification

Example: 7 July 2007

7 6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

absolute humidity (g/m3)

temperature (K)

0-6:50 UTC: dew on radome

retrieved profiles:temperature andhumidity

Example: 7 July 2007

8 6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

retrieved profiles:liquid water content

radar reflectivity (dBZ)

liquid water content (g/m3)

liquid water path (g/m2)

Example: 7 July 2007

9 6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

LWC error (gm-3)T error (K) q error (gm-3)

Hei

ght a

bove

sur

face

(km

)

Hei

ght a

bove

sur

face

(km

)

Hei

ght a

bove

sur

face

(km

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estimate of mean error

LWP (g/m2)

IWV (kg/m2)

stat. retrieval

HATPRO

GPS

Example: 7 July 2007

Time in decimal hours on 20070707

10 6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

„New“: Inclusion of multispectral IR measurements

Atmospheric Emitted Radiance Interferometer (AERI)

Objectives: Enhancement of accuracy & vertical resolution for temperature and humidity

profile retrieval in clear-sky cases (and below cloud) Future: develop a powerful, complementary retrieval tool (AERI+MW+cloud radar)

more accurate retrieval of cloud properties over a wider range of LWP (from very low to precipitating), inclusion of ice microphysics

11 6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

Humidity retrieval shows potential of combining MW and AERI measurements

only MW MW+AERI

Application to COPS measurements (May 25, 2007 – AMF)

12 6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

Application to COPS measurements (May 4, 2007 – AMF) Retrieval of ice microphysics from AERI (and MW)

13 6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

Derivation of physically consistent profiles of temperature, humidity and cloud liquid water within an optimal estimation framework (Integrated Profiling Technique)

Inclusion of target information in the retrieval (CloudNet target categorization) Combination of MW and AERI shows potential for high-quality humidity

retrieval

Application of IPT to whole AMF dataset (April-Dec. 2007) Be careful: problems when clouds are not detected

Attempt to derive cloud droplet concentration Use IPT output for SW/LW radiative transfer applications, cloud fraction

contained in MW-volume scans ( see poster S. Kneifel) Combine MW & AERI in cloudy cases (liquid, ice & mixed-phase)

Summary

Outlook