1. 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. Only possible through integration of multiple measurement principles
Motivation
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 …
temporally highly resolved
continuously and automatically
Only possible through integration of multiple measurement principles
6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

3. 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
HATPRO
DPR 90/150
Cloud radar
Radiometer
Radiosondes
6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

4. Integrated Profiling Technique (IPT) – Overview 2
provides continuous vertical profiles of temperature, humidity and liquid water content (LWC)
needs information of different active and passive remote sensing instruments
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
6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

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

6. Example: 7 July 2007 CloudNet Target Classification
6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

7. Example: 7 July 2007 retrieved profiles: temperature and humidity
temperature (K)
retrieved profiles: temperature and humidity
absolute humidity (g/m3)
0-6:50 UTC: dew on radome
6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

8. Example: 7 July 2007 retrieved profiles: liquid water content
radar reflectivity (dBZ)
liquid water content (g/m3)
liquid water path (g/m2)
6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

9. Example: 7 July 2007 estimate of mean error LWP (g/m2) IWV (kg/m2)
LWC error (gm-3)
T error (K)
q error (gm-3)
Height above surface (km)
LWP (g/m2)
stat. retrieval
HATPRO
IWV (kg/m2)
GPS
Time in decimal hours on
6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

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

11. Application to COPS measurements (May 25, 2007 – AMF)
Humidity retrieval shows potential of combining MW and AERI measurements
only MW MW+AERI
6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

12. Application to COPS measurements (May 4, 2007 – AMF)
Retrieval of ice microphysics from AERI (and MW)
6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008

13. Summary
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)
Outlook
6th COPS Workshop, University of Hohenheim, 27 – 29 February 2008