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Integrated Profiling Technique: Current status, developments and future ideas

Integrated Profiling Technique: Current status, developments and future ideas Kerstin Ebell, Ulrich Löhnert Institute for Geophysics and Meteorology, University of Cologne. IPT in a nutshell. Information on macrophysical cloud properties. Measurements: MWR TBs, cloud radar Z

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Integrated Profiling Technique: Current status, developments and future ideas

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  1. Integrated Profiling Technique: Current status, developments and future ideas Kerstin Ebell, Ulrich Löhnert Institute for Geophysics and Meteorology, University of Cologne

  2. IPT in a nutshell Information onmacrophysical cloud properties Measurements:MWR TBs, cloud radar Z + uncertainties A priori information of the parameters to beretrieved: T, q, LWC+ uncertainties radiosondes climatology cloud model 1 D variational retrieval algorithm (optimal estimation equation, e.g. Rodgers, 2000) optimal solution: profiles of T, q, LWC+ uncertainties

  3. Core of IPT state vector prior information measurements forward model

  4. IPT framework • idea: flexible framework in which different kind of observations from ground and space are combined to retrieve profiles of temperature, humidity and cloud properties in a physically consistent way • different options for a priori and first guess profiles as well as forward models sensitivity studies can be easily performed • easy applicable due to a control file • written in IDL except for some forward models:e.g. PAMTRA (PYTHON, FORTRAN), LBLRTM (FORTRAN), LBLDIS( C++?), RTTOV (FORTRAN)

  5. Cloudnet categorization • categorization is the basis of the IPT: • retrieval performed for the temporal grid of the categorization • provides information if and which cloud parameters need to be retrieved in which height • uses attenuation-corrected cloud radar Z • no retrieval for profiles with melting layers and precipitation!

  6. Atmospheric state vector x - Status • atmospheric state vector x: • temperature profile • absolute humidity profile • log10(LWC) profile for liquid clouds (cloud droplets and drizzle) • T and q are retrieved for a fixed height grid with 47 layers up to 30 km • vertical resolution gradually decreases with height • from 50 m (below 250 m) to 5 km (above 10 km) • T and q are typically retrieved up to 10 km (climatology used above) • height grid specified in the a priori data file • cloud properties are defined on the cloud radar height grid as specified in the categorization file: 30 m resolution @ JOYCE

  7. Atmospheric state vector x - Developments • inclusion of IWC retrieval using IWC-Z-T method (Hogan et al. (2006) further constraint on humidity in (ice) cloudy parts • effective radius profile for liquid clouds • currently, diagnozed afterwards from LWC and and assuming a lognormal drop size distribution • idea: semi-implicit approach following using Frisch et al. (2002): reff updated with LWC of each iteration step and used to update the forward model • effective radius profile for ice clouds • IWC retrieval from TROPOS

  8. Prior information xa - Status • prior T and q profiles from radiosonde climatology (12-year long, from Essen station data):  seasonal mean profile • Sa for T and q from same radiosonde climatology • LWC prior information from Lindenberg Cloudnet data (01-04-2004 – 31-08-2012): profiles of LWC (Frisch et al., 1998) and reff,liq (Frisch et al., 2002) for single-layer liquid water clouds mean cloud profiles for 4 different cloud thicknesses calc. corresponding Sa • no drizzle prior information!

  9. Prior information xa - Developments • still looking for other liquid cloud data sets ... LES? • for IWC, no prior information available  large prior error

  10. Measurement vector y and Se - Status • MWR brightness temperaturesfor different frequencies (GHz) ....K band: 22.24, 23.04, 23.84, 25.44, 26.24, 27.84, 31.40V band: 51.26, 52.28, 53.86, 54.94, 56.66, 57.30, 58.00....and elevation angles5.4, 10.2, 19.2, 30., 42., 90. full error covariance matrix  correlation of the uncertainties of the different MWR channels is taken into account • zenith cloud radar reflectivity Z and uncertainties as provided by the Cloudnet target categorization file;uncorrelated uncertainties  Se becomes diagonal

  11. Measurement vector y and Se - Developments • lidar observations for IWC retrieval(TROPOS) probably not used for the standard HD(CP)2 level 2 product but work on • inclusion of Raman lidar q profiles ( Andreas Foth, Maria Barrera-Verdejo, HOPE BASIL data) • use of higher order moments of cloud radar spectra to retrieve cloud droplet and drizzle parameters ( Claudia Acquistapace) • include satellite observations form IR channels (TBs) of SEVIRI onboard MSG to improve T, q, and cloud property profiles ( Kerstin Ebell)

  12. Forward models F - Status • MWR TBs : • RTO for non-scattering cases (Simmer, 1994) with Rosenkranz (1998) gas absorption model and Liebe et al. (1991) liquid cloud absorption model (interface for other absorption models) • PAMTRA (accounts for scattering  same as RTO in clear-sky but not in cloudy cases • zenith cloud radar reflectivity Z:forward model: • empirical Z=a LWCb relations heaviy drizzle light drizzle / transition no drizzle

  13. Forward models F - Developments • cloud radar reflectivity Z: • IWC-Z-T relation • PAMTRA to explicitely simulate Z using LWC/IWC and effective radii as input  to get rid of Z-LWC/IWC relation... currently waiting for version release for experimental use: • for SEVIRI IR channels • RTTOV • LBLRTM: line-by-line RTM for IR

  14. Test case • latest IPT version is currently tested and applied to JOYCE data

  15. Post-processing • Monitoring of retrieval performance, e.g. • compare results from IPT with statistical retrievals (T, q, LWP, IWV) • convergence criteria monitoring • Definition of output data format: CF convention, HD(CP)2 conform

  16. References: Frisch, A., G. Feingold, C. Fairall, T. Uttal, and J. Snider, On cloud radar and microwave radiometer measurements of stratus cloud liquid water profiles, Journal of Geophysical Research, 103, 23195–23197, 1998. Frisch, A., M. Shupe, I. Djalalova, G. Feingold, and M. Poellot, The retrieval of stratus cloud droplet effective radius with cloud radars, Journal of Atmospheric and Oceanic Technology, 19, 835–842, 2002. Hogan, R., M. Mittermaier, and A. Illingworth, The retrieval of ice water content from radar reflectivity factor and temperature and its use in evaluating a mesoscale model, Journal of Geophysical Research, 45, 301–317, 2006. Rodgers, C., Inverse methods for atmospheric sounding: theory and practice, World Scientific, 2000, 238 pp.

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