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Integrated Profiling Technique

Integrated Profiling Technique. Kerstin Ebell, Ulrich Löhnert. Information on macrophysical cloud properties. Measurements: MWR TBs, Z, (IR radiances) + uncertainties. A priori information of the parameters to be retrieved: T, q, LWC, (r eff ) + uncertainties. radiosondes climatology

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Integrated Profiling Technique

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  1. Integrated Profiling Technique Kerstin Ebell, Ulrich Löhnert

  2. Information onmacrophysical cloud properties Measurements:MWR TBs, Z, (IR radiances)+ uncertainties A priori information of the parameters to beretrieved: T, q, LWC, (reff)+ uncertainties radiosondes climatology cloud model 1 D variational retrieval algorithm (optimal estimation equation, e.g. Rodgers, 2000) optimal solution: profiles of T, q, LWC, (reff)+ uncertainties Retrieval Blindtest Workshop, 7.-9.2.2012

  3. Optimal estimation equation derived from Bayes theorem assumptions: pdfs Gaussian distributed (LWC  log10LWC), forward model moderately non-linear Retrieval Blindtest Workshop, 7.-9.2.2012

  4. Forward model • brightness temperatures: • radiative transfer operator for non-scattering cases (Simmer, 1994) • fast absorption predictor for water vapor and oxygen based on the Rosenkranz absorption model (Rosenkranz, 1998) • absorption due to liquid water according to Liebe et al. (1991) • radar reflectivities:LWC retrieval for pure liquid bins only Retrieval Blindtest Workshop, 7.-9.2.2012

  5. A priori profiles and uncertainties T and q: • AMF cases: • temporally interpolated from 6-hourly radiosondes • Sa variances and covariances from 10-y Lindenberg radiosonde data: compare interpolated profiles at 06 and 18 UTC to actual ascents at those times • blind test cases • T and q of model • use Sa to fix T and q: no coavariance, variances very small no retrieval of T and q LWC: modified adiabatic approach • Sa: variances and convariances from randomly perturbed LWCMODAD profiles  high correlation between cloud layers Retrieval Blindtest Workshop, 7.-9.2.2012

  6. Measurements and uncertainties MWR TBs: K band: 22.24, 23.04, 23.84, 25.44, 26.24, 27.84, 31.40 (noise 0.4 K)V band: 51.26, 52.28, 53.86, 54.94, 56.66, 57.30, 58.00 ( 0.5, 0.5., 0.5, 0.2, 0.2, 0.2, 0.2 K)assume uncorrelated measurements+ uncertainties due to fast absorption predictor cloud radar Z:assume noise of 1 dB + uncertainty due to Z-LWC relationassume non-drizzling clouds in blindtest cases! Retrieval Blindtest Workshop, 7.-9.2.2012

  7. Convergence and estimated retrieval uncertainties convergence criterion: d = dimension of yhere convergence assumed, when < #measurements/10 error covariance matrix of the optimal solution: Retrieval Blindtest Workshop, 7.-9.2.2012

  8. Interplay of a priori and measurement uncertainty Retrieval Blindtest Workshop, 7.-9.2.2012

  9. Current developments inclusion of spectral infrared measurements into the IPT information on T, q, LWP, reff (and ice clouds) inclusion of Z-LWC-reff relation information content studies combining ground-based and satellite / airborne(SEVIRI TBs, HAMP on HALO) measurements DFG project ICOS extend set of a priori LWC profiles:in-situ measurements? analyzed data of ARMs RACORO experiment: sample too small to apply a statistical analysis to Retrieval Blindtest Workshop, 7.-9.2.2012

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