Minutes of the sixth International Radiative Transfer Workshop, June 2004

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BREDBECK  2004  Minutes

Monday, 21/06/04


AD: Adrian Doicu
AH: Arash Houshangpour
BR: Bengt Rydberg
CD: Cory Davis
CE: Claudia Emde
CJ: Carlos Jimenez
CM: Christian Melsheimer
CT: Claas Teichmann
CV: Carmen Verdes
EB: Emmanuel Brocard
FS: Franz Schreier
GH: Gang Hong
JM: Jana Mendrok
ME: Mattias Ekström
MK: Mashrab Kuvatov
MM: Mario Mech
NC: Nathalie Courcoux
NM: Nizy Mathew
OL: Oliver Lemke
PE: Patrick Eriksson
PM: Peter A. T. Mills
SB: Stefan Buehler
SE: Stephen English
SK: Susanne Korn
SR: Sreerekha Ravi
TK: Thomas Kuhn
UK: Una O'Keeffe
UL: Ulrich Löhnert
VJ: Viju Oommen John

Peter Mills  Retrieval of water vapor isolines

* Motivation
* contour advection
* how to retrieve isolines 
   - classification problem
* estimation of probability density function
* classification data - AMSU-B data
* training data - ECMWF
* RT model - ARTS
* Result - Contours are well retrieved - Confidence level worse where
  gradient is high
* Error sources - Surface emissivity, collocation errors, 
           cloud contamination, measurement errors
* Simulations with collocated radiosonde profiles

Discussion: Gaussian PDF is an assumption
   Scheme works best around water vapor line
   Scheme similar to Carlos Monte Carlo Scheme ?

Arash Houshangpour: UTWV / UTH retrieval from AMSU radiances

* Importance of UTWV and UTH
* Methodology - channel 18 and channel 19 are used
* Relationship between radiance and UTWV
* Retrieval of temperature parameters using AMSU-A 6 and 7
* Transformation of TB
* Determination of Model parameters on a Global Scale
  - ECMWF dataset, ARTS simulations
* Fit parameters (Exponential Fit) for UTWV
* UTH retrieval

Discussion: Use of Channel 20

Adrian Doicu: Iteratively regularized Gauss-Newton method for
              atmospheric inverse problems 

* alternative approaches to OEM
* Criteria for selection of appropriate regularization method
* OEM: fully stochastic approach
* alternative: semi-stochastic approach
* Criterium 1: Tikhonov Regularization (not efficient)
    Iteratively regularized Gauss-Newton Method
* Criterium 2: Iteratively regularized Gauss-Newton Method with simple bounds
* Criterium 3: Multi-parameter regularization method
* Criterium 4: Error analysis

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