Minutes of the seventh International Radiative Transfer Workshop, June 2005

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Wednesday, 22/06/05
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Participants:
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CD - Cory Davis
SB - Stefan Bühler
DF - Dietrich Feist
AD - Amy Dorothy
OL - Oliver Lemke
PE - Patrick Eriksson
AB - Alessandro Batta
NC - Nathalie Courcoux
JM - Jana Mendrok
MK - Mashrab Kuvatov
SM - Stefan Müller
CM - Christian Melsheimer
UR - Uwe Raffalski
SR - Sreerekha Ravi
AJ - Adam Jaczewski
BR - Bengt Rydberg
MP - Mattias Palm 
CE - Claudia Emde



Chair: CD

 
Presentation of the working groups
===================================

CD: Scattering models: ARTS-MC, ARTS-DOIT, Alessandros model, 
    Janas model, RTTOV-SCAT
    - Discussion about comparisons 
    - suggestion by Alessandro, uplooking simulation, 3D, 
	    with polarization, rain

SB: ARTS users
    - problems in using ARTS/QPack for up-looking instruments
    - always problems with compatibility, feedback needed
    - contact webpage to be created
    - DF: installation of ARTS on Mac
    - practical part: sort out problems in retrieval

AD: Cloud microphysical assumptions
    - many free parameters
    - consider bulk properties
    - more information from other sources should be used, e.g. radar

DF: ARTS Beamcat
    - Script for producing arts linefiles from beamcat produced by DF and OL

CE: New ARTS development
    - new agenda concept
    - analytical jacobians, ppath array structure
    - data format, xml not practical for some cases
    - Monte Carlo: include sensor characteristics for EOS MLS and surface 
    - include new option: sensor inside cloudbox
    - port absorption from ARTS-1-0 to ARTS-1-1
    - general ARTS-1-1 paper
    - Documentation, new ARTS Wiki, try to keep user guide complete
    - discussion about implementation of fast scattering model 

MK: AMSU UTH and climatology
    - water vapor daily cycle in Mediterranian Sea 
    - investigate this area in AMSU-B data, is it possible to investigate
      boundary layer?
    - trend over several days
    - cycle could not be seen in AMSU data
    - suggestion by AB: use meteosat data   
    
DF: ECMWF AMSU comparisons
    - AJ found bias, 3K in centre of H2O line, 15K in surface channels
    - Start with 3K bias
    - bias is a technical bug, could not be sorted out
    - NC: no bias between RTTOV and ARTS
    - possible explanation: antenna patterns, less impact on nadir radiances



Talk session
=============

PE: Odin-SMR cloud ice retrieval
--------------------------------- 
- Odin: 2 instruments: SMR, OSIRIS
- used Odin-SMR data, stratospheric mode (501.18 - 502.38 GHz)
- tangent points below 9.5 km
- measurement principle, blackbody radiation below ~10km
- with cloud BT decreases, would become more complicated for higher 
  tangent altitudes
- lower retrieval limit about 10 km
- example spectra and retrievals, cloud detection
- ice columns above 260 hPa - similar results for SMR-MH97 and ECMWF
- similar retrievals are done for EOS-MLS
- particle size distributions, large deviations, main uncertainty
- outlook: improve Odin-SMR retrievals, collaboration with EOS-MLS,


Chair: PE

CD: AURA-MLS cloud observations
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- dI for thin clouds approx. proportional to IMC
- polarization signals can be quite large, explained by horizontally 
  aligned ice particles
- AURA-MLS: First dual polarized measurements
- Focus on radiometer 1, measures H and V, centered at O2 line
  -> not ideal for cloud detection
- polarized simulation for different particle shapes
- BT depression does not depend much on shape, but polarization
- polarization signal lerge for horizontally aligned particles
- understanding Q: low tangent altitudes -> Q>0
- measurements: polarization signal abou 10% of BT depression
- interpretation of observations: moderate aspect ratios reproduce 
  measurements
- conclusion: randomly oriented particle assumption seems to be justified
- MLS observations and MODIS cloud height 

Discussion
(CE) higher polarization signal for other radiometers - may be assumption of 
randomly oriented particles not valid
(PE) with 122 GHz mainly convective clouds are observed -> randomly oriented
particles, probably different in thin cirrus


BR: Gereration of cirrus retreival test data base
--------------------------------------------------
- database content: ~100 000 cloud cases, depending on number of parameters
  to be retrieved
- retrieval parameters: Column IWC, IWC profiles, 
  size parameters (mean mass diameter, median mass diameter, ...), shape
- Cloudnet radar data, Products: radar reflectivity factor, 
  inverted IWC and LWC
- microphysical assumptions: use PyARTS for computations, but not very
  realistic size assumption, gamma size distribution
- try to match gamma-distr. parameters by Heymsfield 2003
- simulations using DOIT for CIWSIR frequencies
- database is under construction, plan: use information from radar

Discussion:
- (CE): Gamma distribution can be reproduced using one particle type
  (CD): Does not work because you have altitude dependant size distributions


(JM) Pseudo-spherical RT modelling for emitting and scattering atmospheres
--------------------------------------------------------------------------
- devide solar term and emission term cause problems in IR (2.5 - 4.0 microns)
- features from troposphere can be observed in limb geometry because of 
  scattering into the LOS
- rte with four partial derivatives
- simplified spherical RTE (integral form of RTE, 1D, local panarity of 
  atmosphere 
- existing modules used: Absorption (F. Schreier)
  precalculated optical properties, DISORT
- parabolic parameterization of extinction for calculation of optical depth
- source terms: emission, solar radiation in spherical geometry
   multiple scattering term in plane-parallel geometry
- with solar radiation 2D problem
- validation using ARTS and KOPRA
  model without multiple scattering compared to KOPRA
  with multiple scattering compared to ARTS
- comparison with MCScia for different solar angles with single scattering
  with multiple scattering
  problems with very low sun cases
- reconstruction of measured MIPAS spectra
- intercomparison with McSCIA for more setups 


(MP) Taking Bayes's Theorem seriously
-------------------------------------
- inverse problem -> Bayes theorem
- application: electromagnetic conductivity imaging (ECI)
- discretizations in cells of constant conductivities
- ECI as probability problem
- Likelyhood : Gaussian noise  A priori: Pott's model
- Motivation for using MCMC
- Definition and properties of MCMC algorithm
- Example: largest errors at conductivity boundaries
- Finding optimal size of state space, problem: correlated samples
- Metropolis coupled MCMC

(PE) Article using method in JGR, Tamminen and Kyrölä
(SB) Include this into MC retrieval scheme similar to Evans methods

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