Talk:Participants

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| 1|| Ackerman  || Stefan    || University of Wisconsin-Madison || USA        || [[Image:USA.gif]]    || ''Keynotes passive instruments''  || oral  ||  
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| 1|| Ackerman  || Steve    || University of Wisconsin-Madison || USA        || [[Image:USA.gif]]    || ''Keynotes passive instruments''  || oral  ||  
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| 2|| Baum      || Bryan      || University of Wisconsin-Madison || USA        || [[Image:USA.gif]]    || MODIS Collection 6 Cloud Top Height and IR Thermodynamic Phase  || oral  ||  
| 2|| Baum      || Bryan      || University of Wisconsin-Madison || USA        || [[Image:USA.gif]]    || MODIS Collection 6 Cloud Top Height and IR Thermodynamic Phase  || oral  ||  
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|15|| Horváth    || Ákos      || MPI, Hamburg              || Germany    || [[Image:Germany.gif]]    || Evaluation of MISR Stereo Cloud-Top Height Retrievals    || oral  || yes  
|15|| Horváth    || Ákos      || MPI, Hamburg              || Germany    || [[Image:Germany.gif]]    || Evaluation of MISR Stereo Cloud-Top Height Retrievals    || oral  || yes  
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|16|| Hünerbein || Anja      || Free University of Berlin || Germany    || [[Image:Germany.gif]]    || Synergetic cloud top height retrieval for a passive and an active sensor    || oral  || no       
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|16|| Hünerbein || Anja      || Leibniz Institute for Tropospheric Research || Germany    || [[Image:Germany.gif]]    || Synergetic cloud top height retrieval for a passive and an active sensor    || oral  || no       
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|17|| Jonkheid  || Bastiaan  || KNMI                      || Netherlands || [[Image:Netherlands.gif]]|| A MSG/SEVIRI simulator for the validation of climate models    || oral  || yes
|17|| Jonkheid  || Bastiaan  || KNMI                      || Netherlands || [[Image:Netherlands.gif]]|| A MSG/SEVIRI simulator for the validation of climate models    || oral  || yes
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|19|| Kahn      || Brian      || NASA, JPL                || USA        || [[Image:USA.gif]]        || New AIRS Version 6 cloud retrievals: cloud thermodynamic phase, cirrus cloud optical thickness and effective diameter    || o or p || yes   
|19|| Kahn      || Brian      || NASA, JPL                || USA        || [[Image:USA.gif]]        || New AIRS Version 6 cloud retrievals: cloud thermodynamic phase, cirrus cloud optical thickness and effective diameter    || o or p || yes   
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|20|| Karlsson  || Kar-Goeran || SMHI                      || Sweden      || [[Image:Sweden.gif]]    || Adding uncertainty information to cloud mask products – impact on Level 2 and Level 3 products    || oral  || no
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|20|| Karlsson  || Karl-Göran || SMHI                      || Sweden      || [[Image:Sweden.gif]]    || Adding uncertainty information to cloud mask products – impact on Level 2 and Level 3 products    || oral  || no
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|21|| King      || Michael    || University of Colorado    || USA        || [[Image:USA.gif]]        || -    || none      || no   
|21|| King      || Michael    || University of Colorado    || USA        || [[Image:USA.gif]]        || -    || none      || no   
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|30|| Moroney    || Catherine  || NASA, Jet Propulsion Laboratory || USA  || [[Image:USA.gif]]        ||    || coauther || no   
|30|| Moroney    || Catherine  || NASA, Jet Propulsion Laboratory || USA  || [[Image:USA.gif]]        ||    || coauther || no   
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|31|| Müller    || Jennifer  || Institute for Space Science || Germany  || [[Image:Germany.gif]]    ||     || poster || no   
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|31|| Müller    || Jennifer  || Institute for Space Science || Germany  || [[Image:Germany.gif]]    || Integrating Cloud Observations from Ground and Space – a Way to Combine Time and Space Information
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|| poster || no   
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|32|| Musial    || Jan        || University of Bern        || Switzerland || [[Image:Switzerland.gif]]|| An Enhanced cloud classification scheme based on radiative transfer simulations and aggregated ratings    || oral  || no   
|32|| Musial    || Jan        || University of Bern        || Switzerland || [[Image:Switzerland.gif]]|| An Enhanced cloud classification scheme based on radiative transfer simulations and aggregated ratings    || oral  || no   
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|33|| Palikonda  || Rabindra  || NASA, Langley            || USA        || [[Image:USA.gif]]        || LaRC real-time satellite derived products – Overview: Applications and Limitations    || oral  || no   
|33|| Palikonda  || Rabindra  || NASA, Langley            || USA        || [[Image:USA.gif]]        || LaRC real-time satellite derived products – Overview: Applications and Limitations    || oral  || no   
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|34|| Pavolonis  || Michael    || NOAA/NESDIS/STAR          || USA        || [[Image:USA.gif]]        ||     || oral  || yes   
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|34|| Pavolonis  || Michael    || NOAA/NESDIS/STAR          || USA        || [[Image:USA.gif]]        || Cloud phase determination using infrared absorption optical depth ratios    || oral  || yes   
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|35|| Pincus    || Robert    || University of Colorado    || USA        || [[Image:USA.gif]]        || Small decisions with big impacts: MODIS, ISCCP, and the evaluation of clouds in climate models    || oral  || no   
|35|| Pincus    || Robert    || University of Colorado    || USA        || [[Image:USA.gif]]        || Small decisions with big impacts: MODIS, ISCCP, and the evaluation of clouds in climate models    || oral  || no   
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|38|| Puygrenier  || Vincent    || Météo France              || France      || [[Image:France.gif]] ||  A new spectrally consistent adiabatic method to derive cloud properties from MODIS measurement  || poster  || no     
|38|| Puygrenier  || Vincent    || Météo France              || France      || [[Image:France.gif]] ||  A new spectrally consistent adiabatic method to derive cloud properties from MODIS measurement  || poster  || no     
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|39|| Riedi      || Jérôme    || University of Lille 1    || France      || [[Image:France.gif]]    || Intercomparison of liquid cloud properties retrieved from POLDER/PARASOL, MODIS/AQUA and SEVIRI/MSG<br>Use of A-Train observations to assess cloud phase retrievals from SEVIRI/MSG    || 2 x oral || yes  
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|39|| Rausch      || John      || University of Wisconsin-Madison || USA        || [[Image:USA.gif]]    ||Estimation of cloud properties though a spectrally consistent adiabatic model  || poster  ||
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|40|| Riedi      || Jérôme    || University of Lille 1    || France      || [[Image:France.gif]]    || Intercomparison of liquid cloud properties retrieved from POLDER/PARASOL, MODIS/AQUA and SEVIRI/MSG<br>Use of A-Train observations to assess cloud phase retrievals from SEVIRI/MSG    || 2 x oral || yes  
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|41|| Roebeling  || Rob        || KNMI                      || Netherlands || [[Image:Netherlands.gif]]||  The CREW workshop: an overview  || oral  || yes   
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|42|| Schulz  || Jörg      ||  Eumetsat          || Europe        || [[Image:Europe.gif]]        || ''Keynote lecture instrument calibration'' (GSICS)    || oral      || 
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|43|| Scheirer  || Ronald || SMHI                      || Sweden      || [[Image:Sweden.gif]]    || Adding uncertainty tbc    || oral  || no
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|40|| Roebeling  || Rob        || KNMI                      || Netherlands || [[Image:Netherlands.gif]]|| The CREW workshop: an overview  || oral || yes   
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|44|| Sèze      || Geneviève  || LMD                      || France      || [[Image:France.gif]]     || Evaluation of the global cloud cover distribution obtained from multi-geostationary data in the frame of the MEGHA-TROPIQUES mission with CALIPSO lidar observations    || oral   || no
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|41|| Schultz  || Jörg      || Eumetsat          || Europe         || [[Image:Europe.gif]]        || ''Keynote lecture instrument calibration'' (GSICS)   || oral      ||  
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|45|| Smith      || William L Jr. || NASA, Langley          || USA         || [[Image:USA.gif]]        || Improved Methods To Resolve The Vertical Distribution Of Cloud Water From Passive Satellite Data   || o or p || no   
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|42|| Sèze      || Geneviève  || LMD                       || France      || [[Image:France.gif]]     || Evaluation of the global cloud cover distribution obtained from multi-geostationary data in the frame of the MEGHA-TROPIQUES mission with CALIPSO lidar observations   || oral  || no
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|46|| Stengel    || Martin    || DWD                       || Germany    || [[Image:Germany.gif]]   || The inter-comparison of retrieved cloud properties within the ESA Cloud CCI project   || oral  || no  
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|43|| Smith      || William L Jr. || NASA, Langley          || USA        || [[Image:USA.gif]]        || Improved Methods To Resolve The Vertical Distribution Of Cloud Water From Passive Satellite Data   || o or p || no   
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|47|| Stephans  || Graeme      || Atmosphere Colorado State University          || USA        || [[Image:USA.gif]]        || ''Keynote lecture active sensors''   || oral      ||  
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|44|| Stengel    || Martin     || DWD                      || Germany    || [[Image:Germany.gif]]   || The inter-comparison of retrieved cloud properties within the ESA Cloud CCI project   || oral  || no  
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|48|| Thomas    || Gareth     || University of Oxford || UK|| [[Image:Europe.gif]]       || Application and evaluation of the Oxford-RAL Retrieval of Aerosol and Cloud algorithm to MODIS data   || poster  || no  
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|45|| Stephans   || Graeme      || Atmosphere Colorado State University          || USA        || [[Image:USA.gif]]       || ''Keynote lecture active sensors''   || oral      ||  
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|49|| Thoss   || Anke || SMHI                      || Sweden      || [[Image:Sweden.gif]]     || tbc   || tbc  || tbc
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|46|| Trepte    || Qing      || Science Systems & Application Inc. || USA || [[Image:USA.gif]]      || A Comparison of Cloud Detection between CERES Ed4 Cloud Mask and CALIPSO Version 3 Vertical Feature Mask    || o or p  || no  
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|50|| Trepte    || Qing      || Science Systems & Application Inc. || USA || [[Image:USA.gif]]      || A Comparison of Cloud Detection between CERES Ed4 Cloud Mask and CALIPSO Version 3 Vertical Feature Mask    || o or p  || no  
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|47|| Vidot      || Jerome    || Météo France              || France      || [[Image:France.gif]]    || -    ||  none  || no  
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|51|| Vidot      || Jerome    || Météo France              || France      || [[Image:France.gif]]    || -    ||  none  || no  
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|48|| Walther    || Andi      || University of Wisconsin-Madison  || USA        || [[Image:USA.gif]]        || Sources of error in satellite derived cloud products    || o and p || yes   
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|52|| Walther    || Andi      || University of Wisconsin-Madison  || USA        || [[Image:USA.gif]]        || Sources of error in satellite derived cloud products    || o and p || yes   
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|49|| Watts      || Philip    || Eumetsat                  || Europe      || [[Image:Europe.gif]]    || Progress on optimal estimation cloud property retrieval from SEVIRI observations    ||  oral  || yes  
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|53|| Watts      || Philip    || Eumetsat                  || Europe      || [[Image:Europe.gif]]    || Progress on optimal estimation cloud property retrieval from SEVIRI observations    ||  oral  || yes  
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|50|| Wind      || Galina    || NASA GSFC / SSAI, Inc    || USA        || [[Image:USA.gif]]        || Improvements in Night-time Low Cloud Detection and MODIS-Style Cloud Optical Properties from MSG SEVIRI    || oral  || yes  
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|54|| Wind      || Galina    || NASA GSFC / SSAI, Inc    || USA        || [[Image:USA.gif]]        || Improvements in Night-time Low Cloud Detection and MODIS-Style Cloud Optical Properties from MSG SEVIRI    || oral  || yes  
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|51|| Wolters    || Erwin      || KNMI                      || Netherlands || [[Image:Netherlands.gif]]|| Evaluation of a 30-year NOAA-AVHRR cloud property climate data record    || oral  || no
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|55|| Wolters    || Erwin      || KNMI                      || Netherlands || [[Image:Netherlands.gif]]|| Evaluation of a 30-year NOAA-AVHRR cloud property climate data record    || oral  || no
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|52|| Xiong      || Xianxiong (Jack) || NASA GSFC                || USA        || [[Image:USA.gif]]  ||  MODIS Radiometric Calibration and Uncertainty Assessment  || oral  || no
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|56|| Xiong      || Xianxiong (Jack) || NASA GSFC                || USA        || [[Image:USA.gif]]  ||  MODIS Radiometric Calibration and Uncertainty Assessment  || oral  || no
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|53|| Zhang      || Zhibo      || University of Maryland    || USA        || [[Image:USA.gif]]        || An assessment of differences between cloud effective particle radius retrievals for marine water clouds from three MODIS spectral bands: observational and modeling studies    || oral  || no
+
|57|| Zhang      || Zhibo      || University of Maryland    || USA        || [[Image:USA.gif]]        || An assessment of differences between cloud effective particle radius retrievals for marine water clouds from three MODIS spectral bands: observational and modeling studies    || oral  || no
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Latest revision as of 17:43, 1 July 2011

Participant list for CREW-3, Madison/Wisconsin (USA), 2011 (preliminary)

abstract wish data
1 Ackerman Steve University of Wisconsin-Madison USA USA.gif Keynotes passive instruments oral
2 Baum Bryan University of Wisconsin-Madison USA USA.gif MODIS Collection 6 Cloud Top Height and IR Thermodynamic Phase oral
3 Bennartz Ralf University of Wisconsin-Madison USA USA.gif Cloud liquid water path of warm clouds from passive microwave and visible/near-infrared imagers oral
4 Bojkov Bojan ESA/ESRIN Europe Europe.gif - none no
5 Bugliaro Luca DLR Oberpfaffenhofen Germany Germany.gif Realistic Simulations of MSG/SEVIRI Scenes for Cloud Algorithm Validation oral yes
6 Carbajal Henken Cintia Free University of Berlin Germany Germany.gif Synergistic MERIS-AATSR cloud properties retrievals using optimal estimation technique poster yes
7 Chang Fu-Lung NASA, Science Systems & Applications Inc. USA USA.gif Using CALIPSO/CloudSat Data to Evaluate the Multilayer Cloud Properties Retrieved from MODIS and SEVIRI Data oral
8 Delanoë Julien Observations Spatiales (LATMOS)/UVSQ/CNRS/IPSL France France.gif DARDAR: CloudSat and CALIPO synergy product for cloud studies oral yes
9 Deneke Hartwig Leibniz Institute for Tropospheric Research Germany Germany.gif Cloud analyses with passive satellite imagery viewed from the radiative perspective oral no
10 Derrien Marcel Météo France France France.gif coauther none no
11 Doelling Dave NASA, Langley USA USA.gif The calibration of geostationary visible sensors using MODIS as a reference oral no
12 Hamann Ulrich KNMI Netherlands Netherlands.gif The CREW workshop: an overview oral no
13 Heck Patrick W. University of Wisconsin-Madison USA USA.gif Improved Methods for and Validation of Nighttime Cloud Property Retrievals from SEVIRI, GOES and MODIS poster no
14 Heidinger Andrew NASA, Nesdis USA USA.gif State of the NOAA AWG Cloud Algorithms and their application in the Great Lakes Region oral yes
15 Horváth Ákos MPI, Hamburg Germany Germany.gif Evaluation of MISR Stereo Cloud-Top Height Retrievals oral yes
16 Hünerbein Anja Leibniz Institute for Tropospheric Research Germany Germany.gif Synergetic cloud top height retrieval for a passive and an active sensor oral no
17 Jonkheid Bastiaan KNMI Netherlands Netherlands.gif A MSG/SEVIRI simulator for the validation of climate models oral yes
18 Joro Sauli Eumetsat Europe Europe.gif - none no
19 Kahn Brian NASA, JPL USA USA.gif New AIRS Version 6 cloud retrievals: cloud thermodynamic phase, cirrus cloud optical thickness and effective diameter o or p yes
20 Karlsson Karl-Göran SMHI Sweden Sweden.gif Adding uncertainty information to cloud mask products – impact on Level 2 and Level 3 products oral no
21 King Michael University of Colorado USA USA.gif - none no
22 Kinne Stefan University Hamburg Germany Germany.gif GEWEX Cloud Assessment: a review oral no
23 Kokhanovsky Alexander University of Bremen Germany Germany.gif Retrieval of cloud properties using synthetic datasets oral yes
24 Le Gleau Hervé Météo France France France.gif SAFNWC / MSG cloud products oral yes
25 Lockhoff Maarit DWD Germany Germany.gif Accuracy Assessment of SEVIRI Cloud Detection and Cloud Top Height Retrievals Using Active Remote Sensing Data from CloudSat and CALIPSO oral yes
26 Lutz Hans Joachim Eumetsat Europe Europe.gif Multi-layer cloud detection within the SCE/CLA algorithm oral yes
27 Marchant Benjamin NASA Goddard Space Flight Center USA USA.gif Optical Property Cloud Phase Retrievals for MODIS Collection 6: assessment from CALIOP/CALIPSO poster no
28 Meirink Jan-Focke KNMI Netherlands Netherlands.gif Using MSG-SEVIRI for the inter-calibration of visible and near-infrared reflectance from polar imagers oral no
29 Minnis Patrick NASA, LaRC USA USA.gif Updated NASA Langley Cloud Property Retrievals oral yes
30 Moroney Catherine NASA, Jet Propulsion Laboratory USA USA.gif coauther no
31 Müller Jennifer Institute for Space Science Germany Germany.gif Integrating Cloud Observations from Ground and Space – a Way to Combine Time and Space Information poster no
32 Musial Jan University of Bern Switzerland Switzerland.gif An Enhanced cloud classification scheme based on radiative transfer simulations and aggregated ratings oral no
33 Palikonda Rabindra NASA, Langley USA USA.gif LaRC real-time satellite derived products – Overview: Applications and Limitations oral no
34 Pavolonis Michael NOAA/NESDIS/STAR USA USA.gif Cloud phase determination using infrared absorption optical depth ratios oral yes
35 Pincus Robert University of Colorado USA USA.gif Small decisions with big impacts: MODIS, ISCCP, and the evaluation of clouds in climate models oral no
36 Placidi Simone TU Delft Netherlands Netherlands.gif A novel technique for validating liquid water cloud properties oral no
37 Platnick Steven NASA, GSFC USA USA.gif Overview of the MODIS Collection 6 Optical Property Algorithm
MODIS Optical Property Pixel-Level Uncertainty Estimates in Collection 6 		2 x oral yes
38 Puygrenier Vincent Météo France France France.gif A new spectrally consistent adiabatic method to derive cloud properties from MODIS measurement poster no
39 Rausch John University of Wisconsin-Madison USA USA.gif Estimation of cloud properties though a spectrally consistent adiabatic model poster
40 Riedi Jérôme University of Lille 1 France France.gif Intercomparison of liquid cloud properties retrieved from POLDER/PARASOL, MODIS/AQUA and SEVIRI/MSG
Use of A-Train observations to assess cloud phase retrievals from SEVIRI/MSG 		2 x oral yes
41 Roebeling Rob KNMI Netherlands Netherlands.gif The CREW workshop: an overview oral yes
42 Schulz Jörg Eumetsat Europe Europe.gif Keynote lecture instrument calibration (GSICS) oral
43 Scheirer Ronald SMHI Sweden Sweden.gif Adding uncertainty tbc oral no
44 Sèze Geneviève LMD France France.gif Evaluation of the global cloud cover distribution obtained from multi-geostationary data in the frame of the MEGHA-TROPIQUES mission with CALIPSO lidar observations oral no
45 Smith William L Jr. NASA, Langley USA USA.gif Improved Methods To Resolve The Vertical Distribution Of Cloud Water From Passive Satellite Data o or p no
46 Stengel Martin DWD Germany Germany.gif The inter-comparison of retrieved cloud properties within the ESA Cloud CCI project oral no
47 Stephans Graeme Atmosphere Colorado State University USA USA.gif Keynote lecture active sensors oral
48 Thomas Gareth University of Oxford UK Europe.gif Application and evaluation of the Oxford-RAL Retrieval of Aerosol and Cloud algorithm to MODIS data poster no
49 Thoss Anke SMHI Sweden Sweden.gif tbc tbc tbc
50 Trepte Qing Science Systems & Application Inc. USA USA.gif A Comparison of Cloud Detection between CERES Ed4 Cloud Mask and CALIPSO Version 3 Vertical Feature Mask o or p no
51 Vidot Jerome Météo France France France.gif - none no
52 Walther Andi University of Wisconsin-Madison USA USA.gif Sources of error in satellite derived cloud products o and p yes
53 Watts Philip Eumetsat Europe Europe.gif Progress on optimal estimation cloud property retrieval from SEVIRI observations oral yes
54 Wind Galina NASA GSFC / SSAI, Inc USA USA.gif Improvements in Night-time Low Cloud Detection and MODIS-Style Cloud Optical Properties from MSG SEVIRI oral yes
55 Wolters Erwin KNMI Netherlands Netherlands.gif Evaluation of a 30-year NOAA-AVHRR cloud property climate data record oral no
56 Xiong Xianxiong (Jack) NASA GSFC USA USA.gif MODIS Radiometric Calibration and Uncertainty Assessment oral no
57 Zhang Zhibo University of Maryland USA USA.gif An assessment of differences between cloud effective particle radius retrievals for marine water clouds from three MODIS spectral bands: observational and modeling studies oral no
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