Minutes of the IOMASA Mid-term Review,
12-11 May, 2004
held at met.no, Oslo, Norway

Start of meeting: 10 May, 13:30
End of meeting: 11 May, 12:30

Participants:


IUP: 
Georg Heygster                    GH
Christian Melsheimer 		  CM

DTU-DCRS: 			    
Leif Toudal Pedersen 		  LTP
Roberto Saldo 			  RS

DMI: 				    
Søren Andersen 			  SA
Rasmus Tonboe                     RT 

met.no: 			    
Harald Schyberg 		  HS
Frank Thomas Tveter 		  FT
Steinar Eastwood                  SE
				    
SMHI: 				    
Per Dahlgren                      PD
Tomas Landelius                   TL

1. Introductory items:

2. Progress of Phase 2: Status and Results of Phase 2 of each Partner:

2.1 Part 1 (IUP): WP 2.1: Atmospheric remote sensing algorithms

(C. Melsheimer):

Total water vapour (TWV) from AMSU-B: 

//    TL: Areas of no retrieval because of too high TWV will cause a
//       bias in the model since only TWV data from dry area will be
//       assimilated.
//    HS: Merge with TWV over water from AMSU-A? (emissivity of sea
//       with FASTEM model?)
//    SA: TWV over ice is also very interesting for sea ice part, for
//       the atmospheric correction
//    HS: Does the algorithm work over land as well?
//    CM: Yes, provided TWV is not too high.
(G. Heygster):

Surface emissivity at AMSU-A frequencies

Daily AMSR ice charts

//
//    LT: Have you corrected for the difference in AMSR A-scan and B-scan? 
//    GH: Not yet.							   
//    LT: What do the red spots in cloud signature image mean?		   
//    GH: NaN (not a number), caused by negative argument of the logarithm.
//

2.2 Part 2 (met.no/SMHI): WP 2.2 Improve Arctic high-resolution NWP

(H. Schyberg):

temperature data assimilation (AMSU-A)

(T. Landelius):

Humidity Assimilation (AMSU-B):

(P. Dahlgren):

Quality control

(T. Landelius):

Improved modeling of surface heat flux

2.3 Part 3 (DTU): WP 3.2: Construction of sea ice emissivity forward model:

(L.Toudal):

Advanced statistical retrieval

//
//    SA: How are the different AMSR channels weighted? You might see
//        more variation in the H-pol channels
//    LT: Maybe we should adjust the error covariance matrix to give
//        the H-pol channels less weight
//    GH: Validation with SAR
//    LT: Yes,  RADARSAT and Envisat, but with Envisat data there are
//        ordering problems...
//

Time series analysis (AMSR-E, AMSU)

//
//    GH: AMSU-A: theta-dependence?!
//    LT: First try without. Then see if theta makes much difference
//    GH: Penetration depth?
//    LT: Not considered yet - might be cause of some errors.
//    GH: Are varying penetration depth (with freq. and time) and
//        varying footprint size the main problems?
//    LT: Not necessarily.
//

2.4 Part 4 (DMI): WP 4.2: Construction of algorithm for sea ice concentration retrieval:

Activities focussed on: (R. Tonboe):

Emission modeling

(S. Andersen):

Time series analysis

(R. Tonboe):

Sea ice (satellite data assimilation) model

(S. Andersen):

Plans

SAR classification

3. Review of Phase 2:

We are right in the middle of phase 2 (development phase), thus it is not easy to judge if everything is exactly on time. In any case, all partners are ready now to provide new data to others and to use and apply new data from others. Details in the next section.

4. Inter-task communication and exchange

Part 1, Atmospheric algorithms (IUP)

Part 2, Data Assimilation Activities (met.no/SMHI):

//
//    GH: Progress/Results?
//    TL/HS: Had to get quality control ready.
//

Part 3 Sea ice emissivity forward model (DTU):

AMSR emissivity maps (AMSU-A frequencies) and SST 
//
//    TL: AMSU-B emissivities?
//    LT: We have only 89 GHz (highest AMSR channel)
//    TL: Good enough.
//    GH: Time series?
//    LT: Using for validation/checking
//

5. Project Management

5.1 Management and resource usage report:

Location and Venue of next meetings

6. Action items for MTR

6.1 Discuss upcoming Deliverables

6.2 Discuss input for next Management Report (covering period Nov 2003 till April 2004), due at the end of June, 2004

See section 4. above

7. Any Other Business

7.1. Review of User Advisory Group comments from PM1, one year ago

Main statements:

THE FUTURE: Follow on projects?

7.3 IOMASA Brochure

7.4. MTR presentations

Electronic files of the presentations of this meeting to be sent to Christian as soon as possible, as usual.

GLOSSARY/ACRONYMS:

ASI ARTIST sea ice algorithm
CARE Climate of the Arctic and its Role for Europe
CLW cloud liquid water
CMIS Conical-Scanning Microwave Imager/Sounder
CWV column water vapour (= TWV = PWV)
DMI Danish Meteorological Institute
DTU-DCRS Technical Univ. of Denmark, Danish Center for Remote Sensing
EuroClim European climate change (http://euroclim.nr.no)
GMES Global Monitoring of Environment and Security
FP Framework Programme
FY first-year (ice)
HIRVDA HIRLAM variational data assimilation)
IC ice concentration
ICEMON Sea ice monitoring in the polar regions
IPY International Polar Year
IR infra-red
IUP Institut für Umweltphysik (Environm. Physics), Univ. Bremen
LWC liquid water content
met.no Norwegian Meteorological Institute
MIZ marginal ice zone
MY multi-year (ice)
NCEP National Centers for Environmental Prediction
NSIDC National Snow and Ice Data Center
NT NASA TEAM (algorithm for sea ice concentration retrieval)
NT2 NASA TEAM 2 (algorithm for sea ice concentration retrieval)
NWC SAF SAF in Support to Nowcasting and Very Short Range Forecasting
NWP numerical weather prediction
OEM optimal estimation method
OSI SAF SAF on Ocean and Sea Ice
OW open water
PWV precipitable water vapour (= TWV = CWV)
RTE radiative transfer equation
SAF Satellite Application Facility
SMHI Swedish Meteorological and Hydrological Institute
SST sea surface temperature
T temperature
Tb brightness temperature
TCW total cloud water
TWV total water vapour (= CWV = PWV)
WV water vapour
Minutes prepared by Christian Melsheimer