Minutes of the IOMASA Progress Meeting 4,
16-17 June, 2005
held at met.no, Oslo, Norway

Start of meeting: 16 June, 13:00 CEST
End of meeting: 17 June, 13:00 CEST

Participants:

IUP: 
Georg Heygster                    GH
Christian Melsheimer              CM


DTU-DCRS: 			    
Leif Toudal                       LT
Roberto Saldo                     RS

DMI: 				    
Søren Andersen                    SA

met.no: 			    
Frank Thomas Tveter               FT
Harald Schyberg                   HS
Vibeke Thyness                    VT
				    
SMHI:
Tomas Landelius                   TL

1. Introductory items:

• Welcome address.
• Overview of project schedule: Phase 3 (Production experiment on historic data set) has started in April

2. Progress of Phase 3 (Production experiment on historic data set) and Start of Phase 4 (Validation and real time experiment):

2.1 Part 1 (IUP): WP 1.3: Produce atmospheric fields; WP 1.4: Validation-evaluation of atmospheric algorithms/products

Total water vapour (TWV) from AMSU-B:

• Extended algorithm: Use lowest AMSU-B channels (channel 16, 89 GHz) to retrieve TWV above 7 kg/m²
• Retrieval depends critically on ratio of reflectivities r (reflectivity = 1 - emissivity) at 150 and 89 GHz.
• Emissivity over ice determined from SEPOR/POLEX data (N. Selbach's work at IUP, 2 years ago): from linear fit we get emissivity at 89GHz as a function of emissivity at 150 GHz
• If we impose the condition ε₈₉ = 1 when ε₁₅₀ = 1
, the reflectivity ratio r is constant.
• Consistency checks of the regressions for deriving the calibration constants were acceptable for all sub-algorithms.
• Further validation with radiosonde and/or ECMWF data

C     HS: Rather use radiosonde data for validation, in spite of the
C         limitations and problems of these data 

Emissivity for AMSU channels:

• Algorithm working: Comparison of emissivities retrieved over open water with open water emissivities from FASTEM model mostly consistent. Exception: 50 GHz channel
• Comparison of this emissivity algorithm with emissivity algorithm at DTU: They are equivalent, DTU's algorithm as presented at PM3 contained just more simplifications (isothermal atmosphere, no water vapour) that are not used any more (see below)
• Correlation of retrieved emissivities at the various AMSU A and B channels: ...
• various maps of emissivity, e.g., of the entire Arctic, can be produced: daily/monthly means etc.

C     HS: Monthly emissivity maps are not so useful, rather: better
C         info on emissivity of major ice types.

2.2 Part 2 (met.no, SMHI): WP 2.3: Prepare real time assimilation; WP 2.4: NWP Production and validation

Improved NWP (humidity)

• Bias correction
• Needs to exclude cloudy pixels: AVHRR-derived cloud mask from NWP SAF
  • works over water
  • works reasonably also over ice
• Sensitivity study RTTOV, AMSU-A channel 1-4: More sensitive to emissivity than to skin temperature
• Assimilation of AMSU-B-derived TWV:
  • check NWP-TWV vs. AMSU-B-TWV:
    • over water: bias and skewed
    • over ice: fine
  • Validation of TWV assimilation:
    • Data north of 75°N
    • 1 Jan 2005: Significant impact, decreases with time
    • Even impact on stratiform precipitation in some areas
• Improved surface flux:
  • Improved model results (inversion better modelled)
  • Bias reduced (mean sea level pressure, 2m temperature, 2m relative humidity

C   GH: How is the decision taken if a new scheme is included into
C       NWP model?
C   HS: If somebody can prove that a new scheme improves model on
C       average, then it is included in reference HIRLAM.
C   GH: Will the new turbulence scheme be included in HIRLAM reference
C       model?
C   HS: For the turbulence scheme, improvement is very welcome
C       (⇒ =>high chances)

Improved NWP (temperature)

• Assimilating AMSU-A (related to temperature) easier than assimilating AMSU-B (related to humidity) since humidity more "volatile" (easily generated/destroyed)
• Various impact studies: Feb 8 - Mar 31:
  • "Reference": all obs. except ???/AMSU-A over ocean
  • "Experiment": AMSU-A over ice added
  • Verification with EWGLAM (European Working Group on Limited Area Modelling): MSLP (mean sea level pressure): bias, error reduced; smaller influence on surface T, surface humidity

C   GH: Why highest impact on MSLP?
C   HS: AMSU-A data (ch. 6-16) are related to upper troposphere
C       (weighting function peaks higher) => influence on
C       pressure. Surface T and humidity more dependent on local
C       parameters, less on high troposphere.

• Time series (2 months), pressure verification: Mostly reduced errors, sometimes increased.
• Examining 1 case of particularly much reduced error: Different position of a low over Barents Sea.
• Emissivities
  • add seasonal/regional dependence of emissivities from IUP/DTU data
  • not all new developments about emissivity at IUP can be included in last assimilation runs until the end of IOMASA but ???

Quality control

• Bayesian risk of using an observation (with non-normal error distribution) from distribution of actual innovation (= obs. − 1st guess)

2.3 Part 3 (DTU): Emissivity, validation of sea ice forward model

Emissivity

• Colocation files (i.e. HIRLAM atmospheric profiles) used for emissivity calculation (in contrast to ECMWF reanalysis data that IUP uses)
• Switched to RTTOV (not MWMOD as IUP)
• New version of emissivity model coming up, including looking angle θ
• Maybe make the emissivity code portable so it can be directly plugged into collocation chain.

C   TL: Also AMSU-B emissivities?
C   LT: AMSU-B data are not in colocation (HIRLAM-AMSU) data (yet)

Validation of ice forward model

• AMSR-E: Used in OEM retrieval; looking at rms differences
• Rasmus Tonboe compared modelled data with observations

WP 4.4: Validate sea ice algorithm

• Validation methods:
  • Ship/in-situ observations:
    • good ice type distinction
    • small area coverage, difficult viewing geometry
  • Aerial observations:
    • better observation geometry, medium area coverage
    • clouds are a problem, ice type ambiguities
  • Satellite SAR data:
    • all-weather, large area, good resolution
    • ice-type ambiguities (thin ice)
  • High resolution ice dynamics RGPS (RADARSAT Geophysical Processor System) data:
    • independent method
    • incertainties in ice growth parameterisations
  • Satellite VIS/NIR data
    • Objective ice concentration estimate, sufficient resolution
    • cloud problem
• Comparing RGPS divergence/convergence fields with ice concentration (IC) from various IC algorithms: Expecting decreasing IC for convergence, increasing IC for divergence, but: almost no correlation. Reason: Effect of thin ice, e.g.: NT2 (NASA Team 2) IC vs. Polarstern summer cruise IC: correlation 0.04
• Next steps:
  • Coupled thermodynamic and emissivity model
  • Analyse SAR data set
  • Polarstern ice diaries
  • Will not use RGPS raw deformation data
• Results/conclusions:
  • Paper on Coupled thermodynamic and emissivity model
  • Input findings into OSI SAF (reanalysis in November)

C   GH: Will Near-90-GHz (N90) algorithm be implemented at OSI SAF (N90 ≈ ASI)?
C   SA: Quite likely

2.5 Part 5(DTU): WP 5.3: Setup of production and interface; WP 5.4: Validate production and interface

• Distribution and processing has been running for more than one year now.
• Which data set could be included as well?

  C   CM: R factor (cloud liquid water signature)?
  C   LT: Yes, we should include that.
  

3. Review of Phase 3:

• Production runs/comparison runs: Already done (winter data, see PM3)
• Almost all tasks on schedule
• Cloud Liquid Water activities delayed because of additional sensor noise of AMSR-E

Publications planned

1. TWV: IUP
2. Emissivity retrieval and impact on assimilation (see Vibeke Thyness' poster at ITSC (Int. TOVS Study Conference) in China): IUP, DTU, met.no
3. Paper on Impact Study of emissivity on HIRLAM: met.no
4. New surface flux scheme in HIRLAM (V. Perov's work): SMHI
5. Impact of AMSU TWV assimilation into HIRLAM; conference paper: SMHI
6. Validation of microwave ice concentration algorithm: DMI
7. Rasmus Tonboe's simulated TB using couples thermodynamic and dynamic model: DMI
8. Book-keeping model for sea ice: DMI
9. IOMASA in general (for BAMS): all

Project Management

Next Meeting: Final Presentation

• Thu/Fri 6/7 October, 2005 at IUP, University of Bremen.
• Including User Advisory Group: IOMASA work will be partially continued in DAMOCLES, thus advise at end of IOMASA still useful
• Start: Morning of October 6, so arrival the evening before (check flight schedules)
• First day (Thu, 6 Oct): With user advisory group
• Second day (Fri, 7 Oct): Only IOMASA consortium: Discuss Final report.

Reports, Deliverables

• Upcoming management/progress report for the period Nov 2004 - April 2005: Input needed as soon as possible.
• Material for Final Report should be forwarded to coordinator by end of September, so that the Report can be compiled before the project ends (end of October)

GLOSSARY/ACRONYMS: