Minutes of the seventh International Radiative Transfer Workshop, June 2005

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BREDBECK 2005 Minutes

Monday, 20/06/05

Comparison of stratospheric H2O profiles from an airborne microwave radiometer with
the ECMWF humidity product

Dietrich G. Feist

Airborne water vapor measurements near 183 GHz
- flight compaigns 1998-2004
- one campaign per year since 98
- typical flight route covers most of the nothern hemisphere

#Water vapor spectra in the upper side band (183 GHz)

AMSOS vertical resolution
- averiging kernel (broadband radiometer)

AMSOS measurements September 2002
- typical climatology of H2O
- can see upper troposphere in tropics

H2O measurement in the polar vortex (March 2000)
- summer time looks defferent than winter time

ECMWF humidity product
- H2O volume mixing ratio can be derived from ECMWF specific humidty
- assimilated water vapor in the troposphere
- parameterized water vapor (methane oxidation and transport) in the stratosphere
- good vertical resolution and altitude range since ERA-40 dataset

AMSOS/ECMWF intercomparison (single profile)
- good agreement in mid. altitude, bad in low and high

Mission 1 (Aug 98): AMSOS vs. ECMWF
- AMSOS H2O volume mixing ratio vs. ECMWF H2O volume mixing ratio
- horiz structure in experiment folows nicely ECMWF
- in general ageement is rather good
- 3 hour diff
- inside of polar vortex looks different

Mean difference by mission
- fall, summer good agreement
- spring bad, seasonal dependancy

Standard deviation by mission
- all look the same

Effects of ECMWF model versions
- huge change from one ECMWF version to next

- AMSOS measurments and ECMWF humidity product agree well in lower stratosphere.
  Small scale horizontal structure is well resolved.
- systematic deviation in the upper statosphere. ECMWF probably does not model removal
  processes for statospheric HO well.
- strong deviation inside and near polar vortex. Problem with ECMWF vertical
- quality of ageement has a strong correlation with ECMWF model version


Stefan: What temperature profile is used?
Profiles are from Metoffice.

Stefan: Leveleing off of H2O?
60 km, apriori contribution is 40%.
Stefan: Any PSC's in ECMWF?
In EMWF, they not correlate with measurements. I dont think so.


Problems with kernel matrices for a retrieval of H2O using ARTS and Qpack.
Stefan Müller.

H2O radiometer at 183 GHz
- measurements by aircraft once a year during 1 week
- 2003 modificatons, problems started
- 2003-2004 problems with ARTS & Qpack

Measurement example
- strong base line from standing wave
- O3 line from side-band, Qpack can deal with these problems

Simulation of a retrieval
- input: apriori temperature profile, sensor character, etc.
- output: profile, A, Kx, Dy

Typical A from a simulation
- strange peaks in A
- platform altitude 10km, problem?

Where does the problem come from?
- peak from Kx

Search for the problem
- line strength
- platform altitude discontinuity?
- comparison of retrieval setup (working 22 GHz vs. 183 GHz)
- calculaton of Kx numerically

Line strength
- simulation with
  a reduction of line strength of 183 by 10 and 100
- weaker line

Platform altitude
- simulation with different platform altitude

Comparison of setup at 22 GHz
- uses a polynomfit of the spectrum
- peak is smaller

Simulation around hygropause
- platform at 13km, no polfit - peak is still there

Comparison to numerical calculation
- Kx from ARTS

Averaging kernel with numerical Kx'
- A' = DyKx'

- unwanted peaks in averaging kernel matrix
- simulations showed
 o problem comes from the matrix Kx
 o line strength has no effect
 o the peaks disappear when being above hygropause
- numerical calculation of Kx does not show the peaks


Patrik: What H2O profiles are used?
US standard profile, the same as Dietrich used.
Patrik: Numerical calculation should be the same. Small change of ppm is
important. Jacobians of different units look different, altitude dependant.
More discussions off line.


Comparison of AMSU-B brightness temperatures simulated by ARTS and by RTTOV-7
Nathalie Courcoux.

- UTH climatology
 ECMWF fields
 RT model
 scale the obtained bt to UTH
- cannot use ARTS, too time consuming
- use fast RT model, RTTOV-7
- compare ARTS with RTTOV

- global comparison
- 1 day, 1 time, Jan. 1. 2000
- input: temperature, humidity, pressure ECMWF ERA-40 fields
- for both models ECMWF profiles were interpolated and smoothed to RTTOV pressure
- emissivity 0.6 and 0.95

Channel 18, emissivity 0.6
- good ageement
- well spread negative bias
- clear and visible positive bias in specific regions

Channel 18, dependence to IWV
- clear dependence of the possitive bias on low humidity
- dependence documented by Garand et al. for RTTOV 5 and 6

Channel 16, 17, 19, 20; emissivity 0.6
- negative bias in the tropics
- positive bias in the polar regions and mid latititude
- similar pattern for channel 18

Channel 16, 17, 19, 20 dependence to IWV
- for sounding channels strong dependence of positive bias on low IWV
- threshold at which bias changes sign is moving to higher IWV for channels
with lower sounding altitude
- for surface channels, positive biases occur for IWV lower than the mean IWV

Global distribution of IWV from ERA-40 prifiles
- December 99 to november 2000
- highest ocurrences of dry profiles in the polar regions
- only a yearly picture, there are strong seasonal vatiations

- snow emissivity is important beacase most of profiles leading to a 
positive bias are located in snow covered area
- snow emissivity highly variable forom .45 to .95 at 150 GHz

Channel 18, emissivity 0.95
- no positive bias
- few profiles with low IWV

Channel 16, 17, 19, and 20, emissivity 0.95
- also less psitive bias left

Comparison of low and high emissivity simulations
- the low surface emissivity shows the largest discrepancies
- consistent with Garand et al.
- do the biases maximise with decreasing emissivity?

Comparison for emissivity 0.1
- maximum bias with low emissivity

Comparison of the continua
- flat negative bias due to continua

- low emissivity -> larger discrepancies
- biases and their standard deviations between the models are modest
- certain biases always occure over specific regions
- positive bias maximises with decreasing emissivity
- flat biases are due to use of different continuum models

- bias emissivity dependant, models handle surface differently
- channel 18: positive bias in dry regions is up to 1.5 K
- reported biases are important for numerical weather prediction models and climatology
from satellites
Christian:  6 kg/m2 in channel 19 sees the ground
Christian: Why discrapancy in surface channels?
ARTS and RTTOV treat surface differntly.


Total water vapor retrieval in polar regions using AMSU-B
Christian Melsheimer.

TWV retrieval from AMSU-B
- basic: RTE
- assume linear relaton between opacity and total H2O

- measure Tb at 3 different frequencies, ground emissivity is similar,
but H2O absoprtion different
- relationship between Tb and TWV (opacity)

- channels sorted: channel 16, 17, 20, 19, 18
- channel 3, 4, 5 for low TWV 
- channel 2, 3, 4 for high TWV

Algorithm development for AMSU-B
- use radiosond (RS) profile, integrate TWV, simulate Tbs with ARTS
- linear fit for each RS profile
- find focal point
- linear fit for TWV
- use TWV accordingly

Comparison with NCEP reanalysis data
- dayly averages, good agreement

Extension to higher TWV using emissivity information
- use channels 1, 2, 3, but channel 1 is different from the others
- algorithm not independent on emissivity
- from SEPOR/POLEX campaign emission of various surface types in winter
was determined for frequency needed here

- TWV retrieval is possible for TWV < 6 kg/m2
- TWV retrieval from radiometer data with info on emissivity can be extended to TWV
of 10 kg/m2
- EU project IOMASA about assimulation of dirived TWV into NWP models 
- TWV data might also be used together with regional models for water
cycle investigation

Nathalie: More studies for emissivity other than Selbach's?
This project will do this.
Stefan: ECMWF ok for retrievals?
No, but it is better than just using emissivity 1.

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