Minutes of the seventh International Radiative Transfer Workshop,
BREDBECK 2005 Minutes
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.
H2O radiometer at 183 GHz
- measurements by aircraft once a year during 1 week
- 2003 modificatons, problems started
- 2003-2004 problems with ARTS & Qpack
- 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
- simulation with
a reduction of line strength of 183 by 10 and 100
- weaker line
- 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
- UTH climatology
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
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
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
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.