OBS 13: Measuring
Upper Tropospheric
Humidity with
Operational
Microwave Satellite
Sensors
COST 723 UTLS Summerschool
Cargese, Corsica, Oct. 3-15, 2005
Stefan A. Buehler
Institute of Environmental Physics
University of Bremen
www.sat.uni-bremen.de
Overview
Water vapor in the Earths radiation balance
Operational meteorological microwave satellite instruments
(AMSU-B)
AMSU-B measurements of upper tropospheric water vapor
Comparison with radiosonde measurements
Temperature uncertainty and supersaturation
The radiative transfer model ARTS
Summary
Cited papers can be found at
http://www.sat.uni-bremen.de
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Overview
Water vapor in the Earths radiation balance
Operational meteorological microwave satellite
instruments (AMSU-B)
AMSU-B measurements of upper tropospheric water
vapor
Comparison with radiosonde measurements
Temperature uncertainty and supersaturation
The radiative transfer model ARTS
Summary
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Earths Radiation Balance
Incoming Shortwave
Radiation
Sun
Earth
Outgoing Longwave
Radiation OLR
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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λEλ [normalized]
Earths Radiation Balance
Wavelength [μm]
(Wallace und Hobbs, `Atmospheric Science', Academic
Press, 1977.)
Radiative equilibrium temperature: -18°C
Global mean surface temperature: +15°C
34 K natural greenhouse effect
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Clear-Sky OLR Spectrum
A lot of the
radiation
comes from the
UT
Water vapor
and CO2 are
the most
important
greenhouse
gases
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Jacobians
[10-14 W Hz-1 sr-1 m-2]
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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[10-14 W Hz-1 sr-1 m-2]
[10-14 W Hz-1 sr-1 m-2]
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
[10-14 W Hz-1 sr-1 m-2]
[10-14 W Hz-1 sr-1 m-2]
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Important Altitude Range
[10-14 W Hz-1 sr-1 m-2]
MLS
OLR is sensitive to changes of humidity in the upper
troposphere, where it is difficult to measure.
Sensitivity peak below TTL.
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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H2O is a stronger greenhouse gas than CO2
Impact on Tropical OLR
15% change in humidity
= double CO2 - (for a
tropical atmosphere)
(Buehler et al., JQSRT, submitted 2005)
Higher surface temperature = more evaporation
 positive feedback.
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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The Water Vapor Feedback
Convection and cyclones transport moisture into the UT (see
lectures of Heini Wernli and Andrew Gettelman)
Ascending air is dried by condensation processes
High spatial and temporal variability
Residence time of water substance ~10 days
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Variability of Clear-Sky OLR
Simulated OLR [W/m2]
Paradox: More humidity =
more OLR!
Total water vapor [mm]
(Buehler et al., Q. J. R. Meteorol. Soc., submitted 2005)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Variability of Clear-Sky OLR
Simulated OLR [W/m2]
CERES Data
High temperature correlated
with high humidity
Positive temperature signal
outweighs negative humidity
signal
Expected, otherwise runaway
greenhouse effect
Radiosondes
Surface temperature [K]
Water vapor signal strongest
in the tropics
Simulated radiances agree
with CERES OLR data
(Buehler et al., Q. J. R. Meteorol. Soc., submitted 2005)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Variability of Clear-Sky OLR
Delta OLR [W/m2]
Simulated OLR [W/m2]
CERES Data
Radiosondes
(Buehler et al., Q. J. R. Meteorol. Soc., submitted 2005)
No strong temperature variations in the tropics
Temperature and Water Vapor variations are both important for clear-sky OLR
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Climate GCMs indicate that the feedback is positive.
A large part (about half) of the warming predicted by models for a
CO2 rise is due to the water vapor feedback (Held and Soden, Annu.
Rev. Energy Environ., 2000).
The UT is an important altitude region for this feedback, but
humidity there is poorly known.
Radiosonde measurements:
Low spatial coverage
Poor data quality in the UT
Infrared satellite measurements:
Good global coverage, but affected by clouds
Clear sky bias
Microwave satellite measurements (today)
Radio occultation (Friday, OBS 16)
Ice clouds play also an important role in the UT radiation
balance (Friday, OBS 15)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Comparison: Radiosondes ↔ Infrared Satellite Data
Big differences
between the
different data
sets, for example:
+/-15 %RH
difference
between IR
satellite and
radiosonde
= 40% relative
difference in
humidity, as RH
values are low in
the UT.
(Soden and Lanzante, JGR 1996)
Problem: Large discrepancies, true climatology unknown (see e.g.
SPARC UTLS H2O Assessment)
Overview
Water vapor in the Earths radiation balance
Operational meteorological microwave satellite
instruments (AMSU-B)
AMSU-B measurements of upper tropospheric water
vapor
Comparison with radiosonde measurements
Temperature uncertainty and supersaturation
The radiative transfer model ARTS
Summary
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Microwave Satellite Data
SSM-T2 since 1995
AMSU-B since 1999
Passive microwave
instruments (measuring
thermal radiation from the
atmosphere)
Less affected by cloud than
infrared
Well calibrated
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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AMSU-B
Cross-track scanner
90 pixels per scan line
Outermost pixels 49° offnadir
Swath with ≈ 2300 km
Global coverage twice daily
16 km horizontal resolution
(at nadir)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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AMSU-B Channels
Water vapor
Oxygen
(Details:
John and Buehler,
GRL, 31, L21108,
doi:10.1029/2004GL021214)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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AMSU-B Channels
Water vapor
Oxygen
(Figure by Viju O. John)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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AMSU-B Jacobians
20 19 18 19 20
ARTS Simulation,
Atmosphere:
Midlatitude-Summer
(Figure by Viju O. John)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Jacobians depend on Atmospheric State
(Figures by Viju O. John)
Measurement not in TTL, but below
Altitude where OLR is very sensitive to H2O changes
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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AMSU-B Data (Channel 18)
Dry areas in
the UT
(NOAA 16, Channel 18,
15.6.2004.
Figure: Oliver Lemke)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Overview
Water vapor in the Earths radiation balance
Operational meteorological microwave satellite
instruments (AMSU-B)
AMSU-B measurements of upper tropospheric
water vapor
Comparison with radiosonde measurements
Temperature uncertainty and supersaturation
The radiative transfer model ARTS
Summary
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Retrieving humidity usually requires a priori,
problematic for climate applications
Humidity Assimilation can destroy information on
absolute value due to the bias corrections applied
(compare lecture by Francois Bouttier)
Solution: Look for a humidity product that is related
as closely as possible to the radiances
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Regression UTH Retrieval
UTH = Jacobian-weighted
relative humidity ≈ mean
relative humidity between
500 and 200 hPa
Simple relation:
ln(UTH) = a + b Tb
Determine a and b by linear
regression with training data
set
Details:
Buehler and John, JGR, 2004
Method originally invented by Brian Soden for IR data.
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Coefficients independent of training data set
Basically another unit for radiance
Other humidity data must be processed in same way for
comparison
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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AMSU UTH-Climatology
(AMSU-B, Channel 18, NOAA 15, Winter 1999-2000. Figure by Mashrab Kuvatov)
With deep apologies to Mark Baldwin for the weird color scale...
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Walker Circulation during La Nina
NOAA 15
AMSU-B UTH
DJF 99-00
500-200 hPa
HALOE, 82 hPa
Gettelman et al,
2001, J. Clim
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Comparison with an Infrared Climatology
Infrared UTH (1981-1991),
Soden and Bretherton,
JGR, 101 (D5), 9333-9343,
1996
Microwave UTH
(AMSU-B, NOAA 16,
2002),
Mashrab Kuvatov
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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UTH, AMSU-B, Channel 18, NOAA 16, 2002
Difference with and without cloud filter
(Figures by Mashrab Kuvatov)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Overview
Water vapor in the Earths radiation balance
Operational meteorological microwave satellite
instruments (AMSU-B)
AMSU-B measurements of upper tropospheric water
vapor
Comparison with radiosonde measurements
Temperature uncertainty and supersaturation
The radiative transfer model ARTS
Summary
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Case Study for one selected Station
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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(Figure by Viju O. John)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Finding Matches
Define target area
(radius 50 km)
Compare mean
satellite value to
radiosonde
Take standard
deviation σ50km as
measure of sampling
error
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Large variability in σ50km
Lowest values consistent
with nominal radiometric
noise
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Error Model
Sources of error:
Radiometric noise of the AMSU measurement
Sampling error due to atmospheric inhomogeneity
Radiosonde measurement error in humidity and temperature
RT model error
AMSU calibration error
χ2 tests show that C0 can be taken as a global constant with a
value of 0.5K.
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Results
Non-unity radiance
slope
Possible reasons:
RT model
AMSU
Radiosonde
Increasing radiosonde
dry bias under very dry
conditions
(Buehler et al., JGR, 109, D13103,
doi:10.1029/2004JD004605, 2004)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Comparison for a Different Sensor
Kem, Russia (64N, 34E)
Goldbeater’s skin type
sondes
very large wet bias
(expected from Soden
and Lanzante, JGR,
1996 )
(Figure by Viju O. John)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Comparing different Radiosonde Stations
40 European
stations
Sonde data from
BADC
(John and Buehler, ACP, 2005)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Comparing different Radiosonde Stations
(John and Buehler, ACP, 2005)
General dry bias (expected for Vaisala sensor)
Apparently erratic jumps can be understood by sensor and/or
procedure changes for individual stations
Information about stations not readily available
Mystery: UK stations have less dry bias, although the are supposed
to use similar sensors
See also poster by T. Suortti
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Overview
Water vapor in the Earths radiation balance
Operational meteorological microwave satellite
instruments (AMSU-B)
AMSU-B measurements of upper tropospheric water
vapor
Comparison with radiosonde measurements
Temperature uncertainty and supersaturation
The radiative transfer model ARTS
Summary
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Supersaturation in UARS-MLS Data
exponential drop-off
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Gaussian T distr.
Non-Gauss RH distr.
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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MLS Data
Effect of 2K T uncertainty
Some of the observed supersaturation can be due to
temperature uncertainties (Buehler and Courcoux, GRL,
2003).
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Overview
Water vapor in the Earths radiation balance
Operational meteorological microwave satellite
instruments (AMSU-B)
AMSU-B measurements of upper tropospheric water
vapor
Comparison with radiosonde measurements
Temperature uncertainty and supersaturation
The radiative transfer model ARTS
Summary
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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RTTOV
Fast RT model
Freely available from Eumetsat NWP SAF
Already configured for most meteorological sensors
Biases compared to more accurate ARTS model
(see Poster by Nathalie Courcoux)
Not used for the calculations in this lecture
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Public Domain Program, developed together with
Chalmers University, Göteborg and University of
Edinburgh.
Two branches:
ARTS-1-0: Clear-sky
ARTS-1-1: with cloud scattering
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Radiative Transfer
RT Workshop 2004
Core Developers (2005)
Development and
workshops since 1999.
RT Workshop 2005
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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ARTS Overview
Freely available: http://www.sat.uni-bremen.de/arts/
Clear-sky (arts-1-0):
Line spectra (HITRAN, JPL, GEISA)
Continua (H2O, N2, O2, CO2)
Trivial RT
Analytical Jacobians
Cloudy-sky (arts-1-1):
Two different algorithms for cloud scattering:
Monte Carlo (MC) method
Discrete Ordinate Iterative (DOIT) method
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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ARTS Properties
All viewing geometries
Spherical
Polarized (up to 4 Stokes components)
Validated against various other physical RT models
from microwave to infrared
Used as a reference to judge performance of
RTTOV-8 (with scattering) within the NWP SAF
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Overview
Water vapor in the Earths radiation balance
Operational meteorological microwave satellite
instruments (AMSU-B)
AMSU-B measurements of upper tropospheric water
vapor
Comparison with radiosonde measurements
Temperature uncertainty and supersaturation
The radiative transfer model ARTS
Summary
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
54
Summary
Upper tropospheric humidity (UTH) is an important
parameter of the climate system.
Better absolute measurements of the global UTH
distribution are needed.
Operational microwave sensors provide a new
dataset that is independent of the IR satellite data.
Advantage: Less affected by clouds.
Disadvantage: So far short time series (since 1995
SSM T2, since 1999 AMSU-B).
RT model required for work with satellite
measurements.
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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How to Compare Satellite Data to other
Data
Find out which part of the satellite data is believed to
be ok (check documentation and talk to others)
Need enough matches to get statistics (a single insitu measurement is useless for satellite validation)
Set up error model, including sampling error (without
error bars the comparison has no quantitative
meaning)
Comparison in radiance space can avoid problems
due to use of a priori information for satellite retrieval
(you can scale back the radiance differences to
uncertainties in geophysical parameters at the end)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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Outlook
Very promising UT humidity data is now becoming
available from MLS on Aura.
Proposals for radio-occultation humidity
measurements (my last lecture on Friday)
Clouds play also a crucial role for the radiation
balance (my first lecture on Friday)
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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...
Thanks for your
attention.
Questions?
Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005
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