OBS 13: Measuring Upper Tropospheric Humidity with Operational Microwave Satellite Sensors

transcript

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

OBS 13: Measuring Upper Tropospheric

Humidity with Operational

Microwave Satellite Sensors

Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 2005

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

Overview

Summary

Earths Radiation Balance

Outgoing Longwave Radiation OLR

Incoming Shortwave

RadiationSun

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

Clear-Sky OLR Spectrum

A lot of the radiation comes from the UT

Water vapor and CO2 are the most important greenhouse gases

Jacobians[10-14 W Hz-1 sr-1 m-2]

[10-14 W Hz-1 sr-1 m-2][10-14 W Hz-1 sr-1 m-2]

Important Altitude Range

OLR is sensitive to changes of humidity in the upper troposphere, where it is difficult to measure.

Sensitivity peak below TTL.

[10-14 W Hz-1 sr-1 m-2]

15% change in humidity = double CO2 - (for a tropical atmosphere)

H2O is a stronger greenhouse gas than CO2

Higher surface temperature = more evaporation positive feedback.

(Buehler et al., JQSRT, submitted 2005)

Impact on Tropical OLR

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

Variability of Clear-Sky OLR

Paradox: More humidity = more OLR!

(Buehler et al., Q. J. R. Meteorol. Soc., submitted 2005)

Total water vapor [mm]

High temperature correlated with high humidity

Positive temperature signal outweighs negative humidity signal

Expected, otherwise runaway greenhouse effect

Water vapor signal strongest in the tropics

Simulated radiances agree with CERES OLR data

Radiosondes

CERES Data

Surface temperature [K]

No strong temperature variations in the tropics

Temperature and Water Vapor variations are both important for clear-sky OLR

Radiosondes

CERES Data

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)

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

Summary

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

AMSU-B

Cross-track scanner

90 pixels per scan line

Outermost pixels 49° off-nadir

Swath with ≈ 2300 km

Global coverage twice daily

16 km horizontal resolution (at nadir)

AMSU-B Channels

(Details:John and Buehler,GRL, 31, L21108, doi:10.1029/2004GL021214)

Water vapor

Oxygen

AMSU-B Channels Water vapor

Oxygen(Figure by Viju O. John)

AMSU-B Jacobians

ARTS Simulation,

Atmosphere: Midlatitude-Summer

20 19 18 19 20

(Figure by Viju O. John)

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

AMSU-B Data (Channel 18)

Dry areas in the UT

(NOAA 16, Channel 18,

15.6.2004.

Figure: Oliver Lemke)

Overview

Summary

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

Method originally invented by Brian Soden for IR data.

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

Regression UTH Retrieval

Coefficients independent of training data set

Basically another unit for radiance

Other humidity data must be processed in same way for comparison

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...

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

Comparison with an Infrared ClimatologyInfrared UTH (1981-1991), Soden and Bretherton, JGR, 101 (D5), 9333-9343, 1996

Microwave UTH (AMSU-B, NOAA 16, 2002),

Mashrab Kuvatov

(Figures by Mashrab Kuvatov)

UTH, AMSU-B, Channel 18, NOAA 16, 2002

Difference with and without cloud filter

Overview

Summary

Case Study for one selected Station

Finding Matches

Define target area (radius 50 km)

Compare mean satellite value to radiosonde

Take standard deviation σ50km as measure of sampling error

Large variability in σ50km

Lowest values consistent with nominal radiometric noise

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.

Error Model

Results

(Buehler et al., JGR, 109, D13103, doi:10.1029/2004JD004605, 2004)

Non-unity radiance slope

Possible reasons:

RT model

Radiosonde

Increasing radiosonde dry bias under very dry conditions

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 )

Comparing different Radiosonde Stations

40 European stations

Sonde data from BADC

(John and Buehler, ACP, 2005)

Comparing different Radiosonde Stations

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

OBS 13: Measuring Upper Tropospheric Humidity with Operational Microwave Satellite Sensors

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