Retrieval of Methane Distributions from IASI A.Waterfall, R. Siddans, B. Kerridge, G. Miles, B....
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Transcript of Retrieval of Methane Distributions from IASI A.Waterfall, R. Siddans, B. Kerridge, G. Miles, B....
Retrieval of Methane Distributions from IASI
A. Waterfall, R. Siddans, B. Kerridge, G. Miles, B. Latter
Rutherford Appleton Laboratory
Acknowledgements: NCEO Atmospheric Composition Theme
Why measure methane with IASI?
Methane:• Important for its role in atmospheric chemistry and as a greenhouse gas• Concentrations are increasing with time (but not consistently)• Uncertainties in the global methane budget
Satellite measurements available (tropospheric methane):– mid-IR (IASI, TES, AIRS) – near-IR (GOSAT, SCIAMACHY)
Advantages/disadvantages of IASI– Global day/night coverage– Different sensitivity – Long time series of planned instruments (monitoring of trends)
IASI CH4 retrieval scheme
Retrieval Technique: Optimal estimation
Radiative Transfer model: RTTOV (with customised coefficients)
Spectral range: 1240-1290cm-1 (cf. Razavi et al, ACP 2009)
Retrieval species: CH4, N2O, H2O (log vmr)HDO scaling factor, surface temperature
Measurement noise: Derived from spectral fits (dependent on scene radiance)
Apriori + covariance matrix Fixed apriori profile (no latitude dependence)Covariance: ~ 10% error in troposphere, increased in stratosphere, with 6km correlation length
Background profiles Temperature, and apriori surface temperature, H2O from ECMWF
Simulated averaging kernels
• Retrieve methane on a fixed pressure grid (~0,6,12,16,20... km)• Principal sensitivity is in mid to upper troposphere • Limited sensitivity at the surface (dependent on air-surface temperature contrast)
Averaging kernels:
x = vmr
1km retrieval grid, 6km correlation length
Vertical sensitivity
Averaging kernels:
x = ln(vmr)
Example real averaging kernels
dx
xdˆ
Latitude
Expected methane precision (fractional error)
• Apriori error values: 10% in troposphere, higher in stratosphere• Significant improvement over apriori in mid and upper tropospheric
layers
Retrieval error Retrieved/apriori error
Fra
cti
on
al
err
or
Latitude Latitude
Version 1 of methane data• 4 continuous months of data (August –
November 2009), April 2009, August 2008
• Profile retrieval• => Column averaged mixing ratios
XC
H4 /
ppm
v
Day
Night • ‘least cloudy’ out of every 4 pixels
• Nb. retrieval very sensitive to cloud
23rd August 2009
Xch4 averaging kernel
© 2010 RAL Space
September 2009
Day
Night
Monthly mean column averaged mixing ratios• gridded 1x1 degree bins
Nb. Apriori data is a constant profile => N-S gradient comes completely from IASI
Xch4 (ppmv)
Possible reasons for day/night difference: • Difference in sensitivity• Problem with apriori
• CH4 apriori has a low bias, inconsistent with apriori error
• Different cloud sensitivity
DAY
NIGHT
NCEO Model + satellite comparisons: Aug 2009
:…..
IASI (night) GEOSCHEM (U. Edinburgh)
TOMCAT (U. Leeds)
GOSAT (U. Leicester)
Aug 2009, bias: -0.0296
Xch4 (ppmv)
April 2009 August 2009 November 2009
IASI (night time only data)
GOSAT
Monthly mean data, 1x1°bins
GOSAT data produced by R. Parker, U. Leicester, see poster by Byckling et al.
Xch4 (ppmv)
Monthly mean data on retrieval levelsAugust 2009
ppmv
178 hPa 422 hPa Column averaged mixing ratio
DAY
NIGHT
ppmv
Plots from MACC website.
MACC Reanalysis data
300 hPa
500 hPa
IASI night: 422 hPa
August 2009
Are these distributions reasonable?
ppmv
Cloud effects on the retrieval data
• Real retrievals show a tail of high methane retrievals believed to be due to cloud
• Simulations based on AVHRR-3 data indicate that the apparent methane is expected to increase with increasing cloud top height and optical depth
• Multiple scattering simulations based on more complicated (realistic) cloud distribution also show higher methane values due to increasing cloud amount
• Will be hard to filter out as even small amounts of cloud can cause slightly increased radiances.
• Retrieval of cloud top height/cloud fraction may improve retrievals in many cases.
ORAC
IASI cloud sensitivity - AVHRR/3
LUT of retrieval simulations of error due to cloud on IASI CH4 column averaged vmr
Cloud optical depth
Clo
ud A
ltitu
de /
km
Estimated Relative error on CH4 column
Cloud height / km
CH
4 en
hanc
emen
t
Summary and Future Plans
• A scheme for retrieving methane profiles from IASI has been developed at RAL. • Version 1 global distributions of methane are now available for:
August-November 2009, April 2009, August 2008• Agreement with many features seen in models / GOSAT• Current version has some issues related to cloud
contamination and certain land types• Simulations show cloud can introduce a high bias to the IASI data• Further simulations are planned to assess the impact of other
important error sources• Future versions will include improved handling of clouds
and improved apriori data