Assimilation of AIRS Data at
the Met Office
A.D. Collard and R.W. Saunders
Met Office,Bracknell,UK
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Contents
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
Overview of AIRS Processing at the Met Office

Cloud Detection

Channel Selection

Future Work
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AIRS processing at the Met Office
From
NESDIS
BUFR ingest
To other European
NWP centres
1DVar retrieval
3DVar assimilation
of radiances
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Pre-processing
Store incoming data on
MetDB
Monitoring stats
radiances, retrievals O-B
no. of obs and q/c flags
Cray T3E supercomputer
Current Status of AIRS Processing at
the Met Office

Simulated AIRS data is being received from NESDIS (M.
Goldberg) and is being stored in our MetDB system.
– 281 Channels, Reduced Spatial Sampling
– BUFR format
– Surface information added at Met Office before storage
– Additional pre-processing steps may be performed, e.g., EOF
based cloud detection (Lee, Smith and Taylor, 2001)
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Current Status of AIRS Processing at
the Met Office (contd.)

A 1DVar is done as further pre-processing
before the assimilation stage. This includes:
– Bias Correction
– Cloud Detection
– Channel Selection
– Other QC
– Production of Monitoring Stats
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Some 1DVar & Monitoring Details

Uses RTTOV7 for RT (can also use Gastropod)
– See talks by Matricardi et al. and Sherlock et al.
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
Newtonian or Marquardt-Levenberg
Minimisation

Variational Bias Correction (to be implemented)
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Example O-B Plot
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Variational Cloud Detection
(English, Eyre & Smith, 1999)
Attempt to determine the probability of having cloud in the
field of view given the observed radiances and the NWP background
profile
J   Ln{P(cloud ¦ y obs , x b )}
 12 (y )T {H(x b ) T BH(x b )  R}1 ( y )  Const.
y  y obs  y (xb )
Clouds are flagged when J exceeds a certain threshold
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Cloud Detection Example
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Channel Selection
(following Rodgers, 1996)
Method: Choose those channels with the biggest impact on DFS.
1) Starting with A0=B test which channel will most improve the
DFS
2) Update Ai using that channel
3) Repeat until a sufficient number of channels have been selected
Rodgers speeds this process up by noting that, for diagonal (O+F),
on adding a new channel, i, to the retrieval, the solution error
covariance is changed from Ai-1 to Ai thus:

A i  A i 1 I  hi  A i 1hi 
T
(hi is the Jacobian for channel i)
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
1   A i 1hi T hi 


DFS for different channel selections
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“NESDIS 281” vs “Optimal Channels”
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Channel Selection Caveats
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
Channel Selections are based on different
criteria

Ozone is not considered here

“Optimal” channel selection assumes a given
B-matrix (and assumes it’s correct!)

Channel selection is profile dependent
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Conclusions and Future Work

Simulated AIRS data is being ingested and preprocessed at the Met Office

Software for cloud detection, quality control and
the production of monitoring information is in
place.

Work continues on visualisation of monitoring
data.

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Work on variational assimilation continues
Conclusions and Future Work
(contd.)
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
Channel selections issues should be explored
further (after receipt of real data?)

Studies on assimilation of cloudy radiances to
be made.
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