00/XXXX1 Assimilation of AIRS Data at the Met Office A.D. Collard and R.W. Saunders Met Office,Bracknell,UK.

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Presentation transcript:

00/XXXX1 Assimilation of AIRS Data at the Met Office A.D. Collard and R.W. Saunders Met Office,Bracknell,UK

00/XXXX2 Contents Overview of AIRS Processing at the Met Office Cloud Detection Channel Selection Future Work

00/XXXX3 AIRS processing at the Met Office BUFR ingest Pre-processing Store incoming data on MetDB Pre-processing Store incoming data on MetDB 1DVar retrieval 3DVar assimilation of radiances 3DVar assimilation of radiances Monitoring stats radiances, retrievals O-B no. of obs and q/c flags Monitoring stats radiances, retrievals O-B no. of obs and q/c flags From NESDIS To other European NWP centres Cray T3E supercomputer

00/XXXX4 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)

00/XXXX5 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

00/XXXX6 Some 1DVar & Monitoring Details Uses RTTOV7 for RT (can also use Gastropod) –See talks by Matricardi et al. and Sherlock et al. Newtonian or Marquardt-Levenberg Minimisation Variational Bias Correction (to be implemented)

00/XXXX7 Example O-B Plot

00/XXXX8 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 Clouds are flagged when J exceeds a certain threshold

00/XXXX9 Cloud Detection Example

00/XXXX10 Channel Selection (following Rodgers, 1996) Method: Choose those channels with the biggest impact on DFS. 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 A i-1 to A i thus: 1) Starting with A 0 =B test which channel will most improve the DFS 2) Update A i using that channel 3) Repeat until a sufficient number of channels have been selected (h i is the Jacobian for channel i)

00/XXXX11 DFS for different channel selections

00/XXXX12 NESDIS 281 vs Optimal Channels

00/XXXX13 Channel Selection Caveats Channel Selections are based on different criteria Ozone is not considered here Optimal channel selection assumes a given B-matrix (and assumes its correct!) Channel selection is profile dependent

00/XXXX14 Conclusions and Future Work Simulated AIRS data is being ingested and pre- processed 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. Work on variational assimilation continues

00/XXXX15 Conclusions and Future Work (contd.) Channel selections issues should be explored further (after receipt of real data?) Studies on assimilation of cloudy radiances to be made.

00/XXXX16