1. AIRS Radiance and Geophysical Products: Methodology and Validation Mitch Goldberg, Larry McMillin NOAA/NESDIS Walter Wolf, Lihang Zhou, Yanni Qu and M. Divakarla

2. Science Activities  Data compression.  Validate and improve radiative transfer calculations.  Cloud detection and clearing.  Cloud products  Channel selection (super channels).  Validate and improve retrieval algorithms.  Trace gases  Surface emissivity  Use MODIS to improve AIRS cloud detection and cloud clearing  Radiance bias adjustments  Forecast impact studies

3. TOPICS  Use of principal components (a.k.a. eigenvectors) for data compression.  Surface emissivity  Cloud detection

4. AIRS Geophysical Products  Microwave-only retrieval of sfc emissivity, sfc temperature, sfc type and profiles of temperature, water vapor and cloud liquid water.  AIRS retrieval of cloud amount and height, sfc emissivity, sfc temperature, and profiles of temperature, water vapor and ozone.  AIRS has two retrieval steps – very fast eigenvector regression followed by a physical retrieval algorithm.

5. Data Compression  Advanced IR sounder data are very large compared with current sounders (1 orbit ~ 2GB vs. 8 MB) Much larger for GIFTS.  Information is not independent. Principal component analysis (PCA) is often used to reduce data vectors with many components to a different set of data vectors with much fewer components that still retains most of the variability and information of the original data  Data are rotated onto a new set of axes, such that the first few axes have the most explained variance.  Principal component scores are provided instead of the individual channels.  Individual channels can be reconstructed with minimal signal loss with added benefit of noise reduction.

6. Generating AIRS eigenvectors  Collect an ensemble of AIRS spectra (2378 channels).  The radiances are normalized by expected instrumental noise (signal to noise)  Compute the covariance matrix S  Compute the eigenvectors E and eigenvalues  S = E  E T  E = matrix of orthonormal eigenvectors (2378x2378)  = vector of eigenvalues (explained variance)

7. Training Ensemble  Eigenvectors are generated from a spatial subset of AIRS data (200 mbytes vs 30 GB full data)  Eigenvectors are generated daily.  A static set of eigenvectors is used, but the ensemble is occasionally updated with new structures.  When the ensemble is updated a new set of eigenvectors is also updated.

8. Locations used in generating eigenvectors

9. Applying AIRS eigenvectors  On independent data – compute principal component scores.  P = E T R ; elements of R = (r i - r i ) /n i  Invert equation and compute reconstructed radiances R*.  R* = E P  Reconstructed radiances are used for quality control.  Reconstruction score = [ 1/N 3(R* i - R i ) 2 ] 1/2 i = 1 ….N channels

10. 1 7497.60 2 1670.40 3 945.52 4 496.01 5 284.01 6 266.30 7 156.95 8 139.67 9 88.27 10 72.83 11 60.03 12 53.42 13 45.01 14 39.72 15 34.54 16 26.57 17 22.62 18 17.60 19 14.68 20 13.49 21 12.28 22 11.32 23 10.70 24 9.08 25 8.24 26 7.85 27 6.77 28 5.98 29 5.83 30 5.39 31 5.34 32 4.98 33 4.34 34 4.09 35 3.62 36 3.48 37 3.38 38 3.11 39 2.82 40 2.53 41 2.41 42 2.39 43 2.34 44 2.24 45 2.03 46 1.86 47 1.78 48 1.71 49 1.65 50 1.61 51 1.54 52 1.52 53 1.35 54 1.34 55 1.25 56 1.19 57 1.16 58 1.15 59 1.09 60 1.05 61 1.02 62 0.98 63 0.90 64 0.86 65 0.81 66 0.80 67 0.78 68 0.77 69 0.73 70 0.72 71 0.70 72 0.66 Square root of the eigenvalues

11.  Reconstruction score = [ 1/N 3(R* i - R i ) 2 ] 1/2 i = 1 ….N channels

12.  Reconstruction score = [ 1/N 3(R* i - R i ) 2 ] 1/2 i = 1 ….N channels

13. Monitoring Eigenvectors  Monitoring eigenvectors is critical  Eigenvectors may need to be updated due to new structures that were not in the original ensemble

14. 12/4/00 reconstruction scores

15. Monitoring reconstruction score is important Days July Aug Sep Oct Nov Dec Jan Feb

17. Noise Noise free 75 PCS

18. Observed vs noise-free reconstructed vs noise-free. Noise Reduction

19. “ Observed” Reconstructed

20. Observed vs. Reconstructed

21. New Plan  Generate full spatial resolution AIRS principal component score datasets  Size ~ 5 MB instead of 150 MB per six minute granule

22. Surface emissivity

24. Retrieval error based on 18 channels Background Std dev. Retrieval error

25. Clear detection

26. BACKGROUND  NWP centers will assimilate clear radiances  Need very good cloud detection algorithm  Very important for radiance validation and to initiate the testing of the level 2 retrieval code.

27. Cloud Detection over Ocean  Use VIS/NIR channels during day.  Compare SST with 2616 cm-1 at Night.  Predicting SST from 11 and 8 micron channels (works for day and night)  Predict 2616 from 8 micron channels (night)  11 micron window > 270 K

28. ONLY 0.5% residual clouds

29. Cloud detection – Non Sea  Predict AIRS channel at 2390.9 cm-1 from AMSU  FOV is labeled “mostly clear” if predicted AIRS – observed AIRS < 2 AND IF  SW LW IR window test is successful: [ch(2558.224)-CH(900.562)] < 10 K  Variability of 2390.910 radiance within 3x3 < 0.0026

30. Clear Detected Fovs Cloud cleared cases

31. Future Work – Merge MODIS and AIRS  High spatial resolution will improve determination of clear AIRS fovs.  High spatial resolution will greatly improve clear estimate needed for cloud clearing.

32. MODIS Sounder Radiance Product  MODIS has HIRS-like sounder channels – but at high spatial resolution (1 km).  Find a few clear MODIS fovs in a 50 x 50 km area should provide a yield of 80% -- similar to AMSU

33. Summary  Busy getting ready for real AIRS data  Simulating AIRS in real-time has provided a means to develop, test and validate the delivery of products to NWP centers,  AND created a platform to develop scientific tools to analyze the data and test algorithms.  Early releases of the data should be available 3 months after launch  Final radiance products ~ 7 months  Retrievals ~ 12 months  First activity will be to examine biases between measured and computed radiances and validation of the clear detection algorithm.  “Day-2” Utilize MODIS