airs radiance and geophysical products: methodology and validation mitch goldberg, larry mcmillin...
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AIRS Radiance and Geophysical Products:
Methodology and Validation
Mitch Goldberg , Larry McMillin
NOAA/NESDIS
Walter Wolf, Lihang Zhou, Yanni Qu and M. Divakarla
Science ActivitiesScience Activities
Data compression.Data compression. Validate and improve radiative transfer calculations.Validate and improve radiative transfer calculations. Cloud detection and clearing.Cloud detection and clearing. Cloud productsCloud products Channel selection (super channels).Channel selection (super channels). Validate and improve retrieval algorithms.Validate and improve retrieval algorithms. Trace gasesTrace gases Surface emissivitySurface emissivity Use MODIS to improve AIRS cloud detection and cloud Use MODIS to improve AIRS cloud detection and cloud
clearingclearing Radiance bias adjustmentsRadiance bias adjustments Forecast impact studiesForecast impact studies
TOPICS
Use of principal components (a.k.a. eigenvectors) for data compression.
Surface emissivity
Cloud detection
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.
Data Compression Data Compression Advanced IR sounder data are very large compared with current Advanced IR sounder data are very large compared with current
sounders (1 orbit ~ 2GB vs. 8 MB) Much larger for GIFTS.sounders (1 orbit ~ 2GB vs. 8 MB) Much larger for GIFTS.
Information is not independent. Principal component analysis Information is not independent. Principal component analysis (PCA) is often used to reduce data vectors with many (PCA) is often used to reduce data vectors with many components to a different set of data vectors with much fewer components to a different set of data vectors with much fewer components that still retains most of the variability and components that still retains most of the variability and information of the original data information of the original data
Data are rotated onto a new set of axes, such that the first few Data are rotated onto a new set of axes, such that the first few axes have the most explained variance.axes have the most explained variance.
Principal component scores are provided instead of the Principal component scores are provided instead of the individual channels.individual channels.
Individual channels can be reconstructed with minimal signal Individual channels can be reconstructed with minimal signal loss with added benefit of noise reduction.loss with added benefit of noise reduction.
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 ET
E = matrix of orthonormal eigenvectors (2378x2378) = vector of eigenvalues (explained variance)
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.
Locations used in generating eigenvectors
Applying AIRS eigenvectors
On independent data – compute principal component scores.
P = ET R ; elements of R = (ri- ri ) /ni
Invert equation and compute reconstructed radiances R*.
R* = E P
Reconstructed radiances are used for quality control.
Reconstruction score = [ 1/N (R*i - Ri)2 ]1/2
i = 1 ….N channels
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
Reconstruction score = [ 1/N (R*i - Ri)2 ]1/2
i = 1 ….N channels
Reconstruction score = [ 1/N (R*i - Ri)2 ]1/2
i = 1 ….N channels
Monitoring Eigenvectors
Monitoring eigenvectors is critical
Eigenvectors may need to be updated due to new structures that were not in the original ensemble
12/4/00 reconstruction scores
Monitoring reconstruction score is important
Days
July Aug Sep Oct Nov Dec Jan Feb
Noise
Noise free 75 PCS
Observed vs noise-free reconstructed vs noise-free.Observed vs noise-free reconstructed vs noise-free.
Noise ReductionNoise Reduction
“ Observed” Reconstructed
Observed vs. ReconstructedObserved vs. Reconstructed
New Plan
Generate full spatial resolution AIRS principal component score datasets
Size ~ 5 MB instead of 150 MB per six minute granule
Surface emissivity
Retrieval error based on 18 channels
Background Std dev.
Retrieval error
Clear detection
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.
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
ONLY 0.5% residual clouds
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
Clear Detected Fovs Cloud cleared casesClear Detected Fovs Cloud cleared cases
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.
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
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
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