SCC/NN Retrieval Status and Plans In Support of ROSES - - PowerPoint PPT Presentation
SCC/NN Retrieval Status and Plans In Support of ROSES NNH06ZDA001N-EOS William J. Blackwell and Frederick W. Chen AIRS Science Team Meeting October 11, 2007 This work was sponsored by the National Oceanic and Atmospheric Administration under
AIRS Science Team: 1 WJB 10/23/07
MIT Lincoln Laboratory
In Support of ROSES NNH06ZDA001N-EOS
This work was sponsored by the National Oceanic and Atmospheric Administration under contract FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the United States Government.
William J. Blackwell and Frederick W. Chen AIRS Science Team Meeting
October 11, 2007
MIT Lincoln Laboratory
AIR Science Team: 2 WJB 10/23/07
Hong Li) indicate problems with elevated surface terrain and polar regions
– Further data stratification is needed to improve performance in these regions – Preliminary experiments are encouraging – (We think) these are the last remaining issues to be resolved before SCC/NN “Release 1.0”
– Improve quality and quantity of QC flags – Retrieve spectral surface emissivity – Improve performance over land
MIT Lincoln Laboratory
AIR Science Team: 3 WJB 10/23/07
– Non-uniform FOV effects may be corrected as part of the SCC algorithm – SCC algorithm uses PC signatures to characterize/correct clouds and can therefore be trained to ignore “corrupted” PC’s
– Used as preprocessor for neural network estimation – Allows blending of radiance and temperature/moisture vector subspaces – We can therefore relate PC’s to geophysical quantities, which should help us identify/separate “signal” and “noise”.
MIT Lincoln Laboratory
AIR Science Team: 4 WJB 10/23/07
with the NASA/NOAA Level 2 software
– Thanks to Chris Barnet and John Blaisdell for help with this
provide alternate first-guess retrieval and/or cloud-cleared radiances for Version 6 testing
– Subroutines for retrieval initialization and execution – Coefficient data read from binary file (probably HDF)
in heavily clouded areas
MIT Lincoln Laboratory
AIR Science Team: 5 WJB 10/23/07
that global SCC/NN temperature and moisture profile retrieval accuracies are very good.
terrain, polar regions) that we are working on.
and code (Matlab and Fortran) at the end of this year.
(critical for Version 6 testing) and surface retrievals.
MIT Lincoln Laboratory
AIR Science Team: 6 WJB 10/23/07
MIT Lincoln Laboratory
AIR Science Team: 7 WJB 10/23/07
– All latitudes – Ocean and land – Day and night
– Two-three orders of magnitude faster than current operational methods – One-two orders of magnitude faster than iterative, pseudochannel methods
MIT Lincoln Laboratory
AIR Science Team: 8 WJB 10/23/07
– Projected principal components transform – Neural network estimation
– Co-location of sensor measurements with “truth” (Radiosondes, NWP, etc.) – Model-generated data – Data stratification is used for:
Sensor scan angle Latitude Solar zenith angle Surface type Surface elevation
MIT Lincoln Laboratory
AIR Science Team: 9 WJB 10/23/07
seven days:
– 2002: Sep 6 – 2003: Jan 25, Jun 8, Aug 21, Sep 3, Oct 12, Dec 5
located with AIRS/AMSU observations
MIT Lincoln Laboratory
AIR Science Team: 10 WJB 10/23/07
Version 4 Version 4
Land, All latitudes, Radiosondes
MIT Lincoln Laboratory
AIR Science Team: 11 WJB 10/23/07
Version 4
Land, All latitudes, Radiosondes
MIT Lincoln Laboratory
AIR Science Team: 12 WJB 10/23/07
Land, All latitudes, Radiosondes
Version 4 Version 4