CIMSS PARTICIPATION IN THE

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CIMSS PARTICIPATION IN THE GOES-R RISK
REDUCTION  
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Principal roles of Co-investigators T.
Achtor Project Management, Archive System S.
Ackerman Cloud and Top of Atmosphere Flux
Algorithms R. Dedecker Data Processing and
Archive System R. Garcia Software Management A.
Huang Atmospheric Sounding, Data Assimilation,
GIFTS/HES Synergism R. Knuteson Surface
Property Retrieval, Data Processing and Archive
System J. Li Atmospheric Sounding, Trace Gas,
and GIFTS/HES Synergism C. Schmidt Ozone,
Aerosol D. Tobin Validation C. Velden Winds
System T. Whittaker Visualization  
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Overview

• Background (MURI focuses on theoretical issues,
  NOAA on routine operational issues)
• Approach to development of algorithms
• Updates on algorithms
• Summary

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Satellite Observations of the Earths
Environment Accelerating the Transition of
Research to Operations
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How Can Weather Forecast Duration and Reliability
Be Improved By New Space-Based Observations,
Assimilation, and Modeling?
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Global tropospheric winds

• Improvements require
• Focused validation experiments
• New Technology
• Impact Assessments

Improved forecasts
Funded
Continuous lightning
Improved physical dynamical processes
Unfunded
Soil moisture
Field Campaign
Global Precipitation
High-resolution sounding for fast forecast updates
Global monitoring of water, energy, clouds, and
air quality/Operational prototype missions
New, high-resolution temperature and moisture
sounding will provide needed information to
describe the atmospheric dynamics, cloud
distributions for radiation modeling, aerosol
concentrations for air quality projection, and
better imagery of severe weather phenomena like
hurricanes, floods, and snow/ice cover.
High-resolution global measurements of
temperature, moisture, cloud properties, and
aerosols

• By 2015 Weather and severe storm forecasting
  should be improved greatly
• Hurricane landfall accurate enough for
  evacuation decisions
• Winter storm hazards determine at local levels
  for appropriate mitigation
• Regional forecasting of rain and snow accurate
  for economic decisions

Knowledge Base
Use of NOAA operational models to optimize
assimilation of NASAs new satellite data will
ensure realistic and accelerated use of new
technology and techniques.
NASA/NOAA collaborative centers
Satellite-derived localized heating inputs will
allow regional models to have better predictive
capabilities.
Observations of tropical rainfall/energy release
Steady, evolutionary improvement in weather
prediction accuracy due to ongoing model
refinement in operational agencies, finer-scale
model resolution, improved use of probabilistic
and statistical forecasting aided by
multiple-component ensemble initializations, and
incorporation of radar and aircraft-measurements
Weather satellite sensor and technique
development used by NOAA
Systematic meas. of atmosphere, ocean, and
land surface parameters
2007 NRA
2010 NRA
2004
2002
2006
2005
2011
2003
2012
2013
2014 2015
2008
2009
NRA
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CIMSS GIFTS/GOES_R

• Data Processing and Archive System
• Algorithm Development
• Preparing for Data Assimilation
• Demonstration Activities

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GIFTS Ground Processing Plan (Baseline)
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Algorithm Development

• Radiances
• Atmospheric Soundings
• Winds
• Clouds
• Surface
• Composition (trace gas and aerosol)
• Radiation Budget
• Data and Product Access and Visualization

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Algorithm Development Paths
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Sounding Algorithm Tasks Summary

• FY03 focused on algorithm development
  processing approach design
• Model atmosphere simulation set up - MM5
• Radiance measurements simulation set up -
  clear/cloudy
• spectra generation
• Instrument performance simulation
• Sounding retrieval algorithm set up training,
  application, and evaluation
• Simulated IHOP/THORPEX case studies
• MODIS sounding processing demonstration and
  approach adoption
• AIRS sounding processing demonstration and
  approach adoption
• Algorithm write-ups

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• Atmospheric Sounding Retrieval
• statistical sounding product algorithm
  development
• Generalized/multiple-level cloudy radiative
  transfer
• equation development
• Hyperspectral/temporal IR Clear/cloudy detection
• algorithm development
• Information Content Analysis for Optimal Channel
  Set Selection
• Surface and Cloud Emissivity Modeling
• Forward Model Error Quantification and Bias
  Adjustments
• Clear and cloudy sounding retrieval algorithm
• Derived Product Images (DPI)
• Quantification of Retrieval Error and Error
  Correlation

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Soundings

•          Participate in and support GIFTS/HES
  meetings, write ATBD
•          Continue to refine and update training
  data sets, including improved surface emissivity
  modeling and surface skin temperature assignment,
  for baseline sounding retrieval.
•          Provide simulated physical iterative
  water vapor retrievals for altitude resolved
  water vapor wind demonstration.
•          Continue to support cloudy sounding
  retrievals
• .

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Motion Vectors

• Algorithm development
• Tested with model simulations
• Tested with aircraft observations
• Continue development
• Test with AIRS over polar regions
• ATBD

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In 2003, the novel concept of tracking water
vapor features on altitude-resolved moisture
surfaces was demonstrated using both simulated
GIFTS and airborne NAST-I retrieved fields.
Winds derived from retrieved moisture fields were
compared to model winds (simulation cases) and
co-located Doppler LIDAR winds (NAST-I field
experiment).
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Wind testing with models
The following are plots of the wind vectors
derived from tracking 3 sequential 500mb moisture
analyses derived from MM 5 moisture field only
(upper left), MM5 with simulated GIFTS and no
noise (upper right), MM5 with simulated GIFTS
included expected noise (lower left), an MM5 with
simulated GIFTS and amplified noise (lower
right). Have begun working with WRF model.
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500 mb winds
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Comparison of NAST-I winds and DWL wind profiles
on 11 February 2003.
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Clouds/Aerosols

• Fast model development
• Retrieval
• Cloud/aerosol detection algorithm
• Cloud altitude algorithm
• Integration with soundings
• Full testing on appropriate data sets
• Draft V.0 ATBD

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Sample AIRS/MODIS Cloud Mask Histogram
Clear cases as determined by AIRS cloud mask
Fraction of occurance clear cases
MODIS Cloud Mask
Cloudy cases as determined by AIRS cloud mask
Fraction of occurance cloudy cases
Range bins () of MODIS pixels within AIRS
FOVs for each MODIS cloud mask class
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AIRS
BT
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BT Difference?
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AIRS
DIFF BT
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Composition
While not a major effort in 2003, trace gases
composition retrieval work was very fruitful.
High spectral resolution infrared measurements
are expected to provide increased capabilities
for distinguishing atmospheric constituents

• Ozone and other trace gases
• GIFTS/HES provide high spatial and temporal
  resolution vertical ozone profiles 24 hours a
  day, with percent RMS errors less than 15 in the
  upper troposphere and stratosphere (errors in the
  lower atmosphere are large yet are offset by the
  low ozone concentrations at those levels).
• Continue development and testing
• Draft V.0 ATBD

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The weighting function matrices of (a) fixed gas
(constant mixing ratio), (b) water vapor, and (c)
carbon monoxide of NAST-I channels calculated
with U.S. standard atmosphere (1976). The peak
(or valley) of the weighting function of fixed
gas (or water vapor, carbon monoxide) of each
channel indicated in wavenumber is associated
with a pressure altitude.
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Data Access and Visualization

• A reference application that can be used with
  any multi- or hyperspectral data AIRS, MODIS,
  S-HIS, MSG was created.
• Started work on defining the structure for
  storing and making easily accessible large
  volumes of data.
• extends the capabilities of the reference
  application to include non-hyperspectral data
  (numerical model fields, atmospheric soundings,
  etc) for validation.
• Evolve the reference application as more
  scientists start to work with it and suggest
  extensions.

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Visualization of arithmetic combinations, scatter
diagram and pixel outlines.
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Preparing for Data Assimilation

• During 2003 GIFTS forward model operators, such
  as tangent linear, adjoint and Jacobian were
  developed in high-level language MATLAB. These
  operators are essential for the future data
  assimilation and 1-D VAR physical retrieval.
• Continue to maintain fast forward model
  development in support of data assimilation.
• Continue to implement and test GIFTS adjoint and
  linear tangent code
• Provide GIFTS forward model operators to Prof. X.
  Zou

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Summary

• The main research areas CIMSS proposes to focus
  on during 2004 are
•    Writing of ATBDs that will describe and
  justify baseline algorithms, identify potential
  algorithm risks, and propose solutions to reduce
  the risks. Emphasis in 2004 is on writing the
  atmospheric sounding and wind ATBDs and
  preliminary drafts of other ATBDs.
•    Continue with the demonstration of the new
  approach to derive clear-sky winds from retrieved
  moisture sounding fields.
•    Baseline algorithms will be extensively
  tested by applying them to appropriate
  observations (e.g. AIRS and S-HIS) and model
  simulations.
•    Continued development of data access and
  visualization tools,
•    Continued development on algorithms for the
  retrieval of cloud, aerosol and surface
  properties and trace gas amounts
•    The data access and visualization activities
  will continue to make substantial contributions
  to both the science and education of
  hyperspectral data.
•