1. CIMSS PARTICIPATION IN THE GOES-R RISK REDUCTION

2. Principal roles of Co-investigators T. AchtorProject Management, Archive System S. AckermanCloud and Top of Atmosphere Flux Algorithms R. DedeckerData Processing and Archive System R. Garcia Software Management A. HuangAtmospheric Sounding, Data Assimilation, GIFTS/HES Synergism R. KnutesonSurface Property Retrieval, Data Processing and Archive System J. LiAtmospheric Sounding, Trace Gas, and GIFTS/HES Synergism C. SchmidtOzone, Aerosol D. Tobin Validation C. VeldenWinds System T. WhittakerVisualization

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

4. Satellite Observations of the Earth’s Environment: Accelerating the Transition of Research to Operations

5. Systematic meas. of atmosphere, ocean, and land surface parameters How Can Weather Forecast Duration and Reliability Be Improved By New Space-Based Observations, Assimilation, and Modeling? 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 NASA/NOAA collaborative centers 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 Knowledge Base 2002 2004 2007 NRA 20062005 2010 NRA 2011200320122013 2014 201520082009 Weather satellite sensor and technique development; used by NOAA Improvements require: Focused validation experiments New Technology Impact Assessments Observations of tropical rainfall/energy release High-resolution global measurements of temperature, moisture, cloud properties, and aerosols Global monitoring of water, energy, clouds, and air quality/Operational prototype missions Global tropospheric winds Satellite-derived localized heating inputs will allow regional models to have better predictive capabilities. Use of NOAA operational models to optimize assimilation of NASA’s new satellite data will ensure realistic and accelerated use of new technology and techniques. 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 sounding for fast forecast updates Soil moisture Funded Unfunded T Global Precipitation Continuous lightning Improved forecasts Improved forecasts Improved physical & dynamical processes = Field Campaign NRA

6. CIMSS GIFTS/GOES_R Data Processing and Archive System Algorithm Development Preparing for Data Assimilation Demonstration Activities

7. GIFTS Ground Processing Plan (Baseline)

8. Algorithm Development Radiances Atmospheric Soundings Winds Clouds Surface Composition (trace gas and aerosol) Radiation Budget Data and Product Access and Visualization

9. Algorithm Development Paths

10. 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

11. 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

21. 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.

22. Motion Vectors Algorithm development Tested with model simulations Tested with aircraft observations Continue development Test with AIRS over polar regions ATBD

23. 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).

24. 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.

25. 500 mb winds

26. Comparison of NAST-I winds and DWL wind profiles on 11 February 2003.

27. 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

29. Cloudy cases as determined by AIRS cloud mask Clear cases as determined by AIRS cloud mask Sample AIRS/MODIS Cloud Mask Histogram MODIS Cloud Mask Fraction of occurance clear cases Fraction of occurance cloudy cases Range bins (%) of MODIS pixels within AIRS FOVs for each MODIS cloud mask class E.g. when the AIRS cloud mask said it was clear, ~72 percent of the time 90-100 percent of the MODIS pixels within those AIRS FOVs were determined to be confident clear (green) by the MODIS cloud mask, ~12 percent of the time 90-100 percent of the MODIS pixels within those AIRS FOVs were determined to be probably clear (cyan) by the MODIS cloud mask, ~0 percent of the time 90-100 percent of the MODIS pixels within those AIRS FOVs were determined to be uncertain (red) by the MODIS cloud mask, and ~3 percent of the time 90-100 percent of the MODIS pixels within those AIRS FOVs were determined to be cloudy (white) by the MODIS cloud mask.

30. AIRS BT

31. BT Difference?

32. AIRSDIFF BT

33. Composition 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 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

35. 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.

36. 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.

37. Visualization of arithmetic combinations, scatter diagram and pixel outlines.

38. 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

40. 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.