CIMSS PARTICIPATION IN THE

GOES-R RISK REDUCTION 

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

 

Overview

• Background (MURI focuses on theoretical issues, NOAA on routine operational issues)

• Approach to development of algorithms

• Updates on algorithms

• Summary

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

CIMSS GIFTS/GOES_R

• Data Processing and Archive System

• Algorithm Development

• Preparing for Data Assimilation

• Demonstration Activities

GIFTS Ground Processing Plan (Baseline)

Algorithm Development

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

Algorithm Development Paths

Sounding Algorithm Tasks Summary

FY03 focused on algorithm development & processing approach design:

•Model atmosphere simulation set up - MM5•Radiance measurements simulation set up - clear/cloudyspectra 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

•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

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.

Motion Vectors

• Algorithm development

• Tested with model simulations

• Tested with aircraft observations

• Continue development

• Test with AIRS over polar regions

• ATBD

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).

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.

500 mb winds

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

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

Cloudy casesas determined by AIRS cloud mask

Clear casesas determined by AIRS cloud mask

Sample AIRS/MODIS Cloud Mask Histogram

MODIS Cloud Mask

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Range bins (%) of MODIS pixels within AIRS FOVsfor 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.

AIRS BT

BT Difference?

AIRS DIFF BT

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

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.

Visualization of arithmetic combinations, scatter diagram and pixel outlines.

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

SummaryThe 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.