In support of the NOAA GOES-R mission and in concert with the ongoing GOES-R Risk Reduction effort, the Algorithm Working Group (AWG) was created by NESDIS/STAR to manage and coordinate development of GOES-R products and validation activities that will support preparation for GOES-R implementation and operations. The role of the GOES-R AWG management team is to provide oversight of the schedules, activities, and budget needed for an integrated program of GOES-R science studies, data processing development and, most importantly, their transition into operation use. The GOES-R AWG will work collaboratively with the System Prime Contractor to ensure that the new capabilities offered by the GOES-R Program are implemented optimally and economically. In addition, the GOES-R AWG will work closely with its companion Risk Reduction project, which will address areas of high risk for the GOES-R program and assure that scientific solutions are readily available in time for operational implementation. Specifically, the GOES-R AWG program is a government-led effort to: (1) broker algorithms between government, academic and commercial sources, (2) support the prototyping and demonstration of algorithm performance including algorithm/product metadata generation techniques, standards, and formats, (3) provide algorithm software, test data sets, and benchmarks as potential solutions for the product generation functions, and (4) review and assess applicable GOES Incident Reports (GIRs).
CIMSS scientists have been actively collaborating with NOAA in AWG planning since its inception. The assembled CIMSS AWG team consists of experienced researchers with varied areas of expertise and who are fully aware of and understand all the threads that are required to accomplish the end-to-end system. In other words, CIMSS can contribute in all four major threads, described below, of an end-to-end system for which AWG is chartered.
By leveraging previous CIMSS research programs, such as our five-year U.S. Navy supported hyperspectral MURI project, the NASA EOS polar orbiting direct broadcast processing package, more than 20 years of GOES data processing algorithm development and research to operations support, and NOAA’s GOES-R Risk Reduction, CIMSS is capable of creating and providing a complete data processing and validation system for the simulation thread. For example, CIMSS can routinely create the simulation of hemispheric scale - full disk down to the mesoscale, CONUS or regional NWP fields of atmospheric profiles, clouds, surface properties, and thermal dynamic wind and mass at the appropriate time and space specifications. The simulation of the top of atmosphere radiances for hyperspectral and multispectral instruments using those NWP fields have also been developed. The proxy team task will address the detailed planning of how to accomplish this thread in the most efficient way.
These two threads will be accomplished at CIMSS through seven (7) individual team projects in the areas of soundings, winds, clouds, aerosols, ozone, fires, that include most importantly, the approaches and ancillary information required to support these efforts. Seven highly qualified CIMSS scientists will serve as Project Lead Investigators with support scientists and programmers teaming to develop, evaluate and validate proven approaches that will provide quality research results and documentation.
While this thread is not explicitly proposed, it includes complete testing of algorithms, processing scenarios, and addressing latency requirements. CIMSS will leverage GOES-R Risk Reduction efforts in the processing demonstration where current GIFTS end-to-end system requirement analysis is being performed. CIMSS can contribute greatly to improve the current understanding of GOES-R end-to-end operational data processing requirements. CIMSS is capable of building a scalable system to validate the infrastructure needed to address the critical time latency issues.
Specifically, CIMSS scientists will: (a) support the specification of the benchmark hardware capability needed to provide 24x7 operational demonstrations, (b) simulate full spatial and temporal resolution GOES-R instrument level 1b radiance datasets, to convert ATBDs and the available basic research software into pre-operationally validated software modules, (c) integrate pre-operational validated software modules into pre-operational validated product generation software systems, and (d) integrate pre-operational validated observing system performance monitoring and validation systems. The CIMSS AWG team will support and work closely with other external AWG team members to facilitate the best possible cooperation to achieve the GOES-R mission.
In summary, CIMSS is proposing to conduct activities in close collaboration with NOAA and other industry and university partners to address the key issues in the AWG program plan. The proposed research objectives support the broad NOAA mission goal of serving society’s needs for weather and water information. The end users within the NOAA community will include the NESDIS Satellite Analysis Branch, the National Weather Service, the National Centers for Environmental Prediction, and the Joint Center for Satellite Data Assimilation. The GOES-R Imager and Sounder products will also benefit other national and international analysis and forecast centers, as well as research institutes.
|A. Huang||PI and Proxy data set lead|
|Tom Greenwald||Proxy Team PM & Forward Model Lead Scientist|
|Jason Otkin||Proxy Team NWP Model Lead Scientist|
|C. Velden||PS and Atmospheric Motion Vectors Lead|
|S. Ackerman||Atmospheric Aerosol lead|
|J. Li||Atmospheric Sounding Lead|
|C. Schmidt||Ozone Retrieval Lead|
|E. Prins||Biomass Burning Lead|
|A. Heidinger/M. Pavolonis||Cloud Properties Lead|
|D. Tobin||Validation Lead|
There is a strong collaboration in the GOES R AWG program between NOAA/NESDIS/STAR, which is responsible for the successful conduct of the program, and the NOAA Cooperative Institutes, other universities and industrial partners. Table below indicates the primary NOAA collaborators working with CIMSS scientists in the GOES R AWG program
|NOAA Collaborator||Principal Role|
|M. Goldberg||AWG Chair|
|C. Barnett||Sounding Chair|
|J. Daniels||Winds Chair|
|S. Kondragunta||Air Quality /Aerosol Chair|
|A. Heidinger||Clouds Chair|
|Dr. Yunyue Yu||Land Surface Chair|
|F. Weng||Proxy Data Set Chair|
|M. Pavolonis||Cloud Team Member|
|T. Schmit||Proxy Data Set Member|
|J. Key||Winds Team Member|
|M. DeMaria||Winds Team Member|
|G. S. Wade||StAR/CoRP/ASPB Scientist|
The main focus of this project is to provide state-of-the-art proxy data sets, models, software tools, and their associated documents and/or user guides in support of a broad range of GOES-R AWG application and development team activities. The accomplishments from this project will directly enable most of the AWG team members to use the common GOES-R HES (Hyperspectral Environmental Suite) and ABI (Advanced Baseline Imager) datasets and software tools. This will allow the various AWG teams to concentrate on their own area of expertise and allow better sharing of results between groups. In addition, the refined and up-to-date databases will include: 1) global infrared surface emissivity, 2) ice and water cloud microphysical properties, and 3) aerosol/dust microphysical properties, and models such as 1) community NWP models, 2) community infrared radiative transfer models, and 3) community infrared emissivity models will also be included.
In addition, ABI-like datasets (for most ABI bands) will be generated from MODIS imagery. Some of the steps to simulate the spatial, geometric and radiometric features include: acquiring the MODIS hdf format images, selecting bands with similar central wavenumbers, de-striping the IR bands with an algorithm developed at CIMSS, averaging to appropriate ABI resolution, etc.
The main focus of this project is to evaluate and select a sounding algorithm for GOES-R Hyperspectral Environmental Suite (HES) and Advanced Baseline Imager (ABI) data processing. This project requires CIMSS to compare, evaluate and select existing sounding algorithms and to take into account the time continuity and spatial continuity in the algorithms uniquely suitable for GOES-R HES and ABI sounding products generation.
These activities provide essential validation datasets and analysis for assessing candidate HES temperature and water vapor sounding algorithms. The primary focus is on the production and use of highly accurate temperature and water vapor profiles from the Atmospheric Radiation Measurement (ARM) sites and from aircraft campaigns.
We propose to adapt and optimize the CIMSS/NESDIS automated feature tracking algorithm for deriving atmospheric motion vectors (AMV) from sequential satellite imagery for applications to the GOES-R ABI. This project will include software code modifications, testing on proxy datasets, validation, and documentation. This work will insure the readiness of the CIMSS/NESDIS automated AMV algorithm for operational implementation upon the deployment of GOES-R.
This project involves the development of tools and analysis methods to allow for generation of optimal cloud algorithms for the GOES-R cloud application team. The approach of the cloud application team is to develop and optimize the cloud algorithms using real data as opposed to simulated GOES-R data. Given the timetable of the GOES-R AWG effort, there is not sufficient time for a distinct algorithm development and comparison phase. While the physical basis of cloud algorithms is well established, we propose the best way to achieve consensus is through analyzing results from real data. Therefore, a critical component of the cloud application team’s efforts will be evaluating cloud algorithms on real data. We are proposing that the data from SEVIRI imager from the MSG series of European Geostationary Satellites and data from the MODIS imager serve as the basis for this prototype.
In addition, to developing the required prototype systems using SEVIRI and MODIS, the project requires the selection and development of appropriate cloud algorithms. Many of these algorithms will come from other members of the cloud application team. However, several algorithms, including cloud height from CO 2 slicing, will require development under this proposal. Lastly, this project activity will include validation of all algorithms and documentation of their performance.
The Advanced Baseline Imager (ABI) on GOES-R has sufficient spectral coverage, most importantly the 9.6 μm ozone absorption band, to retrieve total column ozone over its coverage area. The legacy GOES I-M Sounder experimental total column ozone (TCO) algorithm from CIMSS can be applied to ABI. ABI ozone will provide high spatial and temporal resolution sampling of ozone features that primarily reflect ozone distribution in the stratosphere and upper troposphere; ABI ozone alone cannot meet requirements for measuring the tropospheric column ozone. ABI ozone will provide continuity with the current ozone capabilities and function as a part of an ABI Sounding package.
Through AWG, CIMSS can contribute to the evaluation and selection of aerosol retrieval algorithms. Emphasis from CIMSS will be on the detection and characterization of dust, smoke and volcanic aerosols. This activity builds on our historical and current expertise in algorithms developed for AVHRR, MODIS, and current GOES observations.
The primary focus of this effort is to evaluate the current GOES Wildfire Automated Biomass Burning Algorithm (WF_ABBA) and adapt the algorithm for application with the GOES-R ABI. This activity will build on historical and current expertise at CIMSS in fire algorithm development for the GOES Imager and the global geostationary fire observation network (MSG, MTSAT-1R, INSAT-3D, etc.). CIMSS will revise the WF_ABBA to address GOES-R ABI observational requirements utilizing the improved fire monitoring capabilities on GOES-R. This will include updating modules that identify and characterize sub-pixel fire activity, demonstrating and validating the prototype GOES-R ABI WF_ABBA using various GOES-R ABI proxy data sets, and providing a version of the algorithm for further evaluation by the AWG science team. This effort will involve collaborating with MODIS and NPOESS VIIRS fire product development experts to maximize future use of multiple data sources (geo and leo) that take advantage of the strengths of each system to create improved fused fire products. This activity will ensure enhanced future geostationary fire detection, diurnal monitoring, and characterization in the GOES-R era.