1. 1 1. FY08 GOES-R3 Project Proposal Title Page  Title: Hazards Studies with GOES-R Advanced Baseline Imager (ABI)  Project Type: (a) Product Development Proposal; (b) GOES Utilization Proposal  Status: New  Duration: 3 years  Leads: »D. Hillger, STAR/RAMMB »B. Connell, CIRA »M. Sengupta, CIRA  Other Participants: »D. Watson, CIRA »K. Micke, CIRA

2. 2 2. Project Summary  Research toward the development of several new (or improved) products and datasets for product development: 1.New fog-stratus and blowing-dust discrimination products  Improve the discrimination of these features in comparison to current operational products, and leveraging past products as a basis  Expected result: new products utilizing the increased number of ABI bands, including possible quantification of product output 2.Other products (volcanic ash, smoke from fires)  New products based on previous work with these phenomena, building on previous work  Expected result: products which provide qualitative and potentially quantitative analysis 3.Use of three-color (RGB) analysis of multi-spectral imagery as a tool 4.Datasets for smoke and trace gas detection in fire scenarios Build on current forest fire datasets to create new datasets containing smoke and trace gas signatures in relevant channels for use by GOES-R AWG land and air quality team. Expected product: Synthetic Imagery datasets for use in developing products for fire, smoke and trace gas detection for use by GOES-R AWG land and air quality team. 5.Real-time online display of new products  Use experimental imagery from MODIS and MSG to emulate GOES-R ABI imagery and products  Final products for post-launch use would be a results of on-going testing and verification

3. 3 3. Motivation/Justification  Supports NOAA Mission Goal: Weather and Water  Datasets for smoke and trace gases are needed by AWG »GOES-R AWG team has identified smoke detection as a high-risk priority area. So creating high quality simulated datasets containing “truth” will enable this team to reduce the risk in this product.  Fire, Volcanic ash and sometime fog are hazards that require rapid response »High temporal resolution of GOES is essential

4. 4 4. Methodology 1.Fog-stratus and Blowing-dust Discrimination  Collect and process ABI-like imagery and ancillary weather observations to overlay on the product images  Compare the improved product to operational analyses of fog-stratus as currently available online 2.Other New products (volcanic ash, smoke from fires, etc.)  Similarly collect and process ABI-like imagery and ancillary weather observations to overlay on the product images  Compare the new products to other/independent sources of information 3.Utilize new image processing techniques as needed  Three-color (RGB) color image processing, especially for processing and display of multi-spectral imagery  Principal component image differencing as a guide to band selection and combination 4.Datasets for smoke and trace gas detection in fire scenarios  Use current fire datasets being delivered to GOES-R AWG land team and include smoke and trace gas signature over fires.  Use this enhanced dataset for product development of fire, smoke and trace gases.

5. 5 5. Summary of Previous Results 1.Fog-stratus and Blowing Dust Discrimination  ABI RGB product publication in second review for Journal of Atmospheric and Oceanic Technology 2.Experimental work with image differencing, 3-color (RGB) analysis, and Principal Components (at least two publications) 3.Smoke and trace gas detection in fire scenarios. Fire scenario dataset currently being delivered to AWG fire product team Journal paper under 2 nd review for the International Journal of Remote Sensing regarding procedure for creating the high quality data sets.

6. 6 Scatter plot of image pixels, fog vs. stratiform cloud: fog is yellow stratus is cyan all other pixels are magenta Fog/stratus product example from ABI- equivalent MODIS imagery

7. 7 6. Expected Outcomes 1. Fog-stratus and Blowing-dust Discrimination  This research serves to improve these products which will be displayed in real-time with simulated ABI data  Products will be available for analysis, forecasting and training 2.Other New Products (volanic ash, and smoke from fires, etc.)  Products will be made available online for analysis and forecasting and tweaked as needed based on feedback and comparison to ancillary information  Possible use of model output and similar products in lieu of ground truth measurements 3.Smoke and trace gas detection in fire scenarios  Datasets containing smoke and trace gas signature will be made available for use by GOES-R AWG land and air quality products teams.  Possible use of simulated datasets to understand the impact of uncertainties of fire location and size on emissions.

8. 8 Simulated-ABI Experimental Products Online

9. 9 7. Major Milestones  FY08 1.Fog-stratus and Blowing-dust Products – Collect and process data for online output and develop web display for feedback from potential users 2.Other New Products (volcanic ash, and smoke from fires, etc.) – Collect and process data and test product changes, as well as output product online 3.Create methodology for introduction of smoke and trace gas on current fire simulations.  FY09 1.Fog-stratus and Blowing-dust Products – Work on quantification of product output 2.Other New Products (volcanic ash and smoke from fires, etc.) – Adjust products as necessary based on comparison to ancillary data and feedback from theoretical and model studies 3.Produce initial dataset containing smoke signature for use by GOES-R AWG land (fire product) and air-quality teams and deliver this initial dataset for use and feedback. 4.Present results at conferences and coordinate with training activities  FY10 1.Fog-stratus and Blowing-dust Products – Continue product improvement and work with potential users to prepare for eventual real-time ABI data 2.Other New Products – Seek potential users and collaborations in the development of products that show promise 3.Produce datasets containing both smoke and trace gas signatures for use by multiple GOES-R AWG teams including the land and air quality teams. 4.Prepare publications of results

10. 10 8. Funding Profile (K)  Summary of leveraged funding »StAR Base funds covers support of Don Hillger »Infrastructure partially supported by CIRA Base Funding SourcesProcurement Office Purchase Items FY08FY09FY10 GOES-R3StAR CIRA Grant 606265 000 000 Other Sources 000 000 000 000

11. 11 9. Expected Purchase Items  FY08 »(55K): 7 months of Res. Sci./Res. Assoc. support (4 people) from 5/08 to 5/09 »(5K): Necessary hardware and software  FY09 »(58K): 7 months of Res. Sci./Res. Assoc. support (4 people) from 5/09 to 5/10 »(2K): Travel to scientific meeting »(2K): Publication charge  FY10 »(61K): 7 months of Res. Sci./Res. Assoc. support (4 people) from 5/10 to 5/11 »(2K): Travel to scientific meeting »(2K): Publication charge