May 3, GOES-R RISK REDUCTION and PROVING GROUND CONCEPT Dr. Kevin J. Schrab NOAA/NWS Office of Science and Technology May 3, 2006

2 Outline GOES-R Risk Reduction Overview Proving Ground Concept Overview Interaction Between the 2 Thoughts for Break-Out Session

May 3, GOES-R Risk Reduction (R3) Overview R3 enables efficient adoption of GOES-R data & products into NOAA services Within 6 months of routine operations –validation of radiometric GOES-R performance –unique first time ever imagery –examples of improved derived products for weather and coastal ocean nowcasting –case studies of NWP impact Within one year –operational utilization of GOES-R data and early products

May 3, GOES-R Risk Reduction (R3) Overview Enables Collaborations among participating organizations GOES-R products and associated testing to evaluate impact and quality Strategy for data assimilation, forecasting, and nowcasting tests Resources needed to conduct these science preparations

May 3, R3 provides the necessary elements for early GOES-R utilization Capable informed users, Flexible inventive providers, Pre-existing data infrastructures, Informative interactions between providers and users, Knowledge brokers that recognize new connections between capabilities and needs, Champions of new opportunities in high positions, Well planned transitions from research demonstrations to operations, and Cost effective use of GOES-R for improved coastal ocean, weather & water, climate, and commerce & transportation, and ecosystems applications GOES-R Risk Reduction (R3) Overview

May 3, Focus Areas FY04-FY08 –data compression –hyperspectral instrument characterization –algorithm development –data assimilation FY09-FY10 –Technique development for merging data from a composite observing system –User training FY11 and beyond –Preparations for GOES-R product demonstrations GOES-R Risk Reduction (R3) Overview

May 3, Currently only true full integration occurs at WFO and NCEP centers –Unanticipated results of testing can cause disruption to operations –May overtax operational system(s) Provide integrated testing of the following in NWS operations –Software –Hardware –Strategies –Concepts Benefits –All changes (technological or procedural) undergo rigorous integrated testing –Testing as staged, critical design and development points to ensure problems identified early Status –Submitted to NWS requirements process in FY05 –Submitted to NOAA PPBES process in FY05 –No funding yet identified (pursue again in FY06) Proving Ground Concept Overview

May 3, R3 contains funding for interaction between NESDIS and NWS forecasters –3 NESDIS experts placed in 3 WFOs from FY08-FY16 –Test and apply hyperspectral algorithms and decision aids and incorporate forecaster feedback Algorithms tailored for simulated GOES-R data and products, capable of running on operational NWS systems Ensuring that the simulated GOES-R products flow reliably from the source to the WFO and can be used both operationally in real time and for post-event research and evaluation Expertise and training in the applications of the simulated GOES-R data and products Interaction with the WFO personnel and use their feedback to improve existing algorithms and develop new algorithms and concepts of operations Documentation of experiences and findings each year and recommendations to ensure that every forecast office will be able to accept and use GOES-R data and products from the beginning of operations NWS Proving Ground would provide an efficient method to test and evaluate changes suggested by R3 Interaction Between GOES-R Risk Reduction (R3) and Proving Ground Concept

May 3, What suggestions or feedback do you have on the risk reduction and/or proving ground concept to help ensure it will contribute to a successful beginning of operations for GOES-R? Any lessons learned from the WSR-88D (or other observing systems) that could benefit this effort? What other data sets could be leveraged through risk reduction and proving ground for GOES-R applications before the launch of GOES-R? What potential uses would you see for the Weather Event Simulator? How will lessons learned be best communicated to you? What other questions or issues would you like to have the group discuss? Thoughts for Break-Out Session

May 3, Backup slides –From previous R3 brief to GUC-3 by Dr. Paul Menzel (NESDIS)

May 3, Major points for R3 Plan R3 embraces all multi- & hyper -spectral experiences for GOES-R preparation AVIRIS, SHIS, NASTI, SeaWIFS, Hyperion, MODIS, AIRS, MSG, IASI, CrIS, GIFTS Time continuous hyperspectral data offer new opportunities balance of temporal, spatial, and spectral for ocean and atm observations Instrument characterization pre-launch vacuum test experience with CrIS and GIFTS important Aircraft, leo, geo-GIFTS (?), & simulated data used for science prep near polar MODIS & AIRS and ER-2 in crop duster flights important data over a variety of coastal and weather situations will be collected R3 plan covers preparations for radiances and derived products design options for ground system and archive considered (implementation resourced elsewhere) R3 plan covers FY04 through FY12 resources are distributed over 10 tasks FY06 starts full strength preparations

May 3, R3 Tasks Data processing and Archive Design (Task 0) helps with timely design and continues advisory capacity during implementation Algorithm Development (Task 1) starts with ATBDs for GIFTS CDR, learns from aircraft and leo data, & grows into prototype ops system Preparations for Data Assimilation (Task 2) start early and expand just before launch HES Design Synergy (Task 3) continues to guide trade space between algorithms & instrument Calibration / Validation (Task 4) exploits CrIS and GIFTS TV in prep for GOES-R TV, prepares for field campaigns Data Assimilation (Task 5) big challenge is addressed early Computer System for NWP (Task 6) one time purchase plus annual maintenance Data impact tests (Task 7) many OSEs of different components of observing system Nowcasting applications development (Task 8) new products and visualizations Education and Outreach and Training (Task 9) distance learning tools & K-16 involvement

May 3, R3 Partners

May 3, R3 Deliverables (and year) * requirements documents (04) and design options (06) for Raw Data Acquisition and Hyperspectral Data and Metadata Archive * h/w and s/w configuration options (06) for Real Time Data Processing that include robustness for evolving product algorithms * radiance calibration reports from field experiments (05) and pre-launch test characterization (06) of hyperspectral sensors (e.g. CrIS, GIFTS) * ATBD on radiance algorithm and geo-location (06) * time sequences of hyperspectral test data accumulated from polar AIRS data and ER2 crop duster deployments as well as GOES-AIRS simulations (05) * ATBDs on GOES-R derived products such as soundings, cloud properties, precip, OLR, SST, algal blooms, air quality,... (06) * proof of concept for deriving winds from moisture sounding retrieval fields (06) * algorithms for utilization of multispectral and derived product images such as atmospheric water vapor, stability, cloud properties, LST,…& conduct research on SST, ocean color, suspended sediment, ecosysteml change, volcanic ash, ERB, trace gases (08) * new dynamical approach for 3-D forecasts using hourly hyperspectral data (06) * optimal configuration of radiance data for generation of timely forecasts (07) * adjoint and forward models for HES assimilation (05) * implementation of a computer system for HES Model Impact Experiments (08) * data assimilation techniques for time continuous hyperspectral observations (06) * model impact tests with hourly hyperspectral IR data (08) * System ready to nowcast daily presence of harmful algal blooms (09) * distance learning modules to assist in user familiarization (09) * demonstration of GOES-R product generation (10)

May 3, End to End GOES-R System Plan (covered in GOES R3 plan) * User Requirements * Instrument Requirements * Tradeoffs between Instrument Design and Science Requirements * Instrument Cal/Val T/V and post-launch checkout (ORA) * Ground System /Archive Design and Implementation (OSD) * Algorithm and Product Development ATBDs (ORA) simulations (ORA) demonstration during science data gathering (ORA, JCSDA) s/w architecture studies (ORA, OSDPD) * Operations * Education and Outreach GOES-R Risk Reduction Overview

May 3, R3 addresses challenges of GOES-R data utilization (1)better use over land, (2)better use in clouds, (3)better use in coastal regions (4)exploitation of spatial & temporal gradients measured by satellite instruments (5)data compression techniques that don’t average out 3 sigma events (ie. retrievals versus super channels), (6)inter-satellite calibration consistency, (7)early demonstration projects before operations, (8)synergy with complementary observing systems (ie. GPS and leo microwave), (9)sustained observations of oceans & atmosphere and ultimately climate

May 3, GOES-R will help find answers to the following basic science questions. Can weather forecast duration and reliability be improved by new remote sensing, data assimilation, and modeling? How are global precipitation, evaporation, and the cycling of water changing? What are the effects of clouds and surface hydrologic processes on weather and forecasting as well as climate? Can satellite data contributions improve seasonal to inter-annual forecasts? Can satellite data contributions help to detect long-term change (decadal to centennial time span)? How are the oceanic ecosystems (open and coastal) changing? What portions are natural versus anthropogenic?