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GOES-R RISK REDUCTION (R3) ACTIVITIES NOAA Satellite and Information Services Office of Research and Applications June 2005 Slide 2 GOES-R RISK REDUCTION (R3) ACTIVITIES NOAA Satellite and Information Services Office of Research and Applications June 2005 What GOES R3 plan covers R3 plan tasks Partners AWG Measure of Success Slide 3 Plan started to organize NOAA participation in GIFTS Adapted to get ready for GOES-R operational geo hyperspectral capabilities Focused on atmospheric applications of ABI/HES-IR Including Coastal Ocean Applications of GOES-R with HES-CWI through COAST Adding Lightning Mapper, Space Environment Sensors GOES-R3 Plan covering science activities Part but not whole program required for end-to-end preparations Slide 4 GOES-R Science Demonstration covered in GOES R3 plan * Initial Instrument Cal/Val T/V and post-launch checkout * Ground System /Archive Design * Algorithm and Product Development science ATBDs simulations product demonstration during science data gathering tested science s/w GOES-R operational scenarios (OSDPD, NWS) * Education and Outreach Slide 5 End to End GOES-R System Plan (parts covered in GOES R3 plan) * User Requirements set forth in GOES Users Conferences (OSD, ORA) * Instrument Requirements drafted in PORD (ORA, OSD, GSFC) Tradeoffs between Inst Design and Science Req dialogue with vendor (OSD, ORA) Initial Instrument Cal/Val T/V and post-launch checkout (ORA) * Ground System /Archive Design, Testing, and Implementation (OSD) * Algorithm and Product Development science ATBDs (ORA) simulations (ORA) product demonstration during science data gathering (ORA, JCSDA) tested science s/w (ORA) s/w architecture studies for real time processing (ORA, OSDPD) * Transition to Operations science s/w optimization and implementation (OSDPD) GOES-R operational scenarios (OSDPD, NWS) operational cal/val science stewardship (ORA, NCDC) archive (NCDC) data assimilation (EMC) * Education and Outreach Slide 6 GOES R3 explore/prepare/gather/test/learn/demonstrate/benchmark Operational GOES R optimize/document/implement/process/validate/archive/assimilate Slide 7 Using GOES-R to help fulfill NOAA Mission Goals (Ecosystems, Weather/water, Climate, and Commerce) GOES-R data and products will support all of NOAAs four mission goals! Timothy J. Schmit, W. P. Menzel, NOAA/NESDIS/ORA (Office of Research and Applications) James J. Gurka, NOAA/NESDIS/OSD (Office of Systems Development) Jun Li, Mat Gunshor, CIMSS (Cooperative Institute for Meteorological Satellite Studies) Nan D. Walker, Coastal Studies Institute, Louisiana State University GOES-R data and products will support all of NOAAs four mission goals! Slide 8 GOES-R RISK REDUCTION (R3) ACTIVITIES NOAA Satellite and Information Services Office of Research and Applications June 2005 What GOES R3 plan covers R3 plan tasks Partners AWG Measure of Success Slide 9 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 Slide 10 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, & builds foundation for prototype ops system Preparations for Data Assimilation (Task 2) start early and expand just before launch Design Tradeoffs (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 Slide 11 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 dont 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 Slide 12 R3 Partners Slide 13 GOES-R improved products include Imagery / Radiances Sea Surface Temperature (SST) Dust and Volcanic Ash Detection Precipitation Estimations Atmospheric Motions Hurricane Location and Intensity Biomass Burning / Smoke Fog Detection Aircraft Icing Radiation Budget Atmospheric Profiles Water Vapor Processes Cloud Properties Surface Characteristics Atmospheric Constituents Ocean Color (Ocean water-leaving radiances or reflectances) Chlorophyll concentration Suspended sediment concentration Water clarity / visibility Coastal Currents Harmful Algal Blooms Coastal Normalized Difference Vegetation Index (NDVI) Erosion and Bathymetric Changes Slide 14 GOES-R RISK REDUCTION (R3) ACTIVITIES NOAA Satellite and Information Services Office of Research and Applications June 2005 What GOES R3 plan covers R3 plan tasks Partners AWG Measure of Success Slide 15 GOES-R product development consists of 5 phases: 1)Exploratory (akin to NPOESS IGS Studies, included in GOES R3) 2)Product Development and Research Demonstration (ATBDs and science demonstration in GOES R3) 3)Operational Testing (Algorithm Working Group guiding Single Prime) 4) Operational Transition (collaborative effort between Prime, ORA, and OSDPD) 5) Operational Production (established by Prime & maintained by OSDPD) Slide 16 GOES-R Product Generation Development Exploratory Product Development & Demonstration Operational Testing Operational Transition Operational Production GOES-R Risk Reduction AlgWG System Prime AlgWG will continue to develop and improve algorithms over the life cycle of GOES-R Slide 17 GOES-R RISK REDUCTION (R3) ACTIVITIES NOAA Satellite and Information Services Office of Research and Applications June 2005 What GOES R3 plan covers R3 plan tasks Partners AWG Measure of Success Slide 18 Climate Current GOES -- a wide variety of Applications Natural Hazards Weather Ocean Hydrology Numerical Weather Prediction Slide 19 Can weather forecast duration and reliability be improved by new remote sensing, data assimilation, and modeling? GOES-R improves 3-5 day forecast accuracy by 1 day, halves the 3-6 hour severe weather watch and warning boxes and the associated false alarm rates, supports 1 hr lead time for tornado warnings How are precipitation, evaporation, and the cycling of water changing? GOES-R fills in radar rainrate detection gaps and extends radar quality rain detection over all of CONUS What are the effects of clouds and surface hydrologic processes on weather and forecasting as well as climate? GOES-R enables model to double skill of cloud/no-cloud and precipitation forecasts out to 3-days Can satellite data contributions improve seasonal to inter-annual forecasts? GOES-R halves error of wet-dry region seasonal forecast Can satellite data contributions help to detect long-term change (decadal to centennial time span)? GOES-R provides IR calibration reference and distinguishes diurnal cycle so that 20 year climate mission goals are met. How are the oceanic ecosystems (open and coastal) changing? What portions are natural versus anthropogenic? GOES-R distinguishes tidal effects on ocean productivity and enables forecasts of harmful algal bloom GOES-R will be measured by these science accomplishments Slide 20 View from ground View from space OK tornado 3 May 99 530 CDT (2330 UTC) 1800 UTC 2300 UTC Slide 21 GOES axis of high LI indicates subsequent storm track 24 Jul 2000 Slide 22 Atmospheric Instability NWS Forecaster responses (Summer of 1999) to: "Rate the usefulness of LI, CAPE & CINH (changes in time/axes/gradients in the hourly product) for location/timing of thunderstorms." There were 248 valid weather cases. - Significant Positive Impact (30%) - Slight Positive Impact (49%) - No Discernible Impact (19%) - Slight Negative Impact (2%) - Significant Negative Impact (0) Figure from the National Weather Service, Office of Services Slide 23 Space - based GOS KALPANA (India) & Slide 24 Evolution of NWP skill in northern & southern hemispheres NH SH Lessons learned * good pre-launch characterization of instrument measurements is vital * dedicated expert supports NWP center for new instrument assimilation * innovation and flexibility important (eg., radiances from layers, 4DVAR) * satellite data having greater impact than raobs in both hemispheres * five day fcst anomaly corr improved 25% in last twenty years Example from ECMWF Slide 25