1. GOES-R RISK REDUCTION (R3) ACTIVITIES Paul Menzel NESDIS Office of Research and Applications April 2004

2. End to End GOES-R System 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) Instrument Cal/Val T/V and postlaunch 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 s/w implementation (OSDPD) science stewardship (ORA, NCDC) archive (NCDC) data assimilation (EMC)

3. End to End GOES-R System Plan (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) Instrument Cal/Val T/V and postlaunch 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 s/w implementation (OSDPD) science stewardship (ORA, NCDC) archive (NCDC) data assimilation (EMC)

4. R3 enables efficient adoption of GOES-R data & products into NOAA Wx and Climate 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

5. Since December Briefing * GOES R3 Plan has been updated NOAA goals are addressed coastal waters and ocean activities are included Role of JCSDA is more explicit Milestone schedule is revised * GOES R3 budget for FY04 through FY12 has been set OSD expanding resources in FY06 * Coastal Waters Science Working Group being formed Stan Wilson is coordinating review of inst req, study of applications, & linkage of two * GOES R Risk Reduction Activities Review was held 25 Mar 04 good progress from partners was evident Technical Advisory Committee urges priority for ATBDs for GIFTS CDR GIFTS T/V participation * Examples of GOES-R capabilities provided to movie team

6. Using GOES-R to help fulfill NOAA’s Mission Goals (Ecosystems, Weather/water, Climate, and Commerce) GOES-R data and products will support all of NOAA’s 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 NOAA’s four mission goals!

7. Enhanced GOES Capabilities Support NOAA Strategic Goals Weather and Water * Improved disaster mitigation with hurricane trajectory forecasts benefiting from better definition of mass and motion fields. * Improved knowledge of moisture and thermal fields provide better data for agricultural forecasting and nowcasting. * Better general weather announcements affecting public health from improved forecasting and monitoring of surface temperatures in urban and metropolitan areas during heat stress (and sub-zero conditions). Climate * Hourly high spectral resolution infrared calibrated geo-located radiances facilitate radiance calibration, calibration-monitoring, and satellite-to-satellite cross-calibration of the full operational satellite system; and provide measurements that resolve climate-relevant (diurnal, seasonal, and long-term interannual) changes in atmosphere, ocean, land and cryosphere. Ecosystems and Coastal Water * Huge increase in measurements beneficial to ecosystem management and coastal & ocean resource utilization. * First time ever, characterization of diurnal ocean color as a function of tidal conditions and observation of phytoplankton blooms (e.g. red tides) as they occur. * Improved coastal environment monitoring of a) response of marine ecosystems to short-term physical events, such as passage of storms and tidal mixing; b) biotic and abiotic material in transient surface features, such as river plumes and tidal fronts; and c) location of hazardous materials, such as oil spills, ocean waste disposal, and noxious algal blooms Commerce * Better information regarding conditions leading to fog, icing, head or tail winds, and development of severe weather including microbursts en route makes air traffic more economical and safer. Better depiction of ocean currents, low level winds and calm areas, major storms, and hurricanes (locations, intensities, and motions) benefits ocean transportation. Information regarding major ice storms, fog, flooding and flash flooding, heavy snowfall, blowing snow, and blowing sand already assists train and truck transportation. * Power consumption in the United States can be regulated more effectively with real-time assessment of regional and local insolation as well as temperatures.

8. 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

9. 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) starts 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

10. R3 provides the necessary elements for early GOES-R utilization (1)capable informed users, (2)flexible inventive providers, (3)pre-existing data infrastructures, (4)informative interactions between providers and users, (5)knowledge brokers that recognize new connections between capabilities and needs, (6)champions of new opportunities in high positions, (7)well planned transitions from research demonstrations to operations, and (8)cost effective use of GOES-R for improved coastal ocean, weather & water, climate, and commerce applications

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 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

12. R3 Partners

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

14. 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)

15. Extra Slides with examples of early GOES-R research and expectations from GOES-R enhanced capabilities

16. GOES-R HES temporal (15 min), spectral (0.5 cm-1), spatial (1-10 km), & radiometric (0.1 K) capabilities will * depict water vapor as never before by identifying small scale features of moisture vertically and horizontally in the atmosphere * track atmospheric motions much better by discriminating more levels of motion and assigning heights more accurately * characterize life cycle of clouds (cradle to grave) and distinguish between ice and water cloud ( very useful for aircraft routing) and identify cloud particle sizes (useful for radiative effects of clouds) * measure surface temperatures (land and sea) by accounting for emissivity effects (improved SSTs useful for sea level altimetry applications) * distinguish atmospheric constituents with improved certainty; these include volcanic ash (useful for aircraft routing), ozone, and possibly methane plus others trace gases.

17. Hyperspectral Reference for Intercalibration of Broadband Sensors  T = Geo - AIRS

18. 4 hrly LEO obs can’t monitor atm instability & cloud formation Observations every 4 hours are not often enough. Atmospheric process not observed.

19. GOES obs monitor atmospheric instability and cloud formation Hourly observations help track atmospheric changes

20. HES HES’

22.   Atmospheric transmittance in H2O sensitive region of spectrum Spectral change of 0.5 cm-1 causes BT changes > 10 C AIRS BT[1386.11] – BT[1386.66] Studying spectral sensitivity with AIRS Data

23. Twisted Ribbon formed by CO 2 spectrum: Tropopause inversion causes On-line & off-line patterns to cross Blue between-line T b warmer for tropospheric channels, colder for stratospheric channels Signature not available at low resolution 15  m CO2 Spectrum --tropopause--

24. Characterizing Land and Sea Surfaces AIRS is enabling surface emissivity estimates from atmospheric window channel measurements. Example shows  sfc ( ) over the Mediterranean Sea to Algeria to the Sahara Desert. Transect from Mediterranean to Sahara LW SW Wavenumber LW SW

25. Inferring surface properties with AIRS high spectral resolution data Barren region detection if T1086 < T981 T(981 cm -1 )-T(1086 cm -1 ) T(1086 cm -1 ) Barren vs Water/Vegetated AIRS data from 14 June 2002

26. Profile Retrievals in Cirrus Clouds with NAST-I These retrievals, uncorrected for cloud attenuation, demonstrate the ability to sense spatial structure of moisture below a scattered and semi-transparent cirrus cloud cover 16.0 UTC 14.9 13.8 Depressions due to Cloud Attenuation Temperature (K) Log 10 {VMR (g/Kg)}

27. Profile Retrievals in Cirrus Clouds with NAST-I NASA SRL data May 30, 2002 (19.5 – 23 UT) (37.8N, 100W) Aerosol Backscatter Thin cirrus produces little effect on retrieval May 30, 2002 o o GOES IR (2208UT) o SRL Flight Track GOES IR (2208UT) o GOES Visible (2208 UT) Flight Track

28. AERI & Profiler Network Depiction of Moisture Advection evolution of temperature and moisture fields using AERI data evolution of wind fields using Profiler data

29. Best products will be realized from combinations of ABI and HES (Hyperspectral Environmental Suite) data (IR and Visible/near IR on the HES-Costal Water)! ABI HES Better cloud clearing, better spatial, etc Better surface emissivity, better spectral, etc

30. GOES-R spectral improvements provide more capability to help see icing in clouds. Example from MSG of Ch01 (0.6 um visible) and Ch03 (1.6 um NIR) seeing the development of deep convection and the associated icing on 28 July 2003 pictures courtesy of EUMETSAT

31. CH03; 10.30 Ch01: black Ch03: black cloudfree Ch01: black Ch03: black cloudfree Ch01: white Ch03: black start of icing (!?)

32. CH01; 10.30 Ch01: black cloudfree Ch01: black cloudfree Ch01: white cloud

33. GOES-R spectral improvements help to see icing in clouds.

34. Current GOES Imagers MODIS/MTG/ Aircraft, etc ABI Bands MSG/AVHRR/ Sounder(s) Based on experience from:

35. Visible and near-IR channels on the ABI The current GOES has only one visible band. Haze Clouds Veg. Cirrus Part. size Snow, Phase AVIRIS spectra

36. IR channels on the current GOES and on the ABI The spectral coverage of the ten IR ABI bands. Spectral coverage from the GOES-12 imager and a sample high-spectral resolution earth-emitted spectra.

37. The Advanced Baseline Imager: ABICurrent Spectral Coverage 16 bands5 bands Spatial resolution 0.64  m Visible 0.5 km Approx. 1 km Other Visible/nearIR 1.0 kmn/a Bands (>2  m) 2 kmApprox. 4 km Spatial coverage Full disk 4 per hourEvery 3 hours CONUS 12 per hour~4 per hour

38. GOES-R Coastal Water Imaging Function GOES-R provides first ocean color capability from geo orbit –Can make measurements in constant tidal conditions GOES-R enables more frequent views of U.S. coastal ocean color – Routine coverage of U.S. East Coast every 3 hours, with 1 hour refresh for high priority areas GOES-R provides more opportunities for cloud-free viewing –Better detect/monitor/track rapidly changing phenomena such as Harmful Algal Blooms, sediment plumes, and chaotic coastal zone currents magnitude that could be underestimated due to diurnal behavior GOES-R coastal water imaging function offers higher spatial resolution (~300 meters) –Fisheries researchers are limited by spatial resolution of current systems—better than 1 km needed to improve measurement and modeling of small scale phenomena such as migration pathways for salmon fisheries

39. GOES-R Coastal Water Imaging Function Enables Coastal reef monitoring –Improved study of coral reefs that are an integral part of the earth’s biogeochemical processes and respond more quickly than other marine ecosystems to environmental changes Scientists estimate that 90% of these reefs have never been assessed Harmful Algal Bloom monitoring –annual HAB are very costly (tens of million of dollars per year) –Better understanding of processes that trigger HABs and their dispersal Benefits of improved monitoring –Increased and safer shellfish harvest: More targeted shellfish fishery closures could result in increased harvesting without harm to public health; and could enable harvesting before contamination occurred –Reduced losses to recreation/tourism: Earlier and more targeted knowledge of bloom direction could result in fewer beach closures without harm to public safety –Reduced HAB monitoring/managing costs: more efficient use of resources for in-situ sampling

40. HES Coastal Waters Imaging Function

41. GOES-R enables water type classification Example from ER2 MAMS Vis image (left), split IR window (right) Fresh and salt water identified in river delta ecosystems

42. MODIS examples from SSEC Direct Broadcast Haze Current GOES Visible Image GOES-R resolves more details Turbidity Atoll Waters

43. 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?