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Tri-agency Effort to Leverage and Combine Environmental Satellite Activities

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Tri-agency Effort to Leverage and Combine Environmental Satellite Activities

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  1. NPOESSEntering a New EraNational Polar-orbiting Operational Environmental Satellite SystemDelivering Global Data for Improved Numerical Weather PredictionAMS Symposium on the 50th Anniversary ofOperational Numerical Weather PredictionJohn D. CunninghamSystem Program DirectorCollege Park, Maryland14-17 June 2004

  2. METOP NPOESS NPOESS Specialized Satellites NPOESS Tri-agency Effort to Leverage and Combine Environmental Satellite Activities • Mission • Provide a national, operational, polar-orbiting remote-sensing capability • Achieve National Performance Review (NPR) savings by converging DoD and NOAA satellite programs • Incorporate new technologies from NASA • Encourage International Cooperation 1730 1330 2130 Local Equatorial Crossing Time Saves as much as $1.3B from the cost of previously planned separate developments

  3. The “Challenge” of Meteorological Satellite Convergence • Eight major “Convergence Studies” (1972-1991) • Examined consolidation of • DoD’s Defense Meteorological Satellite Program (DMSP) • DOC’s Polar-orbiting Operational Environmental Satellite (POES) Program • Primarily motivated by budget reduction/cost savings pressures • Studies resulted in retaining two separate programs • Deemed “highly complementary”; however • Remained separate primarily due to over-riding policy issues • Similar spacecraft with many common components and subsystems • Some measure of modest coordination/cost-savings achieved • Policy and programmatic benefits of two separate systems outweighed projected cost savings and advantages Achievement of significant cost savings while still satisfying civil and military mission requirements now a driving priority

  4. Evolutionary Roadmap 1960 - 2010 2000 - 2010 2010 – 2020+ NPP(NPOESS Preparatory Project) NPOESS (National Polar-orbiting Operational Environmental Satellite System) DMSP(Defense Meteorological Satellite Program) POES(Polar-orbiting Operational Environmental Satellites) EOS (Earth Observing System) Sensor data rate: 1.5 Mbps Data latency: 100-150 min. 1.7 GigaBytes per day (DMSP) 6.3GigaBytes per day (POES) 15 Mbps sensor data rate Data latency: 100-180 min. Data availability: 98% Ground revisit time: 12 hrs. 2.6 TeraBytes per day (EOS) 2.4 TeraBytes per day (NPP) 20 Mbps sensor data rate Data latency: 28 min. Data availability: 99.98% Autonomous capability: 60 days Selective encryption/deniability Ground revisit time: 4-6 hrs. 8.1 TeraBytes per day NPOESS Satisfies Evolutionary Program Needs with Enhanced Capabilities

  5. Building A More Capable SystemThe Historical Context First Image from TIROS-1 EOS-Aqua MODIS Image-250 m Saharan Dust off the Canary Islands 18 February 2004

  6. NPOESS Requirements Convergence of Alternatives • Integrated Operational Requirements Document (IORD-I) • 59 Data Products • 9 Enhancement Products • 1 System Characteristic KPP • Validated by JARC 1996 • IORD-II • 55 Data Products • 21 Enhancement Products • 2 System Characteristic KPPs • Validated by JARC Dec 2001 Convergence of Requirements Converged Requirements Provide Foundation for Combined Program

  7. Atmospheric Vertical Temperature ProfileHighly accurate measurement of the vertical distribution of temperature in the atmosphere in layers from the surface to 0.01 mb Integrated Operational Requirements Document (IORD) Example • Major Applications • Initialization of Numerical Weather Prediction Models • Complementary data for derivation of moisture/pressure profiles and cloud properties Iterative, Disciplined Requirements Process Ensures Users Needs are Met

  8. ERBS (5) TSIS (1) ALT (3) APS (4) NPOESS EDR-to-Sensor Mapping Atm Vert Moist Profile Precipitable Water Cloud Top Pressure Atm Vert Temp Profile Precipitation Type/Rate Cloud Top Temperature Imagery Down LW Radiance (Sfc) Pressure (Surface/Profile) Sea Surface Temperature Down SW Radiance (Sfc) Sea Ice Characterization Sea Surface Winds Electric Fields Sea SFC Height/TOPO Soil Moisture Electron Density Profile Snow Cover/Depth Aerosol Optical Thickness Energetic Ions Solar Irradiance Aerosol Particle Size Geomagnetic Field Supra-Therm-Aurora Prop Ice Surface Temperature Aerosol Refractive Index Surface Type Albedo (Surface) In-situ Plasma Fluctuation Active Fires (Application product) Auroral Boundary In-situ Plasma Temp Surface Wind Stress Ionospheric Scintillation Auroral Energy Deposition Suspended Matter Auroral Imagery Med Energy Chgd Parts Total Water Content Land Surface Temp Cloud Base Height Vegetative Index Net Heat Flux Cloud Cover/Layers LEGEND Net Solar Radiation (TOA) Cloud Effective Part Size Neutral Density Profile Cloud Ice Water Path VIIRS (24) CMIS (19) Cloud Liquid Water Ocean Color/Chlorophyll CrIS/ATMS (3) Cloud Optical Thickness Ocean Wave Character OMPS (1) Cloud Particle Size/Distrib Outgoing LW Rad (TOA) SES (13) Cloud Top Height O3 – Total Column Profile - Key Performance Parameters

  9. NPOESS Enhancement Products(Pre-Planned Product Improvement – P3I) • Tropospheric Winds • CH4 Column • CO Column • CO2 Column • Optical Background • All Weather Day/Night Imagery • Sea and Lake Ice • Littoral Currents • Coastal Ocean Color • Bioluminescence Potential • Coastal Sea Surface Temperature • Coastal Sea Surface Winds • Coastal Sea Surface Height • Coastal Imagery • Ocean Wave Characteristics • Surf Conditions • Bathymetry (Deep Ocean & Near Shore) • Salinity • Oil Spill Location • Vertical Hydrometer Profile • Neutral Wind

  10. Program Schedule 2002 A&O Contract Award 2003 NPP Delta Critical Design Review 2005 NPOESS Preliminary Design Review 2006 NPOESS Critical Design Review NPP Ground Readiness 2006 NPP Launch 2009 NPOESS Ground Readiness 2009 NPOESS C1 Launch 2011 NPOESS C2 Launch Field Terminal Segment Readiness Initial Operational Capability 2013 NPOESS C3 Launch 2015 NPOESS C4 Launch 2017 NPOESS C5 Launch 2019 End of Program Reliable and timely collection, delivery, and processing of quality environmental data

  11. TSKY Field Terminals TOBS SafetyNetReceptors TATM LCL LATM LRN FOG eij NPOESS Operational Concept 2. Downlink Raw Data 1. Sense Phenomena 3. Transport Data to Centrals for Processing Global fiber network connects 15 receptors to Centrals 4. Process Raw data into EDRs and Deliver to Centrals Monitor and Control Satellites and Ground Elements AFWA NESDIS/NCEP MMC (Suitland) Schriever MMC FNMOC NAVO Full Capability at each Central

  12. GPS SpaceSegment NPP(1030) NPOESS1330 NPOESS1730 NPOESS2130 Low Rate Data/High Rate Data(LRD/HRD) Command& ControlSegment NPP Science Data Segment Field Terminal Segment Svalbard CLASS ADS 15 Globally DistributedReceptor Sites FNMOC NAVOCEANO AFWA NESDIS/NCEP Alternate MMCat Schriever AFB Mission ManagementCenter (MMC)at Suitland Interface Data Processing Segment NOAA Comprehensive Large Array Data Stewardship System NPP Data & Control Flow NPOESS Data & Control Flow ADS CLASS NPP Archive & Distribution Seg NPOESS Top Level Architecture • Data Quality • SMD/HRD • LRD 128 attributes above, 724 at, 7 below threshold 305 attributes above, 180 at, 0 below threshold Threshold • Data Latency • SMD • HRD/LRD Objective Data Availability Operational Availability

  13. 1330 1730 2130 VIIRS X X X CMIS X X X CrIS X X ATMS X X SESS X OMPS X ADCS X X SARSAT X X X ERBS X SS X X X ALT X TSIS X APS X CMIS ATMS CrIS VIIRS OMPS ERBS NPOESS 1330 Configuration NPOESS Satellite Single Satellite Design with Common Sensor Locations

  14. SafetyNet – The Key to Low Data Latency and High Data Availability 75% of NPOESS Data Products at the Nation’s Weather Centrals within 15 min........the rest in under 30 min SafetyNet -- 15 globally distributed SMD receptors linked to the centrals via commercial fiber -- enables low data latency and high data availability

  15. Average Data Latency Latency (minutes)

  16. SMD C3S C3S Centrals IDP IDP Space Vehicle 3 Space Vehicle 3 @ @ C3S C3S CONUS CONUS IDP Centrals Centrals SMD SMD DHN & DHN & Gateways Gateways Ground Ground @ Space Vehicle 2 Space Vehicle 2 Long Term Archive FEP FEP (4x) (4x) Science Receptor Receptor Centrals Space Vehicle 1 Space Vehicle 1 (@ IDPS) (@ IDPS) Users Mission Mission Ground Ground Sensors Bus Bus Sensors Management Management Station Station Deliver Data Products Center Center IDPS IDPS Stored Mission Data flow for Centrals and Science Data Users HRD Field Users Field Field LRD Terminal Terminal Field Software Software Terminal Software Field Terminals (LRD, HRD) Field Terminals (LRD, HRD) Deliver Data Products HRD, LRD Data flow for Field Users SARSAT, ADCS Terminals Mission Data Flow Timely, Accurate, Reliable Data from Sensors to Users

  17. Processing Subsystem Central Systems Command,Control, andCommunicationsSegment SDR/TDR Generation EDR Generation FormattedDataProducts RawDataRecords Sensor/Temp DataRecords EnvironmentalDataRecords Long Term Archive StoredMissionData On-Line Data Storage Ingest Subsystem Data Management Subsystem Data Delivery Subsystem Science Data Segment Sensor Data Ancillary Data Auxiliary Data DataFormatting RawDataRecords Cal/ValSubsystem DataRecords FormattedData Products Data Quality Monitoring Infrastructure Subsystem Production Schedulingand Control IDPOperator CVSOperator Interface Data Processing Segment (IDPS) Functional Diagram

  18. Interface Data Processing Segment Approach • High performance IBM computing hardware • Each Central has a complete system (IDP) that will generate all products within required latencies • Each IDPS or Central contains an Operations string, an Integration and Test (I&T) string, and shared disk arrays (RAID) • Operations string carries 100% reserve capacity and additional availability processors • I&T string can be used for integration and test of new software, support for technology insertion, parallel operations, failover, and algorithm development • Modular, workflow-managed software • Receives multiple data streams from C3S, processes data into RDRs, SDRs, TDRs, and EDRs, packages products into form useful for Centrals, and delivers requested products to end users • Centrals have control over what products are created, which ancillary data sets are used, and how products are delivered • Same software is used in field terminals • Will be made available worldwide via download from the internet

  19. NPOESS EDR Processing Timeline Requirement: 95% of data delivered within 28 min. Capability: Delivering in 24.1 minutes Requirement: >77% of data delivered within 15 min. Capability: Delivering 80.3% Average < 10 min Earliest Data Delivered < 3 min

  20. Comprehensive Risk Reduction • Validate technological approach to remote sensing • Early delivery of NPOESS data to users • Sensor demonstrations on non-operational platforms • Lower risk to operational users • Lower risk of launch delays due to operational schedule • Share cost & risk among agencies Proteus WindSat NASA ER2 / NAST LORE SOLSE

  21. NPOESS Preparatory Project (NPP)Joint IPO-NASA Risk Reduction Demo • NPP Spacecraft contract awarded to Ball Aerospace – May 2002 • Instrument Risk Reduction – 2006 Launch • Early delivery / instrument-level test / system-level integration and test • VIIRS - Vis/IR Imager Radiometer Suite (IPO) • CrIS - Cross-track IR Sounder (IPO) • ATMS - Advanced Technology Microwave Sounder (NASA) • OMPS – Ozone Mapping and Profile Suite (IPO) • Provides lessons learned and allows time for any required modifications before NPOESS first launch • Ground System Risk Reduction • Early delivery and test of a subset of NPOESS-like ground system elements • Early User Evaluation of NPOESS data products • Provides algorithms / instrument verification and opportunities for instrument calibration / validation prior to first NPOESS launch • Allows for algorithm modification prior to first NPOESS launch • Continuity of data for NASA’s EOS Terra/Aqua/Aura missions

  22. Real-Time Operational Demonstrations NPP (2006) CrIS/ATMS VIIRS OMPS Coriolis WindSat (2003) METOP (2005) IASI/AMSU/MHS & AVHRR NPOESS (2009) CrIS/ATMS, VIIRS, CMIS, OMPS & ERBS Aqua (2002) AIRS/AMSU/HSB & MODIS Use of Advanced Sounder Data for Improved Weather Forecasting/Numerical Weather Prediction NOAA Real-Time Data Delivery Timeline Ground Station Scenario NWS/NCEP GSFC/DAO ECMWF UKMO FNMOC Meteo-France BMRC-Australia Met Serv Canada NOAA Real-time User NWP Forecasts IDPS C3S Joint Center for Satellite Data Assimilation

  23. Current Satellite Data Support forNumerical Weather Prediction • Over 97% of the data ingested into the data assimilation system is derived from satellite data (LEO, GEO, operational, and experimental)...Dr. Louis Uccellini, Director, NCEP, 2003 • POES provides 86% of satellite data for NCEP prediction models[Worldwide forecast models mostly use satellite sounding data which is primary mission of POES and secondary on DSMP] …Dr. Stephen Lord, Director, EMC/NCEP • ATOVS temperature super-obs produced the largest reduction in 72h forecast error of any observation type • Early study results indicate advanced sounders with capabilities similar to those being developed for NPOESS (e.g., CrIS and ATMS) indicate measurable positive impact on model accuracy - European Centre for Medium Range Weather Forecasts

  24. G.A.Kelly (ECMWF) Satellite Soundings and Forecast SkillCourtesy Dr. Bill Smith • Before the availability of satellite sounding data, useful forecasts for the Southern Hemisphere were limited to data rich areas such as New Zealand and Australia and these were limited to very short range (i.e., < 2 days). • Today Southern Hemisphere forecasts have about the same scale and useful range as Northern Hemisphere forecasts, primarily the result of the global satellite sounding system.

  25. More • Channels • Better • Soundings Radiances and Temperature & Moisture Profiles • Current • fidelity Higher Spectral Resolution Soundings • Data from atmospheric sounders provide the primary input to Numerical Weather Prediction models at Operational Processing Centers • NPOESS • fidelity NPOESS will deliver higher spectral resolution soundings with improved data latency to initialize NWP models and improve weather forecasts

  26. NPOESS Challenges for NWP • Orders of magnitude increase in data volume from NPOESS will require commensurate increases in computational power for data assimilation and modeling • How can we make best use of higher spatial and temporal resolution data through assimilation • New methods will be required to take advantage of higher spectral resolution data from atmospheric sounders (CrIS, AIRS, and IASI) • How can all the radiance information be used in NWP – as radiances or retrievals? • How do we take advantage of improved data latency? • More rapid update cycles through 4-dimensional data assimilation • What is the impact of clouds on soundings? • How do we use VIIRS for enhancing high vertical resolution CrIS retrieval reliability above clouds • How can we use ATMS/CMIS for providing sub-cloud level profile information?

  27. NPOESS Era Data VolumeCourtesy Dr. Stephen Lord Five Order of Magnitude Increase in Satellite Data Over Next Ten Years Daily Satellite Observation Count 2003 125 M obs 2002 100 M obs Count (Millions) 1990 2000 2010 2010-10%of obs

  28. Expected NPOESS Instrument Impact on NWS Forecast PerformanceCourtesy Dr. Stephen Lord P: PrimaryS: SecondaryN: None

  29. T H E N P O E S S T E A M

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