npoess entering a new era national polar-orbiting operational environmental satellite system...

NPOESSNPOESSEntering a New EraEntering a New Era

National Polar-orbiting Operational Environmental National Polar-orbiting Operational Environmental Satellite SystemSatellite System

Delivering Global Data for Improved Numerical Delivering Global Data for Improved Numerical Weather PredictionWeather Prediction

AMS Symposium on the 50AMS Symposium on the 50thth Anniversary Anniversary ofof

Operational Numerical Weather Operational Numerical Weather PredictionPrediction

John D. CunninghamJohn D. CunninghamSystem Program DirectorSystem Program Director

College Park, MarylandCollege Park, Maryland14-17 June 200414-17 June 2004

2NPOESS Program Overview John D. Cunningham

MissionMission

• Provide a national, operational, polar-orbiting Provide a national, operational, polar-orbiting remote-sensing capabilityremote-sensing capability

• Achieve National Performance Review (NPR) Achieve National Performance Review (NPR) savings by converging DoD and NOAA savings by converging DoD and NOAA satellite programssatellite programs

• Incorporate new technologies from NASAIncorporate new technologies from NASA

• Encourage International CooperationEncourage International Cooperation

Saves as much as $1.3B from the cost of previously planned Saves as much as $1.3B from the cost of previously planned separate developmentsseparate developments

METOP

NPOESS

Specialized Satellites Local Equatorial

Crossing Time

1730

1330

2130

NPOESS

NPOESS

Tri-agency Effort to Leverage and Combine Environmental Satellite Activities


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 Achievement of significantsignificant cost savings while still satisfying cost savings while still satisfyingcivil and military mission requirements now a civil and military mission requirements now a drivingdriving priority priority

Achievement of Achievement of significantsignificant cost savings while still satisfying cost savings while still satisfyingcivil and military mission requirements now a civil and military mission requirements now a drivingdriving priority priority


DMSP (Defense Meteorological

Satellite Program)

EOS (Earth Observing

System)

NPOESS (National Polar-orbiting

Operational Environmental Satellite System)

Sensor data rate: 1.5 MbpsData latency: 100-150 min.

1.7 GigaBytes per day (DMSP)6.3 GigaBytes per day (POES)

15 Mbps sensor data rateData 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 rateData latency: 28 min.Data availability: 99.98%Autonomous capability: 60 daysSelective encryption/deniabilityGround revisit time: 4-6 hrs.

8.1 TeraBytes per day

Evolutionary Roadmap

POES (Polar-orbiting

Operational Environmental Satellites)

NPP (NPOESS

Preparatory Project)

1960 - 2010 2000 - 2010 2010 – 2020+

NPOESS Satisfies Evolutionary Program Needs with Enhanced CapabilitiesNPOESS Satisfies Evolutionary Program Needs with Enhanced CapabilitiesNPOESS Satisfies Evolutionary Program Needs with Enhanced CapabilitiesNPOESS Satisfies Evolutionary Program Needs with Enhanced Capabilities


Building A More Capable SystemThe Historical Context

First Image from TIROS-1 EOS-Aqua MODIS Image-250 m

Saharan Dust off the Canary Islands18 February 2004


NPOESS Requirements

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 Alternatives

Convergence of

Requirements

Converged Requirements Provide Foundation for Combined ProgramConverged Requirements Provide Foundation for Combined Program Converged Requirements Provide Foundation for Combined ProgramConverged Requirements Provide Foundation for Combined Program


Atmospheric Vertical Temperature ProfileHighly accurate measurement of the vertical distribution of temperature in the atmosphere in layers from the surface to 0.01 mb

Major Applications

1) Initialization of Numerical Weather Prediction Models

2) Complementary data for derivation of moisture/pressure profiles and cloud properties

Integrated Operational Requirements Document (IORD) Example

Iterative, Disciplined Iterative, Disciplined Requirements Process Requirements Process

Ensures Users Needs are MetEnsures Users Needs are Met

Iterative, Disciplined Iterative, Disciplined Requirements Process Requirements Process

Ensures Users Needs are MetEnsures Users Needs are Met


NPOESS EDR-to-Sensor Mapping

Precipitation Type/Rate

Sea SFC Height/TOPO

Solar IrradianceSupra-Therm-Aurora PropSurface Type

Suspended MatterTotal Water ContentVegetative Index

Surface Wind Stress

Snow Cover/Depth

Cloud Top PressureCloud Top TemperatureDown LW Radiance (Sfc)Down SW Radiance (Sfc)Electric Fields

Energetic IonsGeomagnetic Field

In-situ Plasma FluctuationIn-situ Plasma Temp

Med Energy Chgd Parts

Net Solar Radiation (TOA)Neutral Density ProfileOcean Color/ChlorophyllOcean Wave CharacterOutgoing LW Rad (TOA)O3 – Total Column Profile

Electron Density Profile

Ionospheric Scintillation

Ice Surface Temperature

Land Surface TempNet Heat Flux

Imagery

Sea Surface Winds

Aerosol Refractive IndexAlbedo (Surface)Auroral BoundaryAuroral Energy DepositionAuroral Imagery

Cloud Cover/LayersCloud Effective Part SizeCloud Ice Water PathCloud Liquid WaterCloud Optical ThicknessCloud Particle Size/DistribCloud Top Height

Atm Vert Moist ProfileAtm Vert Temp Profile

Sea Surface Temperature

Cloud Base Height

VIIRS (24)CMIS (19)

CrIS/ATMS (3)

SES (13)OMPS (1)

TSIS (1)ERBS (5)

ALT (3)APS (4)

LEGEND

- Key Performance Parameters

Precipitable Water

Soil MoistureAerosol Optical ThicknessAerosol Particle Size

Pressure (Surface/Profile)Sea Ice Characterization

Active Fires (Application product)


NPOESS Enhancement Products(Pre-Planned Product Improvement – P3I)

1. Tropospheric Winds

2. CH4 Column

3. CO Column

4. CO2 Column

5. Optical Background

6. All Weather Day/Night Imagery

7. Sea and Lake Ice

8. Littoral Currents

9. Coastal Ocean Color

10. Bioluminescence Potential

11. Coastal Sea Surface Temperature

12. Coastal Sea Surface Winds

13. Coastal Sea Surface Height

14. Coastal Imagery

15. Ocean Wave Characteristics

16. Surf Conditions

17. Bathymetry

(Deep Ocean & Near Shore)

18. Salinity

19. Oil Spill Location

20. Vertical Hydrometer Profile

21. Neutral Wind


Program Schedule

2002 A&O Contract Award

2003 NPP Delta Critical Design Review

2005 NPOESS Preliminary Design Review

2006 NPOESS Critical Design ReviewNPP Ground Readiness

2006 NPP Launch

2009 NPOESS Ground Readiness

2009 NPOESS C1 Launch

2011 NPOESS C2 LaunchField Terminal Segment

ReadinessInitial Operational Capability




2019 End of Program

Reliable and timely collection, Reliable and timely collection, delivery, and processing of delivery, and processing of quality environmental dataquality environmental data

Reliable and timely collection, Reliable and timely collection, delivery, and processing of delivery, and processing of quality environmental dataquality environmental data


NPOESS Operational Concept

1. Sense Phenomena

2. Downlink Raw Data

3. Transport Data to Centrals for Processing

Monitor and Control Satellites and Ground Elements

4. Process Raw data into EDRs and Deliver to Centrals

Full Capability at each Central

T

O

B

S

L

A

T

M

L

C

L

FOG

L

R

N

TATM

TSKY

ei

j

Field Terminals SafetyNet

Receptors

Global fiber network connects 15 receptors to Centrals

MMC (Suitland)

Schriever MMC

NESDIS/NCEP AFWA

FNMOC NAVO


NPOESS Top Level Architecture

Data LatencySMDHRD/LRD

Data Availability

Operational Availability

Threshold Objective

128 attributes above, 724 at, 7 below threshold128 attributes above, 724 at, 7 below threshold305 attributes above, 180 at, 0 below threshold305 attributes above, 180 at, 0 below threshold

Data QualitySMD/HRDLRD

SpaceSegment

Command& ControlSegment

Command& ControlSegment

NPP(1030)

NPOESS1330

NPOESS1730

NPOESS2130

Mission ManagementCenter (MMC)at Suitland

Mission ManagementCenter (MMC)at Suitland

Alternate MMCat Schriever AFBAlternate MMCat Schriever AFB Interface Data Processing SegmentInterface Data Processing Segment

15 Globally DistributedReceptor Sites15 Globally DistributedReceptor Sites

Field Terminal SegmentField Terminal Segment

FNMOC NAVOCEANO AFWA NESDIS/NCEP

GPS

Low Rate Data/High Rate Data(LRD/HRD)

NPP Science Data Segment

CLASS ADS

NPP Data & Control Flow NPOESS Data & Control Flow CLASS ADSNOAA Comprehensive Large Array Data Stewardship System NPP Archive & Distribution Seg

SvalbardSvalbard


NPOESS Satellite

CMIS

VIIRS

CrIS

ATMS

ERBSOMPS

NPOESS 1330 Configuration

Single Satellite Design with Common Sensor LocationsSingle Satellite Design with Common Sensor Locations Single Satellite Design with Common Sensor LocationsSingle Satellite Design with Common Sensor Locations

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


SafetyNet – The Key to Low Data Latency and High Data Availability

SafetyNet -- 15 globally distributed SMD receptors linked to the centrals via SafetyNet -- 15 globally distributed SMD receptors linked to the centrals via commercial fiber -- enables low data latency and high data availabilitycommercial fiber -- enables low data latency and high data availability

SafetyNet -- 15 globally distributed SMD receptors linked to the centrals via SafetyNet -- 15 globally distributed SMD receptors linked to the centrals via commercial fiber -- enables low data latency and high data availabilitycommercial fiber -- enables 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


Average Data Latency

Latency (minutes)


Mission Data Flow

Timely, Accurate, Reliable Data from Sensors to UsersTimely, Accurate, Reliable Data from Sensors to UsersTimely, Accurate, Reliable Data from Sensors to UsersTimely, Accurate, Reliable Data from Sensors to Users

Space Vehicle 2

C3SDHN &

FEP(@ IDPS)

IDP@

Centrals

C3S

Sensors Bus

Space Vehicle 1

IDPS

Centrals SMD

Field Terminals (LRD, HRD)

FieldTerminalSoftware

SARSAT, ADCSTerminals

CONUS Gateways

(4x)

SMD

LRD

HRD

Space Vehicle 3Ground

Receptor

GroundStation

Mission Management Center

Space Vehicle 2

Stored Mission Data flow forCentrals and Science Data Users

C3SDHN &

FEP(@ IDPS)

IDP@

Centrals

IDP@

Centrals

C3S

Bus

Space Vehicle 1

IDPS

Science Users

SMD

Field Terminals (LRD, HRD)



HRD, LRD Data flow for Field Users

Field Users

CONUS Gateways

(4x)

Space Vehicle 3Ground

Receptor

GroundStation

Mission Management Center

Deliver DataProducts

Long TermArchive

Deliver DataProducts

Sensors


Interface Data Processing Segment (IDPS) Functional Diagram

Data Delivery Subsystem

DataFormatting

Production Schedulingand Control

Infrastructure Subsystem

Data ManagementSubsystem

On-LineData Storage

Processing Subsystem

SDR/TDRGeneration

EDRGeneration

Ingest Subsystem

Sensor DataAncillary DataAuxiliary Data

Cal/ValSubsystem

Data Quality Monitoring

CentralSystems

Long Term

Archive

Science Data

Segment

Command,Control, and

CommunicationsSegment

StoredMission

Data

RawData

RecordsData

Records

Sensor/TempData

Records

RawData

Records

EnvironmentalData

Records

FormattedData

Products

IDPOperator

CVSOperator

FormattedData Products


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 developmentModular, 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

Interface Data Processing Segment Approach


Current End-to-End EDR Latency

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25 30 35 40 45 50

Time from Observation to Delivery (minutes)

Per

cen

t o

f E

DR

Pro

du

cts

Del

iver

ed

28

77%

NPOESS EDR Processing Timeline

Requirement: 95% of data delivered within 28 min. Capability: Delivering in 24.1 minutes

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%

Requirement: >77% of data delivered within 15 min. Capability: Delivering 80.3%

Average < 10 minAverage < 10 min

Earliest Data Delivered < 3 minEarliest Data Delivered < 3 min


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 agenciesProteus

SOLSE

LORE

WindSatNASA ER2 / NAST


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)VIIRS - Vis/IR Imager Radiometer Suite (IPO)• CrIS - Cross-track IR Sounder (IPO)CrIS - Cross-track IR Sounder (IPO)• ATMS - Advanced Technology Microwave Sounder (NASA)ATMS - Advanced Technology Microwave Sounder (NASA)• OMPS – Ozone Mapping and Profile Suite (IPO)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


Real-Time Operational Demonstrations

NPP (2006)CrIS/ATMS

VIIRSOMPS

Aqua (2002)AIRS/AMSU/HSB & MODIS

METOP (2005)IASI/AMSU/MHS & AVHRR

NPOESS (2009)CrIS/ATMS, VIIRS, CMIS,

OMPS & ERBS

CoriolisWindSat

(2003)

NWS/NCEP

GSFC/DAO

ECMWF

UKMO

FNMOC

Meteo-France

BMRC-Australia

Met Serv Canada

NWS/NCEP

GSFC/DAO

ECMWF

UKMO

FNMOC

Meteo-France

BMRC-Australia

Met Serv Canada

NWPForecasts

NWPForecasts

NOAA Real-Time Data Delivery TimelineGround Station Scenario

NOAAReal-time

UserC3SC3S IDPSIDPS

Joint Center for Satellite Data Assimilation

Use of Advanced Sounder Data for ImprovedUse of Advanced Sounder Data for ImprovedWeather Forecasting/Numerical Weather PredictionWeather Forecasting/Numerical Weather Prediction


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


• 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.

G.A.Kelly (ECMWF)

Satellite Soundings and Forecast SkillCourtesy Dr. Bill Smith


Higher Spectral Resolution Soundings

Data from atmospheric sounders provide the primary input to Numerical Weather Prediction models at Operational Processing Centers

Current fidelity NPOESS

fidelity

More Channels

Better Soundings

Radiances andTemperature & Moisture Profiles

NPOESS will deliver higher spectral resolution soundings NPOESS will deliver higher spectral resolution soundings with improved data latency to initialize NWP models and with improved data latency to initialize NWP models and

improve weather forecastsimprove weather forecasts

NPOESS will deliver higher spectral resolution soundings NPOESS will deliver higher spectral resolution soundings with improved data latency to initialize NWP models and with improved data latency to initialize NWP models and

improve weather forecastsimprove weather forecasts


•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?

NPOESS Challenges for NWP


Five Order of Magnitude Increase in Satellite Data Over Next Ten YearsFive Order of Magnitude Increase in Satellite Data Over Next Ten YearsFive Order of Magnitude Increase in Satellite Data Over Next Ten YearsFive Order of Magnitude Increase in Satellite Data Over Next Ten Years

Count

(Mill

ions)

Daily Satellite Observation Count

20001990 2010 2010-10%of obs

2002 100 M obs

NPOESS Era Data VolumeCourtesy Dr. Stephen Lord

2003 125 M obs


Expected NPOESS Instrument Impact on NWS Forecast PerformanceCourtesy Dr. Stephen Lord

Instrument Hurricane Seasonal Temp

Global 500mb

Accuracy

Marine Aviation Precip

VIIRS S S N P P S

CMIS S S N P S S

CrIS S N N S S N

ATMS P S P P P P

OMPS N S N N N N

SESS N N N N N N

ADCS S P N P N N

RADAR

Altimeter

S S N S N N

P: Primary S: Secondary N: None

npoess entering a new era national polar-orbiting operational environmental satellite system...

Documents

npoess program overviewjohn

combined program

evolutionary program

data availability

iordii55 data products

systemnpoess national

savings achievedpolicy

projected cost savings