S' Johnston to NPP Science Team

1
Concept of Operations - for Today and On-orbit
Shaida JohnstonNASA/GSFC FEB 9, 2004 
2
Objectives

• Flesh out the concepts of how an on-orbit Science
  Data Segment (SDS) would operate
• SDS basic functionality and implementation still
  in flux
• Focus today on EDR characterization/evaluation
  functions
• Sensor calibration and characterization functions
  will be worked with individual sensor Level 1
  groups
• Walk through general scenarios to develop
  specificity
• Product evaluation and some CDR generation
  scenarios covered
• Identify stake holders concepts for SDS
• Project scientist (Code 900), NPP Science team
  (science community), NPP project (Code 400),
  Program Executive (HQ)
• Identify commonality and issues to be worked
• Consensus vs. compromise
• Minimum functionality required to perform

3
Introduction and Disclaimer

• Johnston to facilitate discussions
• No vested interest in a particular approach
• Capture consensus and commonality if possible
• Document concepts of operation in evolving draft
• Background material used to generate concepts
• Interagency agreements
• NPP Mission Specification
• Previous SDS architecture proposals and Con Ops
• IDPS specification (no Con Ops available)
• EDR interdependencies
• NPP Science Team NRA
• MODIS products, production and algorithms (less
  familiar with ozone and sounding instruments)
• Discussions with stake holders
• Last Ops Con meeting focused on questions of how
  this science team would operate and interact with
  those developing systems

4
Different Mission Approaches
Operational Mission
Science Mission
5
NPP in the NPOESS Context

• 11 NPOESS Instruments - 4 on NPP
• 56 NPOESS EDRs - 27 provided by NPP
• NPP Launch Date 4QCY2006
• Concurrent Activities of Interest
• Calibration Plans - development and review
• Test Plans - development and review
• Algorithm deliveries
• EDU testing and characterization
• FU development and integration
• IDPS definition and operational concepts
• QA flags
• Intermediate products

6
SDS Context - NPP System
GPS
TDRSS
WSC LEO A Backup TC
NESDIS Central
AFWA Central
I/F Data Proc Segment
Command, Cntrl CommSegment
Data Del
Data Del
Science Data Segment
GSE - Svalbard Primary TC NPP SMD
Cal/Val
Cal/Val
Process
Process
Infra
Infra
Data Mgt
Data Mgt
Ingest
Ingest
Archive Dist Segment
Ingest

• Mission Mgmt Center - Suitland
• Flight Operations Team
• Enterprise Management
• Mission Management
• Satellite Operations
• Data Monitoring Recovery

Archive Interchange
Datastore
Data Handling Node
Inventory
RoboticStorage
Archiver
Data Routing Retrieval
Climate User Community
NPP Mission Data
Command and Telemetry
Key
Climate User Community
7
Working Definitions (1)

• Ops Con Con Ops Concept of Operations
• RDR Raw Data Record CEOS/NASA Level 1A
• SDR Sensor Data Record CEOS/NASA Level 1B
• EDR Environmental Data Record CEOS/NASA Level
  2
• 4 Types of EDRs generated in the IDPS
• Simple Single sensor, single algorithm
• Blended Multiple sensors, multiple algorithms
• Fused Multiple sensors, single algorithm
• Combined Multiple EDRs combined for a single EDR
  (Level 3)
• Ancillary data From outside the program (e.g.
  NCEP)
• Auxiliary data From within the program (e.g.
  spacecraft)
• Proxy data Synthetic data generated from
  heritage sensors
• Simulated data generated from models

8
Working Definitions (2)

• MMCDR Multi-mission climate data record (aka
  CDR)
• High quality long-term data record across
  platforms and missions
• with quantified error characteristics
• Essential parameter to monitor and understand
  Earths climate variability and trends
• Example Ocean color from CZCS-gtSeaWiFS-gtMODIS-gtVI
  IRS
• Example Ozone from SBUV/2 -gtTOVS/HIRS-gtOMPS
• NPPCDR NPP climate data record (aka cDR)
• Enhancement or higher level processing of an NPP
  EDR to allow multi-mission climate data records
  to be generated
• Example Ocean color EDRs reprocessed after
  post-launch calibration updates or instrument
  change
• Example NDVI EDRs reprocessed with definitive
  ephemeris providing tighter geolocation knowledge
• Example Temporally and spatially aggregated EDRs
  i.e global, monthly average SSTs

9
Operations in Two Time Frames
Simulation Tools
Algorithm Deliveries
RDRs SDRs EDRs
EDU FU Test Data (Charac/Cal)
Algorithm Updates
Synthetic Test Data
Algorithm Updates
On-orbit Cal (RDRs)
NPP Analysis System (NAS) Needed now to
- Support 1st NPP science team - Evaluate
EDRs - Process test data MODIS-gt VIIRS
conversion s/w Prototype for sensor cal/charac
-Improved Algorithms -EDR Characterizations -Calib
ration Parameters -Sensor Characterization

On-Orbit
Now
2004
2010
2006
Today
NPP Launch
1st NPOESS Launch
10
NPP Analysis System (NAS)

• Document Stores
• eRooms (NGST)
• NPP Electronic Library

11
A Concept for SDS - 1
CDR NPP CDR and MM CDR
IDPS
C3
MODIS
Ocean Color CARS (GSFC) CZCS SeaWiFS
EDR X CARS (Univ.B)
Level 3 Processing -Global -Monthly (Location C)
SST CARS (Univ.A)
Atmos. Sounding CARS (NOAA)
EDRs
ADS
Climate User Community
SDRs,IPs EDRs
RDRs SDRs

• Product Evaluation
• Algorithm Testing
• Validate EDRs
• Characterize EDRs
• -Stratification
• -Quantify error
• Test new algorithms

Sensor Calibration Characterization -Process
cal RDRs -Lunar cal,deep space,
internal/BB -Verify RDRs -Characterize
SDRs -Generate calibration parameters - 4 sensors
CDRs
CDRs
CDRs
CDRs
CDRs
Climate User Community
EOSDIS
CDRs
CDRs
CDRs
CDRs
Aerosols Theme CARS (Location F)
Land Theme CARS (Location G)
Ocean Theme CARS (Location E)
Ozone Theme CARS (Location D)
Algorithm Updates
IPO
NGST/Raytheon
EDRs
EDRs
EDRs
EDRs
IDPS
Cal Parms
12
A Concept for SDS - 2
CDR NPP CDR and MM CDR
EDRs
VIIRS SDRs
CrIMS EDRs
SDRs
EDRs
IDPS
C3
MODIS
Ocean Color CARS (GSFC) CZCS SeaWiFS
EDR X CARS (Univ.B)
Level 3 Processing -Global -Monthly (Location C)
SST CARS (Univ.A)
Atmos. Sounding CARS (NOAA)
EDRs
ADS
Climate User Community
RDRs SDRs
SDRs,IPs EDRs
Sensor Calibration Characterization -Process
cal RDRs -Lunar cal,deep space,
internal/BB -Verify RDRs -Characterize
SDRs -Generate calibration parameters - 4 sensors

• Product Evaluation
• Algorithm Testing
• Validate EDRs
• Characterize EDRs
• -Stratification
• -Quantify error
• Test new algorithms

CDRs
CDRs
CDRs
CDRs
CDRs
Climate User Community
EOSDIS
CDRs
CDRs
CDRs
CDRs
Aerosols Theme CARS (Location F)
Land Theme CARS (Location G)
Ocean Theme CARS (Location E)
Ozone Theme CARS (Location D)
Algorithm Updates
IPO
NGST/Raytheon
EDRs
EDRs
EDRs
EDRs
IDPS
Cal Parms
13
A Concept for SDS - 3
CDR NPP CDR and MM CDR
IDPS
C3
Ocean Color CARS (GSFC) CZCS SeaWiFS
EDR X CARS (Univ.B)
Level 3 Processing -Global -Monthly (Location C)
SST CARS (Univ.A)
Atmos. Sounding CARS (NOAA)
Climate User Community
EDRs
ADS
RDRs SDRs
Sensor Calibration Characterization -Process
cal RDRs -Lunar cal,deep space,
internal/BB -Verify RDRs -Characterize
SDRs -Generate calibration parameters - 4 sensors
CDRs
CDRs
CDRs
CDRs
CDRs
Climate User Community
Algorithm Updates
EOSDIS
CDRs
CDRs
CDRs
CDRs
Aerosols Theme CARS (Location F)
Land Theme CARS (Location G)
Ocean Theme CARS (Location E)
Ozone Theme CARS (Location D)
IPO
NGST/Raytheon
Cal Parms
Algorithm Updates
IDPS
14
SDS Open Issues

• Implementation of CARS
• Interdependence of products
• IDPS - SDS interface
• Mechanism to get early mission data to SDS for
  evaluation
• .

15
Methodology

• Regardless of where the functions are performed,
  they need to be defined and documented
• Develop an operations concept which then leads to
  functional requirements
• Focus on SDS Concept 2
• Discuss strawman scenarios to draw out routine
  and exceptional capabilities needed for product
  evaluation and algorithm assessment
• Identified specialized operations where
  one-on-one discussions are needed
• Draft an operations concept
• Iterate draft with further discussions with group
  or individuals as needed
• Budget realities will constrain the system
• Identify minimum functionality needed as a lower
  bound
• Complete functionality is the upper bound

16
Product Evaluation Scenarios

• Compare NPP EDR with equivalent Level 2 product
• Independently generate EDR to compare with IDPS
  generated EDR
• Use NPP EDR as input to higher level application
• a. Level 3 product generation
• b. Climate model
• Evaluate inputs to EDR and algorithm (i.e. SDR,
  ancillary)
• a. Modeling an EDR
• b. Document review

RDRs and SDRs are assumed to be evaluated in the
sensor calibration and characterization system
17
Product Evaluation Scenario 1 Compare NPP EDR
with equivalent Level 2 product
Tools needed ? - Reprojection - IDL/ENVI
Orbital Variations By lat, temp, etc.
Compare and Characterize
Global comparisons By location, biomes,etc.
Does EDR meet spec?
Seasonal Variations Annual Variations Time Series
Repeat for validation sites or global
samples e.g. 274 MODLAND sites (geographic
subsets)
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Product Evaluation Scenario 2 Compare NPP EDR
with independently generated EDR
Example CrIS/ATM Vertical
Moisture Profile
-VIIRS LST -S/C data
Orbital Variations By lat, temp, etc.
Compare and Characterize
EDR
Global comparisons By location, biomes,etc.
- CrIS - ATMS
Seasonal Variations Annual Variations Time Series
Repeat for validation sites or global samples
-Land/Water Mask -NCEP Surf. Pressure -Terrain
height
Additional or different ancillary data than
original
Results could provide quantified scientific
justification for reprocessing
19
Product Evaluation Scenario 3a Use NPP EDR as
input to higher level application

• a. Level 3 product generation

Characterize
Data buffer
Level 3 Product
Order EDRs
Run Level 3 Application
- SST
-Global, Monthly Average SST
20
Product Evaluation Scenario 4a Evaluate inputs
to EDR and algorithm - model EDR
Example ATMS calibration uncertainty effects on
Temperature Profile EDR
Model/assume Vicarious Calibrations
Space Radiance
Calibration Accuracy
Internal Calibrator Characteristics
Radiometric Sensitivity (NE?T)
Model (ATMS) Sensor
Simulated SDR
Scan Bias
EDR Characteristics (retreival accuracy) as a
functions of input variables
Calibration Nonlinearities
Repeat through variable ranges
21
CDR Generation Scenarios
Intermediate Product
RDR
SDR
EDR
NPP CDR
MultiMission CDR
3

• EDR post-processing (i.e. regridding or time
  integration)
• Reprocess EDR using better or more ancillary data
• - What is the right threshold of improvement to
  reprocess?
• Recalibrate and remove discontinuities from NPP
  EDRs for multi-mission CDR generation

22
CDR Generation Scenario 1 EDR post-processing
Data buffer
CDRs
Run Level 3 Application
- SST
-Global, Monthly Average SST
23
CDR Generation Scenario 2 Reprocess
EDR using better or more ancillary data
Example CrIS/ATMS Vertical Moisture Profile
-VIIRS LST -S/C data
Data buffer
CDRs
Store Distribute
- CrIS - ATMS
-Land/Water Mask -NCEP Surf. Pressure -Terrain
height
Additional or different ancillary data than
original
24
CDR generation Scenario 3 Recalibrate
reprocess discontinuities
SDR
CDR
25
Next Steps

• Conduct one-on-one discussions with
• Develop scenarios for sensor calibration and
  characterization functions
• RDR and SDR product evaluations
• Generation of calibration parameters
• Draft an operations concept document
• Iterate draft with group or individuals

26

Back up slides
27
Characteristics of MM CDRs

• A collection of data that can be used to
  construct a high quality time series with
  quantified error characteristics
• Time series free of instrument artifacts and
  changes in algorithms
• Validation of calibration processes,
  reprocessing, and blending of in situ and
  satellite measurements across platforms/missions
• MM CDRs provide information to
• monitor change (climate variability and trends)
  of the Earths climate
• predict change
• input to model re-analyses
• validate climate prediction model
• understand processes (i.e. water
  vapor-cloud-radiation feedback)

28
NPP Science Algorithm Drop Schedule(Proposed New
Drop Dates)
29
NPP Science Algorithm New Drop Schedule Up to 9
Months Right Shift