Meteorological Satellite Instrumentation - I by wulinqing


									                     Meteorological Satellite

             I – Instrument Selection Process

                            John Forsythe
                             Feb. 9, 2005

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How are Satellite Instruments Chosen?
  • We will examine the process using
  illustrations from GOES – R and NPOESS (~
  2012 and 2010 launch respectively)
  • These will be the prime weather forecasting
  satellite instruments for much of your careers!
  The NPOESS system has a planned 25 year
        - You may be asked to advise on instrument
        specifications in the future
  • Most decisions and choices have already been
  made: 7 – 10 years before launch.
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          Two Paradigms for Instrument Design
    • Requirements-driven         • Specification-driven
      instrument                    instrument
    • Sponsoring agency           • Sponsoring agency, with
      mandates what they            input from science
      want to measure:              community, tells
      designers compete             instrument builders
      and innovate to               specific type of instrument
      develop an                  • The “traditional” approach
      appropriate                 • Example: GOES-R, most
      instrument                    Metsats.
    • Example: NPOESS             • NOAA / NASA
    • EDR-driven
    • IPO
   - a goldmine!
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       Where do requirements originate?
 • User community
       – NOAA (weather, oceanography)
       – DoD
       – Climate monitoring community
       – Space weather
       – Future needs (e. g. data assimilation)
 • Academia
 • Often based on heritage instruments

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For NPOESS, 6 Key EDR’s have been determined

      • Moisture profile                 There are dozens more
                                        EDR’s (ocean, land, ice,
      • Temperature Profile             atmosphere) which have
                                           varying degrees of
      • Sea Surface Winds                     importance
      • Imagery
      • Sea Surface Temperature
      • Soil Moisture
Competitors for the NPOESS instruments (CMIS, VIIRS, CRIS,
OMPS, ATMS…) each have a set of key EDR’s they must be
able to measure within the government’s specifications.
- Realistic simulations are critical here. As scientists, we want
to make sure the instrument is capable by itself.
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   Example from NPOESS IORD (see

                                        Must meet…        Desired…

                     Note: No mention of specific frequencies,
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                     algorithms…                                     7
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                     Sample requirements for GOES-R Advanced
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                     Baseline Imager (“Specification-Driven”)
   Sample text from GOES-R Mission Requirements Document
  “The imager shall be capable of acquiring data of a given area in both of
  the following time scales, although scan mode 4 is anticipated to be the
  normal operation mode:
          a) Scan mode 3: Full Earth disk (stepped-edge acceptable) every
  15-minutes (THRESHOLD); plus CONUS, or the equivalent of a nadir-
  viewed rectangle 5000 by 3000 Kilometers in dimension, every 5 minutes
  (THRESHOLD) and At least one 1000 by 1000 kilometer area (nadir) every
  30 seconds (THRESHOLD)
  b) Scan mode 4: Full Earth disk (stepped-edge acceptable) every 5-
  minutes (THRESHOLD)
  c) The CONUS and full disk imaging must be done concurrently with all
  other image activities, such as space looks, blackbody calibrations and
  star observations.
  d) The ABI image acquisition control system must be flexible such that
  different observational regions and timing can be easily commanded from
  the NESDIS SOCC within the general limitations of the scan rate, buffers
  and data rate limits of the system.”

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       Currently Selected NPOESS Instruments

Visible/IR Imaging Radiometer                   Cross-track IR Sounder (CrIS)
              Suite                                          ITT
        (VIIRS) Raytheon

                         Conically scanning Microwave
                                 (CMIS) Boeing       Advanced Technology
  Ozone Mapping & Profiler                        Microwave Sounder (ATMS)
       Suite (OMPS)                                   Northrup Grumman
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           NPOESS Satellite and Sensors
                                                     1330   1730 2130   NPP
                                            VIIRS     X      X    X      X
                                            CMIS      X      X    X
                                            CrIS      X      X           X
                                            ATMS      X      X           X
                                            SESS      X      X    X
                                            OMPS      X                  X
                                            ADCS      X      X
                                            SARSAT    X      X    X
                                            TSIS             X
                                            ERBS      X
                                            ALT              X
                                            APS                   X
                                            SS        X      X    X

                                              X = changed
 Single Satellite Design with Common Sensor Locations and “ring” Data
              Bus Allows Rapid Reconfiguration and Easy Integration
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                     A bad day building satellites!

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                                                ANOMALY REPORT
PROGRAM/PROJECT:                    GOES/POES Program/POES Project
DATE OF ANOMALY: September 6, 2003\
LOCATION OF ANOMALY:                Lockheed Martin, Sunnyvale CA
As the NOAA-N Prime spacecraft was being repositioned from vertical to horizontal on the “turn over cart” at
approximately 7:15 PDT today, it slipped off the fixture, causing severe damage. (See attached photo). The 18’
long spacecraft was about 3’ off the ground when it fell.

The mishap was caused because 24 bolts were missing from a fixture in the “turn over cart”. Two errors
occurred. First, technicians from another satellite program that uses the same type of “turn over cart” removed
the 24 bolts from the NOAA cart on September 4 without proper documentation. Second, the NOAA team
working today failed to follow the procedure to verify the configuration of the NOAA “turn over cart” since
they had used it a few days earlier.

The shock and vibration of the fall undoubtedly caused tremendous damage. Significant rework and retest will
be required. NOAA-N Prime is planned for launch in 2008.
 Lockheed Martin formed an Accident Review Team in which GSFC is participating. The immediate actions
 concern safety (preventing the spacecraft from rolling, discharging the batteries, and depressurizing the
 propulsion system). NOAA-N Prime is under guard, all records have been impounded, and the personnel
 interviewed. After the safety issues are addressed, attention will focus on assessing the damage to NOAA-N
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       Sensor Characteristics and the Trade Space
How many sensor
characteristics can you
There are relationships
and tradeoffs between
these characteristics.
Engineers and scientists
trade these properties
against each other to
develop a best value
• Requires reliable
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                     Sensor Characteristics

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                     Sensor Characteristics
  • Horizontal / vertical resolution
  • Time / space sampling (swath width, overlap between FOV’s)
  • Wavelengths, channelization
  • Instrument Noise
  • Calibration Strategy: Vicarious or ground-based needed?
  • Data Rate
  • Latency Time
  • Long-term stability (for climate)
  • Radio Frequency Interference
  • Spacecraft Accomodation
  • Engineering (mass, power, thermal, reliability…)
  • $$$

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            System optimization process ties requirements
              to system design for assured performance
                     and requirements traceability
                          Requirements                 System Architecture              System Design
                            Analysis                    Optimization and                 Optimization
   Customer                                               Requirements
 Requirements                                            Decomposition                   RFI Analysis
 – Cost                  Phenomenology
                                                          EDR Attributes                    EDR
                                                               CAIV                                          Optimized
                                                         SRD Flowdown                    Calibration          Design
                                                           (non-EDR)                      Design /
                           Feasibility                                                  Optimization
                            Analysis                           CAIV
                                                             System                     Optimization
                                                           Architectural                  Trades

                           Key Outputs:                    Key Outputs:                  Key Outputs:
                     • Required Frequencies           • Design Features             • Final Allocations
                       and Polarizations              • Initial Allocations         • System Design /
                     • Driving System                 • System Functional             Performance Baseline
                       Requirements / EDRs              Architecture
                     • Requirements Issues
                     • Strawman Architecture
                                         Risk Items, Risk Reduction Results, and TPMs

AT 737 Feb 9, 2005                   Courtesy of Ball Aerospace
         Cost As Independent Variable
                     • Balance of Cost and


                                             Knee in Curve



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        Error Budgets Really Tell the Story…
                                                                              Uncertainty (K)                Comments
    Compare                                                           271-285 K 285-299 K 299-313 K
   integrated                                                                                         Channel sensitivity plus
                                                                                                      effects of footprint
  parameters to      1 Instrument Noise                                 0.67      0.50      0.45
                                                                                                      matching. Assumes
sensor / algorithm                                                                                    Tscene=200K
   predictions                                                                                        Residual systematic error
                         Instrument Calibration and Antenna Pattern                                   expressed as an
                     2                                                  1.55      1.16      1.04
     Identify            Correction                                                                   uncertainty. Assumes 0.5
                                                                                                      interchannel correlation
sensor / algorithm                                                                                    Linear Regression
  level margin       3 Algorithm Error                                  0.85      0.72      0.69
                                                                                                      6.925 V & H, 10.85 V
                                                                                                      Based on a 1-sigma sensor
                     4 Earth Incidence Angle Knowledge                  0.32      0.24      0.23
     Iterate on                                                                                       pointing uncertainty
    parameters,                                                                                       Based on a 1-sigma sensor
                     5 Center Frequency Stability                       0.20      0.16      0.15
                                                                                                      frequency uncertainty
    and margin       Combined Standard Uncertainty                      1.93      1.49      1.36      RSS of parameters 1 - 5

                     Requirement                                        0.50      0.50      0.50      TBR
    Identify risk
       issues        RSS Margin                                          ---       ---       ---

                     Linear Margin                                      -1.43     -0.99     -0.86

                                       Sea Surface Temperature Example
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    Algorithm Theoretical Basis Documents (ATBD’s)

• An excellent reference source
• Usually written by algorithm developers before launch
• Describes interplay between instrument and desired science
• Examples:


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                     Backup Slides

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                                                             Soil moisture remote
RFI is a Growing Problem, Particularly
                                                              sensing (< 10 GHz)
with Passive Microwave at < 30 GHz                              highly affected


                                 22.3 GHz water vapor band (SSM/I)
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                     Earth remote sensing allocations (orange)                      23
  Atmos Vert Moist Prof     Dn Lwave Rad (Sfc)        Ozone-Tot Col/Profile
  Atmos Vert Temp Prof      Electric Field            Precipitable Water
  Imagery                   Electron Density Prof     Precip Type / Rate
  Sea Surf Temp             Aero Refractive Index     Pressure (Surf/Profile)
  Sea Surf Winds            Geomagnetic Field         Sea Ice Age Char
  Soil Moisture             Ice Surface Temp          Sea Surface Hgt/Topo
  Aero Opt Thickness        Energetic Ions            Snow Cover/Depth
  Aerosol Particle Size     In-situ Plasma Fluct      Solar Irradiance
  Albedo (Surface)          In-situ Plasma Temp       ST- Auroral Particles
  Auroral Boundary          Downward Swave Rad        Surface Wind Stress
  Auroral Imagery           Med Energy Particles      Suspended Matter
  Cloud Base Height         Ionospheric Scint         Auroral Energy Depos
  Cloud Cover/Layers        Land Surface Temp         Out Lwave Rad (TOA)
  Cloud Eff Particle Size   Surface Type
  Cloud Ice Water Path      Net Heat Flux             Atmospheric
  Cloud Liquid Water        Net Solar Rad (TOA)       Oceanic
  Cloud Opt Thickness       Neutral Density Profile   Terrestrial
  Cloud Top Height          Total Water Content       Environment
  Cloud Top Pressure        Vegetation Index          Climate
  Cloud Top Temp            Ocean Color / Chlor        EDRs with Key Performance
  Cloud Part Size / Dist    Ocean Wave Char            Parameters
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