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NOAA-18 Instrument Calibration and Validation Briefing . NOAA/NESDIS/Office of Research and Applications As of the Week of June 27, 2005 For archived activities and latest news, please visit http://www.orbit.nesdis.noaa.gov/smcd/spb/n18calval. Weekly Highlights (June 27-July1). HIRS

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noaa 18 instrument calibration and validation briefing

NOAA-18 Instrument Calibration and Validation Briefing

NOAA/NESDIS/Office of Research and Applications

As of the Week of June 27, 2005

For archived activities and latest news, please visit

http://www.orbit.nesdis.noaa.gov/smcd/spb/n18calval

weekly highlights june 27 july1
Weekly Highlights (June 27-July1)
  • HIRS
    • HIRS noises remain fluctuating
    • Noises for all LW channels tend to be coherent
    • ORA recommended ITT to examine the source for generating these noise (e.g. clamp system)
  • AMSU-A geolocation errors
    • A2 is off less than 0.7 MHS scanline along track and 0.36 MHS FOVs across track
    • A1 display 2-3 K cross-track asymmetry
  • MHS
    • MHS is off by about one scanline along track
noaa 18 instrument payload
NOAA-18 Instrument Payload

We focus on these instruments:

  • AVHRR/4 Advanced Very High Resolution Radiometer
  • HIRS/4 High Resolution Infrared Sounder
  • AMSU-A Advanced Microwave Sounding Unit-A
  • MHS Microwave Humidity Sounder
  • SBUV/2 Solar Backscattered Ultraviolet Radiometer
calibration and validation legend
Calibration and Validation Legend
  • PRT: Platinum Resistance Thermometers
  • NEDN/T: Noise Equivalent Delta Radiance/Temperature
  • ATOVS: Advanced TIROS Operational Vertical Sounder (TOVS)
  • TOAST: Total Ozone Analysis using SBUV/2 and TOVS
  • MSPPS: Microwave Surface and Precipitation Product System
  • NDVI: Normalized Difference Vegetation Index
  • SST: Sea Surface Temperature
  • UV: Ultraviolet
  • TPW: Total Precipitable Water
  • CLW: Cloud Liquid Water
ora noaa 18 instrument cal val mission goals
ORA NOAA-18 Instrument Cal/Val Mission Goals
  • Monitor and improve NOAA-18 instrument post-launch calibration
  • Assess and quantify instrument noises though analyzing calibration target counts and channel measurements
  • Monitor possible instrument anomaly and provide recommended solution
  • Quantify satellite geolocation errors
  • Characterize other biases in radiance and products such as cross-track asymmetry through forward modeling and inter-satellite calibration
  • Validate NESDIS NOAA-18 products (ATOVS and MSPPS, TOAST, UV index, NDVI, SST) for operational implementation
  • Provide early demonstration and assessments of NOAA-18 data for improving numerical weather prediction through JCSDA
our team
Mitch Goldberg: ORA/SMCD Division Chief, - Management and Technical Oversight

Fuzhong Weng: ORA/SMCD/Sensor Physics Branch Chief and NOAA-18 cal/val team leader, instrument asymmetry and microwave products and algorithms, radiance bias assessments for NWP model applications

Changyong Cao: HIRS instrument calibration

Fred Wu: AVHRR VIS/IR instrument calibration

Tsan Mo: AMSU/MHS instrument calibration

Jerry Sullivan: AVHRR thermal channel calibration/ NDVI validation

Tony Reale: HIRS/AMSU/MHS sounding channel/products validation

Mike Chalfant: HIRS/AMSU/MHS sounding channel/products validation /geolocation

Ralph Ferraro: AMSU/MHS window channels/MSPPS products validation

Larry Flynn: SBUV product validation

Tom Kleespies: AMSU on-orbit verification

Hank Drahos: Sounding product validation

Dan Tarpley: AVHRR product NDVI monitoring

John LeMashall: Impacts assessments of NOAA-18 data for NWP applications

Our Team
an important signature of the noaa 18 hirs noise found in dds1 dwell data set 1
An important signature of the NOAA-18/HIRS noise found in DDS1 (Dwell Data Set 1)
  • Description of the NOAA-18/HIRS noise problem:
    • For each LW channel, the count jumps from one FOV to the next significantly, and almost randomly, regardless of SV, IWT, or EV.
  • Despite the randomness of the count variation from one FOV to the next for a given channel, the count for one channel can be calculated from another channel with a linear function with small residuals. This is a distinct signature (more like a signal than a noise).
  • This signature has the following characteristics:
  • 1). For a given FOV, the volt/count for ch1-12 must always go up or down in tandem, and in linear proportion
  • 2). Between FOVs, they jump up and down together, dramatically, and randomly (or somewhat randomly).

56 samples for all 12 channels

Space dwell data 300 scanlines, June 9, 2005

Unexpected correlation between ch1 and ch11

time sequence and channel correlation analysis
Time Sequence and Channel Correlation Analysis
  • For each HIRS FOV cycle,

dVch = Vch – V0,

Where dVch = delta volt for a channel

Vch = volt for a channel

ch = 1, 2, 3, 4, 11, 7, 8, 10, 6, 5, 12, 9 (in order)

V0 = value for the clamp target, which is set once for each cycle (every 0.1 sec, or one rotation of the filterwheel).

  • Unlike a FPA based system, a filterwheel based radiometer such as HIRS generates each channel for an FOV separately, one at a time with the rotation of the filterwheel. It is very difficult to get signals that are correlated across channels at all times.
  • Very few places in the instrument can produce this signature. Possibilities:
    • Clamp system and related circuit.
      • Clamp system itself
      • Interference of the clamp system or through the clamp system (many possible freqs. ).
    • Other possibilities: must be able to produce this signature, which is difficult.

Clamp target

HIRS filterwheel (inner ring for LW channels)

Vch

V0

update from dds3 dwell data set 3
Update from DDS3(Dwell Data Set 3)
  • The magnitude of the noise has come down significantly since DDS1. As expected, this signature becomes weaker relative to the random noise which attenuated the correlation.
  • Two groups of correlation emerged, which were not apparent in DDS1 (or perhaps overshadowed by the strong correlation among all channels).
    • Group 1: ch 1, 2, 3, 4, 11, and 7.
    • Group 2: ch 5, 6, 7, 8, 9, 10, 12

Note: ch 11 is weakly correlated with both groups

Group1

Ex: ch 2 vs. ch5

Ch2 vs. ch5 correlation disappeared

Group 2

Ex: ch6 vs. ch5

relationship to frequency analysis
Relationship to Frequency Analysis
  • For a given channel, the jump from one FOV to the next (0.1 sec or 10 Hz) appears to be random (more so in recent data).
  • May not show on the Fourier spectrum (Fourier transform of a random function)
  • External interference with different freq.: The signature is more likely to be generated if it is a disturbance through the clamp system
  • Direct interference on individual channels will require both specific frequency (such as 10 Hz) and synchronization with 10 Hz, which is difficult to sustain.
amsu cal val news
AMSU Cal/Val News
  • AMSU-A geo-location and co-registration are investigated by Tom Kleespies (ORA) and Ralf Bennartz (UWisc)
    • Accurate FOV matching through Back-Gilbert convolution
    • A2 is off less than 0.7 MHS scanline along track and 0.36 MHS FOVs across track
  • AMSU-A cross-track asymmetry
    • Global simulated brightness temperatures using JCSDA CRTM and NCEP GDAS outputs
    • A1 displays larger than expected cross-track asymmetry
amsu a1 asymmetry
AMSU-A1 Asymmetry

Shown are the NOAA-18 AMSU-A window channel asymmetry characterized by the differencesbetween observed and simulated brightness temperatures. NOAA-18 AMSU-A1 module displays much

larger cross-track asymmetry than previous AMSU-A1 and will impact sounding products.