Cloud and Aerosol Products From GIFTS/IOMI

1

Cloud and Aerosol Products From GIFTS/IOMI

• Gary Jedlovec and Sundar Christopher
• NASA Global Hydrology and Climate Center
• University of Alabama - Huntsville
• Research goals (year 1)
• Identifying cloud and surface characteristics in
  high spectral resolution data that best delineate
  clouds, aerosols, and surface characteristics
  from one another, and leads to a superior cloud
  product.
• Refine the Tracking Error Lower Limit (TELL)
  parameter to include instrument characteristics
  and observing requirements of the GIFTS/IOMI.
• Presentation centers on capabilities to satisfy
  these goals

2
Cloud Detection

• Current geostationary cloud property retrieval
  technique at GHCC detect clouds and retrieve
  cloud information, mask for atmospheric surface
  parameter retrieval (http//wwwghcc.msfc.nasa.gov/
  goesprod)
• Cloud detection
• Bi-spectral THreshold (BTH) method (Jedlovec and
  Laws 2001)
• Used operationally at the GHCC (24h a day)
• GOES Imager or Sounder
• Single pixel resolution (4 or 10 km)
• 3.7 - 11 micrometer difference provides key cloud
  signature
• Three (3) tests applied to difference image
• 2.8K spatial pixel deviation (edge detection)
• 2.1K adjacent pixel (element direction) change
  (fills in clouds)
• Historical (20 day) minimum difference image
  check for each time (detects low clouds/fog, and
  incorporates synoptic influences)
• Performance documented against NESDIS products
  (Jedlovec and Laws 2001) link

3
Cloud and Aerosol Products

• Parameter retrieval
• Cloud height (CTP) infrared look-up with model
  guess for GOES imager and opaque clouds
• Easy to implement, uses model T(p) as a reference
• Highly accurate for opaque clouds
• CO2 slicing H2O intercept possible with Sounder
  (currently not implemented)
• Cloud phase water or ice, mixed reflective
  information at 3.7 micrometers (under
  development)
• Aerosol optical thickness (AOT) visible channel
  approach to retrieve AOT in cloud-free regions
  (Zhang and Christopher, 2001)
• DISORT model (Ricchiazzi et al. 1998) used to
  generate look up tables describing radiance, AOT,
  ?, ?
• Correlation with sun photometer data as high as
  0.97 chart

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Cloud Product Comparisons
GOES-8 CTP 1645 UTC 18 April 2002
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Cloud Product Comparisons
GOES-8 vs MODIS - 18 April 2002
GHCC BTH - GOES Imager CTP (1645 UTC)
MODIS CTP (1635 UTC)
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Cloud Research Focus

• Examine spectral signature of clouds, aerosols ,
  and dust for unique features
• Use AIRS radiance data for selected periods
• Begin to adapt the Bi-spectral Threshold method
  for for high spectral measurements for the
  retrieval of cloud products

7
Satellite-derived Wind Errors

• Sources of wind tracking errors
• When clouds and wv features are
  non-conservative tracers of wind
• Changes in cloud shape (often result of too
  large of image separation)
• Improper height assignment
• Mis-identification of targets (dependent on
  tracking algorithm)
• Incorrect image displacements (navigation and
  registration inaccuracies)
• The effect of incorrect image displacements on
  the cloud-tracked wind is a function of image
  registration, image separation time, and image
  resolution.
• Tracking Error Lower Limit (Tell) is the
  theoretical lower limit error in wind tracking
  algorithms due to image resolution (?), time
  separation (?), and image stability or
  registration accuracy (?) uncertainties.
  TELL (? ? ?) / ?
• GOES
• infrared pixel resolution (?) is 4km
• image-to image registration accuracy (?) is
  typically about 2km (0.5 pixel)
• For 15 minute images (? 15), TELL 2.22 ms-1
• This means that GOES derived winds under these
  conditions will typically have a 2 ms-1 error
  component due to these image uncertainties alone!

8
Imaging Requirements for Cloud-drift Winds
Image Interval, Resolution, and Registration
Accuracy Constraints
TELL (R?)/?

• Science Requirement
• Accurate mesoscale winds for diagnostic and
  modeling studies (lt2.0 ms-1)
• use small time intervals
• high resolution imagery
• accurate image-to-image
• registration
• Imaging Requirement
• Resolution trades/constraints
• as image separation (?) is decreased (point 1 to
  2), the registration accuracy (R) must improved
  to maintain quality
• of wind data
• if image resolution (?) is improved,
  registration accuracy can be relaxed (point 2 to
  3) for an equivalent image separation interval
  (?)

TELL Surface of 0.55

• GOES-R
• ? 15 min
• R 0.125
• 4km
• TELL 0.55

60
55
50
45
40
? - Image Separation Time (min)
35
30
1
25
20
15
10
2
3
5
0
? - Image Resolution (km)
R - Image Registration Accuracy (m)
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Wind Tracking Error Emphasis

• Refine Tracking Error Lower Limit (TELL) for
  GIFTS
• Instrument characteristics
• Observing scenarios

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Summary / Deliverables

• Focus of research
• Examine spectral signature of clouds, aerosols ,
  and dust for unique features
• Use AIRS radiance data for selected periods
• Begin to adapt the Bi-spectral Threshold method
  for for high spectral measurements for the
  retrieval of cloud products
• Refine Tracking Error Lower Limit (TELL) for
  GIFTS
• Instrument characteristics
• Observing scenarios
• Deliverables
• Key spectral signatures and wavelengths for the
  detection of clouds and aerosols
• Insight on how these characteristics can be
  included in a cloud product algorithm
• Estimates of the lower limit on satellite derived
  wind errors from GIFTS

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Cloud and Aerosol Products From GIFTS/IOMI
Gary Jedlovec and Sundar Christopher NASA Global
Hydrology and Climate Center University of
Alabama - Huntsville
Backup Charts
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Cloud Detection Validation

Case Study September 11 October 8, 2001

• 15 points (locations on the image to right) used
  each hour to
• validate cloud detection schemes
• subjective determination of clouds (man in the
  loop)
• visible, multiple channel IR
• any pixel cloudy in 32x32km area, then all
  cloudy
• Statistical performance at hourly intervals - 2
  times below
• Results are for
• CLC ground truth clear retrieval scheme
  correct
• CLI ground truth clear retrieval scheme
  incorrect
• CDC ground truth cloudy retrieval scheme
  correct
• CDI ground truth cloudy retrieval scheme
  incorrect
• NESDIS NESDIS operational algorithm (Hayden et
  al. 1996)
• BSC Bi-spectral Spatial Coherence method
  (Guillory et al. 1998)
• used operationally at GHCC
• BTH Bi-spectral Threshold algorithm under
  development

Night 0645 Statistics
Daytime 1845 Statistics
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1
0.9
0.9
BTH
BSC
NESDIS
BTH
BSC
NESDIS
0.8
0.8
0.7
0.7
CLD
CDC
CDC
0.6
0.6
CLC
CLC
CLR
CDC
CLD
CDC
0.5
0.5
CLC
CLC
CLR
0.4
0.4
CDC
CLC
0.3
Ratio
0.3
Ratio
CLC
0.2
0.2
CDC
0.1
0.1
0
0
CLI
CLI
CLI
CDI
CLI
CDI
CDI
-0.1
-0.1
CDI
-0.2
-0.2
CDI
-0.3
-0.3
CLI
CLI
-0.4
-0.4
CDI
-0.5
-0.5
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MODIS IR C31 1635 UTC 18 April 2002
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