Cloud Detection: Optical Depth Thresholds and FOV Considerations

1
Cloud Detection Optical Depth Thresholds and FOV
Considerations
Steven A. Ackerman, Richard A. Frey, Edwin
Eloranta, and Robert Holz
Cooperative Institute for Meteorological
Satellite Studies, Space Science and Engineering
Center, University of Wisconsin-Madison,
Cloud Mask and Spectral Tests
Cloud Detection Issues
What is a cloud?
A note on thresholds

• The answer to that question is determined by the
  application. What is considered a cloud in some
  applications may be defined as clear in other
  applications. For example, detection of thin
  cirrus clouds is important for applications of
  infrared remote sensing of sea surface
  temperature, but of little concern for microwave
  remote sounding of atmospheric temperature. So,
  in one regard, cloud detection capabilities is
  determined by the application.
• Detection of clouds is also a function of
  instrument capability and algorithm design.
  Cloud detection is a function of contrast between
  the target (e.g. cloud) and the background.
  Contrast can be
• Spatial Large fov are generally more uniform
  lowering contrast
• Temporal Clouds can be detected in a sequence
  of images if the clouds are moving
• Spectral Spectral contrast is determined by the
  radiative properties of the cloud and surface.

The above two figures show the 0.86 reflectance
(x-axis) versus the percentage of pixels less
then that value (e.g. cloud fraction if this
reflectance was a threshold for ocean scenes
equatorward of 60 degrees and away from sun glint
for various solar zenith angles and viewing angle
s between 0- 10 degrees (left) and 30 40 degrees
(right). The geometrically defined sun-glint
areas (reflectance angle 0-36 degrees). For
different viewing geometries, the cloud detection
threshold varies. At low reflectances (less then
10 ) a small change in the threshold can result
in a large change in cloud amount. Comparison of
satellite cloud amounts should account for the
varying viewing and solar geometry.
GCMs make extensive comparison with satellite
derived cloud amount. Total cloud amount from
different satellite algorithms can vary
significantly even among accepted standards, as
shown below in a comparison of annual zonal mean
cloud fraction from CLAVR, ISCCP and UW-HIRS.
Global distributions demonstrate expected
patterns but can differ in magnitude by more then
10. This paper investigates how spectral
testing, field-of-view size and cloud optical
depth impact cloud detection algorithms.
These histograms show observations of radiance
data as a function of final clear sky confidences
according to the MODIS cloud mask. The top two
figures also define thresholds for the tests
depicted. For example, the top left plot shows
how the distribution of visible ratios changes
with clear sky confidence. The vertical lines
define the threshold interval for this cloud test
(1.0 at left to 0.0 at right). One may conclude
that the thresholds have been chosen properly as
very few, if any, clear sky confidences gt0.95
fall within the interval. In the figure above,
however, one sees that part of the distribution
of observations denoted as clear (blue) or
probably clear (green) falls inside the threshold
interval. One could conclude that these
thresholds should be made smaller (moved left on
the graph).
Top Zonal mean frequencies of cloudy conditions
for October 16,2003, daytime ocean scenes as a
function of three cloud detection tests and the
combination of all 16 tests from MODIS. Bottom
Zonal mean frequencies of cloudy conditions for
October 16,2003, daytime land scenes as a
function of three cloud detection tests and the
combination of all 16 tests from MODIS. Right
Accumulated zonal mean cloud occurrence as
various tests are added to the detection
algorithm for daytime land (top) and daytime
ocean (bottom) scenes.
snow
Field of View
Top Thresholds accuracy is a function of
instrument noise and spectral contrast with
background. In this example, the optical depth of
the cloud detection at 1.38 microns is estimated
through simulations of an water scene.
Observations indicate that optical depth
detection limits are more on the order of 0.1 to
0.3. Bottom Cloud fraction increase due to
addition cloud detected by the MODIS 1.38 micron
channel.
Total Cloud Amount
The percent of total observations of clear
(blue), high cloud (green) and total cloud (red)
as a function of MODIS fov size. Observations
are over ocean between 60 S and 60N during the
daytime and away from sun glint.
To estimate cloud optical detection limits cloud
mask results from the MODIS and GLI were compared
to ground based observations from the
High-Spectral Resolution Lidar (HSRL), which
measures visible optical depth. Comparisons were
also made using the ER-2 borne cloud physics
lidar and collocated observations of the MODIS
Airborne Simulator (MAS)
Left GLI (and MODIS) observations were compared
to the HSRL site over the University of
Wisconsin-Madison. The HSRL directly measures
cloud optical depth at visible wavelengths.Initial
results indicate that when the MODIS or GLI flag
a cloudy region as Uncertain Clear, the optical
depth is less then approximately 0.3.
MODIS Cloud MPL/MMCR Cloud 24 cases
MODIS No Cloud MPL/MMCR Cloud 9 cases
Discussion
Cloud fraction has been derived from various
satellite platforms for over 25 years. New
satellites have more capability, in terms of
improved performance, more spectral channels and
higher spatial resolution. Fractional cloud
coverage is a function of the optical depth
threshold limit of the instrument, and will also
vary with the spatial, spectral and temporal
resolution of the instrument. In some scenes, a
single (or few) channel method yields similar
results to mutlispectral tests. Cloud detection
thresholds also vary as a function of viewing
geometry, scene illumination and thermal
structure of the scene. Comparison of various
cloud detection methods/results from different
instruments or platforms need to account for
these differences. The dependence of cloud
detection on these parameters and the need to
monitor with changing instruments and satellites,
will likely make it difficult to compare cloud
amounts from different approaches and achieve the
1 accuracy needed for long-term monitoring of
cloud amount.
MODIS Cloud MPL/MMCR No Cloud 12 cases
MODIS No Cloud MPL/MMCR No Cloud 8 cases
Lidar/radar/cloud mask validation results are
shown in the above table. MODIS cloud mask output
(5x5-pixel regions) and combined SGP CART site
ground-based MPL (Micro-pulse Lidar) and MMCR
(Millimeter-wavelength Cloud Radar) cloud data
were collocated for the month of November, 2000.
MODIS 5x5s were considered to be cloudy if at
least half of the individual 1-km pixels had
clear sky confidences lt 0.95. The distribution of
the four possible cloud/no cloud results between
the cloud mask and the MPL/MMCR is indicated by
the numbers in the table. There are inherent
difficulties in comparing data with vastly
different spatial and temporal resolutions and
sensitivities.
snow
Above The total cloud fraction as a function of
cloud optical depth, and the cloud fractions when
considering the plus and minus sigma values of
optical depth from June 2004. Each optical depth
time profile has an associated error bar due to
the molecular return and the density profile.
Right A temporal resolution comparison of
cloud fraction for the month of June 2004, for a
300-meter spatial average and a 1, 3, and
5-minute temporal average of the HSRL. When the
time average is reduced to a 1-minute average,
the lidar signal tends to see the same amount
of optically thin cloud and a slight increase in
the percentage of thicker clouds (optical depths
greater than 1.0). Increasing to a 5-minute
average, the amount of detectable thin clouds
remains the same, the cloud fraction for thicker
clouds is reduced. This is due to averaging the
edges of clouds with surrounding clear sky
effectively reducing the measured optical
depth. Far right The impact of vertical average
on the derived cloud fraction from the HSRL.
Top The number of occurrences that MAS scene was
identified as clear and the cloud physics lidar
detected a cloud optical depths (visible
wavelengths). This figure suggests that the
cloud limit is less then approximately 0.4
Bottom The number of occurrences of lidar
determined optical depth when the MAS cloud mask
algorithm detected a cloud.