The Aerospace Corporation (Aerospace) Prelaunch Assessment of the Northrop Grumman VIIRS Cloud Mask Thomas Kopp, The Aerospace Corporation Keith Hutchison, Northrop Grumman Andrew Heidinger, NOAA/STAR Richard Frey, University of Wisconsin IGARSS 25 July 2011
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The Aerospace Corporation (Aerospace) Prelaunch Assessment of the Northrop Grumman VIIRS Cloud Mask Thomas Kopp, The Aerospace Corporation Keith Hutchison,
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The Aerospace Corporation (Aerospace)
Prelaunch Assessment of the Northrop Grumman VIIRS Cloud Mask
Thomas Kopp, The Aerospace CorporationKeith Hutchison, Northrop GrummanAndrew Heidinger, NOAA/STARRichard Frey, University of Wisconsin
• The VCM itself determines one of four cloud cover conditions for each pixel– Confidently Cloudy– Probably Cloudy– Probably Clear– Confidently Clear
• All downstream EDR products, except for imagery, require the VCM as an input
• Downstream products will use either the confidently cloudy or confidently clear condition– The probably clear/cloudy cases account for pixels that are not completely
cloud covered but due either to the difficulty of the scene or partial clouds such as cumulus, are not sufficiently clear to reliably determine the conditions at the surface
•Results intended to show “where we were” in late 2009
•Neither of the two I-band tests could be simulated using the proxy data, a significant source of error that will not be quantified until the post-launch validation of the VCM
•Thin cirrus has a major impact on the results
•Analysis limited to near-nadir views (MODIS viewing zenith angle of +/- 20 degrees)
• Pre-launch tuning is based on 14 granules which employed Global Synthetic Data (GSD)– Of these 14, 11 contained land backgrounds
•These granules covered each VCM geographic type and ranged from straightforward to difficult scenes • GSD provides unique data to set the mid-point thresholds
– Typical methods of tuning, using on-orbit sensor data, rely upon 100% cloudy and 100% cloud free distributions
– GSD alone allows cloud distributions to be evaluated at the mid-point (50% cloudy) condition
•GSD allows setting thresholds and then minimize the distance between the confidently cloudy and confidently clear thresholds
• Reduce the number of probably clear and probably cloudy (PCPC) classifications by adjusting the overall cloud confidence threshold
• Identify tests that generated the highest percentage of false alarms for each VCM background condition and tune the mid-point thresholds (i.e. 50% cloud cover condition) accordingly – only possible with GSD
• Further reduce the number of PCPC classifications, as necessary, by adjusting the distance between the mid-point thresholds of a given individual cloud test and the low and/or high threshold using cloud distributions in the GSD.
• Identify the tests causing the largest number of errors• Use GSD with MODIS RSRs to generate cloud cover
distributions for the cloud detection tests identified above– Generate distributions for 0%, 50%, and 100% cloud cover – Set key mid-point threshold using the 50% cloud cover, then
minimize low- and high thresholds
• Update VCM using these thresholds• Execute the updated algorithm on the set of MODIS
granules• Evaluate the performance using the manually generated
• Reflectance test over desert (M1)• Reflectance test over land (M5)• Reflectance test over water (M7)• Ratio test over land (GEMI)• Ratio test over water (M7/M5)• Mid-Wave minus long wave infrared over snow (M12 – M15)• Mid-Wave infrared difference over snow (M12 – M13)
• The previous analyses reveal quantitative aspects of the VCM, but lack context
• Historically the capability to visualize the output from each individual cloud detection test has been used operationally at the Air Force Weather Agency
• Key to a useful visualization are two fundamental factors– It must overlay each test on applicable imagery– It must contain the reflectance/brightness temperatures used within
the cloud mask• This reveals if any bands have bad or saturated values
• The visualization should also note if any degraded conditions of note exist in the scene– These include aerosols, sun glint, and shadows
• The following pair of slides show this capability
• Pre-launch validation of the VCM uses three different approaches to verify the VCM will meet expectations– Large scale quantitative analysis– Small scale quantitative analysis via GSD– Visualization of individual granules with each component cloud
detection test
• Results show promise that the VCM will meet or exceed its requirements
• Each of these methods will be employed in some form post-launch, though we will no longer need GSD as actual VIIERS data will be available