The document summarizes the pre-launch assessment of the VIIRS cloud mask (VCM) as follows: 1) It reviewed the VCM logic and contents, and presented global results using the pre-launch VCM without tuning, which showed room for improvement. 2) It described methods used for pre-launch tuning using synthetic data, which helped reduce false alarms and improve performance over land and desert. 3) Quantitative results showed the tuning effort significantly improved the VCM's probability of correct typing, especially over land.

1. 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

2. Outline Definitions of VIIRS Cloud Mask (VCM) contents and validation conditions High level review of the VCM logic Global results with the pre-launch VCM without any tuning Quantitative Improvements Using the Northrop Grumman (NG) tuning tool Methods for evaluating individual granules during Intensive Cal/Val (ICV) of the VCM

3. VCM Contents 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

5. PCT = (1 - Binary Cloud Mask Error) = {1 – [(VCM = conf. clear) & (Truth = conf. cloudy) OR ((VCM = conf. cloudy) & (Truth = conf. clear)]/[total #pixels in each geographic class – PCPC]

6. Cloud Leakage (CL)

7. CL = [(VCM = conf. clear) & (Truth = conf. cloudy)]/total #pixels in the geographic class

8. False Alarm Rate (FA)

9. FA = [(VCM = conf. cloudy) & (Truth = conf. clear)]/total #pixels in the geographic class

10. Fraction of Pixels Classified as Probably Clear/Cloudy (FPCPC)

13. The initial thresholds were used, the VCM for this testing was not tuned

14. Comparisons made with collocated MOD35 C6 cloud mask and CALIOP matchups for comparisonCloudy for the VCM in this case included probably cloudy pixels Clear for the VCM in this case included probably clear pixels Compared only 1-km CALIOP segments with either 0% or 100% cloud cover Resulted in approximately 15 million collocations per month

16. 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

17. Thin cirrus has a major impact on the results

18. Analysis limited to near-nadir views (MODIS viewing zenith angle of +/- 20 degrees)

20. VCM versus CALIOP, Polar and Non-Polar Results

21. VCM versus CALIOP, Day Results

22. VCM versus CALIOP, Night Results

23. VCM versus CALIOP, Land and Desert Results

24. VCM Designed to Exploit VIIRS 1.38-µm Data MODIS OOB Response is as large as the in-band response VIIRS OOB Response is orders of magnitude less MODIS vs VIIRS TOA Radiances MODIS vs VIIRS RSRs Thin cirrus clouds will be more readily detected with VIIRS data than in MODIS

25. VCM Versus Heritage Performance, COT > 1.0 VCM and heritage performance are comparable when thin cirrus clouds are eliminated from the results

26. One Year Means of Hit Rates and Skill

28. GSD provides unique data to set the mid-point thresholds

29. Typical methods of tuning, using on-orbit sensor data, rely upon 100% cloudy and 100% cloud free distributions

30. GSD alone allows cloud distributions to be evaluated at the mid-point (50% cloudy) condition

32. Tuning process is validated by :

33. (1) tuning with GSD truth data developed with MODIS Relative Spectral Responses (RSR)

34. (2) tested in the VCM using MODIS granules

35. (3) quantitatively evaluated using manually-generated cloud data of the MODIS data

37. Initial Untuned VCM Performance - Land

38. Overview of the Pre-Launch Tuning Process 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 cloud masks Assess the changes in performance

39. Example for a Case With Too Many PCPC Pixels Manually-Generated Mask MODA.2001.196.1755 Qthresh = 99% Qthresh = 90% Land - Pre Land - Post

40. Specific Cloud Detection Case, GEMI Test (Land) Changed from 1.95 to 1.87 Changed from 1.90 to 1.82 Changed from 1.85 to 1.78

41. Quantitative Impacts – GEMI Results Previous untuned results

42. Tests Improved by the Pre-Launch Tuning Effort 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)

43. Performance After Daytime Tuning Completed Tuned VCM: November 2010 Untuned VCM: March 2010

44. Tool for Visualization of the VCM 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

45. Aerospace Visualization Tool – Example I

46. Aerospace Visualization Tool – Example 2

47. Conclusion 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