VIIRS/NOAA20 Cloud Properties L2 6-Min Swath 750 m

Given the lack of such spectral channels in the VIIRS instrument, in addition to the spectral offset between the MODIS 2.13 µm and VIIRS 2.25 µm shortwave infrared (SWIR) channels in which ice and liquid cloud absorption differs, a direct porting of the original MODIS MOD35 and MOD06 algorithms to VIIRS is precluded. The commonly applicable CLDPROP algorithm provides a continuity of approach between MODIS and VIIRS, leveraging only those spectral channels that are common to both instruments.

In the CLDPROP algorithm, cloud-top property retrievals are derived through NOAA’s operational algorithms developed for Advanced Very-High Resolution Radiometer (AVHRR), VIIRS, and Geostationary Operational Environmental Satellites (GOES)-16/17, specifically the Clouds from AVHRR Extended (CLAVR-x) processing system for cloud-top phase (algorithm based primarily on infrared spectral channels, with additional information from select SWIR channels) and NOAA’s Enterprise Algorithm Working Group (AWG) Cloud Height Algorithm (ACHA) for cloud-top properties.

Besides a modified version of the NOAA-20 VIIRS Level-1B (including restored bow-tie deleted pixels and radiometric adjustments to several shortwave channels to address known radiometric offsets between MODIS and VIIRS), this product uses the NOAA-20 VIIRS Cloudmask Continuity product, and several ancillary data inputs; they include gap-filled MODIS snow/ice surface spectral albedos, Near-real-time Ice and Snow Extent (NISE) inputs, and National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) forecast analysis fields.

The nominal spatial resolution of the NOAA-20 VIIRS Level-2 Cloud Properties product is 750-meters. The L2 netCDF product, acquired and processed every 5 minutes, contains the following four data groups and Science Data Set (SDS) layers in each group: Cloud model data: 1. Ice particle asymmetry parameter 2.

Liquid water droplet asymmetry parameter 3. Ice particle extinction efficiency 4. Liquid water droplet extinction efficiency 5.

Ice particle single scattering albedo 6. Liquid water droplet single scattering albedo Geophysical data: 1. Atmospherically corrected reflectance used during cloud optical and microphysical properties retrieval 2.

Cloud Effective Emissivity from NOAA CLAVR-x AWG algorithm at 11µm 3. Cloud Particle Effective Radius two-channel retrieval using 2.2 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from best points: not failed in any way, not marked for clear sky restoral 4. Cloud Particle Effective Radius two-channel retrieval using 1.6 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from best points: not failed in any way, not marked for clear sky restoral 5.

Cloud Particle Effective Radius two-channel retrieval using 2.2 µm and 1.6 µm from best points: not failed in any way, not marked for clear sky restoral 6. Cloud Particle Effective Radius two-channel retrieval using 2.2 µm and 1.6 µm from points identified as partly cloudy from cloud edges 7. Cloud Particle Effective Radius two-channel retrieval using 1.6 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from points identified as partly cloudy from cloud edges 8.

Cloud Particle Effective Radius two-channel retrieval using 3.7 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from best points: not failed in any way, not marked for clear sky restoral 9. Cloud Particle Effective Radius two-channel retrieval using 3.7 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from points identified as partly cloudy from cloud edges 10. Cloud Particle Effective Radius two-channel retrieval using 2.2 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from points identified as partly cloudy from cloud edges 11.

Cloud Effective Particle Radius from 2.2 µm Relative Uncertainty (Percent) from both best points and points identified as cloud edge 12. Cloud Effective Particle Radius from 1.6 µm Relative Uncertainty (Percent) from both best points and points identified as cloud edge 13. Cloud Effective Particle Radius Relative Uncertainty (Percent) using 2.2 µm and 1.6 µm from both best points and points identified as cloud edge 14.

Cloud Effective Particle Radius from 3.7 µm Relative Uncertainty (Percent) from both best points and points identified as cloud edge 15. 3-d byte array from which cloud mask information can be extracted 16. Cloud Optical Thickness two-channel retrieval using 2.2 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from best points: not failed in any way, not marked for clear sky restoral 17. Cloud Optical Thickness two-channel retrieval using 1.6 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from best points: not failed in any way, not marked for clear sky restoral 18.

Cloud Optical Thickness two-channel retrieval using 2.2 µm and 1.6 µm from best points: not failed in any way, not marked for clear sky restoral 19. Cloud Optical Thickness two-channel retrieval using 2.2 µm and 1.6 µm from points identified as partly cloudy from cloud edges 20. Cloud Optical Thickness two-channel retrieval using 1.6 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from points identified as partly cloudy from cloud edges 21.

Cloud Optical Thickness two-channel retrieval using 3.7 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from best points: not failed in any way, not marked for clear sky restoral 22. Cloud Optical Thickness two-channel retrieval using 3.7 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from points identified as partly cloudy from cloud edges 23. Cloud Optical Thickness two-channel retrieval using 2.2 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from points identified as partly cloudy from cloud edges 24.

Cloud Optical Thickness from 2.2 µm Relative Uncertainty (Percent) from both best points and points identified as cloud edge 25. Cloud Optical Thickness from 1.6 µm Relative Uncertainty (Percent) from both best points and points identified as cloud edge 26. Cloud Optical Thickness Relative Uncertainty (Percent) using 2.2 µm and 1.6 µm from both best points and points identified as cloud edge 27.

Cloud Optical Thickness from 3.7 µm Relative Uncertainty (Percent) from both best points and points identified as cloud edge 28. Cloud Phase Determination from NOAA CLAVR-x AWG algorithm 29. Cloud Phase Determination Used in Optical Thickness/Effective Radius Retrieval 30.

Cloud Top Height from NOAA CLAVR-x AWG algorithm 31. Cloud Top Height uncertainty from NOAA CLAVR-x AWG algorithm 32. Cloud Top Pressure from NOAA CLAVR-x AWG algorithm 33.

Cloud Top Pressure uncertainty from NOAA CLAVR-x AWG algorithm 34. Cloud Top Temperature from NOAA CLAVR-x AWG algorithm 35. Cloud Top Temperature uncertainty from NOAA CLAVR-x AWG algorithm 36.

Column Water Path two-channel retrieval using 2.2 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from best points: not failed in any way, not marked for clear sky restoral 37. Column Water Path two-channel retrieval using 1.6 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from best points: not failed in any way, not marked for clear sky restoral 38. Column Water Path two-channel retrieval using 2.2 µm and 1.6 µm from best points: not failed in any way, not marked for clear sky restoral 39.

Column Water Path two-channel retrieval using 2.2 µm and 1.6 µm from points identified as partly cloudy from cloud edges 40. Column Water Path two-channel retrieval using 1.6 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from points identified as partly cloudy from cloud edges 41. Column Water Path two-channel retrieval using 3.7 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from best points: not failed in any way, not marked for clear sky restoral 42.

Column Water Path two-channel retrieval using 3.7 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from points identified as partly cloudy from cloud edges 43. Column Water Path two-channel retrieval using 2.2 µm and either 0.65 µm, 0.86 µm or 1.24 µm (specified in Quality_Assurance) from points identified as partly cloudy from cloud edges 44. Cloud Water Path from 2.2 µm Relative Uncertainty (Percent) from both best points and points identified as cloud edge 45.

Cloud Water Path from 1.6 µm Relative Uncertainty (Percent) from both best points and points identified as cloud edge 46. Cloud Water Path Relative Uncertainty (Percent) using 2.2 µm and 1.6 µm from both best points and points identified as cloud edge 47. Cloud Water Path from 3.7 µm Relative Uncertainty (Percent) from both best points and points identified as cloud edge 48.

Low Cloud Temperature from IR Window retrieval using cloud emissivity based on cloud optical thickness 49. 3-D byte array from which much info about quality (QA) of retrieval data can be extracted 50. Retrievals and other information for points that failed to retrieve via standard solution logic for retrieval using 2.2 µm and either 0.65 µm, 0.86 µm or 1.2 µm (specified in Quality_Assurance) 51. Retrievals and other information for points that failed to retrieve via standard solution logic for retrieval using 1.6 µm and either 0.65 µm, 0.86 µm or 1.2 µm (specified in Quality_Assurance) 52.

Retrievals and other information for points that failed to retrieve via standard solution logic for retrieval using 2.2 µm and 1.6 µm 53. Retrievals and other information for points that failed to retrieve via standard solution logic for retrieval using 3.7 µm and either 0.65 µm, 0.86 µm or 1.2 µm (specified in Quality_Assurance) 54. Surface pressure from ancillary data Geolocation data: 1.

Latitude from instrument geolocation 2. Longitude from instrument geolocation 3. Sensor Azimuth Angle, Cell to Sensor 4.

Sensor Zenith Angle, Cell to Sensor 5. Solar Azimuth Angle, Cell to Sun 6. Solar Zenith Angle, Cell to Sun Scanline attributes: 1.

Scan start time (TAI93) Consult the EOS MODIS and SNPP VIIRS Cloud Properties product user guide for additional information regarding the Aqua MODIS CLDPROP product and its variables, its algorithm, output file, file naming conventions, and quality flags.

Users are also encouraged to consult the User Guide and ATBD for the original MODIS version of the Cloud Top/Optical Properties product to understand this product’s heritage.