The EOS Aqua MODIS NASA Level-2 (L2) Cloud Properties is one of two continuity products designed to sustain the long-term records of both Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) heritages. CLDPROP_L2_MODIS_Aqua is the shortname for the Aqua MODIS incarnation of the Cloud Properties continuity product. This product ensures continuity of approach through a common algorithm that is applicable to both MODIS and VIIRS data.

The original MODIS cloud-top and cloud optical properties algorithms (MOD06 suite) benefit from their access, via the MODIS instrument, to thermal infrared absorption channels that provide information on cloud thermodynamic phase and cloud-top properties, including the 7.5 µm water vapor channel and several channels within the 13 µm CO2 absorption band that facilitate a CO2-slicing method to retrieve cloud-top pressure. However, because these IR absorption channels are absent on VIIRS, 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 the Aqua MODIS Level-1B, this product uses the Aqua MODIS CLDMSK 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 Aqua MODIS L2 Cloud Properties product is 1 km.

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, referenced in the links below, to understand this product’s heritage.