1. VIIRS Cloud Products Andrew Heidinger, Michael Pavolonis Corey Calvert
NOAA/NESDIS/Center for Satellite Applications and Research
Corey Calvert
University of Wisconsin / CIMSS
Madison, Wisconsin
August 15, 2006

2. Outline VIIRS as a cloud observing platform
Baseline NPOESS VIIRS Cloud Products
Our (CIMSS/ASPB) VIIRS Research
Conclusions

3. VISIBLE Infrared Imaging Radiometer Suite (VIIRS)
Multiple VIS and IR channels between 0.3 and 12 microns
Imagery Spatial Resolution: NADIR / EOS
Mass Comparison: VIIRS 199 kg; MODIS 230 kg; AVHRR 46 kg
VIIRS replaces the paddle wheel mirror on MODIS with a rotating telescope (SEAWIFS) and a half-angle mirror design.
VIIRS has the same solar diffuser and solar diffuser monitor as on MODIS.
VIIRS also has DMSP-like capabilities
(controlled pixel growth and day/night
visible imagery)
Most of the VIIRS hardware issues have
been solved. Vacuum Testing is going on
now.

4. VIIRS Spatial Resolution Improvements
Most VIIRS bands are referred to as moderate resolution (740 meters). VIIRS will also provide a few imaging bands much higher spatial resolution with a spectral resolution of the AVHRR.
Improves upon MODIS which had 2 high resolution bands and GOES imager which gave one high resolution band (visible).
Taken from Tom Lee (NRL)

6. MODIS vs VIIRS vs ABI visible/near-IR
0.94 mm H2O
Taken from Tim Schmit

7. MODIS vs VIIRS vs ABI Infrared
14 mm CO2
4 mm CO2
7 mm H2O
Taken from Tim Schmit

8. Baseline NPOESS VIIRS Cloud Products
9 of 24 EDRS are Cloud Products for VIIRS (including cloud mask + cloud phase)

9. Baseline VIIRS Cloud Algorithms for Cloud Optical Thickness, Particle Size and Cloud Height
Cloud Mask: The VIIRS cloud mask is a modified version of the 1998 (pre-launch) MODIS cloud mask. NESDIS/CIMSS has worked with NGST (Keith Hutchison) to fit some of the lessons learned with MODIS into the VIIRS cloud mask. Some of the NOAA AVHRR approaches have also been adopted.
Cloud Phase: NGST adopted at method developed at CIMSS funded under my IGS project (Pavolonis and Heidinger, 2004; Heidinger and Pavolonis, 2005). NGST adopted this approach because they wanted the multi-layer detection capability offered in this approach.
Daytime Properties: Algorithm developed by Prof K. N. Liou at UCLA. Uses 0.65, 1.6, 2.1, 3.75 and 10.8 um channels in a traditional “Nakajima-King” approach. Does not use same clear-sky reflectance fields and ice scattering models as used by NASA GSFC/MODIS and NESDIS and therefore results are understandably different for ice clouds.
Nighttime Properties: UCLA has just proposed a switch from a two channel approach (3.75 and 12 mm) to a four channel approaches (3.75, 8.5, 10.8 and 12 mm). This change was needed to independently estimate particle size and cloud temperature (before they were constrained to a pre-existing relationship). This method is new but the VOAT supported the move to more channels and will help test it.
Note, baseline resolution of most VIIRS cloud products is not pixel-level.

10. Our (CIMSS/ASPB) VIIRS Research (funded under IGS)
Development of VIIRS Cloud Products that account for multi-layer conditions
Development of VIIRS Cloud Products that are consistent for all orbits (day/night).
Global Testing of VIIRS Algorithms
Day/night Consistent Products from VIIRS
Constructing Optimal Cloud Products from multiple NPOESS sensors

11. Contributions to the Baseline NPOESS VIIRS Cloud Products
Development of VIIRS Cloud Products that account for multi-layer conditions
MODIS product is similar to initial VIIRS. It does well distinguishing ice from water.
Original VIIRS baseline was based on MODIS IR method and did a good job of separating ice from water phase clouds. But no multi-layer information,

12. Contributions to the Baseline NPOESS VIIRS Cloud Products
Development of VIIRS Cloud Products that account for multi-layer conditions
Using various spectral signatures that are unique to multi-layer cloud (cirrus over low), we included multi-layer detection into the VIIRS cloud typing product.
We are developing techniques to estimate properties of both cloud layers.

13. Day/Night Continuity in VIIRS Cloud Products
One area where CIMSS/NESDIS is doing research with VIIRS algorithms is to develop ways to remove day/night discontinuities in cloud products by using IR only
No vis retrieval for qo > 70o
NOAA-15 Data near Equator
Formulation of multi- channel IR retrieval allows for near-seamless operation through terminator – critical for diurnal studies which are possible with AVHRR/MODIS. Infrared approaches lose sensitivity for optical depths > 6.

14. A Tool for Testing VIIRS Cloud Algorithms Globally
(Low Earth Orbiting Cloud Algorithm Testbed - LEOCAT)
M. Pavolonis has developed a processing system that allows multiple algorithms for the same product (ie cloud height) to be run on the same MODIS granule simultaneously.
This tools allows for direct comparison and isolation of algorithmic / spectral differences.
We plan to run VIIRS approaches along with AVHRR and MODIS approaches though we can multiple VIIRS algorithms as well.
Our goal is to use the CASANOSA code as one of the algorithms in the suite and we plan to compare directly to the results from the CASANOSA system using the available test data.
One weakness is that this tool uses a common clear-sky modeling framework (the CRTM) that is not used by NGST. This is not a large limitation.

15. Example Results: Cloud Height Emissivity
AVHRR (CLAVR-x)
MODIS (MOD06)
VIIRS (Coming Soon)
By doing these, we know differences are not caused by cloud mask, cloud type or clear-sky assumptions (surface temp, RTM, …)
VIIRS code provided by NGST, we are implementing it now.

16. Conclusions
VIIRS offers some new (spatial, day/night) capabilities that should improve NOAA’s real-time cloud products
VIIRS is undergoing vacuum testing now and we’ll soon know how well VIIRS should perform.
CIMSS/ASPB (through the VOAT) have had limited success in impacting NPOESS baseline algorithms (ie cloud mask and cloud typing).
CIMSS/ASPB playing a larger role in testing VIIRS algorithms (in conjunction with the NPP PEATE).
Multi-sensor approaches offer most significant potential for product improvements (ie CrIS fills spectral voids in VIIRS, Cloud liquid water from ATMS and VIIRS).
Work is needed to develop ways of reconciling cloud climatologies from different sensors otherwise NPOESS climatologies will not build on 30 yrs of POES climatologies). CloudSat and CALIPSO offer tools to do this.

17. Backup Material

18. Characteristics of Cloud Products from Different NPOESS sensors: Cloud Liquid Water Path from ATMS and VIIRS.
ATMS (left) can see cloud water that is under cloud ice – VIIRS can not. This explains why most high values seen by AMSU (like ATMS) are missing in AVHRR (like VIIRS).
AVHRR / VIIRS can detect smaller amounts of cloud water missed by AMSU
Both EDRS are not redundant and complement each other (same for ATMS/VIIRS)
AMSU data from MSPPS site
AVHRR data from CLAVR-x site

19. Characteristics of Cloud Products from Different NPOESS sensors: Cloud Liquid Water Path from ATMS and VIIRS.
AMSU-B (left) is less sensitive to presence of some ice than AVHRR (right) but is more to uniquely detect ice signatures.
Much of the signal detected by AVHRR as Cloud Ice Water Path is due to the presence of Cloud Liquid Water underneath the ice. This holds true for VIIRS.
AVHRR data from CLAVR-x site
AMSU data from MSPPS site

20. Using New Cloud Observing Systems (GLAS) to estimate optical depth sensitivity of cloud climatologies
By filtering out GLAS results with optical depths below some minimum, we can estimate the sensitivity of our passive cloud climatologies:
Minimum GLAS optical depth to match observed High Cloud Amount:
AVHRR Day – 0.23
AVHRR Night – 0.1
MODIS/TERRA – 0.12
ISCCP Day – 0.27
ISCCP Night – 0.40
HIRS Day/Night – 0.04

21. Reconciling VIIRS Cloud Climatologies with those from AVHRR and MODIS
The VIIRS Cloud climatologies will be most relevant if they placed in context of those from AVHRR and MODIS (its predecessors).
LIDARS (such as GLAS and CALIPSO) provides profiles of cloud optical depth so that we can estimate at what optical depth are clouds not detected by different passive sensors. This also a truer basis of comparison. .

22. Example Verification of NWP using Satellite Radiances (11 mm)
While the global comparison indicate agreement on the synoptic scales, there are difference revealed in smaller scales.
AVHRR 11 mm BT at 6Z
GFS Simulated 11 mm BT at 6Z

23. Comparison of products can explain differences noticed in 11 mm radiances
AVHRR (CLAVR-x) Optical Depth
Derived NWP (GFS) Optical Depth