1. Cloud Products and Applications: moving from POES to NPOESS (A VIIRS/NOAA-biased perspective) Andrew Heidinger, Fuzhong Weng NOAA/NESDIS Office of Research and Applications (STAR)

2. Outline Review of POES Cloud Products and Applications EDRs NWP Applications Climate Applications Expected Improvements in EDRs with NPOESS Expected Improvements in CDRs with NPOESS Conclusions

3. NOAA is producing many of the NPOESS Cloud EDRs now from POES and our customers should be ready for the improvements offered by NPOESS. AVHRR (CLAVR-x) 1.Cloud Detection using the CLAVR-x cloud mask 2.Cloud Typing (water, cirrus, opaque ice, multi-layer cirrus) 3.Cloud Optical thickness and particle size during the day for ice and water clouds separately. 4.Cloud Temperature and emissivity from a split-window approach – day/night insensitive. Products from MSPPS (AMSU A/B) 1.Cloud liquid water path 2.Ice water path 3.Ice crystal size Products from HIRS 1.Cloud Top Pressure 2.Effective Cloud Amount

4. Where are we with the current observing system (POES) The following slides will review the abilities of the NOAA AVHRR processing to derive cloud properties produced by VIIRS for the following scene from the Eastern Tropical Pacific.

5. Cloud Detection We can detect clouds well enough over ice-free oceans for SST estimation We estimate our detection level requires a cloud of optical depth 0.3 – 0.5. Thin cirrus contamination is an issue for some POES products Detect of cloud over the poles is difficult and requires advanced algorithms.

6. Cloud Typing / Phase Outside of terminator conditions, the AVHRR does well on opaque clouds. AVHRR approaches often have difficulty uniquely detecting thin cirrus Cirrus detection actually better at night without solar component to 3.75  m channel Detection of multi-layer clouds but only thin and high over thick and low.

7. Cloud Optical Depth / Particle Size Most common technique retrieve these simultaneously from 0.65 and 3.75  m observations. Split-window approaches offer some night-time capability though limited in optical depth range. Separation of 3.75  m component into solar and thermal contributions is commonly done and is a major source of error in particle size. Lack of on-board calibration is another source of error in AVHRR retrievals. At 0.65 and 0.86  m, optical depth retrieval over snow difficult for optically thin cloud.

8. Cloud Top Height Without CO 2 slicing, AVHRR can only estimate cloud temperature directly. Most common approaches (also VIIRS) to retrieve T c with  and r e during the day. Split-window approaches can be used for day/night independent estimations.

9. Comparison between Microwave (AMSU) and VIS/IR Cloud Products AMSU-A (left) can see cloud water that is under cloud ice – AVHRR can not. This explains why most high values seen by AMSU are missing in AVHRR. A AVHRR can detect smaller amounts of cloud water Both EDRS are not redundant and complement each other (same for CMIS/VIIRS) AMSU data from MSPPS siteAVHRR data from CLAVR-x site

10. 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. AMSU data from MSPPS site AVHRR data from CLAVR-x site Comparison between Microwave (AMSU) and VIS/IR Cloud Products

11. Reasons to Expect VIIRS Cloud EDRs to surpass AVHRR EDRs Cloud Detection/Typing. 1.38  m channel will aid in thin cirrus detection and typing lack of H 2 O or CO 2 channels will hinder polar cloud detection DNB and more infrared channels will help at night compared to AVHRR Spatial resolution also a big benefit to cloud detection Cloud Optical Properties More reflectance channels will lead to better particle size and optical dep. More infrared channels (8.5  m) and DNB will greatly enhance nighttime COP Cloud Top Parameters Lack of H2O and CO2 channels causes Cloud Top Parameters to rely on Cloud Optical Properties – therefore performance suspect in some regions (poles/termin.) Nighttime performance should be better than AVHRR with 8.5 mm channel VIIRS will produce a cloud base product for the first time.

12. Fortunately, the MODIS has allowed to see what VIIRS should provide (Cloud Optical Properties - COP) Aqua platform 20 November 2000 1710 UTC MODIS granule from the northeastern coast of South America

13. Cloud Top Temperature (K) Cloud Top Pressure (hPa) Aqua platform 20 November 2000 1710 UTC Again, MODIS has provided a good experience of Cloud Top Props (CTP) though lack of CO 2 channels requires different approaches.

14. NWP Applications Now and During NPOESS The complexity of cloud parameterization schemes in NWP models is increasing to the point of allowing meaningful comparisons between forecast clouds and satellite-derived clouds. JCSDA is pursuing microwave cloudy radiance assimilation methods and infrared radiance assimilation will follow. Direct assimilation of cloud EDRs seems less attractive to NWP centers than cloud EDR assimilation. This may change in the NPOESS era.

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

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

17. Global Comparisons of SSM/I Cloud Liquid Water Inter-comparison of Cloud Liquid Water from SSM/I and NWP Model – One Example of how NWP models can be validated/tuned to Satellite-derived cloud products.

18. Liquid Water PathIce Water Path Research is being conducted to explore assimilation of cloudy microwave radiances and derived LWP and IWP. Below are simulated LWP and IWP fields from a model that assimilated AMSU radiances (F. Weng) NWP Applications during NPOESS

19. Climate Applications POES has provided roughly 25 years of data and is therefore a major source of data for decadal climate variability studies. The interest in satellite-derived climatologies is increasing and will certainly be a large application during NPOESS. NOAA has made climatologies of cloud products from AVHRR, SSM/I and HIRS.

20. Current State of Imager Cloud Climatologies. For the same CDR (i.e. high cloud fraction), different sensors produce time series with different magnitudes and signs. (see below) Many of the VIIRS cloud algorithms differ from those used AVHRR and MODIS and therefore making the AVHRR – MODIS – VIIRS time series consistent will require some effort.

21. Note that NOAA-15 & NOAA-16 are out of family (HIRS 3) HIRS has also produced an excellent time series of cloud heights and amounts (and so will CrIS). Frequency of High Clouds (< 440 hPa) and All Clouds Tropics (20 o S to 20 o N) over ocean D. Wylie and P. Menzel

22. Example of the difficulties in making a consistent time series from POES and NPOESS (AVHRR and MODIS) The time-series below is the mean water cloud particle size for a region off the coast of Western South America. In 2001-2003, NOAA-16 AVHRR used 1.6  m instead of 3.75  m channel. This effects the derived particle size. MODIS uses 2.1  m for particle size Some of this difference is expected due to spectral differences, some is not. NOAA-16 1.6  m period

23. NPOESS Improvements for Cloud Climatologies Lack of spectral characterization has limited our ability to make seamless time-series of some CDRs Orbital drift on the POES series has also hindered climate research NPOESS will offer several improvements Constant orbit times (POES/DMSP drifted) Onboard VIIRS reflectance calibration (missing on POES) Improved spectral characterization More overlap between sensors VIIRS DNB offers a chance for better day/night continuity in cloud products.

24. Motivation to move beyond the Baseline VIIRS products VIIRS channels do not span any h 2 o or co 2 absorption bands. This makes performance in polar regions marginal. In addition, the VIIRS cloud top height for thin clouds is very sensitive to errors in optical depths. Use of CrIS data will solve many of these issues Fusion with CrIS will: improve cloud detection in the poles Provide the best cloud top height for thin cirrus and for all orbits. Provide cloud top particle and opacity information for thin clouds that is less sensitive to uncertainties in particle size than VIIRS CTP. Fusion with CMIS CMIS will benefit from sub-pixel cloud detection/type and height information. Fused microwave/visible/infrared approaches will certainly become mature before launch of NPOESS APS (at a minimum) will provide a basis for improving all VIIRS cloud products. Limited extent of APS limits utility of combined products.

25. Conclusions While VIIRS will not essentially offer new cloud products (except for cloud base) from those we have been deriving from AVHRR, its products will be At a higher spatial resolution Better calibrated Available more quickly For cloud climatology research, VIIRS will improve upon POES with On board calibration Controlled orbits. The presence of CMIS, CrIS and VIIRS together on a single platform will provide a much better cloud observing system than possible with any one sensor.