GOES-R AWG Product Validation Tool Development

1. GOES-R AWG Product Validation Tool Development Cloud ProductsAndrew Heidinger (STAR) Michael Pavolonis (STAR) Andi Walther (CIMSS) Pat Heck and Pat Minnis (NASA Larc) William Straka (CIMSS)

2. Cloud Products While the cloud team as 10 products from 5 algorithms, we really have 14 when doing validation. • Clear sky mask (binary, 4-level, and test results) • Cloud Phase • Cloud Type • Cloud-top Height • Cloud-top Temperature • Cloud-top Pressure • Daytime Cloud Optical Depth • Daytime Cloud Particle Size • Daytime Liquid Water Path • Daytime Ice Water Path • Nighttime Cloud Optical Thickness • Nighttime Cloud Particle Size • Nighttime Liquid Water Path • Nighttime Ice Water Path

3. Validation Strategies • ABI Cloud Mask (ACM) • CALIPSO cloud layer product. • Comparison to other clouds masks (NASA, EUMETSAT) • ABI Cloud Phase/Type (ACT) • A-train – CALIPSO and potentially CloudSat • ABI Cloud Height Algorithm (ACHA) • CALIPSO cloud-top height • Comparison to other sensors (GOES, SEVIRI, MODIS) • Cloud Optical and Microphysical Properties (DCOMP and NCOMP) • Comparison to same products from other algorithms on similar sensors (DCOMP) • Microwave radiometers (AMSRe) provide Liquid Water Path • Various data-sets from field campaigns. • CALIPSO to provide cloud optical depth for thin cirrus (NCOMP). • Validation Tools • All Deep-Dive tools developed in IDL • Routine validation imagery done in IDL and prototype websites exist showing images using Java Applets and Google Earth. • McIdas-V interaction beginning.

4. Routine Validation Tools • Visual analysis – Cloud Type • Easiest to perform in an operational manner. Visualization below from the CIMSS GEOCAT website. Images generated in IDL. • Cloud phase and type should visually correlate with features seen in false color images that include the appropriate channels. • Visual analysis of all cloud products is an important tool.

5. Deep Dive: ACM Comparison to other Masks • The MODIS cloud mask from NASA (developed at CIMSS) provides a well-characterized mask designed for an advanced imager. • The image on the right shows a comparison of the ACM run on data from AQUA as compared to the MODIS cloud mask from that scene. • This can be done with any polar orbiting satellite (NASA EOS, JPSS, Metop) that has a cloud mask product. 5

6. Deep Dive: ACM Comparison to other Masks • The EUMETSAT Meteorological Product Extraction Facility (MPEF) Cloud Mask cloud mask provides a well-characterized mask designed for the imagery used as proxy • The animation on the right shows a comparison of the ACM run on data from SEVIRI as compared to the EUMETSAT cloud mask from that scene. • Inter-satellite comparisons of cloud mask (and other products) can provide insight as to deficiencies and improvement in both products (next slide) 6

7. Deep Dive: ACHA Comparison with CALIPSO/CALIOP Example Comparison of CALIPSO and ACHA for one AQUA/MODIS Granule . • CALIPSO/CALIOP remains our main source of cloud height validation. • CALIPSO results compared to MODIS, GOES, SEVIRI and AVHRR • See ACHA Poster for more details. See ACHA poster for more CALIPSO/CALIOP comparisons.

8. Deep-Dive Validation DCOMP Liquid water path with AMSRe • Image shows result of four arbitrary chosen days in October 2006 and April 2007. • Accuracy and precision specs are met. DCOMP LWP (SEVIRI) AMSRe LWP 8 See DCOMP poster for more AMSRe and MODIS comparisons.

9. Deep Dive: Field Campaigns • The SSFR is a shortwave spectrometer operated by U. Colorado / LASP. • During CALNEX 2010 it was operated on a ship looking up. • It provides retrievals of DCOMP cloud properties using radiation that travelling through the cloud (not reflected off the top like DCOMP). • This provides a more independent validation. Red lines are the DCOMP Error Bars 9

10. NCOMPValidation Summary Routine Validation Tools using satellite-based sources forLWPand COD have been developed with F&PS met. Tools using satellite-based sources for CPS and IWP have been developed but sources are not well-understood and are subject to the sources’ evolving retrieval algorithms. Have met or are approaching F&PS requirements. Deep-dive Validation Tools using satellite-based sources for LWP, COD, CPS and IWP have been developed. Same caveats on satellite sources. Tools using surface-based sources have been developed for usage within non-GOES-R systems and are being adapted to GOES-R requirements. Integrating multiple sources and tools for deep-dive looks is in development. 10

11. Deep Dive Validation: NCOMP • Liquid Water Path NASA LaRC SEVIRI/ABI SIST2 products Retrievals from AMSR-E1 SIST LWP Retrieval 2 Apr 2011 00:00 UTC LWP from SEVIRI SIST can be compared to NCOMP over entire disk LWP from SEVIRI SIST compared to AMSR-E for thin water clouds • SEVIRI/ABI SIST near-realtime • Validation tool imminent, IDL based, some McIDAS • AMSR-E comparison meets specs for thin water clouds • IDL-based • TBD: is AMSR-E a feasible source for other water cloud types? Is CloudSat LWP appropriate? Ground-based Retrievals1 LWP from ARM MWR and radar/lidar are compared to MODIS SIST at ARM SGP • Need to transition NCOMP to MODIS • Subject to ground-based retrievals’ availability • IDL-based 11 1 completed2 in development

12. Validation through Application • Another way to validate products is by tracking their impact on applications that use cloud products. • GOES-R AWG cloud algorithms are currently being fed into the following applications. • NWS Forecaster Support in GOES-R Proving Ground • Cloud-drift winds by the AMV team. • Cloud-top Height Assimilation by NWP (potentially)

13. Impact of Cloud-top Heights on AMV Performance • AMV Performance is dependant on the accuracy of the cloud height • Image below from AMV team shows an impact of a cloud height change (black line) on the wind speed bias (blue) and vector difference (green) Wayne Bresky, AMV Team

14. Impact of Cloud-top Heights in NWP Data Assimilation: Cloud Analysis Verification: Impact on moisture analysis & forecast 1-h Forecast Humidity RMS, Jan 4 - 13, 2010 Analysis Humidity RMS, Jan 4 - 13, 2010 oprRR: w/ cloud analysis oprRR => better moisture forecasts at all levels devRR: no cloud analysis Error Error Inclusion of LaRC clouds yields more accurate moisture analyses & forecasts => ultimately better forecasts of aviation hazards *Ultimately, only Ztop is being used operationally because inconsistencies with other parameters occurred when assimilating LWP/IWP. So, research continues. Cloud Assimilation

15. Conclusions • Cloud team is making use of all available space-based validation sources. (CALIPSO, CLOUDSAT, AMSRe, MODIS …) • Application of these algorithms to current sensors is an important part of our algorithm validation and evolution. (In start contrast to JPSS). • We are starting to get feedback from applications on our products by other groups. • We hope some of the proposals we have submitted are funded or we have additional opportunities so that we can benefit from the data and insights offered by the airborne and field campaign communities.

16. End of Talk, Thank You !

17. Deep Dive Comparison to MODIS Example: DCOMP Comparison to NASA MODIS MYD06 Products A W G P R O D U C T S (D C O M P ) N A S A M O D I S P R O D U C T S ( M Y D 0 6 )

18. Routine Validation Tools • Visual Analysis: DCOMP Cloud Water Path • Cloud Water Path (Liquid or Ice) should show a correlation with the visible reflectance. • Areas of very low values over bright surface are indicative of false cloud. • Imagery shows performance of clouds near edges and across boundaries.