1. 1 Shobha Kondragunta NOAA/NESDIS Center for Satellite Applications and Research Introducing VIIRS Aerosol Products for Global and Regional Model Applications

2. VIIRS Aerosol Cal/Val Team 2 NameOrganizationMajor Task Kurt F. BrueskeIIS/RaytheonCode testing support within IDPS Ashley N. GriffinPRAXIS, INC/NASAJAM Brent HolbenNASA/GSFCAERONET observations for validation work Robert HolzUW/CIMSSProduct validation and science team support Nai-Yung C. HsuNASA/GSFCDeep-blue algorithm development Ho-Chun HuangUMD/CICSSM algorithm development and validation Jingfeng HuangUMD/CICSAOT Algorithm development and product validation Edward J. HyerNRLProduct validation, assimilation activities John M. JacksonNGASVIIRS cal/val activities, liaison to SDR team Shobha KondraguntaNOAA/NESDISCo-lead Istvan LaszloNOAA/NESDISCo-lead Hongqing LiuIMSG/NOAAVisualization, algorithm development, validation Min M. OoUW/CIMSSCal/Val with collocated MODIS data Lorraine A. RemerUMBCAlgorithm development, ATBD, liason to VCM team Andrew M. SayerNASA/GESTARDeep-blue algorithm development Hai ZhangIMSG/NOAAAlgorithm coding, validation within IDEA NOAA Team

3. Navy Global Aerosol Forecasting Assimilation system is “2D-VAR” for total column AOD based on NAVDAS 3DVAR system Large development effort required for producing DA-quality products from off-the-shelf MODIS data Operational AOD assimilation reduces RMS error in analyzed AOD by >50% Operational at FNMOC from September 2009 (over ocean) Land and ocean MODIS assimilated in operations since February 2012 Publications about aerosol DA Zhang, J. L., et al.: Evaluating the impact of assimilating caliop-derived aerosol extinction profiles on a global mass transport model, Geophys. Res. Lett., 38, L14801, doi:/10.1029/2011gl047737, 2011. Zhang, J. L., et al.: A system for operational aerosol optical depth data assimilation over global oceans, J. Geophys. Res.-Atmos., 113, D10208, D10208, doi:/10.1029/2007jd009065, 2008.

4. GEOS-5/GOCART Forecasts CO Smoke SO 4 http://gmao.gsfc.nasa.gov/forecasts/  Global 5-day chemical forecasts customized for each campaign  O3, aerosols, CO, CO 2, SO 2  Resolution: Nomally 25 km  Driven by real-time biomass emissions from MODIS  Assimilated aerosols interacts with circulation through radiation

5. NOAA GFS Aerosol Component (NGAC) 5 Model Configuration:  Forecast model: Global Forecast System (GFS) based on NOAA Environmental Modeling System (NEMS), NEMS-GFS  Aerosol model: NASA Goddard Chemistry Aerosol Radiation and Transport Model, GOCART Phased Implementation:  Dust-only guidance is established in Q4FY12  Dust, sea-salt, OC/BC, and sulfate aerosol forecast once real-time global smoke emissions are developed and tested (NWS/NCEP-NESDIS/STAR- NASA/GSFC collaboration) Near-Real-Time Dust Forecasts  5-day dust forecast once per day (at 00Z), output every 3 hour, at T126 L64 resolution  ICs: Aerosols from previous day forecast and meteorology from operational GDAS

6. 6 Need to know: IP is a pixel level AOT retrieval; EDR is an aggregate of about 8 X 8 IP pixels; IP product has Navy Aerosol Analysis and Prediction System AOT filled in for pixels with no retrievals but this is not included in EDR; no AOT over inland water bodies; no negative retrievals allowed; no AOTs over 2.0 reported; VIIRS products come with several quality flags that are useful to screen the data. VIIRS Granule: 86 seconds; 48 scan lines; 3040 km swath width; 16 M-bands (412 nm to 12 µm); 768 X 3200 fixed array size per granule. Outputs (HDF5): (1) AOT Intermediate Product, IP; (2) AOT Environmental Data Record, EDR; (3) Suspended Matter; (4) Aerosol Model Information; (5) Geolocation file for IP; (6) Geolocation file for EDR.

7. VIIRS vs. MODIS EOS 7 MODISVIIRS Orbit altitude 690 km824 km Equator crossing time 13:30 LT Granule size 5 min86 sec swath2330 km3040 km Pixel nadir0.5 km0.75 km Pixel edge2 km1.5 km EDR AOT Product nadir 10 km6 km EDR AOT Product EOS 40 km12 km

8. IP AOT to EDR AOT 8 IP (750m)EDR (6 km) 8 x 8 750m IP pixels aggregated to 6 km EDRs; For EDR to be of “best quality”, at least a minimum of 16 out of 64 IP pixels should have “best quality” AOT; During the aggregation process, top 40% and bottom 20% “best quality” IP pixels are discarded. In contrast to VIIRS, MODIS retrieves AOT by averaging 500m reflectances in a 10 km x 10 km grid and discarding the top 50% (25%) and bottom 20% (25%) pixels over land (ocean)

9. Daily global mean AOT 9 Production Error Algorithm Upgrade

10. AOT (VIIRS – MODIS) 10 Upgrades to the algorithm to reduce the high bias over land are in the plans. NDVI dependent spectral surface reflectance ratios will be implemented soon. Data: February and March 2013 MODIS C5 VIIRS IDPS AOT data mapped to 0.25 o grids AOT data not paired; some sampling differences exist

11. AOT (VIIRS vs. AERONET) 11 MetricVIIRSMODIS Accuracy0.0140.003 Precision0.0600.056 Uncertainty0.0620.056 MetricVIIRSMODIS Accuracy-0.021-0.017 Precision0.1640.115 Uncertainty0.1650.116

12. 12 Smoke from fires on March 15, 2013 seen in the visible RGB image (left panel) nicely captured in the quantitative retrieval of aerosol optical thickness (right panel). These images are generated at the STAR IDEA (Infusing satellite Data into Environmental Applications) website using direct broadcast SDRs, VCM, and fire hot spot data. Data latency is ~2 hrs. The website (www.star.nesdis.noaa.gov/smcd/spb/aq/) is routinely accessed by air quality forecasters for satellite aerosol imagery that provides the spatial extent of smoke, dust, and haze.www.star.nesdis.noaa.gov/smcd/spb/aq/

13. 13 VIIRS true color image of blowing dust from different sources in Alaska on April 28, 2013 VIIRS Pixel Level AOD VIIRS Dust Flag

14. Important Dates: 28 Oct 2011 2 May 2012 15 Oct 2012 27 Nov 2012 22 Jan 2013 today Initial instrument check out. Tuning cloud mask parameters Aerosol product at Beta status Software production error Beta status Aerosol product candidate provisional status Red periods: DO NOT USE the product. Beta: Use with caution. Known biases. Provisional: Use data bearing in mind that further validation is ongiong 14

15. Data (not SM) are available through CLASS: http://www.class.noaa.gov A “back door” for data via the IDEA site: https://www.star.nesdis.noaa.gov/smcd/spb/aq/index_viirs.php?product_id=4 Users’ guide available at: http://www.star.nesdis.noaa.gov/jpss/ATBD.php#S126472 README file under VIIRS aerosol (AOT) at: http://www.nsof.class.noaa.gov/saa/products/welcome Other documents are available at: http://npp.gsfc.nasa.gov/science/documents.html 15

16. JCSDA Atmospheric Composition (AC) Working Group Update  WG is made up of scientists working on data assimilation methods for atmospheric composition modeling  Mostly there are operational customers behind these efforts  Only one telecon held since the last annual meeting. All working group members working on AC working group topics with “leveraged” funding

17. JCSDA Atmospheric Composition Working Group Update  Several Aerosol-related applications are making their way to operations  Zhiquan Liu at NCAR has developed and implemented aerosol optical depth DA in GSI. Air Force Weather Agency intends to operationalize this.  NASA LANCE is now producing DA-ready AOD from MODIS based on Naval Research Lab / University of North Dakota algorithm  Dust aerosol is now operational in GFS  NASA GMAO is assimilating MODIS data for aerosols, and includes aerosol direct effects in GEOS-5  Development of global biomass burning emissions as input for NGAC is progressing well

18. JCSDA Atmospheric Composition Working Group Update  CRTM is being used in these efforts, with difficulty  CRTM trunk has GOCART speciation and microphysical properties hard-coded; cannot modify without altering source code;  CRTM for CMAQ exists; this is another fixed, inflexible configuration  CRTM developers could future-proof current systems and speed development of future systems by extracting the aerosol microphysics into a flexible framework

19. 19 Aerosol Algorithm Differences between VIIRS and MODIS VIIRSMODIS Dark Target Retrieval every pixel (0.75 km resolution) Retrieval after grouping pixels (10 km resolution) Aggregates AFTER retrieval, discarding outliers in groupAggregates BEFORE retrieval, discarding outliers in group Robust AOT at standard 6 km resolution at nadir, 12 km at edge Robust AOT at standard 10 km resolution at nadir, 40 km at edge LAND: chooses aerosol modelLAND: mixes fixed fine and coarse models LAND: fits to atmospherically corrected surface reflectance ratios LAND: fits to top of atmosphere reflectances LAND Spectral AOT at 11 wavelength with 1 primary wavelength LAND AOT at 3 wavelengths with 1 primary wavelength OCEAN: Based on Tanré retrieval OCEAN spectral AOT at 11 wavelength with 1 primary wavelength OCEAN spectral AOT at 7 wavelength with 1 primary wavelength Heavily dependent on cloud mask from outsideMostly dependent on internal cloud mask. NO negative values. Max AOT = 2.0YES negative values. Max AOT = 5.0 Bands used: 0.411, 0.466, (0.550), 0.554, 0.646, 0.856, 1.242, 1.629, 2.114 µm Bands used: 0.412, 0.445, 0.488, (0.550), 0.555, 0.672, 0.746, 0.865, 1.24, 1.61, 2.25 µm No level 3 gridded products yetLevel 3 daily, 8-day, monthly mean