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Shobha KondraguntaNOAA/NESDIS Center for Satellite
Applications and Research
Introducing VIIRS Aerosol Products for Global and
Regional Model Applications
VIIRS Aerosol Cal/Val Team2
Name Organization Major TaskKurt F. Brueske IIS/Raytheon Code testing support within IDPS
Ashley N. Griffin PRAXIS, INC/NASA JAM
Brent Holben NASA/GSFC AERONET observations for validation work
Robert Holz UW/CIMSS Product validation and science team support
Nai-Yung C. Hsu NASA/GSFC Deep-blue algorithm development
Ho-Chun Huang UMD/CICS SM algorithm development and validation
Jingfeng Huang UMD/CICS AOT Algorithm development and product validation
Edward J. Hyer NRL Product validation, assimilation activities
John M. Jackson NGAS VIIRS cal/val activities, liaison to SDR team
Shobha Kondragunta NOAA/NESDIS Co-lead
Istvan Laszlo NOAA/NESDIS Co-lead
Hongqing Liu IMSG/NOAA Visualization, algorithm development, validation
Min M. Oo UW/CIMSS Cal/Val with collocated MODIS data
Lorraine A. Remer UMBC Algorithm development, ATBD, liason to VCM team
Andrew M. Sayer NASA/GESTAR Deep-blue algorithm development
Hai Zhang IMSG/NOAA Algorithm coding, validation within IDEA
NOAA Team
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.
GEOS-5/GOCART Forecasts
CO
Smoke
SO4
http://gmao.gsfc.nasa.gov/forecasts/
Global 5-day chemical forecasts customized for each campaign O3, aerosols, CO, CO2, SO2
Resolution: Nomally 25 km
Driven by real-time biomass emissions from MODIS
Assimilated aerosols interacts with circulation through radiation
NOAA GFS Aerosol Component (NGAC)
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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
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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.
VIIRS vs. MODIS EOS7
MODIS VIIRS
Orbit altitude
690 km 824 km
Equator crossing time
13:30 LT
13:30 LT
Granule size
5 min 86 sec
swath 2330 km
3040 km
Pixel nadir
0.5 km 0.75 km
Pixel edge
2 km 1.5 km
EDR AOT Product nadir
10 km 6 km
EDR AOT Product EOS
40 km 12 km
IP AOT to EDR AOT8
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)
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Daily global mean AOT
Production Error
Algorithm Upgrade
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AOT (VIIRS – MODIS)
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 0.25o
grids• AOT data not paired; some
sampling differences exist
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AOT (VIIRS vs. AERONET)
Metric VIIRS MODIS
Accuracy 0.014 0.003
Precision 0.060 0.056
Uncertainty
0.062 0.056
Metric VIIRS MODIS
Accuracy -0.021 -0.017
Precision 0.164 0.115
Uncertainty
0.165 0.116
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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.
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VIIRS true color image of blowing dust from different sources in Alaska on
April 28, 2013
VIIRS Pixel Level AOD
VIIRS Dust Flag
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Important Dates:
28 Oct2011
2 May2012
15 Oct2012
27 Nov2012
22 Jan2013
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
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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
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
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
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
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Aerosol Algorithm Differences between VIIRS and MODIS
VIIRS MODIS Dark Target
Retrieval every pixel(0.75 km resolution)
Retrieval after grouping pixels(10 km resolution)
Aggregates AFTER retrieval, discarding outliers in group
Aggregates 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 model LAND: 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: 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 outside
Mostly dependent on internal cloud mask.
NO negative values. Max AOT = 2.0 YES 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 yet Level 3 daily, 8-day, monthly mean