Satellite Aerosol Detection in the NPOESS Era

1. Satellite Aerosol Detection in the NPOESS Era Leslie O. BelsmaThe Aerospace Corporation Leslie.belsma@aero.org 310-336-3040

2. Agenda • Background • Satellite Sensors • Current • National Polar-orbiting Operational Environment Satellite System (NPOESS) • Data Assimilation • Conclusions

3. Background - Need for Detection and Prediction • Air quality • Visual air quality • Health effect • Visibility • Military operations • Civilian and defense aviation • Climatic impact – global warming

4. Surface Networks • Visibility, PM, and aerosol properties have traditionally been measured from ground based networks such as • SLAMS - State and Local Air Monitoring Stations • NAMS - National Ambient Monitoring Stations • SPMS - Special Purpose Monitoring Stations • PAMS - Photochemical Assessment Monitoring Stations • IMPROVE - Interagency Monitoring of Protected Visual Environment • NASA AERONET (AErosol RObotic NETwork) passive aerosol measurements using sun photometers • Sparsity of ground-based measurements limits their utility in understanding climate impact, the transport of aerosols, or ambient detection for operational applications

5. Needs for Satellite Aerosol Detection • Space-based Aerosol Detection is a valuable tool to augment ground measurements • Spatial and temporal heterogeneity of aerosols • Provides coverage in data sparse and rural regions where it might be the only source of data • Large spatial domains allows tracking aerosol transport

6. Space Based Data • A variety of aerosol properties can be retrieved from satellites • Aerosol Optical Thickness AOT & Aerosol Index • Angstrom Coefficient • Single Scatter Albedo • Size Distribution Information • Aerosol Type • Aerosol Shape • Relative Vertical Distribution • Aerosol Layer Height • Backscatter & Extinction CrossSection • Data can be used qualitatively • Imagery and visualizations to provide a regional view of aerosol transport • Data can be used Quantitatively • Initialize and validate weather, climate, and air quality models

7. Qualitative: Wildfire Smoke • Wildfire Smoke plumes evident in both DMSP OLS (Left) and EOS MODIS (Right)

8. Qualitative: Dust storm Air Force Special Operations Command feedback (Operation Iraqi Freedom): “Approximately 20 instances where dust and sand storms were identified in the DMSP imagery …with the lack of ground obs, DMSP became more important than ever… Ref: Lanicci, Polar Max 2004 Conference, Los Angeles,

9. Satellite Sensors Categories • Visible IR solar backscatter sensors • Ozone sensors that detect solar UV absorption and backscatter • Polarimeters • Active Lidar

10. Visible IR backscatter retrievals • Backscattered solar radiation over dark surfaces mainly varies with aerosol type and concentration • Aerosols backscatter solar radiation in proportion to Aerosol Optical Thickness (AOT) and aerosol single scatter phase function • To retrieve AOT, phase function must be known • Phase function depends on aerosol size distribution and composition • Aerosol models used with satellite radiances to retrieve AOT • Simplified over ocean because of low and constant albedo • More difficult over land – complex and variable albedo

11. Visible IR backscatter sensors - AVHRR • NOAA Advanced Very High Resolution Radiometer (AVHRR) • Polar orbiting • Operational single-channel algorithm for Aerosol AOT retrieval over oceans from radiances in channel 1 (0.63 µm) • Aerosol records spanning over two decades • NESDIS generates global daytime cloud-free AOT over oceans • Daily, Weekly, Monthly 1 deg maps http://www.osdpd.noaa.gov/PSB/EPS/Aerosol/Aerosol.html

12. Visible IR backscatter sensors- GOES • GOES Imager • Geostationary orbit: more frequent data • Collaborating with EPA, NOAA/NESDIS recently implemented operational aerosol retrievals over land • Use GOES visible channel to produce AOT • 30 minute intervals with a 4km spatial resolution • Daytime cloud-free conditions http://www.ssd.noaa.gov/PS/FIRE/GASP/gasp.html

13. Visible IR backscatter sensors -MODIS • Moderate Resolution Imaging Spectroradiometer (MODIS) • 36 well-calibrated bands with spatial resolution ranging from 250-1000m • Daytime cloud-free detection of aerosols with high accuracy • Aerosol retrieval uses seven well-calibrated channels from VIS to SWIR • Global coverage over ocean and nearly global over land at 10km res • Near Real Time access through new EPA-NASA-NOAA Collaboration (IDEA-Infusing satellite Data into Environmental Applications) http://idea.ssec.wisc.edu/index.php

14. Vis - IR backscatter sensors – SeaWiFS, MISR • NASA’s Sea-viewing Wide Field-of-view Sensor (SeaWiFS) • Primary mission: ocean color bio-optical properties • AOT at 865nm over oceans is a by-product of atmospheric correction • Routinely produced for the past seven years • Daily, Weekly, Seasonal at 9km resolution • Terra Multi-angle Imaging Spectro-Radiometer (MISR) • Measures solar reflectance in four spectral bands (red, blue, green, and near infrared) • Nine widely spaced viewing angles simultaneously • Allows distinguishing different types of aerosols and land surface covers • AOT over water and dark surfaces & composition products mapped to a 17.6km grid • Beta products: AOT over other surfaces, Ang Exp, Single Scatter Albedo, size, shape, and fractional amounts

15. UV Absorption/Backscatter Sensors • Multispectral bands in near UV detect UV-absorbing tropospheric aerosols over both land and ocean • UV aerosol retrieval is fundamentally different from VIS/SWIR • Strong Rayleigh scattering signature • Reduced, less variable surface reflectivity • Enables detection of aerosols over more land surfaces • Capability to separate aerosol absorption from scattering allows identification of aerosol types • Less spatial resolution

16. UV Absorption/Backscatter Sensors - TOMS • Total Ozone Mapping Spectrometer (TOMS) • First instrument to allow observation of aerosols as they cross the land/sea boundary • 50 km footprint • Aerosol Index product that is related to optical depth, is routinely generated • Earthprobe TOMS – Aerosol Index is in terms of the differences between measurements at 331 and 360 nm

17. UV Absorption/Backscatter Sensors • Other ozone monitors: • GOME (Global Ozone Monitoring Experiment) flying on the European Space Agency (ESA) Environmental Research Satellite (ERS2) • SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) flying on the ESA ENVISAT launched Oct. 01 • OMI (Ozone Monitoring Instrument flying on the NASA Earth Observing System (EOS) Aura mission) • HIRDLS (High Resolution Dynamics Limb Sounder), another NASA Aura mission

18. Aerosol Retrieval Coverage • MODIS provides aerosol data with high accuracy and spatial resolution over most of the globe, but challenges in retrieving AOT over highly reflective land surfaces results in regional coverage that must be filled by other means.

19. Aerosol Polarimetry • Observations of solar reflectance with polarizing filters at multiple angles and wavelengths • Correction for ground reflectance (polarization insensitive to wavelength) • Enables derivation of several aerosol properties • NASA Research Scanning Polarimeter (RSP) • Airborne sensor successfully demonstrated the capability • Paving the way for a new generation of space-based aerosol sensors

20. Aerosol Polarimetry • POLDER (POlarization and Directionality of the Earth’s Reflectances): • Launched Japanese Advanced Earth Observing Satellite (ADEOS I & II) missions, both of which suffered premature deaths. • Planned as the main payload on future French space agency microsatellite PARASOL to complement NASA’s Earth System Science Pathfinder (ESSP) program

21. LIDAR Sensors • Multi-wavelength Lidar uses the wavelength-dependent absorption of atmosphere constituents to measure their range-resolved concentration • Provides information on the vertical distribution of the aerosols • Retrieval of aerosol information both night and day • Demonstrated through measurements campaigns with NOAA Ozone Airborne Lidar - NOAL(formerly UV-DIAL) • Measures vertical profiles of ozone and aerosols from near the surface to the upper troposphere along the flight track

22. LIDAR Sensors • GLAS (NASA Geoscience Laser Altimeter System) • Launched in Jan 2003 aboard ICESat • Retrieves ice, cloud, and aerosol properties both day and night • 1064 and 532 nm channels provide atmospheric backscatter profiles • 1064 nm provides height and vertical distribution of dense aerosols (and clouds) • 532 nm provides vertical distribution of optically thin aerosols • 75 m vertical and 175 m horizontal resolution • Products include Aerosol Layer Height, Backscatter crossSection, Extinction Coefficient, AOT • Reliability of two of three GLAS lasers was much less than planned and NASA is currently operating the system on an intermittent schedule

23. GLAS Layer Heights Data Product Example

24. Sensor Satellite Retrieved Grid Near Ocean Land Day Night Comments Parameter Size RealTimeSensor Satellite Retrieved Grid Near Ocean Land Day Night Comments Parameter Size RealTime OLS DMSP N/A Imagery only AVHRR POES AOT 1 Deg No Yes Rsch Yes Daily, Weekly/Monthly VISSR GOES AOT 4 km Yes Yes Yes Yes AOT Yes Some Yes AOT is for DarkMODIS Aqua & ASD 10 km Yes No Yes Vegetation – Rsch Alg Terra Type No Yes Yes for other Land Types Additional Aerosol Products from ASDC SEAWifs SEAWifs AOT 9 km Yes No Yes AngC TOMS Earthprobe Aindex 50 km Yes Yes Yes No AOT 13x24km Launch Jul 2004: OMI Aura SSA Yes Yes Yes Products not SO2 available yet AOT No Yes Some Yes Rsch over homogeneous Sfcs MISR Terra AngE SSA 17.6 km Beta Beta Beta APS ASD PBL&A Layer HT 7/28 kmGLAS ICESat BSctrCS Yes Yes Yes Yes Quicklook AExtC Vertical Available AOT 76.8 m Aerosol Product Summary AOT = Aerosol Optical Thickness AIndex = Aerosol Indes AngC/E = Angstron Coefficient or Exponent ASP = Aerosol Size Parameter Type = Aerosol Type ASD = Aerosol Size Distribution SSA = Singel Scatter Albedo RelVD = Relative Vertical Distribution PBL&AlayrerHT = Planetary Boundary andAerosol Layer Heights BsctrCS = Backscatter Cross Section AextC = Aerosol Extinction Cross Section

25. NPOESS • National Polar-orbiting Operational Environmental Satellite System • $5.6B NPOESS system marks a new era • Converges operational DoD and NOAA environmental satellites with new NASA technologies • Three orbital planes provide frequent data-refresh • 56 Data Products & 21 Enhancement Products • Rapid-downlink delivers products in 28 minutes • First launch in 2009

26. TSKY Field Terminals TOBS SafetyNetReceptors TATM LCL LATM LRN FOG eij MMC (Suitland) Schriever MMC NPOESS - CONOPS 2. Downlink Raw Data 3. Transport Data to Centrals for Processing 1. Sense Phenomena Global fiber network connects 15 receptors to Centrals 4. Process Raw data into EDRs and Deliver to Centrals 5. Monitor and Control Satellites and Ground Elements Full Processing Capability at each Central: NESDIS, AFWA, FNMOC, NAVO

27. NPOESS Aerosol Capabilities • 3 of 11 NPOESS sensors will provide data related to aerosols • VIIRS (Visible Infrared Imaging Radiometer Suite) • MODIS-like fire, smoke, and aerosol products • APS (Aerosol Polarimetry Sensor) • Dedicated to aerosol detection • OMPS (Ozone Mapping and Profiler Suite) • Aerosol Index Interim Product • APS and OMPS will fly in only one of the NPOESS orbit planes, while VIIRS will fly on all three • VIIRS, and OMPS first fly in 2006 on NPOESS Preparatory Project (NPP) risk reduction mission

28. NPOESS- VIIRS Visible/Infrared Imager Radiometer Suite • Merges attributes of the current operational DMSP OLS and POES AVHRR sensors with state of the art spectro-radiomometer capabilities of the NASA MODIS sensor • 0.4 km imaging and 0.8 km radiometer resolution • 22 spectral bands covering 0.4 to 12.5 mm • Automatic dual VNIR and triple DNB gains • Spectrally and radiometrically calibrated • EDR-dependent swath widths of 1700, 2000, 3000 km • Will deliver enhanced MODIS-like aerosol products • AOT • Size parameter • Suspended Matter (Type) • Product resolution: at 1.6km over ocean, 9.6km over land

29. NPOESS-VIIRS • VIIRS includes a Day-Night Band (DNB) for visible band cloud imagery with a quarter moon illumination • Naval Research Laboratory investigating use of VIIRS DNB measurements of scattered moonlight to retrieve AOT at night over oceans (Shettle, 2004) • Nighttime AOT would improve temporal coverage • Better capture transient aerosol phenomena • Provide information on day/night differences of aerosols • Aid in understanding the impact of aerosols on thermal cooling at night with land/sea breezes in coastal regions

30. NPOESS- APS Aerosol Polarimeter Sensor • Sensor dedicated to measuring global distribution of aerosols • Polarization (all states) • Multiangular (175 angles) • Multispectral (nine spectral bands from 0.4 to 2.25 mm) • Measurements of spectral and angular polarization signature of solar backscatter allow unambiguous retrieval of aerosol amount and size • Most benefit to retrieval of fine particulate data • Wide spectral range needed to understand size distributions and determine fraction of aerosols absorbing vs reflecting • 488 nm measures chlorophyll over-water to separate surface and atmospheric signals • 910 nm band will measure water vapor • 1378 nm will detect cirrus clouds • Remaining bands used to fully characterize the aerosols

31. NPOESS-APS • APS pixel size 5 km to limit sensitivity to cloud cover • APS aerosol products • Optical thickness • Particle size distribution • Refractive index • Single-scatter albedo and shape • APS will allow accurate calibration to improve VIIRS aerosol retrievals

32. NPOESS-OMPS Ozone Mapping and Profiler Suite • Includes both nadir and limb viewing systems • Total column ozone • High vertical resolution ozone profiles • Aerosol correction is an interim processing step in the ozone retrieval • Aerosol index, AI, defined in terms of the difference between the 336 and 377 nm channels, is an “Interim Product” • OMPS sulfate detection can be used in conjunction with VIIRS data for “Suspended Matter” product

33. NPOESS Aerosol Related Sensors and Data Products Sensor Satellite Processed Latency Ocean Land Day Night Comments Products HCS HCS NPOESS AOT 28 min 9.6km RschVIIRS 3 orbit ASP 28 min 1.6 km planes SM 28 min 1.6 km AOT 28 min APS footprint isAPS NPOESS ASP 28 min 5 km 5 km Yes No 5 km, APS/VIIRS 1 orbit SM 28 min TBD product can be ARI, SSA, Sh 90 min finer resolution OMPS NPOESS SO2 28 min 50 km 50 km 1 orbit Aindex 28 min No AOT = Aerosol Optical Thickness ASP = Aerosol Size Parameter SM = Suspended Matter ARI = Aerosol Refractive Index SSA = Single-Scattering Albedo SH = Shape

34. Data Fusion • Satellite data fusion techniques that exploit data from multiple future missions, both domestic and international, will further enhance improved retrievals by reducing backscatter radiance solution space (Labonnote, 2004) • NASA planning formation flying among EOS afternoon constellation of science missions satellites • Aqua • CALIPSO • Cloudsat • Aura • PARASOL (French micro-satellite containing POLDER) • NPOESS continues the Initial Joint Polar Satellite System (IJPS) NOAA and ESA data sharing data sharing agreement • ESA operational METOP will include AVHRR and GOME (enhanced follow-on versions) during the NPOESS era

35. Application of Satellite AOT to PM • Research is underway to relate satellite derived aerosol optical depth to ground-based Particulate Matter (PM) measurements • Comparison between the surface PM2.5 monitors and MODIS AOT(Kittaka, 2004) • IDEA -Infusing satellite Data into Environmental Applications • Joint NASA/EPA project • Prototype system in place • Demonstrates use of MODIS AOT to determine transport of fine aerosols within the lower troposphere http://idea.ssec.wisc.edu/

36. Application of Satellite AOT to PM • Study comparing hourly PM2.5 values from a ground-based monitor in Houston with MODIS AOT - found good statistical correlation (Wang, 2004) • Study underway in Europe to demonstrate that SeaWiFS and MERIS aerosol products can be converted into PM10 and PM2.5 (Ramon, 2003)

37. Data Assimilation • Integration of satellite and ground measurements with numerical models is required to fully characterize large spatial and temporal variations of aerosols • Space based aerosol retrievals are column quantities • Data assimilation into numerical models provides a 3D grid of aerosol distribution • Analysis and forecast • Aerosol transport • Fine particulate contribution to air pollution

38. Data Assimilation • Study to assimilate MODIS AOT into GOCART model (Yu, 2003) • Produced AOT over land in better agreement with ground based AERONET measurements than either the MODIS retrievals or the GOCART simulations alone • Study to assimilate GOES AOT into the CSU RAMS for optimal characterization of the spatial and temporal aerosol distribution (Wang, 2004) • Results indicated that aerosol radiative effects are significant in the simulation of aerosol transport and weather prediction

39. Conclusion • Space-based measurements are an increasingly valuable tool in the detection, tracking and understanding of aerosols by providing observations over large spatial domains and where ground based measurements are sparse or missing. • Numerous satellite missions flying today can retrieve aerosol parameters that can be related to PM concentrations for air quality applications. • Increasingly sophisticated multi-spectral, multi-angle, polarization, and active sensing methods will be employed on future missions. • The NPOESS program will merge the remote sensing technologies of today’s science and operational environmental satellite programs to provide significantly improved data quality, frequent data refresh, and rapid ground processing to deliver products within operational timelines. • Three of the 11 NPOESS sensors will provide aerosol data • It is essential that air quality agencies plan now to procure the capability to acquire, display, and assimilate these valuable sources of data into modeling processes to improve particulate matter forecasting into the NPOESS era.

40. References • Shettle E., NPOESS Integrated Program Office (IPO), Internal Government Study (IGS) Science Team Presentations, Silver Spring, MD, February 24-26 and March 2-4, 2004 • Labonnote, L., Kreidenweis, S., Stephens, G., Multi-Sensor Retrieval of Aerosol Properties. Colorado State/CIRA Annual Review 04 Poster, Accessed via CIRA Website Jul 2004 • Kittaka, C. j. Szykman, B. Pierce, J Al-Saadi, D. Neil, A.Chu, L Remer, E. Prins, J.Holdskom, 2004: Utilizing MODIS Satellite Observations to Monitor and Analyze Fine Particulate Matter, PM2.5, Transport Event, Proceedings of the 84th AMS Annual Meeting, Washington State Convention and Trade Center, Seattle WA 11-15 Jan 2004 • Wang, J, U.S Nair, S. A Christopher., GOES-8 Aerosol Optical Thickness Assimilation in a Mesoscale Model: Online Integration of Aerosol Radiative Effects, JGR, Revised Submission August 5, 2004 • Ramon, D., R. Santer, J. Vidot, Determination of fine particulate matter from MERIS and SeaWiFS aerosol data, Proceedings of the ESA Envisat MERIS User’s Workshop 10-14 Nov 03 • Yu, H., R. E Dickinson, M. Chin, Y. J Kaufman, B. N. Holben, I.V. Geogdzhayev, M. I Mishchenko, Annual cycle of global distributions of aerosol optical depth from integration of MODIS retrievals and GOCART model simulations JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. D3, 4128, 14 February 2003