ARSET NASA’s Applied Remote Sensing Education and Training Program Richard Kleidman Science Systems and Applications, Inc. NASA Goddard Space Flight Center Ana Prados Joint Center for Earth Systems Technology (JCET) University of Maryland Baltimore County

NASA and Earth Science Applied Sciences Program Ecological Forecasting Agricultural Efficiency Air Quality Weather (Aviation) Climate Water Resources Disaster Management Public Health Applications to Decision Making: Eight Thematic Areas

Remote Sensing and Air Quality NASA’s Applied Remote Sensing Education and Training Our Current Focus :

ARSET Program Motivation NASA data products are underutilized

Barriers to Remote Sensing Data Utilization 1. Lack of knowledge. 1. Too much information to digest.

Barriers to Remote Sensing Data Utilization 1. Lack of knowledge. a) So much was promised at the beginning of the satellite era. What can the satellite data really do for us?

Current Application Areas of NASA Remote Sensing Data Not a comprehensive list Long Range Transport of air pollution (regional scale). Attainment is a combination of local and upwind sources

Long Range Transport of Air Pollution October 20, 2007October 21, 2007October 22, 2007 October 23, 2007October 24, 2007October 25, 2007

Current Application Areas of NASA Remote Sensing Data Not a comprehensive list Long Range Transport of air pollution (regional scale). Attainment is a combination of local and upwind sources Improve data coverage and knowledge of air pollution trends where monitor data are lacking (e.g. forecasting).

Relating Satellite Column Measurements to Ground Concentrations June 13, 2008 Figure courtesy, A. Huff

Current Application Areas of NASA Remote Sensing Data Not a comprehensive list Long Range Transport of air pollution (regional scale). Attainment is a combination of local and upwind sources Improve data coverage and knowledge of air pollution trends where monitor data are lacking (e.g. forecasting). Exceptional Event analysis: allows states to obtain an exclusion for a NAAQS exceedance

Exceptional Event Submittals Long range transport of air pollution Virginia, Maryland and North Carolina

Current Application Areas of NASA Remote Sensing Data Not a comprehensive list Long Range Transport of air pollution (regional scale). Attainment is a combination of local and upwind sources Improve data coverage and knowledge of air pollution trends where monitor data are lacking (e.g. forecasting). Exceptional Event analysis: allows states to obtain an exclusion for a NAAQS exceedance Trace Gas Emissions Inventories and regulatory effectiveness: (U.S and China Coal Plants)

Earth Satellite Observations Advantages and Limitations

Air Quality/Pollution Advantages –Adds value when combined with surface monitor and models –Provides coverage where there are no ground monitors –Synoptic and transboundary view (time and space) –Visual appeal –Qualitative assessments and indications of long range transport –Emerging Application areas

Earth Satellite Observations Advantages and Limitations Air Quality/Pollution Limitations –Lack of specificity about pollutants type –Resolution and temporal scales sometimes too coarse –Vertical distribution often unknown (sum over column of air) –Satellite data cannot be used quantitatively for enforcement purposes such as for example to determine whether a region is in attainment or not (Hoff and Christopher 2009).

Remote Sensing and Air Quality NASA’s Applied Remote Sensing Education and Training Aerosol ProductsTrace Gas Products Fire Products

Applied Remote Sensing Education and Training Workshops and materials are designed to help overcome many of the barriers to proper utilization of remote sensing data.

Training Philosophy - at the heart of facilitating proper data usage Training is the transmitting of experience not just knowledge. People learn best when they are actively engaged through hands-on activities. Training activities must be tailored to the target audience therefore trainers must have the skills and resources to be flexible. Workshops alone are insufficient to transmit the experience needed to build expertise and capacity.

Barriers to Remote Sensing Data Utilization 1. Lack of knowledge. a) So much was promised at the beginning of the satellite era. What can the satellite data really do for us? b) I didn’t know that so much data was available and/or that NASA data is free.

Negotiating the maze of information overload Sources for MODIS atmospheric products Ladsweb – NASA archive site (3 interfaces plus ftp) iCARE – French CNES archive site LANCE – NASA real time data Giovanni – NASA on-line analysis tool (6 different instances) NEO – NASA Earth Observations

Negotiating the maze information overload Sources for MODIS images MODIS Rapid Response NASA’s Earth Observatory NASA’s Visible Earth MODIS today MODIS-atmos website Naval Research Lab Just one among many products!

NASA Satellite Products for Air Quality Applications Particulate Pollution (dust, haze, smoke) - Qualitative: Visual imagery - Quantitative*: Column Products and vertical extinction profiles Fire Products: Fire locations or ‘hot spots’ Trace Gases - Quantitative*: Column Products - Vertical profiles: mostly mid-troposphere 25

Barriers to Remote Sensing Data Utilization 2.Too much information to digest. a) Too many new developments to keep track of them all. b) Too steep a learning curve.

Barriers to NASA Data Utilization 3. Institutional : prioritization, lack of man-power and needed technical expertise. 4. Access to research results: - Policy-relevant research remains largely inaccessible beyond the relatively small research community - cost of journals - knowledge gaps about data sets and their application to air quality management activities

Methodology - Our philosophy in practice Provide context for the overwhelming mass of satellite data. - lectures, comparison charts, and materials archive.

“Directed Messing Around” We use the benefit of our experience to create hands on exercises to facilitate directed exploration of the most pertinent sources of information for the needs of our audience.

An example of “Directed Messing Around” The MODIS-atmos site. The most important reference site for MODIS atmospheric products.

ProductsImagesToolsReference Brief and complete descriptions of all the Atmosphere products as well as processing and other information. A complete image archive of Aqua and Terra true color images for all 5 minute granules as well as samples of other types of images. Brief descriptions of, and links to, important tools for MODIS analysis. All tools listed on this page are free. A searchable data base of many important articles related to MODIS Atmosphere products. ATBD (algorithm theoretical basis documents) available for download These are complete and very detailed descriptions of the algorithms used to create the MODIS level 2 products. Several Power Point presentations are also available for download.

Training Philosophy Training is the transmitting of experience not just knowledge. NASA’s Giovanni is one of the most user friendly on-line tools for remote sensing data analysis. It’s ease of use makes it extremely useful and frequently cited. All data sets must be well understood to draw proper conclusions. We use our experience to stress the proper understanding and use of remote sensing data.

Evaluating Remote Sensing Data Or How to Avoid Making Great Discoveries by Misinterpreting Data Richard Kleidman ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences

Aqua Daily Overpass ~ 1:30 PM local time Terra Daily Overpass ~ 10:30 AM local time MODIS 1 x 1 Degree Data from the on-line Giovanni tool

Possible ways to interpret these differences Real world differences Differences due to other factors - Sensor error - Algorithm error - Sampling error.

Training Philosophy Training is the transmitting of experience not just knowledge. People learn best when they are actively engaged through hands-on activities. Training activities must be tailored to the target audience therefore trainers must have the skills and resources to be flexible. Workshops alone are insufficient to transmit the experience needed to build expertise and capacity.

Methodology - Our philosophy in practice Provide context for the overwhelming mass of satellite data. - lectures, comparison charts, and materials archive. Hands-on activities to explore individual sources of data - Directed messing around Overview and Review - Bringing it all together with case studies.

Case Studies and Hands-On Activities Air Quality Event Template with step-by- step instructions: 1) Access to imagery 2) Access to meteorological, model or other information 3) Utilization of image analysis tools 4) Air Quality Assessment: - Type of event: smoke, dust ? -Where is the pollution coming from? -Potential health impacts University of North Carolina, October 2009

Visualization Tools: May 16 th, 2007: Global View of Transported Dust and Regional Smoke Dust Smoke Source: NASA MODIS Giovanni Image on Google Earth

Case Study Analysis Image from one of the training Case Studies showing MODIS fire locations and True Color Imagery – dust and smoke. Google imagery provided by the NRL Fire Locating and Modeling of Burning Emissions (Flambé) Program. s. Image from one of the training Case Studies, showing MODIS fire locations and modeled pollution – dust and smoke - from the NRL Fire Locating and Modeling of Burning Emissions (Flambe) Program, which incorporates NASA Satellite real-time observations in its model predictions.

Who are we training ? Expertise Air Quality Managers and Regulators EPA, state and local regulatory agencies, US Forest Service Scientists/Technical: Meteorologists, air quality forecasters and modelers, health scientists, AQ researchers Other/public: project managers, reps. from health agencies, World Bank ANY Audience can span a large range in expertise: - No background in remote sensing and little science background - No background in remote sensing and some science background - Introductory expertise with satellite data - Moderate expertise with satellite data

Workshop Goals Teach appropriate use of remote sensing data Navigate the maze of information sources Collaborate with applied end-users to: - Identify areas that can benefit from inclusion of remote sensing data - Plan future training activities

Workshops Range from 1 day overview to 4 day in depth presentations. Provide a framework and structure that can be applied to other remote sensing products. - Focus on relatively few products. Work with target audience and sponsors to design content and length.

Typical Content of Remote Sensing Workshops Basics of remote sensing: instruments, orbits, product overview, data formats Critical Thinking of Remote Sensing: Strengths and caveats in the data products, retrieval characteristics Visualization Tools: online tools and visualization via Google Earth: greatly improved access to NASA Earth Science Data !! Case Studies and Hands-on Activities

Accomplishments Developed a set of re-usable instructional modules Conducted 16 national and international training activities reaching several hundred participants since January Built a project website that provides NASA Earth Science Data users and potential trainers with free access to air quality training modules Developed a Case Study Inventory Workshop Attendees: Local, Regional Federal Policy-makers Air Quality Professionals and Managers Students and Researchers 7 SEAS Workshop, Singapore

Training Schedule for 2011 Slots are still available – ask about scheduling a training Host LocationDates Griffith University Gold Coast, AustraliaApril 4 – 7, 2011 NASA ARSET Training for EPA Region 4 Appalachian State University North Carolina June 2011 Community Modeling and Analysis (CMAS) Chapel Hill, North CarolinaSept. - Oct International Society of Exposure Science Pre- Conference Workshop Baltimore, MarylandOctober 23, 2011 Air and Waste Management Association Montreal, CanadaNovember 2011 NASA ARSET Training for EPA Region 6 TBD Texas TBD October - December California Air Resources Board (Basic and Advanced Courses) Sacramento, CaliforniaDecember 2011

What’s new in 2011 and beyond Application-specific training modules and workshops - Biomass burning and dust events - Satellite/model comparisons - Health (e.g. PM2.5) - Exceptional event case studies for EPA area 6. Expanded products and topics for inclusion - Aerosols: CALIPSO, MISR, AIRS - Trace Gases - European Data Sets

Acknowledgements Lawrence Friedl Director NASA Applied Sciences Program, for providing funding for past and ongoing NASA Satellite training activities

For More Information

Extras

Van Donkelaar et al. relate satellite-based measurements of aerosol optical depth to PM 2.5 using a global chemical transport model Approach Estimated PM 2.5 = η· τ Combined MODIS/MISR Aerosol Optical Depth GEOS-Chem Following Liu et al., 2004: vertical structure aerosol type meteorological effects meteorology diurnal effects η van Donkelaar et al., EHP, in press

MODIS and MISR AOD MODIS AOD 1-2 days for global coverage Requires assumptions about surface reflectivity MISR AOD 6-9 days for global coverage Simultaneous surface reflectance and aerosol retrieval Mean τ at 0.1º x 0.1º τ [unitless] MISR MODIS r = 0.40 vs. in-situ PM 2.5 r = 0.54 vs. in-situ PM 2.5 van Donkelaar et. at.

Satellite vs. ARONET: Varies with surface type 9 surface types, defined by monthly mean surface albedo ratios, evaluation against AERONET AOD MODIS MISR July van Donkelaar et. at.

Combining MODIS and MISR improves agreement with PM 2.5 MODIS r = 0.40 (vs. in-situ PM 2.5 ) MISR r = 0.54 (vs. in-situ PM 2.5 ) Combined MODIS/MISR r = 0.63 (vs. in-situ PM 2.5 ) τ [unitless] van Donkelaar et. at.

Global CTMs can directly relate PM 2.5 to AOD Detailed aerosol-oxidant model 2º x 2.5º 54 tracers, 100’s reactions Assimilated meteorology Year-specific emissions Dust, sea salt, sulfate- ammonium-nitrate system, organic carbon, black carbon, SOA GEOS-Chem η [ug/m] van Donkelaar et. at.

Significant agreement with coincident ground measurements over NA Satellite Derived In-situ Satellite-Derived [ μ g/m3] In-situ PM 2.5 [μg/m 3 ] Annual Mean PM 2.5 [ μ g/m 3 ] ( ) r MODIS τ 0.40 MISR τ 0.54 Combined τ 0.63 Combined PM van Donkelaar et. at.