Air and Waste Management Association Professional Development Course AIR-257: Satellite Detection of Aerosols: Satellite data and tools for the RPO FASTNET project Instructor: Rudolf Husar, Ph.D. Professor of Mechanical Engineering Washington University, St. Louis, MO October 25, 2004, 9:00 a.m. - 12:00 p.m. Asheville, NC

Syllabus 9:00-9:30 Introduction to satellite aerosol detection and monitoring 9:30-10:00 Satellite Types and their Usage 10:00-10:30 Satellite detection of aerosol events: fires, dust storms, haze 10:30-10:45 Break 10:45-11:00 Satellite data and tools for the RPO FASTNET project 11:15-11:30 Satellite Data Use in AQ Management: Issues and Opportunities 11:30-12:00 Class-defined problems, feedback, discussion, exam(?)

9:00-9:30 Introduction to satellite aerosol detection and monitoring

9:30-10:00 Satellite Types and their Usage

10:00-10:30 Satellite detection of aerosol events: fires, dust storms, haze

10:30-10:45 Break

10:45-11:00 Satellite data and tools for the RPO FASTNET project

11:15-11:30 Satellite Data Use in AQ Management: Issues and Opportunities

11:30-12:00 Class-defined problems, feedback, discussion, exam(?)

Please Visit http://datafed.net

Web-based data delivery: Analysts Console

MODIS Rapid Response FASTNET Event Report: 040219TexMexDust Texas-Mexico Dust Event February 19, 2004 Contributed by the FASNET Community Correspondence to R Poirot , R Husar

Satellites detect dust most storms in near real time The MODIS sensor on AQUA and Terra provides 250m resolution image s of the dust storm Visual inspection reveals the dust sources at the beginning of dust streaks. The NOAA AVHRR sensor highlights the dust by its IR sensors In the TOMS satellite image, the dust signal is conspicuously absent – too close to the ground

Monte Carlo simulation of dust transport using surface winds (just a toy, 3D winds are essential!) See animation Note, how sensitive the transport direction is to the source location (according to this toy)

See animation Note, how sensitive the transport direction is to the source location (according to this toy)

VIEWS Fine Mass, Sulfate, OC, Dust, 02-07-01 OC OC Mass SO4 Dust

OC

SeaWiFS AOT – ASOS FBext, 02-07-01

July 2020 Quebec Smoke Event Superposition of ASOS visibility data (NWS) and SeaWiFS reflectance data for July 7, 2002 – PM2.5 time series for New England sites. Note the high values at White Face Mtn. Micropulse Lidar data for July 6 and July 7, 2002 - intense smoke layer over D.C. at 2km altitude.

Superposition of ASOS visibility data (NWS) and SeaWiFS reflectance data for July 7, 2002

PM2.5 time series for New England sites. Note the high values at White Face Mtn.

Micropulse Lidar data for July 6 and July 7, 2002 - intense smoke layer over D.C. at 2km altitude.

Pattern of Fires over N. America The number of ATSR satellite-observed fires peaks in warm season Fire onset and smoke amount is unpredictable Fire Pixel Count: Western US North America

The number of ATSR satellite-observed fires peaks in warm season

Fire onset and smoke amount is unpredictable

Near Real Time Public Satellite Data Delivery

NCore Integration NOAA/NASA Satellite: Global/Continental transport Other Networks: Deposition, Ecosystems Intensive/diagnostic Field Programs Longer Term Goal: Integrated Observation-modeling Complex Similar to Meteorological Models (FDDA) Model Adjustments Through Obs. All in Near Real Time Full Model Dims (x, y, z, t, chemistry, size)

NCore Integration

NOAA/NASA Satellite: Global/Continental transport

Other Networks: Deposition, Ecosystems

Intensive/diagnostic Field Programs

Longer Term Goal:

Integrated Observation-modeling Complex

Similar to Meteorological Models (FDDA)

Model Adjustments Through Obs.

All in Near Real Time

Full Model Dims (x, y, z, t, chemistry, size)