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Feb TexMex Dust Storm
Analysis
Satellite
250m image
• The National Polar-orbiting Operational Environmental Satellite System (NPOESS) represents this
country’s next generation of polar-orbiting environmental satellite.
• These projects involve improved tactical communications - direct broadcast field terminals, data mining
techniques for large heterogeneous data bases, data retrieval algorithm development, data assimilation
for nowcasting applications, combat simulations quantifying the value of data to the manager exploring
other remote sensing technologies to augment NPOESS and.
• NPOESS Preparatory Project (NPP) to launch in 2006, will provide a bridge from NASA’s EOS research
missions (Terra, Aqua, and Aura) to the operational NPOESS mission in the years that follow.
• America’s future (2012 period) geostationary satellite series, GOES-R, is expected to be a geostationary
constellation whose major meteorological observing instruments are an Advanced Baseline Imager (ABI)
with up to 16 channels, and a Hyperspectral Environmental Suite (HES) that is comprised of a
hyperspectral imager operating in the 0.4 to 1 micron range (HES-I) and an atmospheric sounder
operating across the 4-15 micron portion of the spectrum (HES-II). That instrument is the GOES-R HES-
I with a spatial resolution on the order of 100 to 150 meters operating at 10 nanometer spectral
resolution across the 0.4 to 1.0 micron range across a domain of 100x100 kilometers and capable of
being refreshed at 5 to 10 times per minute
• This report documents a significant dust storm outbreak that occurred on 3 March 2003 over the Gulf of
Oman. The case study demonstrates the effective synergy of the AOD product together with various
other satellite-derived products on the NRL Satellite Focus page and the Navy Aerosol Analysis and
Prediction System (NAAPS) to characterize visibility conditions over data sparse/data denied regions.
Nearby synoptic surface reports serve as validation to the satellite and model -derived products
presented herein. This report also describes the limitations and shortcomings of the current AOD
product arising from sun glint, clouds and water turbidity contamination factors.
• Presently, the Naval Research Laboratory’s global and regional dust models (NAAPS and COAMPS TM
Dust) use the USGS land use characteristic dataset to determine dust emission areas. Since its
compilation a decade ago, two major weaknesses in the USGS land use characteristic dataset have
become apparent. 1. The land uses describing arid and semi-arid regions in Asia and Southwest Asia
have quickly become outdated. To update and to improve the USGS dataset, we are using GIS-like
software named ENVI (Environment for Visualizing Images), 1 km National Geophysical Data Center
(NGDC) global topographical data, satellite imagery, maps, atlases, and recently released governmental
reports.
• The science behind the Air Force Weather Agency’s (AFWA) Dust Transport Application (DTA) is
discussed and the results of an extensive verification of DTA over Africa, central and southwest Asia are
presented. DTA ingests AFWA MM5 45km resolution surface wind data, which is used to calculate the
surface dust flux based on wind threshold velocity. There are differing threshold velocities based upon
the dust particles’ diameter, air and particle density, and soil moisture. DTA also accounts for the
vertical transport of dust through the calculation of horizontal divergence and a second parameter that
calculates vertical diffusion. In addition, DTA uses a dust source region database that was developed on
the basis of land use, topography, the use of Advanced Very High Resolution Radiometer (AVHRR), and
Total Ozone Mapping Spectrometer (TOMS) data.
• A dust aerosol model has been developed and fully embedded in the Navy’s COAMPSTM as an on-line
module of the prediction system, using the exact meteorological fields at each time step and each grid
point of all nests. COAMPSTM is being applied to the experimental dust forecasts for Southwest Asia
including Iraq and Persian Gulf in Spring 2003, the season of high frequency of sandstorms in the
region. The model is run twice a day at 00Z and 12Z to produce 3-day forecasts at 9, 27 and 81-km grid
resolutions.
• SATELLITE FOCUS: A DYNAMIC, NEAR REAL-TIME SATELLITE RESOURCE FOR THE DoD. (NRL) in Monterey accelerated the
development and transition to operations of a new web-based satellite imagery interface. The philosophy of the “Satellite
Focus” web page is sector-centric; a wide variety of value-added products populate the website in near real-time over co-
registered domains. This provides one stop shopping for the analyst, thereby mitigating the often -burdensome task of
searching for necessary information across a myriad of independent resources of variable coverage, capability, quality, and
timeliness. A completely dynamic tool, the interface evolves with the introduction of new sectors and products. Intelligent
architecture and site navigation, customizable animation, image mosaics, satellite overpass prediction and on -line product
tutorials support cutting-edge satellite multi-spectral and model-fusion products developed by NRL Satellite Meteorological
Applications Section scientists using a full complement of polar/geostationary satellites and NWP fields. Highlighted among
these products are the high-resolution multi-spectral applications available from the Moderate Resolution Imaging
Spectroradiometer (MODIS), a telemetry received in near real-time via special arrangement between NOAA, NASA, and DoD
agencies in direct support of the War on Terrorism. A mirror website transitioned to Fleet Numerical Meteorology and
Oceanography Center has made Satellite Focus available upon Secure Internet bandwidth and thereby more readily
accessible to assets in theater. Constructive feedback from a wide variety of operational users during OEF and OIF has
helped to further develop and optimize this resource. Constructive feedback from a wide variety of operational users during
OEF and OIF has helped to further develop and optimize this resource.
•
The FNMOC Dust Discussion. Dust event forecasting is an emerging, but still immature science. With the onset of war in Iraq,
forecasting dust has become an important issue, and forecasters in theater have been doing their best to forecast dust
effects on operations for pilots, ground forces, and ships at sea. As forces move further inland, dust events present both a
problem and an opportunity for effective deployment of U.S. forces. Toward this end, FNMOC has taken a two -pronged
approach by 1) upgrading its array of satellite and model dust products, and 2) reorganizing its operational watch team to
focus on dust analysis and forecasting. FNMOC began to make use of the NRL/MRY Aerosol Group's Navy Atmospheric
Aerosol Prediction System (NAAPS) products prior to formal transition to operations. At NRL, the Coupled
Ocean/Atmosphere Mesoscale Prediction System (COAMPS) was enhanced to provide aerosol prediction for Southwest Asia,
and preliminary model aerosol output from COAMPS was made available for evaluation starting in March of 2003
An important aspect of FNMOC’s new strategy is to increase situational awareness and interaction with forward deployed
forecasters who directly support the warfighter. To accomplish this objective, the Operations Department watch standers’
duties were restructured to include a daily analysis of the dust products available on the Satellite Focus and NAAPS Web
pages. This daily analysis was termed the “Dust Discussion”. The procedures for this analysis and the content of the Dust
Discussion were developed by a group of watch standers, scientists and forecasters from FNMOC and NRL, who meet on a
weekly basis to provide guidance, review results, and modify procedures or content as necessary. The watch standers have
undergone training to learn how to forecast dust events. Training has included analysis of satellite imagery, basic dust sto rm
physics, forecasting tips, and resource utilization topics.
• FIRES: Unlike aerosol species such as dust and smoke who’s source functions can be determined
through dynamical fields, most fires are anthropogenic in nature and hence emissions vary considerably
from day to day. To support field operations that rely on EO systems, propagation models need to be
able to quickly adapt to new fires. The smoke component of the Navy Aerosol Analysis and Prediction
System (NAAPS) utilizes real time fire detection algorithms from geostationary satellites with the
NOAA/NESDIS Automated Biomass Burning Algorithm (ABBA) and from MODIS with the University of
Maryland RapidFire and NRL fire hotspot algorithms. We also discuss and contrast the physical optical
properties of biomass and oil fire smokes and how they relate to light extinction in visible and IR
wavelengths.
• 2D-4D grid data distribution system and its use in support of tactical operations. The data have been used
in secondary modeling systems (surf, EM propagation, and chemical dispersion forecasts), planning tools
for flight and landing missions (JMPS, Brandes Associates), and for display on a common operational
desktop (WebCOP/XiS, Polexis). We store NOGAPS, COAMPS, WW3, SWAN and other model grids. Other parts of
Metcast store derived data (ship routes, surf forecasts). The built-in fine-grained access control allows
the system to be used for coalition support and joint operations.
• The system incorporates the Grid DataBlade (Barrodale Computing Services) —stores tiles of scalar and
vector grids arranged in time and the vertical dimension. The DataBlade can compute a subgrid, select a
vertical post, re-project and interpolate in any dimension. Because these computations are performed
within the database engine, they are highly efficient. A flexible query system lets the user select a 1D-
4D (sub)grid based on a model, geographical region, valid time and other criteria. The user can also
request a desired interpolation mode or remapping, e.g. from a Lambert-Conformal projection to spherical
coordinates. The data distribution system is reflective and can describe, in various levels of detail,
which gridded data are potentially or currently available.
Project isGoal and Objective on:
The goal of the project to provide technical support to EPA & RPOs
Estimation of Natural Visibility Conditions over the
US
Tasks and Approach:
1. Conceptual Evaluation of Natural PM and Visibility Conditions
• Establish Virtual Workgroup with representatives from EPA, RPOs,
scientific community
2. Quantitative Estimation of Recent Regional Natural Contribution
Statistics
• Conduct Data Analysis for estimating natural contributions (1995+, surf. and
satellite obs)
Task 1: Conceptual Evaluation of Natural PM
and Visibility Conditions
• Establishing the main natural source types, e.g.
– Windblown dust (local and distant)
– Biomass smoke (forest, grass and other uncontrolled fires, local and
distant)
– Biogenic emissions (trees, marshes, oceans)
– Sea salt
• Physico-chemical properties of natural aerosols
– Size distribution
– Chemical composition
– Optical properties
• Evaluate suitable metrics for statistically describing
natural conditions
– Relevant aerosol components (e.g. SO4, NO3, OC, EC, Dust)
• Atmospheric aerosol system has three extra dimensions (red),
Background
compared to gases (blue):
– Spatial dimensions (X, Y, Z)
– Temporal Dimensions (T)
– Particle size (D)
– Particle Composition ( C )
– Particle Shape (S)
• Bad news: The mere characterization of the 7D aerosol system
is a challenge
– Spatially dense network -X, Y, Z(??)
– Continuous monitoring (T)
– Size segregated sampling (D)
– Speciated analysis ( C )
– Shape (??)
• Good news: The aerosol system is self-describing.
– Once the aerosol is characterized (Speciated monitoring) and
multidimensional aerosol data are organized, (see RPO VIEWS effort),
Aerosols: Many Dimensions
• Compared to gases (X, Y, Z, T), the aerosol system
has four extra dimensions(D, C, F, M).
– Spatial dimensions X, Y Satellites, dense networks
– Height Z Lidar, soundings
– Time T Continuous monitoring
– Particle size D Size-segregated sampling
– Particle Composition C Speciated analysis
– Particle Shape/Form F Microscopy
– Ext/Internal Mixture M Microscopy
•Bad News:The mere characterization requires many tools.
Some tools sample a small subset of the xDim aerosol data space
These need extrapolation, e.g. single particle analysis
Other tools get integral measures of several dimensions
These require de-convolution of the integral, e.g. satellite sensors
Satellite-Integral
Aerosols: Opportunity and Challenge
• Good news: The aerosol system is self-describing.
– Once the aerosol is characterized (size-composition, shape) and
– Spatio-temporal pattern are established,
– => The aerosol system describes much of its history through the
properties and pattern, e.g source type (dust, smoke, haze),
formation mechanisms, atmospheric interactions. and
transformations.
– The ‘aerosol’ dimensions (D, C, F, M) are most useful for
establishing the sources and effects, including some of the
processes.
– The Source of can be considered an additional, ‘derived’ aerosol
dimension.
• Analysts challenge: Deciphering the handwriting contained in
the data
– Chemical fingerprinting/source apportionment
– Meteorological transport analysis
– Multidimensional data extrapolation, de-convolution and fusion
Local, Sahara and Gobi Dust0.8
Sahara SW US
0.7
over N. America 0.6
0.5
0.4
0.3
0.2
0.1
0
Al/Si Fe/Si Ca/Si K/Si Ti/Si
• The dust over N. America originates from local sources
as well as from the Sahara and Gobi Deserts
• Each dust source region has distinct chemical signature in
the crustal elements.
Seasonal and Secular Trends of Sahara
Dust over the US
Seasonally, dust peaks
Regional Sahara Dust events occur sharply in July when
several times each summer the Sahara plume
swings into the
Caribbean.
(Poirot, 2003)
Dirty dust composition based on Positive Matrix
Factorization, PMF
At Brigantine, NJ, dust
composition is enriched
by SO4 (30% dirty dust
mass) and NO3 (8%)
‘Dirty’ dust and salt
composition
Direction of Dust Origin at 5
IMPROVE Sites
High ‘dust’ concentration at 5 sites
indicate the same airmass pathway from
the tropical Atlantic
NOAAARL Ad hoc Data Processing Value Chain
ATAD
ATAD Traject
Gebhart (2002)
Weather Serv.
Upper Air Data
CATT Tool Aggregation
Husar (2003) Poirot (2003)
PMF Tool
Pareto (2001)
PMF “Sources”
Coutant (2002)
NPS-CIRA
IMPROVEData
The Influence of Emissions,
Dilution and Transformations
• The PM
concentration, C, at
any given location
and time is
determined by the
combined
interaction of
emissions, E,
atmospheric
dilution, D, and
chemical
transformation and
removal, T,
processes:
Seasonal Pattern of PM2.5
• The seasonal cycle results
from changes in PM
background levels,
emissions, atmospheric
dilution, and chemical
reaction, formation, and
removal processes.
• Examining the seasonal
cycles of PM2.5 mass and
its elemental constituents
can provide insights into
these causal factors.
• The season with the
highest concentrations is a
good candidate for PM2.5 Key reference: CAPITA
control actions.
Seasonal PM2.5 During 1988
• At Washington DC and Philadelphia,
(Mid-Atlantic) the PM2.5 • At urban Southwestern sites,
concentrations are 60% higher in PM2.5 concentrations in the
summer than in winter.
winter are 50% higher than in
• In the rural Appalachians, the summer
PM2.5 concentrations are a factor of the summer.
three higher than during the winter. • At rural Southwestern sites,
PM2.5 concentrations are 50%
Key reference: CAPITA
Regional Haze Goal: Attain natural
conditions by 2064
Pattern of Fires over N.
America peaks in warmsatellite-observed
The number of ATSR
fires season
Fire onset and smoke amount is
unpredictable
Fire Pixel Count:
Western US
North America
Asian Dust Cloud over N.
America Asian Dust 100 mg/m3
Hourly PM10
On April 27, the dust cloud arrived in
North America.
Regional average PM10
concentrations increased to 65 mg/m3
In Washington State, PM10
concentrations exceeded 100 mg/m3
Origin of Fine Dust Events over
Gobi dust in spring
the US
Sahara in summer
Fine dust events over the
US are mainly from
intercontinental
transport
Daily Average Concentration
over the US
Sulfate is seasonal with noise
Noise is by synoptic weather
VIEWS Aerosol
Chemistry Database
Dust is seasonal with
noise
Random short spikes
added
Sahara and Local Dust
Apportionment: Annual and July
The Sahara and Local dust was apportioned based on their respective source profiles.
July Annual
• In July the Sahara dust contributions are 4-8 • The maximum annual Sahara dust
mg.m3 contribution is about 1 mg.m3
• Throughout the Southeast, the Sahara dust • In Florida, the local and Sahara dust
exceeds the local source contributions by w contributions are about equal but at Big
wide margin (factor of 2-4)
Bend, the Sahara contribution is < 25%.
Supporting Evidence: Transport
Analysis
The air masses arrive to Big Bend, TX Satellite data (e.g. SeaWiFS) show Sahara
form the east (July) and from the west Dust reaching Gulf of Mexico and
(April) entering the continent.
Seasonal Fine Aerosol Composition,
Upper Buffalo Smoky Mtn
E. US
Big Bend, TX Everglades, FL
Sahara PM10 Events over July 5, 1992
Eastern US
Much previous work by Prospero, Cahill, Malm,
Scanning the AIRS PM10 and IMPROVE chemical
databases several regional-scale PM10 episodes
over the Gulf Coast (> 80 ug/m3) that can be
attributed to Sahara.
June 30, 1993
June 21 1997
The highest July, Eastern US, 90th
percentile PM10 occurs over the Gulf
Coast ( > 80 ug/m3)
Sahara dust is the dominant contributor
May 9, 1998 A Really Bad Aerosol Day for N. America
Asian Smoke
Canada
Smoke
What kind of C. American
Smoke
neighborhood is
this anyway?
Seasonal PM2.5 Dependence on
Elevation in Appalachian Mountains
Monitor Locations and topography
• During August, the PM2.5 concentrations are independent of
elevation to at least 1200 m. Above 1200 m, PM2.5 concentrations
decrease.
• During January, PM2.5 concentrations decrease between sites at 300
and 800 m by about 50% . PM2.5 concentrations are approximately
Key reference:
constant from 800 m to 1200 m and decrease another ~50% from
Local, Sahara and Gobi Dust 0.8
Sahara SW US
0.7
over N. America 0.6
0.5
0.4
0.3
0.2
0.1
0
Al/Si Fe/Si Ca/Si K/Si Ti/Si
• The dust over N. America originates from local sources as well as from the
Sahara and Gobi Deserts
• Each dust source region has distinct chemical signature in the crustal elements.
Fine Dust
(<2.5mm)
Local and
Sahara
The two dust peeks at Big Bend have
different Al/Si ratios
During the year, Al/Si = 0.4
In July, Al/Si reaches 0.55, closer to the
Al/Si of the Sahara dust (0.65-0.7)
The spring peak is identified as as ‘Local
• In Florida, July peak is Fine
Dust’, while the virtually all thedominated by
Particle Dust appears to originate
dust.
Sahara from Sahara throughout the year
• At other sites over the Southeast,
Sahara dominates in July
• T he Spring and Fall dust is evidently
of local origin
Supporting Evidence: Aerosol Pattern
and Transport Analysis
There are large seasonal differences in the directions that air masses • In July (1998) elevated levels of absorbing aerosol (Sahara Dust) reaches the
arriving in Big Bend, TX have taken. Gulf of Mexico and evidently, enters the continent .
During winter and into spring, they come from the west and the • High TOMS dust levels are seen along the US-Mexican borders, reaching
northwest,while during the summer, they come mainly from the east. New Mexico. Higher levels also cover the Caribbean Islands and S. Florida.
• Another patch of absorbing aerosol (local dust?) is seen over the Colorado
Plateau, well separated from the Sahara dust.
Illustration of RAW: Quebec Smoke, July 6,
2002
Right. SeaWiFS satellite and METAR surface
haze shown near-real time in the Voyager
distributed data browser
Below. SeaWiFS, METAR and TOMS
Absorbing Aerosol Index superimposed
Satellite data are fetched from NASA GSFC;
surface data from NWS/CAPITA servers
Incremental Transport
Probalility
Analysis Value Chain: CATT’s
AE R O S O L Habitat
Collection Integration CATT-In CATT CAT
IMP. EPA VIEWS CAPITA CAPITA
Aerosol Aerosol Integrated AerData Aggreg. Next
Sensors Data AerData Cube Why? How?
Aerosol Process
Weather Gridded Traject. When?Aggreg.
TrajData Where? Next
Data Meteor. Data Cube Traject. Process
Assimilate Trajectory CATT-In CATT TAT
NWS ARL CAPITA CAPITA
TRAN S PO RT
Transport Probability Metrics
• The transport metric is calculated from two
residence time grids, one for all trajectories and
another for trajectories on selected (filtered days).
Both residence time grids are normalized by the
sum of all resdence times in all grid cells:
pijf=rij/SS rij pija=rij/SS rij
• pijf, is the filtered and pija is the unfiltered
residence time probabilitiy that an airmasses
passes through a specific grid. There is a choice of
transport probaility metrics:
• The Incremental Residence Time Probability
(IRTP) proposed by Poirot et al., 2001 is obtained
by subtracting the chemically filtered grid from
• Currently, there is a choice of two different transport
Transport Metric Selection
probability metrics:
• Incremental Residence Time Probability (IRTP)
proposed by Poirot et al., 2001 is the difference
between the chemically filtered and unfiltered
residence time probalbilities. Positive values of
IRTP in a grid indicates more than average liekihood
of transport; (red); negative IRTP values (blue)
represent less than average likeihood of transport.
• Potential Source Contribution Function (PSCF)
proposed by Hopke et al., 19?? is computed as the
ratio of the filtered and unfiltered residence time
SUMMARY
• The atmospheric dust system occupies at least 8 key dimensions
g (x, y, x, t, size, comp, shape, mixture)
• The current observational revolution (satellites, surface networks) allows monitoring
many aspects of the global daily aerosol pattern and transport.
• Each sensor/system measures different aspects of aerosols, usually resolving some and
integrating over other dimensions.
• Data from multiple sensors/systems (satellites AND surface) along with models are
required to characterize the 8D system and to derive actionable knowledge.
• Current data and analysis tools allow the estimation of transcontinental transport of dust
to N. America.
• The yearly average fine (<2.5 um) Sahara dust concentration over the SE US is 0.2 – 1
ug/m3, with July peak concentration of 2-6 ug/m3.
• During specific transcontinental dust transport episodes from Africa and Asia, the
globally transported surface dust concentrations approach 50-100 ug.m3 over 1000 km -
scale regions of North America.
SUMMARY: New Opportunities
• We are in the midst of a sensory revolution regarding the
detection of global aerosol sources, transport and some of
the effects. Satellite and surface network provide daily
pattern of aerosol.
• Still, the available aerosol data provides only a sparse
characterization of the aerosol system.
• The Internet facilitates communication and the sharing,
(reuse) of data and tools. There is a growing
collaborative-sharing spirit in the scientific community;
The winds of change are here – but we need to harness
them for faster learning
Combined Aerosol
Trajectory Tool (CATT)
Example: Airmass origin for high
(2.5*average) nitrate
Boundary Waters Doly Sods
Lye Brook Smoky Mtn.
Triangulation indicates nitrate source in the corn belt
CATT: A Community Not There!
Further Analysis
When?
Tool! Where?
GIS
Part of an Analysis Value Grid Processing
Emission
Why?
There!
Chain Comparison
How?
AEROSOL
Collection Integration CATT-In
IMP. EPA VIEWS CAPITA
Aerosol Aerosol Integrated AerData Aggreg. Next
Sensors Data AerData Cube Aerosol Process
CATT
Weather Gridded Traject. TrajData Aggreg. Next
Data Meteor. Data Cube Traject. Process
Assimilate Trajectory CATT-In
NWS ARL CAPITA
TRANSPORT
Haze by RPO
Judged qualitatively based on
WRAP MANE-VU
current surface and satellite data
Local Smoke Canada Smoke
Local Dust
Asian Dust
CENRAP MRPO VISTAS
Local Smoke Local Smoke Local Smoke
Mexico/Canada Smoke Canada Smoke Sahara Dust
Local Dust Local Dust
Sahara Dust
• Natural forest fires and windblown dust are judged to be the key
contributors to regional haze
• The dominant natural sources include locally produced and long-
Scientific Challenge: Description
Particulate matter is complex because of its multi-dimensionality
of PM
It takes at leas 8 independent dimensions to describe the PM concentration pattern
Dimension Abbr Data Sources
Spatial dimensions X, Y Satellites, dense networks
.
Height Z Lidar, soundings
Time T Continuous monitoring
Particle size D Size-segregated sampling
Particle Composition C Speciated analysis
Particle Shape/Form F Microscopy
Ext/Internal Mixture M Microscopy
• Gaseous concentration: g (X, Y, Z, T)
• Aerosol concentration: a (X, Y, Z, T,
D, C, F, M)
• The ‘aerosol dimensions’ size D, composition C,
shape F, and mixing M determine the impact on
health, and welfare.
Technical Challenge:
• PM characterization requires many different
Characterization
instruments and analysis tools.
• Each sensor/network covers only a limited fraction
of the 8-D PM data space.
• Most of the 8D PM pattern is extrapolated from
sparse measured data.
• Some devices (e.g. single particle electron
microscopy) measure only a small subset of the
PM; the challenge is extrapolation to larger space-
time domains.
• Others, like satellites, integrate over height, size,
composition, shape, and mixture dimensions; these
data need de-convolution of the integral measures.
Data Analysis and Decision
Retrospective Anal. Now Analysis Predictive Analysis
Days Days-years
Support
Months-years
Data Sources & All the Real-Time data + EPA PM2.5Mass NAAPS MODEL Forecast
NPS IMPROVE Aer. Chem. NWS ASOS Visibility, WEBCAMs NOAA/EPA CMAQ?
Types EPA Speciation NASA MODIS, GOES, TOMS, MPL
EPA PM10/PM2.5 NOAA Fire, Weather & Wind
EPA CMAQ Full Chem. Model NAAPS MODEL Simulation
Data Analysis Tools Full chemical model simulation Spatio-temporal overlays Emission and met. forecasts
Diagnostic & inverse modeling Multi-sensory data integration Full chemical model
& Methods Chemical source apportionment Back & forward trajectories, CATT Data assimilation
Multiple event statistics Pattern analysis Parcel tagging, tracking
Communication Tech Reports for reg. support Analyst and managers consoles Open, public forecasts
Peer reviewed scientific papers Open, inclusive communication Model-data comparison
Collab. & Coord. Science-AQ mgmt. interaction Data assimilation methods Modeler-data analyst comm.
Methods Reconciliation of perspectives Community data & idea sharing
Analysis Products Quantitative natural aer. concr. Current Aerosol Pattern Future natural emissions
Natural source attribution Evolving Event Summary Simulated conc. pattern
Comparison to manmade aer. Causality (dust, smoke, sulfate) Future location of high conc.
Decision Support Jurisdiction: nat./manmade Jurisdiction: nat./manmade Statutory & policy changes
State Implementation Plans, (SIP) Triggers for management action Management action triggers
PM/Haze Crit. Documents, Regs Public information & decisions Progress tracking
July 2020 Quebec Smoke Event
–
Superposition of ASOS • PM2.5 time series for New England sites.
Note the high values at White Face Mtn.
visibility data (NWS) and • Micropulse Lidar data for July 6 and July 7,
SeaWiFS reflectance data 2002 - intense smoke layer over D.C. at 2km
altitude.
for July 7, 2002
GLAS Satellite Lidar (Geoscience Laser
Altimeter System)
California Fires, Oct for continuous global
First satellite lidar 7, 2003
observations of Earth
Quebec
Smoke
over the
Northeast
Smoke
(Organics) and
Sulfate
concentration
data from
VIEWS
integrated
database
DVoy overlay of
sulfate and
organics
during the
