Feb TexMex Dust Storm Analysis

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Feb TexMex Dust Storm Analysis
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Satellite
250m image
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• The National Polar-orbiting Operational
  Environmental Satellite System (NPOESS)
  represents this countrys 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 NASAs EOS
  research missions (Terra, Aqua, and Aura) to the
  operational NPOESS mission in the years that
  follow.
• Americas 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.

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• Presently, the Naval Research Laboratorys global
  and regional dust models (NAAPS and COAMPSTM
  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 Agencys
  (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 Navys 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.

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• 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.
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• 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 FNMOCs 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 storm
  physics, forecasting tips, and resource
  utilization topics.

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• FIRES Unlike aerosol species such as dust and
  smoke whos 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.

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Project Goal and Objective

• The goal of the project is to provide technical
  support to EPA RPOs on
• Estimation of Natural Visibility Conditions over
  the US
• Tasks and Approach
• Conceptual Evaluation of Natural PM and
  Visibility Conditions
• Establish Virtual Workgroup with representatives
  from EPA, RPOs, scientific community
• Quantitative Estimation of Recent Regional
  Natural Contribution Statistics
• Conduct Data Analysis for estimating natural
  contributions (1995, surf. and satellite obs)
• Real-Time Estimation of Natural Aerosols and
  Visibility
• Implement a Web-based Tool for routine real-time
  estimation of natural aerosols/visibility

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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)
• Spatial scales and resolution of natural
  events/conditions
• Temporal scales and resolution of natural
  events/conditions

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Background

• Atmospheric aerosol system has three extra
  dimensions (red), 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), unique
  opportunities exists for extracting information
  about the aerosol system (sources,
  transformations) from the data directly.
• Analysts challenge Deciphering the handwriting
  contained in the data
• Chemical fingerprinting/source apportionment

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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 NewsThe 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
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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,
• gt 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

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Local, Sahara and Gobi Dust over N. America

• 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.

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Seasonal and Secular Trends of Sahara Dust over
the US

• Seasonally, dust peaks sharply in July when the
  Sahara plume swings into the Caribbean.

Regional Sahara Dust events occur several times
each summer
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Sahara Dust Passage over the EUS (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
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Direction of Dust Origin at 5 IMPROVE Sites
High dust concentration at 5 sites indicate
the same airmass pathway from the tropical
Atlantic
Ad hoc Data Processing Value Chain
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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
• C f (E, D, T)
• Each of the three processes has its own pattern
  at secular, yearly, weekly, synoptic, diurnal and
  micro time scales.
• The yearly, weekly and the diurnal scales are
  periodic

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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 control actions.

Key reference CAPITA
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Seasonal PM2.5 During 1988

• At Washington DC and Philadelphia, (Mid-Atlantic)
  the PM2.5 concentrations are 60 higher in summer
  than in winter.
• In the rural Appalachians, the summer PM2.5
  concentrations are a factor of three higher than
  during the winter.

• At urban Southwestern sites, PM2.5 concentrations
  in the winter are 50 higher than in the summer.
• At rural Southwestern sites, PM2.5 concentrations
  are 50 higher during June than January.

Key reference CAPITA
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Regional Haze Goal Attain natural conditions by
2064
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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
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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
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Origin of Fine Dust Events over the US
Gobi dust in spring Sahara in summer
Fine dust events over the US are mainly from
intercontinental transport
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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

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Sahara and Local Dust Apportionment Annual and
July
The Sahara and Local dust was apportioned based
on their respective source profiles.

• The maximum annual Sahara dust contribution is
  about 1 mg.m3
• In Florida, the local and Sahara dust
  contributions are about equal but at Big Bend,
  the Sahara contribution is lt 25.

• In July the Sahara dust contributions are 4-8
  mg.m3
• Throughout the Southeast, the Sahara dust exceeds
  the local source contributions by w wide margin
  (factor of 2-4)

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Supporting Evidence Transport Analysis
Satellite data (e.g. SeaWiFS) show Sahara Dust
reaching Gulf of Mexico and entering the
continent.
The air masses arrive to Big Bend, TX form the
east (July) and from the west (April)
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Seasonal Fine Aerosol Composition, E. US
Smoky Mtn
Upper Buffalo
Everglades, FL
Big Bend, TX
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Sahara PM10 Events over Eastern US
July 5, 1992
Much previous work by Prospero, Cahill, Malm,
Scanning the AIRS PM10 and IMPROVE chemical
databases several regional-scale PM10 episodes
over the Gulf Coast (gt 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 ( gt 80 ug/m3)
• Sahara dust is the dominant contributor to peak
  July PM10 levels.

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May 9, 1998 A Really Bad Aerosol Day for N.
America
Asian Smoke
Canada Smoke

• What kind of neighborhood is this anyway?

C. American Smoke
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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 constant
  from 800 m to 1200 m and decrease another 50
  from 1200 to 1700 m.

Key reference
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Local, Sahara and Gobi Dust over N. America

• 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.

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Attribution of Fine Dust (lt2.5mm) 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 Dust, while the July
peak is dominated by Sahara dust.

• In Florida, virtually all the Fine Particle Dust
  appears to originate from Sahara throughout the
  year
• At other sites over the Southeast, Sahara
  dominates in July
• The Spring and Fall dust is evidently of local
  origin

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Supporting Evidence Aerosol Pattern and
Transport Analysis
There are large seasonal differences in the
directions that air masses arriving in Big Bend,
TX have taken. During winter and into spring,
they come from the west and the northwest,while
during the summer, they come mainly from the east.

• In July (1998) elevated levels of absorbing
  aerosol (Sahara Dust) reaches the Gulf of Mexico
  and evidently, enters the continent .
• High TOMS dust levels are seen along the
  US-Mexican borders, reaching 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.

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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
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Incremental Transport Probalility
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Analysis Value Chain CATTs Habitat
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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
• pijfrij/SS rij pijarij/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 the
  unfiltered residence time grid, IRTP pijf -
  pija
• The other metric is the Potential Source
  Contribution Function (PSCF) proposed by Hopke et
  al., 19xx which is the ratio of the filtered and
  unfiltered residence time probabilities, PSCF
  pijf / pija

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Transport Metric Selection

• Currently, there is a choice of two different
  transport 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 probabilities. Higher values of PSCF is
  indicative of inreased source contribution.
• The desired metric is selected through a dialog
  box invoked by clicking on the right-most button
  in the TRAJ_CHEM layer.

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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 (lt2.5 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.
• These events constitute significant perturbations
  to the aerosol pattern of North America.

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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
• Establishing trans-continental source-receptor
  relationship for dust is attainable with
  available observational and modeling tools but
  will require
• Open flow of data/knowledge and sharing of tools
  
• Creation of scientific value-adding chains
• Decomposition and reintegration of the 8D aerosol
  system

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Combined Aerosol Trajectory Tool (CATT)

• Example Airmass origin for high (2.5average)
  nitrate

Boundary Waters
Doly Sods
Lye Brook
Smoky Mtn.
Triangulation indicates nitrate source in the
corn belt
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CATT A Community Tool! Part of an Analysis
Value Chain
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Significant Natural Contributions to Haze by RPO
Judged qualitatively based on current surface
and satellite data
WRAP Local Smoke Local Dust Asian Dust
MANE-VU Canada Smoke
VISTAS Local Smoke Sahara Dust
MRPO Local Smoke Canada Smoke Local Dust
CENRAP Local Smoke Mexico/Canada Smoke 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-range transported smoke and dust

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Scientific Challenge Description of PM
Particulate matter is complex because of its
multi-dimensionality It takes at leas 8
independent dimensions to describe the PM
concentration pattern

• 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.

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Technical Challenge Characterization

• PM characterization requires many different
  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.

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Data Analysis and Decision Support
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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.

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GLAS Satellite Lidar (Geoscience Laser Altimeter
System) First satellite lidar for continuous
global observations of Earth
California Fires, Oct 7, 2003
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2002 Quebec Smoke over the Northeast

• Smoke (Organics) and Sulfate concentration data
  from VIEWS integrated database
• DVoy overlay of sulfate and organics during the
  passage of the smoke plume