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Feb TexMex Dust Storm Analysis


Feb TexMex Dust Storm Analysis – PowerPoint PPT presentation

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Title: Feb TexMex Dust Storm Analysis

Feb TexMex Dust Storm Analysis
250m image
  • 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
  • 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.

  • 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
  • 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.

    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 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.

  • 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
  • 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
  • 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

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
  • Implement a Web-based Tool for routine real-time
    estimation of natural aerosols/visibility

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
  • Temporal scales and resolution of natural

  • 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

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
  • 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
  • Some tools sample a small subset of the xDim
    aerosol data space
  • These need extrapolation, e.g. single particle
  • Other tools get integral measures of several
  • These require de-convolution of the integral,
    e.g. satellite sensors

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
  • 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 Dust over N. America
  • The dust over N. America originates from local
    sources as well as from the Sahara and Gobi
  • 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 sharply in July when the
    Sahara plume swings into the Caribbean.

Regional Sahara Dust events occur several times
each summer
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
Direction of Dust Origin at 5 IMPROVE Sites
High dust concentration at 5 sites indicate
the same airmass pathway from the tropical
Ad hoc Data Processing Value Chain
The Influence of Emissions, Dilution and
  • 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

(No Transcript)
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
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
Regional Haze Goal Attain natural conditions by
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
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 the US
Gobi dust in spring 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
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
  • Throughout the Southeast, the Sahara dust exceeds
    the local source contributions by w wide margin
    (factor of 2-4)

Supporting Evidence Transport Analysis
Satellite data (e.g. SeaWiFS) show Sahara Dust
reaching Gulf of Mexico and entering the
The air masses arrive to Big Bend, TX form the
east (July) and from the west (April)
Seasonal Fine Aerosol Composition, E. US
Smoky Mtn
Upper Buffalo
Everglades, FL
Big Bend, TX
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.

May 9, 1998 A Really Bad Aerosol Day for N.
Asian Smoke
Canada Smoke
  • What kind of neighborhood is this anyway?

C. American Smoke
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
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
  • Each dust source region has distinct chemical
    signature in the crustal elements.

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
  • At other sites over the Southeast, Sahara
    dominates in July
  • The Spring and Fall dust is evidently of local

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.
  • 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 CATTs Habitat
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 -
  • 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

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.

  • 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
  • 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.

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
  • 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

Combined Aerosol Trajectory Tool (CATT)
  • Example Airmass origin for high (2.5average)

Boundary Waters
Doly Sods
Lye Brook
Smoky Mtn.
Triangulation indicates nitrate source in the
corn belt
CATT A Community Tool! Part of an Analysis
Value Chain
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
  • The dominant natural sources include locally
    produced and long-range transported smoke and dust

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,
  • The aerosol dimensions size D, composition C,
    shape F, and mixing M determine the impact on
    health, and welfare.

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

Data Analysis and Decision Support
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.

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