Satellite Measurements of Atmospheric Aerosols
Introduction
Aerosol sensors on Earth orbiting satellites provide near-real time, kilometer resolution
data on the global distribution of atmospheric aerosols. The data from satellite sensors
with global coverage and kilometer-scale spatial resolution now provide real-time
snapshots which depict the pattern of haze, smoke and dust in stunning detail.
Space-borne sensors are contributing to Earth system science and support societal
decision making in many areas, including weather forecasting, climate change, and
disaster management. Atmospheric aerosols are exceptionally well suitable for satellite
remote sensing, because the sun provides the light source and the scattered light by
aerosols is easily detectable by spectral radiation sensors with high spatial resolution.
In situ aerosol sensors can detect the aerosol characteristics in a small volume of air, in
some cases the size, the composition, and the shape of individual particles. With the aid
of such sensors, it is possible to construct the full size-chemical composition distribution
function as defined by Friedlander [dust smoke and Haze]. Satellite sensors on the other
hand, can only detect integral measures of the aerosol population,
While the science of aerosol remote sensing is based on well established theory of
radiative energy transfer, the engineering of satellite remote sensing systems has not been
fully mastered. The design of radiation sensors, the satellite platforms, the transmission
and near-real time processing of high-bandwidth digital data streams, and the distribution
of derived data products to the end users are among most complex large scale
information-engineering systems. Given the strong demand for satellite data products in
many societal benefit areas, it is expected that these remote sensing systems will continue
to expand their utility.
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This chapter is a brief overview of satellite aerosol remote sensing systems. We begin
with a historical overview of satellite aerosol measurements, followed by a more detailed
description of the physical principles and the limitations of satellite aerosol detection. A
section is devoted to the information processing and distribution systems of satellite
aerosol products. We conclude with a section on the application of satellite products to a
range of scientific and societal benefit areas.
Historical Background
Aerosol detection by satellites begun in the mid-1960’s with advent of the Defense
Meteorological Satellite Program (DMSP) [ref DMSP], which operated a series of
meteorological satellites. The presence of atmospheric aerosols in gray-scale satellite
images was detected even with the crude single channel DMSP sensors. However, the
interpretation of the aerosol signal has eluded physical interpretation. The aerosol-laden
areas were simply referred to as ‘anomalous grey shades’ until the early 1970’s [ref
Isaac]. Throughout the 1970’s, qualitative measurements of aerosols were reported over
different parts of the world. In fact, observers identified Sahara dust [ref Sahara dust],
forest fire smoke over Central America [ref Parmenter 1972], and other major aerosol
events. The potential of satellites to augment surface-based particulate air pollution
measurements was illustrated by [ref Lyons and Husar]. By the 1980’s, a significant
number of satellites were deployed that yielded increasingly better characterization of
global aerosols.
Early satellite sensors were designed for a variety of applications, including the detection
of the Earth surface (LANDSAT), meteorological parameters (DMSP, GOES, AVHRR),
atmospheric columnar ozone (TOMS), etc. It soon became evident that atmospheric
aerosols constitute a significant nuisance to satellite remote sensing. Clouds completely
obscure the ground for satellite detection of surfaces, while water vapor absorption which
is significant can be avoided since it is confined to specific spectral bands. However, the
role of atmospheric aerosols in the remote sensing of surfaces and atmospheric
parameters is more subtle since the aerosol perturbation stretches over the entire solar
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spectrum from UV to near IR. Hence, passive remote sensing that use sun light can not
completely avoid the interference from atmospheric aerosols. Much of the early work on
aerosol detection was focused on removing the spurious aerosol signal by applying
aerosol correction algorithms. Unfortunately, eliminating the aerosol perturbation is a
major challenge, because of the unknown aerosol optical properties and highly variable
spatio-temporal pattern. In fact, aerosol correction for the remote sensing of land and
ocean surfaces and atmospheric chemical constituents remains active research area up to
the present [ref aerosol correction].
The earliest quantitative global scale aerosol retrievals were obtained as by-products
arising from ’aerosol correction’ for the remote sensing of other parameters. For example,
the sea surface temperature detection by the AVHRR sensor required a significant aerosol
correction [ref Stowe]. The columnar ozone detection by TOMS was also distorted by
absorbing aerosols and yielded the ‘Absorbing Aerosol Index’ as a by-product [ref
Torres]. The ocean color and chlorophyll detection by SeaWiFS also yielded significant
advances in both aerosol corrections of chlorophyll and aerosol retrievals over the
oceans.
By the 1990s, a new generation of satellites sensors were deployed that were designed
from ground up for aerosol detection. The multi-spectral MODIS, the multi-angle MISR,
and the POLDER-CALIPSO space-borne lidar instruments represent the new sensor
dedicated to aerosol characterization. [ref Robert Fraser and Yoram Kaufman ..NASA].
The distribution of satellite aerosol products began in the late 1990’s, and is being
incorporated in the Global Observing System of Systems (GEOSS)
Satellite Orbits and Platforms
Current Earth-observing satellites are circulating the Earth either in geostationary or polar
orbit, representing two different aerosol sensing and monitoring opportunities.
Geostationary Earth Observing Satellites (GOES) orbit roughly in the plane of the
Equator at about 36,000 km, such that their rotational speed is identical to the Earth daily
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rotation (Figure 1a). Hence, they appear to be stationary over a fixed location on Earth,
e.g. Amazon (GOES East), Africa (METEOSAT). This fixed perspective allows the
monitoring of dynamic meteorological phenomena such as cloud pattern, major dust
clouds, smoke plumes or regional pollution events as they evolve throughout the day.
Current geostationary meteorological sensors provide only qualitative indication on
aerosols since they carry a single broadband channel in the visible range. However, for
the elucidation of fast changing dust, smoke and other aerosol events, the animation of
30-minute visible GOES images provides vivid documentation of aerosol emission and
transport dynamics. From the perspective of the geostationary sensor, the sun illuminates
a given aerosol layer from different angles between sunrise and sunset. Geostationary
aerosol observations in the ‘twilight zones’ (Figure 1a) at dawn and dusk are particularly
helpful in detecting very thin aerosol layers, because of the large aerosol forward
scattering angle. Data from geostationary meteorological satellites are distributed through
the Internet, minutes after they are taken and they can be used to aid now-casting,
forecasting and other decision support applications. However, the large distance from the
Earth limits the sensor’s spatial resolution to about 1km in the visible range and their
spectral sensors are limited.
Polar orbiting platforms rotate around the Earth in a plane that passes through the two
poles. The satellite elevation is adjusted to about 100 km above the Earth surface which
corresponds to a full rotation every 90 minutes. Due to the low orbit, each consecutive
rotation samples only a swath of the Earth surface that is at most 3000 km wide. Each
swath is incremented such that the polar orbit is sun-synchronous, i.e. the sensor will
always observe the ‘sunny side’ of the Earth at the same local time of the day. However,
polar orbit means that any location on Earth can only be observed only once a day or less.
The low orbit permits sensors to have higher spatial resolution and geo-location accuracy.
However, polar orbit means that any location on Earth can only be observed once a day
or less. Most quantitative aerosol data are derived from polar orbiting satellites and
extensively documented in the literature (ref global aerosol pattern).
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Figure 1. a. Satellite orbits. b. Multi Angle Imaging Spectro-radiometer. C. A-Train
satellite constellation.
There is a rich array of satellite sensors that have been deployed for the characterization
of atmospheric aerosols. These include sensors of backscattered solar radiation in
multiple narrow wavelength channels (AVHRR, MODIS, SeaWiFS, OMI), polarization
of backscattered radiation (POLDER), combined angular and spectral backscattering
(MISR) (Figure 1b), as well as, aerosol absorption (TOMS, OMI). The aerosol retrieval
technique is typically tailored to the characteristics of the particular sensor and provides a
unique characterization of the aerosols. It is fair to say, however, that none of these
sensors can by itself fully characterize the complex, dynamic, multi-dimensional aspects
of the global aerosol system. The prudent combination and integration of these sensors
offers an opportunity for significant advances. Such an opportunity is being pursued by a
synchronized constellation of satellite sensors that are arranged to fly on near-identical
orbits as a pack of satellites. One such constellation, the ‘A-Train’ (Figure 1c) provides
near-simultaneous observations of the same part of the Earth through dozens of sensors
on eight satellites.
Principles of Satellite Aerosol Detection
Satellite aerosol detection and day-time vision by the human eye operate on the same
principle of passive remote sensing. The radiation source is the sun, the target is the
Earth’s surface, the aerosol signal is the ‘haze’ that distorts the targets image, and the
detector is a set of color sensitive array of sensors. Accordingly, the main components of
aerosol remote sensing can be identified as shown schematically in Figure 2: (1) sun as
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the source (2) the reflecting objects such as the surface and clouds (3) the radiatively
active atmosphere containing light scattering pure air, absorbing gases, aerosols and
clouds (4) the radiation sensor that detects and measures the total reflectance and
scattered solar radiation.
The first challenge of satellite aerosol detection consists of separating and isolating the
faint aerosol signal from air scattering, cloud scattering and surface reflectance. The
second challenge is to interpret the aerosol backscattering in terms of the physical and
optical characteristics of the sensed aerosol column, i.e. ‘inversion’ of aerosol properties
from measured radiation signal. The final, most difficult challenge is to estimate or infer
the vertical distribution of the aerosol from the sensed 2D image.
Cloud Effects
Clouds are generally optically thick and tend to obscure the detection of underlying
surfaces and aerosols. Clouds are masked as blank areas unavailable for retrieval. Clouds
are typically detected as a bright and spectrally uniform (white) reflectance delineated by
a ‘cloud mask’. The edges of clouds are not easy to delineate, particularly during humid,
hazy conditions. Thin cirrus clouds are also difficult to identify. ‘Cloud contamination’
due to improper clouds masks is a major source of error in satellite-derived aerosol fields.
Cloud shadows also constitute a nagging problem in aerosol retrieval. [ref Raffuse]. Since
large portions of the tropics and also at northern latitudes are cloudy throughout the year,
cloudiness is a major limitation of satellite remote sensing of both aerosols and surfaces.
Rayleigh Scattering and Molecular Absorption
Under cloud-free, clear air conditions, most of the solar radiation passing through the
atmosphere reaches the surface (>90%) either in the direct solar beam or as diffuse
‘airtight’ radiation from the blue sky. Rayleigh scattering can be corrected for by rigorous
calculations based on known optical properties and radiative transfer theory.
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Absorption by stratospheric ozone and tropospheric water vapor also attenuate selected
bands in the solar spectrum passing through the atmosphere. Stratospheric ozone layer
has an absorption band in the visible range (520-740 nm). Whenever possible, the
satellite sensor bands are located in transmission windows, i.e. at wavelengths away from
major molecular absorption bands.
Surface Reflectance
Surface reflectance plays a major role in aerosol detection over land and ocean. The
surface reflectance i.e. the fraction of solar radiation reflected, ranges from 1-4% for dark
ocean, 5-15% for thick vegetation and over 30% for bright rocky or man-made surfaces.
From the perspective of aerosol detection the highly textured and colorful land reflection
constitutes a significant source of noise.
Figure 2. Radiation detected by a satellite aerosol sensor originates from the sun but
reflected or scattered by multiple sources.
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Figure 3.
Figure 4. a. Spectral reflectance of characteristic surfaces. b. Seasonal variation of
spectral reflectance for Illinois farmland
The amount of radiation reflected by a surface depends on many parameters most notably
on the spectral and geometrical distribution of incoming and reflected radiation. Ocean
surfaces are dark, and reflect only 1-4% of the incoming radiation, mostly by specular
reflection, Figure 3a. However, ocean waves tend to broaden the angular spread of the
mirror-like specular reflection. Specular reflection from water bodies a major interference
with aerosol detection. The areas of the ‘sun glint’ can be identified from the sun-surfacesenor geometry and can be masked out just like clouds.
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Over land, the angular dependence of the reflected radiation depends both on the angle of
the incoming as well as the angle of reflected radiation, and it is defined by the
Bidirectional Diffuse Reflectance Function (BDRF). The directional features of BDRF’s
for land, and vegetated surfaces are shown in Figure 3b. Solid land surfaces are mostly
diffuse Labmertian reflectors, with minor specular component. Vegetation is nearly
Labmertian but also shows an extra reflection ‘hot spot’ back toward the sun.
The BDRF is also strongly wavelength dependent which gives the color of reflecting
objects. The spectral reflectance of water is a few percent in the visible range and
vanishes in the near IR. Vegetation reflectance has a peak in the green (500nm) and also
rises to over 40% in the near IR. Soil reflectance gradually increases from about 10-15%
in the blue to over 40% in the near IR. As shown in Figure 4. The spectral reflectance of
many surfaces is strongly seasonal. For example, Figure 4b shows the strong seasonal
changes of farmland reflectance at blue, green and red wavelengths.
Unfortunately, the a-priory quantification of the spectral BDRF is not available for the
fast changing Earth surface. Given a series of spectral reflectance values for the same
pixel on different days (a time series), the cleanest day is the one with the lowest sum of
the first four spectral bands. This value is taken to be most representative of the hazefree surface color. By utilizing a large enough time window, a clean day is found for all
pixels.
Relevant Aerosol Physical and Optical Properties
Key aerosol microphysical parameters relevant to aerosol optics are particle size
distribution, chemical composition and particle shape. Typical atmospheric particles are
shown in Figure 5a. While most of the particle number concentration is in the size range
below 100nm, the aerosol volume of mass is typically distributed among two modes: fine
particle mode (100-1000nm) and coarse particle mode (1000-10000nm). Satellite aerosol
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detection is confined to the size range 200-10000nm, since particles below 200nm are
inefficient scatterers, and giant particles larger then 10m are low in concentration.
Figure 5.
Aerosol particles > 1000nm in size are generally originate as windblown dust and sea salt
from sea spray and bursting bubbles and tend to be irregular in shape. Aerosols smaller
than 1 m are mostly formed by combustion and condensation processes, resulting in
spherical liquid droplets.
The relevant aerosol optical parameters include the volume scattering coefficient, bscat [m1
), which is the fraction of radiant energy scattered in all directions, per unit length of
transit. The scattering coefficient depends on the size distribution and on a lesser degree
on the chemical composition which determines the real part of the aerosol refractive
index. The companion absorption coefficient, babs (m-1), absorption per unit length
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depends primarily on the aerosol composition, which determines the imaginary part of
the refractive index.
It is also useful to express these coefficients per unit of aerosol volume. Figure 5b shows
that the aerosol scattering per unit volume (or mass) peaks at about 500-800nm and
declines and vanishes below 100nm and above 1000nm. For this reason this sub-range of
aerosol particles is referred to as the scattering range. Figure 5b also shows that the
absorption per unit mass of aerosol is not significantly dependent on particle size. The
aerosol extinction coefficient, bext=bscat+babs, while the single scatter albedo ω= bscat/bext
The scattering phase function is defined as the ratio of the energy scattering per unit solid
angle into a particular direction to the average energy scattered per unit solid angle into
all directions. Figure 5c shows the phase function for Rayleigh scattering and for typical
aerosol in the optical sub-range.
Some general features of the haze aerosol were documented by [Barteneva (1960)] over
several locations exhibited well defined pattern as a function of the extinction coefficient.
As expected at bext=0.02 km-1 the measured phase function closely resembled the
Rayleigh phase function since the aerosol scattering was small. However, as the aerosol
scattering increased, the phase function became systematically skewed toward the
forward direction.
The aerosol optical thickness, τ, is the total attenuation along a path length, generally a
function of wavelength. Total optical thickness of the atmosphere, Aerosol Optical
Depth, AOD is the total attenuation in a vertical path from the top of the atmosphere
down to the surface.
The radiation signal received by the satellite sensor is an integral which depends on
aerosol column height, chemical composition, and particle size and shape. Thus, the
objective of ‘aerosol retrievals’ is to de-convolute this integral and by an appropriate
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inversion technique, to retrieve atmospheric aerosol properties, such as columnar optical
thickness, size distribution and possibly aerosol type.
The first group of integrals pertains to aerosol type, i.e. industrial haze, wind-blown dust,
biomass smoke, sea salt, etc. Each aerosol type is characterized by its size distribution,
chemical composition and particle shape which together define its optical properties. If
aerosols of different types, e.g. dust or industrial haze are superimposed in different
layers then the analytical separation of the individual types is a challenge. Such a
separation is even more difficult if the aerosol types are mixed in the same volume of air,
or even within individual multi-composition particle aggregates. The schematics and the
atmospheric photographs in Figure 6 indicate some of the complications arising from
layered and well-mixed multi-component aerosol pattern.
Another integration is by the height of the atmospheric column. The scattering from
aerosols in different layers is added to produce the cumulative signal from the entire
atmospheric column. The sensed column integral is typically over a slant angle rather
than perpendicular to the Earth surface. The consequence of this integration is that with
passive remote sensors it is virtually impossible to retrieve the vertical profile of aerosols.
However, with space-borne pulsing lidar instruments such as LITE and CALIPSO, even
the vertical profile of atmospheric aerosols can be mapped under the path of the
spacecraft.
Figure 6.
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In what follows, the physics of photon transmission through a hazy atmosphere will be
expressed via the radiative transfer equation and leading to the haze image perturbation
as a function of optical thickness, τ and scattering phase function, P. The aerosol phase
function is perhaps the most critical parameter in satellite aerosol detection.
In essence, satellite retrieval of atmospheric aerosols is derived from the difference
between a hazy image of the surface reflectance and the inherent surface reflectance void
of the perturbation by atmospheric aerosols, Rayleigh scattering and clouds. The
radiative perturbation by aerosols consists of two competing effects. On one hand, light
scattering particles tend to add reflectance to the brightness of surfaces. This contribution
depends both on the optical thickness τ as well as the aerosol angular scattering phase
function, P, Figure 7. The other role of aerosols is to act as a filter which exponentially
diminishes the upwelling radiation from a reflecting surface. These subtle features of
haze effects on the detected radiation combined with the inadequacy of our understanding
of the aerosol phase functions hampers the quantitative satellite aerosol retrieval methods.
The net effect of these two competing interactions is a subtle as illustrated in Figure 8.
This aerosol-contributed excess radiance is then converted into measures of the aerosol
using radiative transfer theory.
Figure 7.
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Satellite sensors detect the total amount of solar radiation that is reflected from the
Earth’s surface (R0) and backscattered by the atmosphere from aerosol, pure air, and
clouds.
Figure 8.
The dependence of apparent reflectance on aerosol loading and properties is shown in
Figure 8. Since τλ, R0λ, Rλ, are all spectral entities, the chart in Figure 8 is for each
wavelength. The critical parameter whether aerosols will increase or decrease the
apparent reflectance, R, is the ratio P/R0 of the aerosol angular reflectance, P, and the bidirectional surface reflectance, R0. For P/R0>1, with increasing aerosol burden, the
apparent surface reflectance will increase. This is the case for dark surfaces such as
ocean or vegetation at blue wavelength where the surface reflectance is <0.05%, while
P≈0.3. When the aerosol reflectance, P, is greater than the surface reflectance, P/R0<1,
adding aerosols to the optical path will decrease the surface reflectance. This is the case
for bright surfaces such as clouds or vegetation at near-IR wavelength. Hence, an aerosol
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layer over clouds will decrease the brightness of clouds as observed at the top of the
atmosphere. At P~R0 the reflectance is unchanged by haze aerosols (e.g. soil and
vegetation at 800nm).
For dark surfaces, the increase of apparent reflectance with increasing aerosol optical
thickness asymptotically approaches the value of aerosol reflectance P. Similarly, the
diminishing reflectance of bright objects also approaches asymptotically the same value
of P. Hence, at an aerosol optical thickness, τ >4, the inherent reflectance of the surfaces
will be lost, and the only remaining reflectance is that of aerosols. Figure 8 also illustrates
that by increasing optical thickness beyond 4, the reflectance will not change. In this
case, the filtering and scattering effects of aerosols are balanced, i.e. the aerosol system
reaches radiative equilibrium. The implication is that the retrieval of τ for optically thick
aerosol layers (τ>4) is not possible. Aerosol retrieval is also limited when P~R0. Based on
the above consideration, aerosol detection over land is most suitable over dark surfaces
(e.g. vegetation at blue wavelength).
The optical thickness, τ of the aerosol column is derived from the excess reflectance, i.e.
the difference between the apparent reflectance of the top of the atmosphere and the true
reflectance of the surface, R-R0= (P-R0)(1-e-τ). The value of P obtained from fitting the
observed and retrieved surface reflectance spectra. For example in light haze at 0.412μm
it was found that P=0.38. Accurate and automatic retrieval of the relevant aerosol P is the
most difficult part of the retrieval process.
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Figure 9. Spectra of vegetation reflectance with and without atmospheric haze.
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Figure 10 a; b show the spectral reflectance for two representative surfaces under similar
aerosol optical thickness.
At blue wavelength (412nm) (Figure 9a), the apparent reflectance reading of the satellite
sensor is dominated by the haze since the surface reflectance is low over the ocean and
vegetation. The aerosol patterns are clearly discernable, while all the surface features are
obscured by haze. Hence, the blue wavelength is well suited for aerosol detection over
land but surface characterization is difficult. Over the ocean the apparent reflectance in
the blue is also dominated by the added aerosol reflectance.
At green wavelength (555nm) (Figure 9b), the apparent reflectance is still dominated by
haze. However, the aerosol retrieval at green wavelength is more difficult because the
apparent reflectance is significantly influenced by both the surface and aerosol
reflectance over vegetated land. . Over the ocean the aerosol reflectance dominates since
the surface reflectance is low. The green is poorly suited for haze detection over land but
appropriate for detection over the ocean provided that consideration is given to
chlorophyll.
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At red wavelength (677nm) (Figure 9c) the apparent reflectance is contributed
significantly by both surface reflectance and aerosol reflectance. However, the
magnitude of the surface reflectance differs greatly for vegetated from solid surfaces,
such as urban centers. Hence, aerosol detection over land is difficult because it is strongly
influenced by the type of underlying surfaces. Over the ocean the aerosol reflectance is
high compared to the reflectance of water, and therefore quantitative aerosol detection in
the red channel is possible.
The near IR wavelength (865nm) (Figure 9d) is uniquely suitable for aerosol detection
over the ocean since the ocean reflectance is below 1%. Hence, virtually all the apparent
reflectance is due to aerosol back scattering. However, under windy conditions, the
whitecaps from breaking waves may add a spurious signal. Over land, the reflectance of
both vegetated and solid surfaces is high, comparable to the aerosol reflectance P, i.e., P/
Ro ~ 1. Under these conditions, the aerosol neither diminishes, nor adds to the surface
reflectance and this precludes satellite aerosol detection.
Satellite Data Transmission, Processing, Integration and Usage
Earth observing satellites are the most prolific sources of monitoring data. Literally
terabytes of digital data are collected, transmitted and processed. An integral part of Earth
observing satellite systems is the data flow and processing network, i.e the information
system that collects and distributes the relevant processed data to the end users. This first
stage of the dataflow is from the satellite sensor to the data silo where the processed
primary data are deposited. This first stage of the data flow and processing is generally
under control of the agency responsible for the satellite data acquisition and storage.
However, raw satellite data need considerable processing to be useful in specific
applications. The second phase of the data flow is from the satellite data archives to the
application and users of the satellite data products, such as informing the public about
aerosol events, air quality forecasting or for regulatory air quality management.
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The incorporation of NASA data products into the EE DSS is primarily through the rich
capabilities and keen interest of the Co-I partners of this project. The GIOVANNI data
portal is a key is a key access portal to the most widely used satellite products in AQ
analysis, including MODIS and MISR AOT, OMI and more recently CALIPSO.
GIOVANNI also provides an array of useful data processing and fusion services. In the
NAAPS global aerosol model a number of NASA datasets are assimilated and also used
for validation. MODIS-derived fire location is derived hourly in real-time and converted
into model-relevant emissions. The MODIS aerosol optical depth product is operationally
assimilated into NAAPS. The MODIS Dust Enhancement Product is used to identify dust
sources globally for NAAPS. The AIRPACT modeling system actively pursues the
verification of CMAQ model with OMI columnar data for urban-industrial as well as for
major fire emissions.
Fortunately, data dissemination technologies offer outstanding opportunities to fulfill the
information needs of applications. The ‘terabytes’ of data from satellite and other sensors
can now be stored, processed and delivered in near real time. The instantaneous
‘horizontal’ diffusion of information via the Internet now permits, in principle, the
delivery of the right information to the right people at the right place and time.
Standardized computer-computer communication languages and Service-Oriented
Architectures (SOA) now facilitate the flexible processing of raw data into high-grade
‘actionable’ knowledge. Last but not least, the World Wide Web has opened the way to
generous sharing of data and tools leading to faster knowledge creation through
collaborative analysis and virtual workgroups.
Nevertheless, users of satellite data face significant hurdles. The new developments
introduced a new set of problems. The “data deluge” problem is especially acute for
analysts interested in aerosol pollution, since the aerosol processes are inherently
complex, the numerous relevant data range form detailed surface-based chemical
measurements to extensive satellite remote sensing and the integration of these requires
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the use of sophisticated models. As a consequence, Earth Observations (EO) are underutilized in making societal decisions.
A remedy is anticipated from the Global Earth Observation System of Systems (GEOSS),
an emerging public information infrastructure for finding, accessing and applying diverse
data useful for decision makers [1]. A motto of GEOSS is that ‘any single dataset can
serve many applications and any application requires many datasets’. GEOSS is
proactively pursuing the linking together of Earth observing systems so that data can be
combined and turned into actionable knowledge for many societal benefit areas. The
Group on Earth Observations (GEO) [2] is the body that coordinates the construction of
GEOSS by the year 2015, it ensures universal access to EO data as public good and helps
building trust and collaboration among the diverse stakeholders in GEOSS. A unique
contribution of GEOSS is the adoption and promotion of the advanced 'system of
systems' (SoS) approach toward the integration of the multiplicity of autonomous Earth
observations and models. Recent developments in surface and satellite sensing along with
new information technologies now allow real-time, ‘just-in-time’ data analysis for the
characterization and partial explanation of the of major air pollution events as well as
more in-depth post-analysis, integration and fusion can also be performed using the
federated historical data resources and tools.
Applications
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There is increasing demand for satellite aerosol measurement in Earth science, air quality
management and disaster management. The primary satellite data, e.g. aerosol optical
thickness need to be further processed in accordance with the needs of the specific
application. Below, present several key application areas, and highlight the filtering,
aggregation and the fusion of satellite data with other relevant data streams.
Satellite aerosol data are the only available measurements on the global distribution of
aerosols.
Fig The gray shade is AOT 0-1
With increasing concern about global human-induced climate change, quantitative
measurement and monitoring of atmospheric aerosols has become major global concern.
Atmospheric aerosols, including anthropogenic particles contribute significantly to the
Earth’s energy budget. Aerosol scattering tends to reduce the incoming solar radiation,
while aerosol absorption contributes to the heating of the atmosphere. Unfortunately, the
relative contribution and the changes of human induced atmospheric aerosols is not well
understood, neither are the aerosol induced aerosol heating and cooling rates well
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quantified. For these reasons the uncertainty of the human induced Earth energy budget is
caused by the uncertainties associated with global aerosols. It is expected that satellite
aerosol detection will significantly reduce this component of uncertainty in global climate
change [Global dimming].
Satellites offer a unique opportunity to characterize the biomass smoke emanating from
forest fires and from agricultural fires through high spatial resolution snap shots.
However, the satellite data also indicate that some of the forest fire smoke is bluish in
color, while other smoke plumes are yellow. This clearly indicates that size and chemical
composition of these two smoke types is different. There is also considerable evidence
that multiple scattering in a thick smoke plume is prevalent. Thus, the retrieval of the true
vertical optical thickness for such plumes is problematic.
Unfortunately, until recently, the role of satellites in EPAs air quality regulatory process
was very modest. In fact, the EE Rule is the only air quality regulation that we are aware
of, where the use of satellites is explicitly encouraged, as seen on the quote snippets from
the EE Rule in Federal Register [Ref]: Information demonstrating the occurrence of the
event.. ... satellite-derived pixels indicating the presence of fires; satellite images of the
dispersing smoke; Identification of the spatial pattern of the affected area (the size,
shape, and area of geographic coverage). This could include, for instance, the use of
satellite or surface measurement data; The simplest demonstrations could consist of
newspaper accounts or satellite images to demonstrate that an event occurred…
Below is an illustration of satellite application to air quality management. For several
days every spring, the rangeland grass in Kansas-Oklahoma is burned resulting in major
smoke plumes that cover multi-state areas of the Midwest. Figure 3 shows the location of
fires over Kansas derived from the MODIS satellite sensor, the noon aerosol optical
thickness derived from the SeaWiFS satellite sensor and the spatial pattern of organic
fine particle mass concentration derived from speciated aerosol data collected through
several surface monitoring networks. Based on such exploratory analyses, the federal
EPA is considering exceedance waivers for remote sites that were impacted by the
agricultural smoke. The local managers are also using the dynamic analysis tools to
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evaluate alternative grass burning schedules and approaches. The high value of
qualitative real-time air quality information to the public is well demonstrated through
EPA’s successful AIRNOW program (Weyland and Dye, 2006).
Fig 3 Multi-sensory characterization of agricultural smoke over the Midwest on April 12,
2003: a. Fire pixels over Kansas from the MODIS sensor; b. Vertical aerosol optical
thickness derived from the SeaWiFS satellite; c. Concentration pattern of organics from
speciated chemical samplers (EPA)-
The global-scale, high spatial resolution satellites remote sensing data are particularly
suitable for detecting and quantifying natural and manmade air pollution events. (dust
smoke, haze). The intense aerosol and gaseous pollutant signal during these events have
made satellites indispensable in detecting and following the evolution of such events.
Additional AQ benefits of satellites is on evaluating and improving emission inventories
for NOx, biogenic VOC and particulates.
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