Difference between revisions of "Satellite Measurements of Atmospheric Aerosols"
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=== Chapter Table of Contents: Satellite Measurements of Atmospheric Aerosols === | === Chapter Table of Contents: Satellite Measurements of Atmospheric Aerosols === | ||
==== Introduction==== | ==== Introduction==== | ||
| − | * Two fields not clear | + | * Two fields not clear at the start - 2 fields are atmospheric aerosols and remote sensing |
| + | * Inverse problem of deriving atmospheric aerosol properties from measured optical effects at the top of the atmosphere is difficult because it requires the knowledge of those aerosol properties. | ||
* Aerosols are described by matching models and observations - uncertainty comes from divergence between model/uncertainty | * Aerosols are described by matching models and observations - uncertainty comes from divergence between model/uncertainty | ||
| − | * Two fields complement each other not replace | + | * Two fields complement each other not replace - surface measurements provide aerosol characteristics and properties; satellites provide transport and spatio/temporal patterns |
==== Aerosol Physical and Optical Properties==== | ==== Aerosol Physical and Optical Properties==== | ||
| Line 28: | Line 29: | ||
Regularities allow simplify the : | Regularities allow simplify the : | ||
| − | # Multi-modal simplifies size distribution if you know mass | + | # Multi-modal simplifies size distribution if you know total mass |
| + | ## Each mode can be treated separately b/c particles of different size are governed by diff. mechanisms of formation, transport. | ||
| + | ## Coarse particles originate from primary emission, are chemically stable, irregular shape do not interact with themselves or fine particles - therefore easier to model? | ||
| + | ## Fine particles size changes in response to environment, form as product of reactions, internally mixed, multi-modal? more difficult to model? | ||
# Mass median diameter increases in Urban area increases concentration. Find mass median using spectral and angular scattering. | # Mass median diameter increases in Urban area increases concentration. Find mass median using spectral and angular scattering. | ||
# For region there is a high correlation between total light scattering and mass concentration of fine particles | # For region there is a high correlation between total light scattering and mass concentration of fine particles | ||
| Line 34: | Line 38: | ||
* P = scattering phase function = energy scattered in given direction/average energy in all directions | * P = scattering phase function = energy scattered in given direction/average energy in all directions | ||
| + | * Extinction Coefficient (Bext) = bscat + babs = fraction of light scattered and absorbed by particle per unit length. | ||
| + | ** if atmosphere is mixture of particles b is the sum of contributions for each source (SDH) | ||
| + | ** B also changes with vary with space/time. | ||
| + | ** AOT = integral of Bext between L1 and L2 (surface to satellite?) (SDH) | ||
| + | * Fig. 4b - If higher bext more pronounced phase function therefore larger characteristic size therefore higher concentration? | ||
* aerosol types have same forward scattering, satellite measures the backscatter, therefore total scattering can be calculated and then correlated to pm2.5 conc. for a region. | * aerosol types have same forward scattering, satellite measures the backscatter, therefore total scattering can be calculated and then correlated to pm2.5 conc. for a region. | ||
| Line 41: | Line 50: | ||
==== Principles of Satellite Aerosol Detection==== | ==== Principles of Satellite Aerosol Detection==== | ||
| − | ==== Challenges of Satellite Aerosol Remote Sensing==== | + | Two competing effects of aerosols are that the light scattering particles tend to add reflectance to the surface brightness and at the same time they act as a filter which exponentially diminishes the total upwelling radiation |
| − | ==== Aerosol Retrievals: Past, Present and Future==== | + | |
| + | The ratio P/R0 determines whether aerosols will increase or decrease the apparent reflectance | ||
| + | *R0=Surface reflectance function | ||
| + | |||
| + | AOT retrieval is not possible either when aerosol system reaches radiative equilibrium or when Phase function approximately matches surface reflectance function(which occur over bright surfaces) | ||
| + | |||
| + | ==== Challenges of Satellite Aerosol Remote Sensing==== | ||
| + | * Clouds obscure the detection of both underlying surface and atmospheric aerosols. Cloud shadows also can complicate aerosol retrieval. | ||
| + | |||
| + | * Sensors has to work in transmission windows to avoid the influence of molecular absorption on aerosol retrieval | ||
| + | |||
| + | * Uncertainties in Bidirectional Diffuse Reflectance Function (BDRF)cause uncertainties in retrievals | ||
| + | |||
| + | ==== Aerosol Retrievals: Past, Present and Future==== | ||
| + | |||
| + | Earliest quantitative retrievals of aerosols was the byproduct from "aerosol correction algorithms" | ||
| + | |||
| + | A wide array of "dedicated" sensors were available by 1990's for the characterization of aerosols, each providing specific characteristics of aerosols | ||
| + | |||
| + | These sensors measure total aerosol burden (as AOT) in multiple wavelength channels which can give a clue on size composition of aerosol column | ||
| + | |||
| + | Performance of aerosol sensors are evaluated against observations by AERONET. Disparities are observed between sensors at pixel level as well as between temporal and spatial averaged aerosol properties. | ||
| + | |||
| + | In future, | ||
| + | |||
| + | * Replacing static aerosol models with dynamic aerosol models would be desirable | ||
| + | |||
| + | * Co-retrieval of surface and aerosol reflectance would be ideal | ||
| + | |||
| + | * Aerosol retrievals could be expanded by multi-sensor data fusion | ||
| + | |||
==== Satellite Information Systems ==== | ==== Satellite Information Systems ==== | ||
| + | |||
==== Applications ==== | ==== Applications ==== | ||
Latest revision as of 10:43, November 11, 2009
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Title: Satellite Measurements of Atmospheric Aerosols | Document Link
Organization/Author: R. Husar
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Year: 2009
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Description of Document: Book chapter for Aerosol Measurement
Chapter Table of Contents: Satellite Measurements of Atmospheric Aerosols
Introduction
- Two fields not clear at the start - 2 fields are atmospheric aerosols and remote sensing
- Inverse problem of deriving atmospheric aerosol properties from measured optical effects at the top of the atmosphere is difficult because it requires the knowledge of those aerosol properties.
- Aerosols are described by matching models and observations - uncertainty comes from divergence between model/uncertainty
- Two fields complement each other not replace - surface measurements provide aerosol characteristics and properties; satellites provide transport and spatio/temporal patterns
Aerosol Physical and Optical Properties
Aerosol Dimensions
- Space
- Time
- Diameter
- Composition
- Shape
- Mixing (internal/external)
Key point is that diameter, composition, shape and mixing are all integral from satellite perspective not just height
signal at TOA is weak compared to surface reflectance
Regularities allow simplify the :
- Multi-modal simplifies size distribution if you know total mass
- Each mode can be treated separately b/c particles of different size are governed by diff. mechanisms of formation, transport.
- Coarse particles originate from primary emission, are chemically stable, irregular shape do not interact with themselves or fine particles - therefore easier to model?
- Fine particles size changes in response to environment, form as product of reactions, internally mixed, multi-modal? more difficult to model?
- Mass median diameter increases in Urban area increases concentration. Find mass median using spectral and angular scattering.
- For region there is a high correlation between total light scattering and mass concentration of fine particles
- aerosol types are in layers
- P = scattering phase function = energy scattered in given direction/average energy in all directions
- Extinction Coefficient (Bext) = bscat + babs = fraction of light scattered and absorbed by particle per unit length.
- if atmosphere is mixture of particles b is the sum of contributions for each source (SDH)
- B also changes with vary with space/time.
- AOT = integral of Bext between L1 and L2 (surface to satellite?) (SDH)
- Fig. 4b - If higher bext more pronounced phase function therefore larger characteristic size therefore higher concentration?
- aerosol types have same forward scattering, satellite measures the backscatter, therefore total scattering can be calculated and then correlated to pm2.5 conc. for a region.
Satellite Orbits
GEO - Geostationary orbits; 36,000 km up. high temporal res, low spatial res Polar orbit - low temporal (pass 1x a day); high spatial res
Principles of Satellite Aerosol Detection
Two competing effects of aerosols are that the light scattering particles tend to add reflectance to the surface brightness and at the same time they act as a filter which exponentially diminishes the total upwelling radiation
The ratio P/R0 determines whether aerosols will increase or decrease the apparent reflectance
- R0=Surface reflectance function
AOT retrieval is not possible either when aerosol system reaches radiative equilibrium or when Phase function approximately matches surface reflectance function(which occur over bright surfaces)
Challenges of Satellite Aerosol Remote Sensing
- Clouds obscure the detection of both underlying surface and atmospheric aerosols. Cloud shadows also can complicate aerosol retrieval.
- Sensors has to work in transmission windows to avoid the influence of molecular absorption on aerosol retrieval
- Uncertainties in Bidirectional Diffuse Reflectance Function (BDRF)cause uncertainties in retrievals
Aerosol Retrievals: Past, Present and Future
Earliest quantitative retrievals of aerosols was the byproduct from "aerosol correction algorithms"
A wide array of "dedicated" sensors were available by 1990's for the characterization of aerosols, each providing specific characteristics of aerosols
These sensors measure total aerosol burden (as AOT) in multiple wavelength channels which can give a clue on size composition of aerosol column
Performance of aerosol sensors are evaluated against observations by AERONET. Disparities are observed between sensors at pixel level as well as between temporal and spatial averaged aerosol properties.
In future,
- Replacing static aerosol models with dynamic aerosol models would be desirable
- Co-retrieval of surface and aerosol reflectance would be ideal
- Aerosol retrievals could be expanded by multi-sensor data fusion
