New sensors and models for complex environmental conditions. John N. Porter University of Hawaii. If one cannot accurately measure the in situ optical properties of the environment, then developing a satellite remote sensing algorithm is like trying to hit a target with poor vision
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John N. Porter
University of Hawaii
While new satellite algorithms are moving forward,
more effort is needed for environmental optical characterization.
Ground-based aerosol polar nephelometer
Modified to make polarization measurements
Field measurements planned for summer.
Sea salt phase function
UAE dust/pollution phase function
(vertical and depolarization)
Mini custom polar nephelometer
- Tried various approaches with low cost components
- System still under testing with more expensive components
Aircraft handheld sun photometer measurements
Many bad values must be removed!
Agreement with ground Cimel
Porter, J.N. A. Clarke, J. Reid, G. Shaw, H. Maring, E. Reid, D. Kress, Handheld Sun Photometer Measurements From Light Aircraft, J. Atmos. Ocean. Tech., 24, 1588-1597, 2007.
I = Io exp(- τ /cos(θ)
(define AirMass = 1/cos(θ) )
V = Vo exp(-τ AirMass)
ln(V) = - τ AirMass + ln(Vo)
determine Vo by extrapolating to zero air mass
New sun-sky photometer system tracked sun automatically on several days with
excellent results. The concept of using webcams for sun alignment works well. The
System upgrades for faster performance are near completion.
Example of complex coastal site (UAE 2, MAARCO site)
AO3D tracks photons through the atmosphere-ocean
and uses Monte Carlo techniques to solve radiation
AO3D accounts for:
ocean surface roughness and whitecaps
multiple aerosol layers
refraction (index of refraction layer changes)
user specified optical properties
AO3D compares well with
Kattawar and Adams
Bates, D. and J. Porter, AO3D: A Monte Carlo Code for Modeling of Environmental Light Propagation, accepted in Journal of Quantitative Spectroscopy and Radiative Transfer, January 2008.
AO3D example of photons entering the ocean surface (laser beam)
to start movie)
AO3D model of laser beam entering ocean.
Points show where photon was absorbed
Monte Carlo time-resolved calculations.
AO3D compared with lidar equation.
Standard Lidar Eq. (green)
Monte Carlo simulation (blue)
AO3D top of the atmosphere radiance compared with Kattawar and Adams (1978)
Kattawar and Adams (circles)
Calculations of total-scatter TOA radiance for spherical-shell molecular atmosphere with no surface reflection.
Sun photometer measurements
made at Mauna Loa
Observatory using new
automated sun tracking sun
photometer out to air mass
20 (small dots). Larger dots
show expected values
calculated with AO3D Monte
Carlo radiation model for
aerosol layer placed at different
Best agreement is
found when aerosol layer is
placed slightly above the
observatory. Only coarse aerosol
positioning was attempted. Further
studies could provide a better fit.
Bates and Porter, 2007
Land | Ocean
SZA = 60deg
Figure above shows sky radiance for a coastal site
with part of the sky over land and the other over
ocean. Three different aerosol optical depths are
shown. Surface inhomogeneity turns out to
significantly affect the sky radiance for all aerosol
loadings ! (unpublished results)
Example of Azimuth Angle Scan
Cloud cover, cloud shape, cloud microphysical properties, and location all affect surface and satellite radiation measurements. In order to model these light fields it is therefore important to quantify cloud properties as much as possible.
In addition to radiation problems, there is also a need for new wind measurements aloft where little data exists. Cloud tracking can be used to derive winds aloft.
For these reasons we have begun testing a new approach to map out cloud fields and to derive wind fields at different heights using ground based stereo cameras.
Example of clouds passing over Honolulu. (double click image)
speed based on
on one of the
Cloud simulation model to test how far apart the stereo cameras need to
be and what the accuracy is needed for camera azimuth and zenith angles.
(work in progress)
In order to derive cloud position accurately from stereo cameras, we need to know the camera internal and external calibrations (azimuth and zenith angles for each pixel). Internal calibration was carried out with a reference pattern and an example is shown on the right.
External calibration is achieved by external reference points. To be discussed in detail in the
In order to test the camera internal calibration we carried out a set of independent measurements using a pan-tilt system. A single bright light source was placed ~80 m away and the camera was panned and tilted under computer control. Preliminary results are shown below. The error seen in the azimuth angle figure is likely due to cases with near zero zenith angle (azimuth angle poorly defined). (work in progress)
Camera on pan-tilt
Two different camera angular calibration
approaches plotted versus each other.