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The climatic importance of atmospheric anthropogenic gases such as carbon dioxide and methane, in addition to the state of the ozone layer, is currently a major scientific and political issue (Schneider, 1990; Chappelaz et al., 1993; Genthon et al., 1987). However, cirrus clouds have a similar climatic role as these `greenhouse gases,' where a one percent increase in cirrus cloud cover is comparable to a four percent increase in carbon dioxide. This is based on cloud cover results published by Rossow and Lacis (1990). An effort directed toward the study of cirrus clouds should be similar to the level and scope of greenhouse gas research.

Analysis of cirrus cloud optical properties is necessary to develop an understanding of the earth's radiative budget. The radiative transfer mechanism drives the atmospheric temperature cycle; where incident solar (shortwave or visible) radiation is imbalanced with upwelling terrestrial (longwave or infrared) radiation. A change in the mean global coverage of cirrus clouds could have a sustained repercussion to the thermally driven global climate. Clouds which consist of ice particles, with a typical altitude range of 4 to 15 km, are referred to as cirrus in this thesis.

Absorption and scattering of radiation are processes which determine the atmospheric column optical thickness. Optically thin cirrus is more transparent to downwelling visible light than to upwelling radiation due to absorption by ice in the infrared (IR). Thus, heating of the lower atmosphere occurs with the presence of thin cirrus. As the cirrus becomes optically thick, it begins to scatter a greater amount of solar radiation back to space, while trapping a greater amount of upwelling IR radiation. The magnitude of radiative forcing is dependent upon the cloud altitude and particle size distribution. High altitude clouds offer greater ability to trap upwelling IR radiation. Cloud particle size affects the amount of solar radiation that reaches the surface. Simultaneous visible and infrared measurement of the cloud optical depth is critical in developing model simulations that will properly account for the presence of cirrus in radiative transfer calculations.

Interest in cirrus clouds and their effect on the radiation budget has increased within the last few decades. Radiative transfer modeling, given various particle sizes and shapes (Deirmendjian, 1969; Plass and Kattawar, 1971; Stephens, 1980; Hess and Wiegner, 1994), have been useful to gauge the importance of cirrus to radiative transfer. Recent technological advancement has demonstrated the feasibility of visible and infrared observations using both ground-based and space-borne instrumentation incorporating active and passive techniques Stone et al., 1990; Platt, 1973; Piironen and Eloranta, 1994; Smith et al., 1993).

Platt (1973) monitors simultaneous visible and infrared properties using a single-channel ground-based lidar and broadband radiometer. A single channel lidar cannot determine the cloud optical depth, due to the inherent coupling of the extinction cross-section to the aerosol and molecular backscatter cross-sections. Therefore, radiometer measurements are required to derive the visible cirrus cloud optical depth. However, the approach makes several assumptions to infer the cirrus optical properties. This includes the assumption that the visible to infrared extinction coefficient is constant, which is used to convert the measured broadband infrared optical depth to a visible value derived from simultaneous infrared and visible measurements (Platt, 1979; Platt and Dilley, 1979). This results in visible optical properties that are dependent upon the infrared measurements. This technique also requires a large data set to extract the backscatter-to-extinction ratio. Cloud radiance is determined by removing a model calculated clear sky radiance from the surface to cloud base; and is often contaminated with atmospheric water vapor. Finally, temporal and spatial differences due to instrument characteristics also affect the data. The use of high spectral resolution instruments to measure visible and infrared properties reduces these problems. Furthermore, it provides the opportunity to spectrally validate the models.

This thesis encompasses my research conducted on cirrus cloud optical properties, incorporating data from two high spectral resolution remote sensing instruments developed at the University of Wisconsin - Madison (UW). The first instrument, the Atmospheric Emitted Radiance Interferometer (AERI), is a passive sensor that measures the emitted spectral radiance from an atmospheric column. The second instrument, the High Spectral Resolution Lidar (HSRL), is an active sensor which emits visible light and measures the aerosol backscattered signal and depolarization as a function of altitude. Each instrument monitors a vertical atmospheric column that is advected into the instrument's respective field of view in the zenith direction. Atmospheric state information is necessary to invert the data collected by these instruments. It is acquired through the use of a local radiosonde launch using the Cross-chain Loran Atmospheric Sounding System (CLASS). Recent theses by Piironen (1994) and Feltz (1994) provide an additional detailed description of the HSRL and AERI, respectively.

AERI measured downwelling radiance, vertical atmospheric temperature and dewpoint temperature profiles, and HSRL measured cloud boundaries will allow inversion of the IR radiative transfer equation for a cloudy atmosphere to yield the cloud infrared optical depth. The high spectral resolution AERI measurements will provide IR optical depths in a number of regions between water vapor absorption lines within the infrared atmospheric window located between 770 and 1200 cm tex2html_wrap_inline2723. The HSRL provides a 532 nm optical depth, which can be compared to the IR measured values.

This analysis will reveal an experimental relationship of the visible to infrared optical depth for each spectral region by two different techniques: a direct comparison of the measured visible to derived infrared optical depth; and iteration of the ratio using a forward solution of the radiative transfer equation, based on the HSRL measured visible optical depth, to derive the downwelling column radiance relative to AERI measured data at the given wavenumber regions. In the former approach, the visible to infrared optical depth ratio will be used to determine the downwelling column radiance. The latter approach will yield the infrared optical depth. Overall, both techniques will produce the same optical properties. However, the latter should achieve better results given the effective cloud weighting inherent to the HSRL data and coupling of the visible and IR data.

Particle size, shape, number density, and phase are additional parameters which affect the radiative transfer mechanism. Particles that are much smaller than the incident wavelength will scatter the light according to Rayleigh theory, whereas particle size on the order of the wavelength will scatter according to Mie theory assuming spherical ice particles. Although ice particles are not spherical, a Mie theory model will be utilized to compare the spectral optical depth measurements with theory.

A brief overview of the AERI, HSRL, and CLASS systems will describe each instrument. The goal is to detail the individual instrument characteristics that allow solution of the problem at hand. This should provide the reader with a fundamental background to complement the theoretical derivations and experimental results that will be presented.

Data was collected during a three-month case study at the UW, which occurred from October through early December, 1995. Additional data sets were acquired at the UW, prior to this experiment, when both the AERI and HSRL were available for simultaneous operation. This provided a fair sample of cirrus events with a few mixed-phase and water cloud cases. Data acquisition in a winter climate also reduced background atmospheric emission due to boundary layer humidity, which increases during the summer season.

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Next: Theory Up: Abstract and Contents Previous: Abstract and Contents

Daniel DeSlover
Sun Aug 11 10:02:40 CDT 1996