Kathy L. Pagan

Analysis of polar stratospheric cloud measurements from AVHRR

This work investigates thermal emission from polar stratospheric clouds (PSCs) measured at 10.9 and 11.9 μm wavelengths (channels 4 and 5) by the advanced very high resolution radiometer (AVHRR). PSCs can be broadly categorized by particle composition as either ice or nitric acid mixtures. This work shows that nitric acid PSCs are invisible to AVHRR, while some ice PSCs can be detected. Methods were developed to discriminate ice PSCs from other cloud types in AVHRR imagery based on the brightness temperature difference between channels 4 and 5. When PSCs are identified in AVHRR imagery, it is possible to estimate the PSC optical depth, effective radius, and ice water path using relationships reported here, however, these estimates may have large uncertainties.

Toward a polar stratospheric cloud climatology using advanced very high resolution radiometer thermal infrared data

Polar stratospheric clouds (PSCs) play a critical role in ozone depletion over both polar regions. To date, the most complete PSC records consist of measurements from limb-viewing satellites that offer limited spatial and temporal coverage. To construct a more complete and long-term PSC climatology, we investigated the use of advanced very high resolution radiometer (AVHRR) satellite imagery for detecting PSCs. Two approaches were examined: (1) a correlative approach relating image-derived data to verification data, and (2) an interpretation of the image-derived data based on a radiative transfer model. The model determined that Type II or ice PSCs can be detected using the AVHRR thermal infrared channels. The image-derived data, namely density-sliced channel 5 temperature data, color composites, and density-sliced channel 4‐5 brightness temperature difference images, provide quick views of potential PSC locations. The model-based approach provides the best method for constructing a long-term ice PSC climatology from the AVHRR archive.

Development of an automated classification scheme for detection of polar stratospheric clouds over Antarctica using AVHRR imagery

Although polar stratospheric clouds (PSCs) are a critical component in the ozone depletion process, their timing, duration, geographic extent, and annual variability are not well understood. The goal of this study is the development of an automated classification scheme for detecting PSCs using NOAA AVHRR data. Visual interpretation, density slicing, and standard multispectral classification detect most optically thick PSCs, but only some thin PSCs. Two types of automated techniques for detecting thin PSCs are being investigated: namely, multispectral classification methods, including the use of texture and other imagederived features, and back-propagation neural networks, including the use of hyperspatial and hypertemporal data. UARS CLAES temperature and aerosol extinction coefficient data are being used as a verification dataset. If successful, this classification scheme will be used to process the entire record of AVHRR data in order to assemble a long-term PSC climatology.