Remote sensing of cloud properties using oxygen A-band spectral measurements.

摘要

Clouds play an important role in the climate system through their radiative effects and their vital link in the hydrological cycle. Detailed knowledge of the three dimensional (3-D) distribution of cloud macrophysical and microphysical properties is crucial to properly characterize radiative forcing by clouds and to quantify the response of the climate. In this study, a multi-layer cloud detection algorithm is developed by utilizing photon path length distributions retrieved from oxygen A-band spectral measurements. Case studies from measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site demonstrate that this photon path length method can detect multi-layer clouds missed by the combined active sensors of millimeter-wave cloud radar (MMCR) and micropulse lidar (MPL). One year statistics at the ARM SGP site suggest that at least 27% of single-layer clouds detected by the MMCR-MPL are influenced by some "missed" clouds or by the 3-D effects of clouds. Also, a synergetic retrieval algorithm has been developed by combining oxygen A-band spectral measurements with MMCR radar reflectivity to retrieve the vertical distribution of cloud droplet effective radius (Re), liquid water content (LWC), and optical depth. Extensive evaluation and validation against other independent measurements and retrievals demonstrate that this retrieval algorithm is feasible and accurate for stratus clouds. Finally a wavelength registration algorithm has been developed for a high resolution oxygen A-band spectrometer (HABS). This algorithm can accurately register measured spectral wavelengths and calibrate wavelength shifts induced by temperature variation of the instrument. Tests on the measurements from the field campaign in the summer of 2011 at Beltsville, MD prove that this algorithm is accurate and applicable for all high resolution oxygen A-band spectral measurements.