Search Results
Abstract
A numerical scheme has been developed to identify multilayer cirrus cloud systems using Advanced Very Higher Resolution Radiometer (AVHRR) data. It is based on the physical properties of the AVHRR channels 1–2 reflectance ratios, the brightness temperature differences between channels 4 and 5, and the channel 4 brightness temperatures. In this scheme, clear pixels are first separated from cloudy pixels, which are then classified into three types: cirrus, cirrus/low cloud, and low clouds. The authors have applied this scheme to the satellite data collected over the FIRE II IFO [First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment II intensive field observations area during nine overseas within seven observation dates. Determination of the threshold values used in the detection scheme are based on statistical analysts of these satellite data. The authors have validated the detection results against the cloudy conditions inferred from the collocated and coincident ground-based lidar and radar images, balloonborne replicator data, and National Center for Atmospheric Research CLASS (Cross-chain Loran Atmospheric Sounding System) humidity soundings on a case-by-case basis. In every case, the satellite detection results are consistent with the cloudy conditions inferred from these independent and complementary measurement. The present scheme is well suited for the detection of midlatitude, multilayer cirrus cloud systems and tropical anvils.
Abstract
A numerical scheme has been developed to identify multilayer cirrus cloud systems using Advanced Very Higher Resolution Radiometer (AVHRR) data. It is based on the physical properties of the AVHRR channels 1–2 reflectance ratios, the brightness temperature differences between channels 4 and 5, and the channel 4 brightness temperatures. In this scheme, clear pixels are first separated from cloudy pixels, which are then classified into three types: cirrus, cirrus/low cloud, and low clouds. The authors have applied this scheme to the satellite data collected over the FIRE II IFO [First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment II intensive field observations area during nine overseas within seven observation dates. Determination of the threshold values used in the detection scheme are based on statistical analysts of these satellite data. The authors have validated the detection results against the cloudy conditions inferred from the collocated and coincident ground-based lidar and radar images, balloonborne replicator data, and National Center for Atmospheric Research CLASS (Cross-chain Loran Atmospheric Sounding System) humidity soundings on a case-by-case basis. In every case, the satellite detection results are consistent with the cloudy conditions inferred from these independent and complementary measurement. The present scheme is well suited for the detection of midlatitude, multilayer cirrus cloud systems and tropical anvils.
Abstract
A new technique for ascertaining the thermodynamic cloud phase from high-spectral-resolution ground-based infrared measurements made by the Atmospheric Emitted Radiance Interferometer (AERI) is presented. This technique takes advantage of the differences in the index of refraction of ice and water between 11 and 19 μm. The differences in the refractive indices translate into differences in cloud emissivity at the various wavelengths, which are used to determine whether clouds contain only ice particles or only water particles, or are mixed phase. Simulations demonstrate that the algorithm is able to ascertain correctly the cloud phase under most conditions, with the exceptions occurring when the optical depth of the cloud is dominated by liquid water (>70%). Several examples from the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment are presented, to demonstrate the capability of the algorithm, in which a collocated polarization-sensitive lidar is used to provide insight to the true thermodynamic phase of the clouds. Statistical comparisons with this lidar during the SHEBA campaign demonstrate that the algorithm identifies the cloud as either an ice or mixed-phase cloud approximately 80% of time when a single-layer cloud with an average depolarization above 10% exists that is not opaque to the AERI. For single-layer clouds having depolarization of less than 10%, the algorithm identifies the cloud as a liquid water cloud over 50% of the time. This algorithm was applied to 7 months of data collected during SHEBA, and monthly statistics on the frequency of ice, water, and mixed-phase clouds are presented.
Abstract
A new technique for ascertaining the thermodynamic cloud phase from high-spectral-resolution ground-based infrared measurements made by the Atmospheric Emitted Radiance Interferometer (AERI) is presented. This technique takes advantage of the differences in the index of refraction of ice and water between 11 and 19 μm. The differences in the refractive indices translate into differences in cloud emissivity at the various wavelengths, which are used to determine whether clouds contain only ice particles or only water particles, or are mixed phase. Simulations demonstrate that the algorithm is able to ascertain correctly the cloud phase under most conditions, with the exceptions occurring when the optical depth of the cloud is dominated by liquid water (>70%). Several examples from the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment are presented, to demonstrate the capability of the algorithm, in which a collocated polarization-sensitive lidar is used to provide insight to the true thermodynamic phase of the clouds. Statistical comparisons with this lidar during the SHEBA campaign demonstrate that the algorithm identifies the cloud as either an ice or mixed-phase cloud approximately 80% of time when a single-layer cloud with an average depolarization above 10% exists that is not opaque to the AERI. For single-layer clouds having depolarization of less than 10%, the algorithm identifies the cloud as a liquid water cloud over 50% of the time. This algorithm was applied to 7 months of data collected during SHEBA, and monthly statistics on the frequency of ice, water, and mixed-phase clouds are presented.
