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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.
Abstract
Using the data obtained from the Advanced Very High Resolution Radiometer (AVHRR) 3.7-µm and 10.9-µm channels, a retrieval scheme has been developed to simultaneously infer cirrus cloud optical depth and mean effective ice crystal size based on the theory of radiative transfer and parameterizations. A numerical scheme is further developed to remove the solar component in the 3.7-µm radiance for applications to daytime satellite data. This scheme is based on the correlation between the 3.7-µm (solar) and 0.63-µm reflectances. Validation of the algorithm has been performed by using various datasets that were collected during the FIRE-II IFO (Nov-Dec 1991) at Coffeyville, Kansas. We have focused on the 26 November and 5 December cases. The retrieval analysis over a 0.5°×1.0° area is performed around Coffeyville for each case based on AVHRR-HRPT data. For validation the authors analyze the photomicrograph data collected by the balloonborne replicator, determine the microphysical and optical properties of the sampled cirrus clouds, and derive their position at the satellite overpass based on sounding data. It is demonstrated that the retrieved cirrus cloud temperature, mean effective ice crystal size, and optical depth closely match the observed values. Further, the retrieved cirrus cloud properties are applied to the computation of surface radiative fluxes using a radiative transfer program that involves a consistent representation of cirrus cloud fields. The computed values are compared with the data measured from ground-based radiometers, and it is shown that the computed downward surface IR and solar fluxes are within 5 and 10 W m−2 of the measured values, respectively, near the time of satellite overpass.
Abstract
Using the data obtained from the Advanced Very High Resolution Radiometer (AVHRR) 3.7-µm and 10.9-µm channels, a retrieval scheme has been developed to simultaneously infer cirrus cloud optical depth and mean effective ice crystal size based on the theory of radiative transfer and parameterizations. A numerical scheme is further developed to remove the solar component in the 3.7-µm radiance for applications to daytime satellite data. This scheme is based on the correlation between the 3.7-µm (solar) and 0.63-µm reflectances. Validation of the algorithm has been performed by using various datasets that were collected during the FIRE-II IFO (Nov-Dec 1991) at Coffeyville, Kansas. We have focused on the 26 November and 5 December cases. The retrieval analysis over a 0.5°×1.0° area is performed around Coffeyville for each case based on AVHRR-HRPT data. For validation the authors analyze the photomicrograph data collected by the balloonborne replicator, determine the microphysical and optical properties of the sampled cirrus clouds, and derive their position at the satellite overpass based on sounding data. It is demonstrated that the retrieved cirrus cloud temperature, mean effective ice crystal size, and optical depth closely match the observed values. Further, the retrieved cirrus cloud properties are applied to the computation of surface radiative fluxes using a radiative transfer program that involves a consistent representation of cirrus cloud fields. The computed values are compared with the data measured from ground-based radiometers, and it is shown that the computed downward surface IR and solar fluxes are within 5 and 10 W m−2 of the measured values, respectively, near the time of satellite overpass.
Abstract
The goals of the current study are threefold: 1) to present a multispectral, multiresolution (MSMR) methodology for analysis of scenes containing multiple cloud layers; 2) to apply the MSMR method to two multilevel cloud scenes recorded by the NOAA Advanced Very High Resolution Radiometer (AVHRR) and the High Resolution Infrared Radiometer Sounder (HIRS/2) instruments during the First International Satellite Cloud Climatology Program (ISCCP) Regional Experiment (FIRE) on 28 November 1991; and 3) to validate the cloud-top height results from the case study analyses through comparison with lidar, radar, aircraft and rawin-sonde data. The measurements available from FIRE Cirrus II enable detailed examination of two complex cloud scenes in which cirrus and stratus appear simultaneously.
A “fuzzy logic” classification system is developed to determine whether a 32×32 array of AVHRR data contains clear sky, low-level cloud, midlevel cloud, high-level cloud, or multiple cloud layers. With the addition of the fray logic cloud classification system, it is possible for the first time to find evidence of more than one cloud layer within each HMS field of view. Low cloud heights are determined through application of the spatial coherence method to the AVHRR data, while mid- to high-level cloud heights are calculated from the HIRS/2 15-µm CO2 band radiometric data that are collocated with the AVHRR data. Cirrus cloud heights retrieved from HIRS 15-µm CO2 band data are improved for optically thin cirrus through the use of the upper-tropospheric humidity profile. The MSMR-derived cloud heights are consistent with coincident lidar, radar, and aircraft data. Cirrus and stratus cloud-top heights and cirrus effective emittances are retrieved for data within an ISCCP 2.5° grid cell that encompasses the FIRE experimental region.
Abstract
The goals of the current study are threefold: 1) to present a multispectral, multiresolution (MSMR) methodology for analysis of scenes containing multiple cloud layers; 2) to apply the MSMR method to two multilevel cloud scenes recorded by the NOAA Advanced Very High Resolution Radiometer (AVHRR) and the High Resolution Infrared Radiometer Sounder (HIRS/2) instruments during the First International Satellite Cloud Climatology Program (ISCCP) Regional Experiment (FIRE) on 28 November 1991; and 3) to validate the cloud-top height results from the case study analyses through comparison with lidar, radar, aircraft and rawin-sonde data. The measurements available from FIRE Cirrus II enable detailed examination of two complex cloud scenes in which cirrus and stratus appear simultaneously.
A “fuzzy logic” classification system is developed to determine whether a 32×32 array of AVHRR data contains clear sky, low-level cloud, midlevel cloud, high-level cloud, or multiple cloud layers. With the addition of the fray logic cloud classification system, it is possible for the first time to find evidence of more than one cloud layer within each HMS field of view. Low cloud heights are determined through application of the spatial coherence method to the AVHRR data, while mid- to high-level cloud heights are calculated from the HIRS/2 15-µm CO2 band radiometric data that are collocated with the AVHRR data. Cirrus cloud heights retrieved from HIRS 15-µm CO2 band data are improved for optically thin cirrus through the use of the upper-tropospheric humidity profile. The MSMR-derived cloud heights are consistent with coincident lidar, radar, and aircraft data. Cirrus and stratus cloud-top heights and cirrus effective emittances are retrieved for data within an ISCCP 2.5° grid cell that encompasses the FIRE experimental region.
