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Hongchun Jin and Shaima L. Nasiri

Abstract

Atmospheric Infrared Sounder (AIRS) infrared-based cloud-thermodynamic-phase retrievals are evaluated using the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud thermodynamic phase. The AIRS cloud phase is derived from spectral information contained within the 8–12-μm window, and CALIPSO provides coincident pixel-scale observations of cloud phase using the depolarization capability of the 532-nm channel. Comparisons are performed between the AIRS and CALIPSO cloud-phase observations for single-layer (48.5% of all clouds), heterogeneous-layer (45.9%), and multilayered (5.6%) clouds. The AIRS ice phase is in agreement with CALIPSO for more than 90% of coincident observations globally, with the largest discrepancies found in high latitudes and multilayered clouds. AIRS water phase generally follows CALIPSO spatial patterns, but the frequency is lower by about a factor of 2. The ice and water phases of AIRS both show misclassifications about 1% of the time when compared with CALIPSO. Not all clouds demonstrate strong phase signatures in the AIRS spectrum, which leads AIRS to classify unknown phase to around 10% of CALIPSO’s ice clouds and 60% of CALIPSO’s water clouds. This study shows that the algorithm is capable of detecting ice clouds within the AIRS field of view and can be used as the first step in further retrievals of ice-cloud optical thickness and effective particle size.

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Shaima L. Nasiri and Bryan A. Baum

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This study reports on recent progress toward the daytime detection of multilayered clouds in satellite multispectral data, specifically for the case of optically thin cirrus overlying lower-level water clouds. The technique is applied to 200 × 200 pixel arrays of data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and is primarily based on the relationship between the near-infrared reflectance (at either 1.6 or 2.1 μm) and the 11-μm brightness temperature. Additional information used by the algorithm includes the operational MODIS cloud mask and cloud thermodynamic phase as inferred from the 8.5- and 11-μm brightness temperatures. The performance of the algorithm is evaluated for two MODIS case studies, and results are compared to coincident cloud physics lidar (CPL) data obtained from an aircraft platform. In both cases, the multilayered cloud detection algorithm results appear reasonable in comparison with the CPL data. The first case study, from 11 December 2002 during the Terra–Aqua Experiment (TX-2002), also examines the behavior of the algorithm when midlevel or mixed-phase cloud is present. The second case study, from 26 February 2003 during The Observing System Research and Predictability Experiment (THORPEX) campaign, sheds light on the sensitivity of the algorithm to optically thin cirrus. In this case, the algorithm does not detect cirrus with a visible (0.564 μm) optical thickness of less than 0.1 when it overlies a lower-level water cloud.

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Shaima L. Nasiri and Brian H. Kahn

Abstract

Determining cloud thermodynamic phase using infrared satellite observations typically requires a priori assumptions about relationships between cloud phase and cloud temperature. In this study, limitations of an approach using two infrared channels with moderate spectral resolutions are demonstrated, as well as the potential for improvement using channels with higher spectral resolution. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument uses a bispectral infrared cloud phase determination algorithm. MODIS observations during January 2005 show that approximately 23% of cloudy pixels are classified as mixed or unknown cloud phase; this increases to 78% when only cloud-top temperatures between 250 and 265 K are considered. Radiative transfer simulations show that the bispectral algorithm has limited ability to discriminate between water and ice clouds in this temperature range. There is also the potential for thin ice clouds at colder temperatures to be misclassified as water clouds. In addition, sensitivities to cloud particle size and cloud height can be larger than sensitivities to cloud phase. Simulations suggest that phase sensitivity may be higher with hyperspectral observations such as those from the Atmospheric Infrared Sounder (AIRS). The AIRS brightness temperature differences between channels at 8.1 and 10.4 μm show phase sensitivities of at least 0.5 K, regardless of cloud particle size, cloud-top temperature, or cloud height. They also demonstrate reduced sensitivity to atmospheric temperature and water vapor variability. The reduced sensitivity of AIRS radiances to these physical quantities shows that hyperspectral sounders will serve an important role in refining estimates of cloud phase.

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Hyoun-Myoung Cho, Shaima L. Nasiri, and Ping Yang

Abstract

In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) infrared-based cloud thermodynamic phase retrievals are evaluated using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) retrievals for the 6 months from January to June of 2008. The CALIOP 5-km cloud-layer product provides information on cloud opacity, cloud-top height, midlayer cloud temperature, and cloud thermodynamic phase. Comparisons are made between MODIS IR phase and CALIOP observations for single-layer clouds (54% of the cloudy CALIOP scenes) and for the top layer of the CALIOP scenes. Both CALIOP and MODIS retrieve larger fractions of water clouds in the single-layer cases than in the top-layer cases, demonstrating that focusing on only single-layer clouds may introduce a water-cloud bias. Of the single-layer clouds, 60% are transparent and 40% are opaque (defined by the lack of a CALIOP ground return). MODIS tends to classify single-layer clouds with midlayer temperatures below −40°C as ice; around −30°C nearly equally as ice, mixed, and unknown; between −28° and −15°C as mixed; and above 0°C as water. Ninety-five percent of the single-layer CALIOP clouds not detected by MODIS are transparent. Approximately ⅓ of transparent single-layer clouds with temperatures below −30°C are not detected by MODIS and close to another ⅓ are classified as ice, with the rest assigned as water, mixed, or unknown. CALIOP classes nearly all of these transparent cold clouds as ice.

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Hyoun-Myoung Cho, Shaima L. Nasiri, Ping Yang, Istvan Laszlo, and Xuepeng “Tom” Zhao

Abstract

Analyses show that several existing Moderate Resolution Imaging Spectroradiometer (MODIS) dust detection techniques, including an approach based on simple brightness temperature difference thresholds, the D-parameter method, and the multichannel image (MCI) algorithm, may be more effective for detection of highly concentrated dust plumes than for thin dust layers. Using the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud and aerosol classification as a reference, the sensitivities of six MODIS radiative parameters (including brightness temperature differences, and standard deviation and ratios of reflectances) to cloud, clear sky, and dust layers are examined in this paper. Reflectance ratios and the standard deviation of reflectances were confirmed to be useful in the discrimination of dust from cloud and underlying ocean surface, while brightness temperature differences alone were not sufficient to separate dust from cloud and clear sky over the ocean surface. Using a collocated MODIS and CALIPSO training dataset from 2008, visible and infrared MODIS radiative parameters from six latitude bands and four seasons were combined using linear and quadratic discriminant analyses to develop a new algorithm for the detection of optically thin dust over the ocean. The validation using collocated MODIS and CALIPSO data from 2009 shows that the present algorithm is effective in detecting thin dust layers having optical thicknesses between 0.1 and 2.0, but that it tends to misclassify optically thicker dust layers as clouds.

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Kevin J. Garrett, Ping Yang, Shaima L. Nasiri, Christopher R. Yost, and Bryan A. Baum

Abstract

The retrieval of ice cloud microphysical and optical properties from satellite-based infrared observation remains a challenging research topic, partly because of the sensitivity of observed infrared radiances to many surface and atmospheric parameters that vary on fine spatial and temporal scales. In this study, the sensitivity of an infrared-based ice cloud retrieval to effective cloud temperature is investigated, with a focus on the effects of cloud-top height and geometric thickness. To illustrate the sensitivity, the authors first simulate brightness temperatures at 8.5 and 11.0 μm using the discrete ordinates radiative transfer (DISORT) model for five cloud-top heights ranging from 8 to 16 km and for varying cloud geometric thicknesses of 1, 2, 3, and 5 km. The simulations are performed across a range of visible optical thicknesses from 0.1 to 10 and ice cloud effective diameters from 30 to 100 μm. Furthermore, the effective particle size and optical thickness of ice clouds are retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) measurements on the basis of a lookup-table approach. Specifically, the infrared brightness temperatures are simulated from the collocated Atmospheric Infrared Sounder (AIRS) level-2 product at 28 atmospheric levels and prescribed ice cloud parameters. Variations of the retrieved effective particle size and optical thickness versus cloud-top height and geometric thickness are investigated. Results show that retrievals based on the 8.5- and 11.0-μm bispectral approach are most valid for cloud-top temperatures of less than 224 K with visible optical thickness values between 2 and 5. The present retrievals are also compared with the collection-5 MODIS level-2 ice cloud product.

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Tong Ren, Anita D. Rapp, Shaima L. Nasiri, John R. Mecikalski, and Jason Apke

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals from the Terra and Aqua satellites currently provide the largest satellite aerosol dataset for investigating relationships to meteorological phenomena, such as aerosol impact on electrification in deep convection. The usefulness of polar-orbiting satellite aerosol retrievals in lightning inference is examined by correlating MODIS AOD retrievals with lightning observations of the thunderstorms in the summers during 2002–14 over northern Alabama. Lightning flashes during the 1400–1700 local standard time peak period show weak but positive correlations with the MODIS AOD retrievals 2–4 h earlier. The correlation becomes stronger in particular meteorological conditions, including weak vertical wind shear and prevailing northerly winds over northern Alabama. Results show that the MODIS AOD retrievals are less useful in predicting enhanced lightning flash rate for lightning-producing storms than the forecasts of other meteorological variables that are more closely linked to the intensification of convective storms. However, when relatively weaker convective available potential energy (CAPE) is forecast, the probability of enhanced lightning flash rate increases in a more polluted environment, making the knowledge of aerosols more useful in lightning inference in such CAPE regimes. The aerosol enhancement of lightning, if present, may be associated with enhanced convergence in the boundary layer and secondary convection.

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Shaima L. Nasiri, Bryan A. Baum, Andrew J. Heymsfield, Ping Yang, Michael R. Poellot, David P. Kratz, and Yongxiang Hu

Abstract

Detailed in situ data from cirrus clouds have been collected during dedicated field campaigns, but the use of the size and habit distribution data has been lagging in the development of more realistic cirrus scattering models. In this study, the authors examine the use of in situ cirrus data collected during three field campaigns to develop more realistic midlatitude cirrus microphysical models. Data are used from the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE)-I (1986) and FIRE-II (1991) campaigns and from a recent Atmospheric Radiation Measurement (ARM) Program campaign held in March–April of 2000. The microphysical models are based on measured vertical distributions of both particle size and particle habit and are used to develop new scattering models for a suite of moderate-resolution imaging spectoradiometer (MODIS) bands spanning visible, near-infrared, and infrared wavelengths. The sensitivity of the resulting scattering properties to the underlying assumptions of the assumed particle size and habit distributions are examined. It is found that the near-infrared bands are sensitive not only to the discretization of the size distribution but also to the assumed habit distribution. In addition, the results indicate that the effective diameter calculated from a given size distribution tends to be sensitive to the number of size bins that are used to discretize the data and also to the ice-crystal habit distribution.

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Shaima L. Nasiri, H. Van T. Dang, Brian H. Kahn, Eric J. Fetzer, Evan M. Manning, Mathias M. Schreier, and Richard A. Frey

Abstract

Comparisons are described for infrared-derived cloud products retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) using measured spatial response functions obtained from prelaunch AIRS calibration. One full day (1 January 2005) of global collection-5 MODIS and version-5 AIRS retrievals of cloud-top temperature Tc, effective cloud fraction f, and derived effective brightness temperature Tb ,e is investigated. Comparisons of Tb ,e demonstrate that MODIS and AIRS are essentially radiatively consistent and that MODIS Tb ,e is 0.62 K higher than AIRS Tb ,e for all scenes, increasing to 1.43 K for cloud described by AIRS as single layer and decreasing to 0.50 K for two-layer clouds. Somewhat larger differences in Tc and f are observed between the two instruments. The magnitudes of differences depend partly on whether MODIS uses a CO2-slicing or 11-μm brightness temperature window retrieval method. Some cloud- and regime-type differences and similarities between AIRS and MODIS cloud products are traceable to the assumptions made about the number of cloud layers in AIRS and also to the MODIS retrieval method. This (partially) holistic comparison approach should be useful for ongoing algorithm refinements, rigorous assessments of climate applicability, and establishment of the capability of synergistic MODIS and AIRS retrievals for improved cloud quantities and also should be useful for future observations to be made by the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP).

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Elaine M. Prins, Christopher S. Velden, Jeffrey D. Hawkins, F. Joseph Turk, Jaime M. Daniels, Gerald J. Dittberner, Kenneth Holmlund, Robbie E. Hood, Arlene G. Laing, Shaima L. Nasiri, Jeffery J. Puschell, J. Marshall Shepherd, and John V. Zapotocny
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