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Elisabeth Weisz, W. Paul Menzel, Nadia Smith, Richard Frey, Eva E. Borbas, and Bryan A. Baum

Abstract

The next-generation Visible and Infrared Imaging Radiometer Suite (VIIRS) offers infrared (IR)-window measurements with a horizontal spatial resolution of at least 1 km, but it lacks IR spectral bands that are sensitive to absorption by carbon dioxide (CO2) or water vapor (H2O). The CO2 and H2O absorption bands have high sensitivity for the inference of cloud-top pressure (CTP), especially for semitransparent ice clouds. To account for the lack of vertical resolution, the “merging gradient” (MG) approach is introduced, wherein the high spatial resolution of an imager is combined with the high vertical resolution of a sounder for improved CTP retrievals. The Cross-Track Infrared Sounder (CrIS) is on the same payload as VIIRS. In this paper Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) data are used as proxies for VIIRS and CrIS, respectively, although the approach can be applied to any imager–sounder pair. The MG method establishes a regression relationship between gradients in both the sounder radiances convolved to imager bands and the sounder CTP retrievals. This relationship is then applied to the imager radiance measurements to obtain CTP retrievals at imager spatial resolution. Comparisons with Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud altitudes are presented for a variety of cloud scenes. Results demonstrate the ability of the MG algorithm to add spatial definition to the sounder retrievals with a higher accuracy and precision than those obtained solely from the imager.

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Bryan A. Baum, Ping Yang, Shaima Nasiri, Andrew K. Heidinger, Andrew Heymsfield, and Jun Li

Abstract

This study reports on the development of bulk single-scattering models for ice clouds that are appropriate for use in hyperspectral radiative transfer cloud modeling over the spectral range from 100 to 3250 cm−1. The models are developed in a manner similar to that recently reported for the Moderate-Resolution Imaging Spectroradiometer (MODIS); therefore these models result in a consistent set of scattering properties from visible to far-infrared wavelengths. The models incorporate a new database of individual ice-particle scattering properties that includes droxtals, 3D bullet rosettes, hexagonal solid and hollow columns, aggregates, and plates. The database provides single-scattering properties for each habit in 45 size bins ranging from 2 to 9500 μm, and for 49 wavenumbers between 100 and 3250 cm−1, which is further interpolated to 3151 discrete wavenumbers on the basis of a third-order spline interpolation method. Bulk models are developed by integrating various properties over both particle habit and size distributions. Individual bulk models are developed for 18 effective diameters D eff, ranging from D eff = 10 μm to D eff = 180 μm. A total of 1117 particle size distributions are used in the analyses and are taken from analysis of the First International Satellite Cloud Climatology Project Regional Experiment (FIRE)-I, FIRE-II, Atmospheric Radiation Measurement Program intensive operation period (ARM-IOP), Tropical Rainfall Measuring Mission Kwajalein Experiment (TRMM-KWAJEX), and Cirrus Regional Study of Tropical Anvils and Cirrus Layers Florida-Area Cirrus Experiment (CRYSTAL-FACE) data. The models include microphysical and scattering properties such as median mass diameter, effective diameter, single-scattering albedo, asymmetry factor, and scattering phase function. The spectral models are appropriate for applications involving the interpretation of the radiometric measurements of ice clouds acquired by infrared spectrometers such as the Atmospheric Infrared Sounder (AIRS) on the NASA Aqua satellite and the Cross-Track Infrared Sounder (CrIS) on the upcoming National Polar-Orbiting Environmental Satellite System (NPOESS) platforms.

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Bryan A. Baum, Richard A. Frey, Gerald G. Mace, Monica K. Harkey, and Ping Yang

Abstract

This study reports on recent progress toward the discrimination between pixels containing multilayered clouds, specifically optically thin cirrus overlying lower-level water clouds, and those containing single-layered clouds in nighttime Moderate Resolution Imaging Spectroradiometer (MODIS) data. Cloud heights are determined from analysis of the 15-μm CO2 band data (i.e., the CO2-slicing method). Cloud phase is inferred from the MODIS operational bispectral technique using the 8.5- and 11-μm IR bands. Clear-sky pixels are identified from application of the MODIS operational cloud-clearing algorithm. The primary assumption invoked is that over a relatively small spatial area, it is likely that two cloud layers exist with some areas that overlap in height. The multilayered cloud pixels are identified through a process of elimination, where pixels from single-layered upper and lower cloud layers are eliminated from the data samples. For two case studies (22 April 2001 and 28 March 2001), ground-based lidar and radar observations are provided by the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains (SGP) Clouds and Radiation Test Bed (CART) site in Oklahoma. The surface-based cloud observations provide independent information regarding the cloud layering and cloud height statistics in the time period surrounding the MODIS overpass.

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Chen Zhou, Ping Yang, Andrew E. Dessler, Yongxiang Hu, and Bryan A. Baum

Abstract

Data from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) indicate that horizontally oriented ice crystals (HOIC) occur frequently in both ice and mixed-phase clouds. When compared with the case for clouds consisting of randomly oriented ice crystals (ROIC), lidar measurements from clouds with HOIC, such as horizontally oriented hexagonal plates or columns, have stronger backscatter signals and smaller depolarization ratio values. In this study, a 3D Monte Carlo model is developed for simulating the CALIOP signals from clouds consisting of a mixture of quasi HOIC and ROIC. With CALIOP’s initial orientation with a pointing angle of 0.3° off nadir, the integrated attenuated backscatter is linearly related to the percentage of HOIC but is negatively related to the depolarization ratio. At a later time in the CALIOP mission, the pointing angle of the incident beam was changed to 3° off nadir to minimize the signal from HOIC. In this configuration, both the backscatter and the depolarization ratio are similar for clouds containing HOIC and ROIC. Horizontally oriented columns with two opposing prism facets perpendicular to the lidar beam and horizontally oriented plates show similar backscattering features, but the effect of columns is negligible in comparison with that of plates because the plates have relatively much larger surfaces facing the incident lidar beam. From the comparison between the CALIOP simulations and observations, it is estimated that the percentage of quasi-horizontally oriented plates ranges from 0% to 6% in optically thick mixed-phase clouds, from 0% to 3% in warm ice clouds (>−35°C), and from 0% to 0.5% in cold ice clouds.

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W. Paul Menzel, Richard A. Frey, Eva E. Borbas, Bryan A. Baum, Geoff Cureton, and Nick Bearson

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This paper presents the cloud-parameter data records derived from High Resolution Infrared Radiation Sounder (HIRS) measurements from 1980 through 2015 on the NOAA and MetOp polar-orbiting platforms. Over this time period, the HIRS sensor has been flown on 16 satellites from TIROS-N through NOAA-19 and MetOp-A and MetOp-B, forming a 35-yr cloud data record. Intercalibration of the Infrared Advanced Sounding Interferometer (IASI) and HIRS on MetOp-A has created confidence in the onboard calibration of this HIRS as a reference for others. A recent effort to improve the understanding of IR-channel response functions of earlier HIRS sensor radiance measurements using simultaneous nadir overpasses has produced a more consistent sensor-to-sensor calibration record. Incorporation of a cloud mask from the higher-spatial-resolution Advanced Very High Resolution Radiometer (AVHRR) improves the subpixel cloud detection within the HIRS measurements. Cloud-top pressure and effective emissivity (εf, or cloud emissivity multiplied by cloud fraction) are derived using the 15-μm spectral bands in the carbon dioxide (CO2) absorption band and implementing the CO2-slicing technique; the approach is robust for high semitransparent clouds but weak for low clouds with little thermal contrast from clear-sky radiances. This paper documents the effort to incorporate the recalibration of the HIRS sensors, notes the improvements to the cloud algorithm, and presents the HIRS cloud data record from 1980 to 2015. The reprocessed HIRS cloud data record reports clouds in 76.5% of the observations, and 36.1% of the observations find high clouds.

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Nadia Smith, W. Paul Menzel, Elisabeth Weisz, Andrew K. Heidinger, and Bryan A. Baum

Abstract

To overcome the complexities associated with combining or comparing multisensor data, a statistical gridding algorithm is introduced for projecting data from their unique instrument domain to a uniform space–time domain. The algorithm has two components: 1) a spatial gridding phase in which geophysical properties are filtered on the basis of a set of criteria (e.g., time of day or viewing angle) and then aggregated into nearest-neighbor clusters as defined by equal-angle grid cells and 2) a temporal gridding phase in which daily statistics are calculated per grid cell from which longer time-aggregate statistics are derived. The sensitivity of the gridding algorithm is demonstrated using a month (1–31 August 2009) of level 2 Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-top pressure (CTP) retrievals as an example. Algorithm sensitivity is tested for grid size, number of days in the definition of a time average, viewing angle, and minimum number of observations per grid cell per day. The average CTP for high-level clouds from a number of different polar-orbiting instruments are compared on a 1° × 1° global grid. With the data projected onto a single grid, differences in CTP retrieval algorithms are highlighted. The authors conclude that this gridding algorithm greatly facilitates the intercomparison of CTP (or any other geophysical parameter) and algorithms in a dynamic environment. Its simplicity lends transparency to understanding the behavior of a given parameter and makes it useful for both research and operational use.

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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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Rob Roebeling, Bryan Baum, Ralf Bennartz, Ulrich Hamann, Andy Heidinger, Anke Thoss, and Andi Walther
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Bryan A. Baum, Andrew J. Heymsfield, Ping Yang, and Sarah T. Bedka

Abstract

This study reports on the use of in situ data obtained in midlatitude and tropical ice clouds from airborne sampling probes and balloon-borne replicators as the basis for the development of bulk scattering models for use in satellite remote sensing applications. Airborne sampling instrumentation includes the two-dimensional cloud (2D-C), two-dimensional precipitation (2D-P), high-volume precipitation spectrometer (HVPS), cloud particle imager (CPI), and NCAR video ice particle sampler (VIPS) probes. Herein the development of a comprehensive set of microphysical models based on in situ measurements of particle size distributions (PSDs) is discussed. Two parameters are developed and examined: ice water content (IWC) and median mass diameter Dm. Comparisons are provided between the IWC and Dm values derived from in situ measurements obtained during a series of field campaigns held in the midlatitude and tropical regions and those calculated from a set of modeled ice particles used for light-scattering calculations. The ice particle types considered in this study include droxtals, hexagonal plates, solid columns, hollow columns, aggregates, and 3D bullet rosettes. It is shown that no single habit accurately replicates the derived IWC and Dm values, but a mixture of habits can significantly improve the comparison of these bulk microphysical properties. In addition, the relationship between Dm and the effective particle size D eff, defined as 1.5 times the ratio of ice particle volume to projected area for a given PSD, is investigated. Based on these results, a subset of microphysical models is chosen as the basis for the development of ice cloud bulk scattering models in Part II of this study.

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Shouguo Ding, Ping Yang, Bryan A. Baum, Andrew Heidinger, and Thomas Greenwald

Abstract

This paper describes the development of an ice cloud radiance simulator for the anticipated Geostationary Operational Environmental Satellite R (GOES-R) Advanced Baseline Imager (ABI) solar channels. The simulator is based on the discrete ordinates radiative transfer (DISORT) model. A set of correlated k-distribution (CKD) models is developed for the ABI solar channels to account for atmospheric trace gas absorption. The CKD models are based on the ABI spectral response functions and also consider when multiple gases have overlapping absorption. The related errors of the transmittance profile are estimated on the basis of the exact line-by-line results, and it is found that errors in transmittance are less than 0.2% for all but one of the ABI solar channels. The exception is for the 1.378-μm channel, centered near a strong water vapor absorption band, for which the errors are less than 2%. For ice clouds, the band-averaged bulk-scattering properties for each ABI [and corresponding Moderate Resolution Imaging Spectroradiometer (MODIS)] solar channel are derived using an updated single-scattering property database of both smooth and severely roughened ice particles, which include droxtals, hexagonal plates, hexagonal hollow and solid columns, three-dimensional hollow and solid bullet rosettes, and several types of aggregates. The comparison shows close agreement between the radiance simulator and the benchmark model, the line-by-line radiative transfer model (LBLRTM)+DISORT model. The radiances of the ABI and corresponding MODIS measurements are compared. The results show that the radiance differences between the ABI and MODIS channels under ice cloud conditions are likely due to the different band-averaged imaginary indices of refraction.

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