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Bryan A. Baum
and
Bruce A. Wielicki

Abstract

In this study we perform an error analysis for cloud-top pressure retrieval using the High-Resolution Infrared Radiometric Sounder (HIRS/2) 15-µm CO2 channels for the two-layer case of transmissive cirrus overlying an overcast, opaque stratiform cloud. This analysis includes standard deviation and bias error due to instrument noise and the presence of two cloud layers, the lower of which is opaque. Instantaneous cloud pressure retrieval errors are determined for a range of cloud amounts (0.1–1.0) and cloud-top pressures (850−250 mb). Large cloud-top pressure retrieval errors are found to occur when a lower opaque layer is present underneath an upper transmissive cloud layer in the satellite field of view (FOV). Errors tend to increase with decreasing upper-cloud elective cloud amount and with decreasing cloud height (increasing pressure). Errors in retrieved upper-cloud pressure result in corresponding errors in derived effective cloud amount. For the case in which a HIRS FOV has two distinct cloud layers, the difference between the retrieved and actual cloud-top pressure is positive in all casts, meaning that the retrieved upper-cloud height is lower than the actual upper-cloud height. In addition, errors in retrieved cloud pressure are found to depend upon the lapse rate between the low-level cloud top and the surface. We examined which sounder channel combinations would minimize the total errors in derived cirrus cloud height caused by instrument noise and by the presence of a lower-level cloud. We find that while the sounding channels that peak between 700 and 1000 mb minimize random errors, the sounding channels that peak at 300—500 mb minimize bias errors. For a cloud climatology, the bias errors are most critical.

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Shaima L. Nasiri
and
Bryan A. Baum

Abstract

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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Bryan A. Baum
and
Qing Trepte

Abstract

The authors propose a grouped threshold method for scene identification in Advanced Very High Resolution Radiometer imagery that may contain clouds, fire, smoke, or snow. The philosophy of the approach is to build modules that contain groups of spectral threshold tests that are applied concurrently, not sequentially, to each pixel in an image. The purpose of each group of tests is to identify uniquely a specific class in the image, such as smoke. A strength of this approach is that insight into the limits used in the threshold tests may be gained through the use of radiative transfer theory. Methodology and examples are provided for two different scenes, one containing clouds, forest fires, and smoke; and the other containing clouds over snow in the central United States. For both scenes, a limited amount of supporting information is provided by surface observers.

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Bryan A. Baum
and
Bruce R. Barkstrom

The Earth Observing System (EOS) will collect data from a large number of satellite-borne instruments, beginning later in this decade. To make data accessible to the scientific community, NASA will build an EOS Data and Information System (EOSDIS). As an initial effort to accelerate the development of EOSDIS and to gain experience with such an information system, NASA and other agencies are working on a prototype system called Version 0 (VO). This effort will provide improved access to pre-EOS earth science data throughout the early EOSDIS period. Based on recommendations from the EOSDIS Science Advisory Panel, EOSDIS will have several distributed active archive centers (DAACs). Each DAAC will specialize in particular datasets. This paper describes work at the NASA Langley Research Center's (LaRC) DAAC.

The Version 0 Langley DAAC began archiving and distributing existing datasets pertaining to the earth's radiation budget, clouds, aerosols, and tropospheric chemistry in late 1992. The primary goals of the LaRC VO effort are the following:

  1. Enhance scientific use of existing data;

  2. Develop institutional expertise in maintaining and distributing data;

  3. Use institutional capability for processing data from previous missions such as the Earth Radiation Budget Experiment and the Stratospheric Aerosol and Gas Experiment to prepare for processing future EOS satellite data;

  4. Encourage cooperative interagency and international involvement with datasets and research;

  5. Incorporate technological hardware and software advances quickly.

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Bryan A. Baum
,
Bruce A. Wielicki
,
Patrick Minnis
, and
Lindsay Parker

Abstract

A technique is developed that uses a multispectral, multiresolution (MSMR) method to improve the overall retrieval of mid-to high-level cloud properties by combining HIRS sounding channel data with higher spatial resolution AVHRR radiometric data collocated with the HIRS footprint. Cirrus cloud radiative and physical properties are determined using satellite data, surface-based measurements provided by rawinsondes and lidar, and aircraft-based lidar data collected during the First ISCCP (International Satellite Cloud Climatology Program) Regional Experiment (FIRE) in Wisconsin during the months of October and November 1986. HIRS cloud-height retrievals are compared to ground-based lidar and aircraft lidar when possible. Retrieved cloud heights are found to have close agreement with lidar for thin cloud, but are higher than lidar for optically thick cloud. The fact that the retrieved cloud height is higher than lidar for optically thick cloud is probably due to the attenuation of the lidar signal before the signal reaches through the cloud, while the satellite is viewing the cloud from above. AVHRR visible (0.63-μm) and infrared (11-μm) radiances are analyzed to determine the cloud emittances and reflectances collocated with each HIRS pixel. The bidirectional reflectances from the AVHRR visible-channel data are corrected for solar direct and diffuse surface reflection to isolate the cloud reflectance. The individual AVHRR pixel emittances are calculated using the cloud-top temperature derived from the HIRS cloud-retrieval analysis. The results of the reflectance-emittance relationships derived in this fashion are compared to theoretical scattering model results for both water-droplet spheres and randomly oriented hexagonal ice crystals. It is found that the assumption of 10-μm water droplets is inadequate to describe the reflectance-emittance relationship for the ice clouds seen here. Use of this assumption would lead to lower cloud heights using the ISCCP approach. The theoretical results show that use of hexagonal ice-crystal phase functions could lead to much improved results for cloud retrieval algorithms using a bispectral approach.

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Bryan A. Baum
,
Vasanth Tovinkere
,
Jay Titlow
, and
Ronald M. Welch

Abstract

A fuzzy logic classification (FLC) methodology is proposed to achieve the two goals of this paper: 1) to discriminate between clear sky and clouds in a 32 × 32 pixel array, or sample, of 1.1-km Advanced Very High Resolution Radiometer (AVHRR) data, and 2) if clouds are present, to discriminate between single-layered and multilayered clouds within the sample. To achieve these goals, eight FLC modules are derived that are based broadly on airmass type and surface type (land or water): equatorial over land, marine tropical over land, marine tropical/equatorial over water, continental tropical over land, marine polar over land, marine polar over water, continental polar over land, and continental polar/arctic over water. Derivation of airmass type is performed using gridded analyses provided by the National Centers for Environmental Prediction.

The training and testing data used by the FLC are collected from more than 150 daytime AVHRR local area coverage scenes recorded between 1991 and 1994 over all seasons and over all continents and oceans. A total of 190 textural and spectral features are computed from the AVHRR data. A forward feature selection method is implemented to reduce the number of features used to discriminate between classes in each FLC module. The number of features selected ranges from 13 (marine tropical over land) to 24 (marine tropical/equatorial over water). An estimate of the classifier accuracy is determined using the hold-one-out method in which the classifier is trained with all but one of the data samples; the classifier is applied subsequently to the remaining sample.

The overall accuracies of the eight classification modules are calculated by dividing the number of correctly classified samples by the total number of manually labeled samples of clear-sky and single-layer clouds. Individual module classification accuracies are as follows: equatorial over land (86.2%), marine tropical over land (85.6%), marine tropical/equatorial over water (88.6%), continental tropical over land (87.4%), marine polar over land (86.8%), marine polar over water (84.8%), continental polar over land (91.1%), and continental polar/arctic over water (89.8%). Single-level cloud samples misclassified as multilayered clouds range between 0.5% (continental polar over land) and 3.4% (marine polar over land) for the eight airmass modules.

Classification accuracies for a set of labeled multilayered cloud samples range between 64% and 81% for six of the eight airmass modules (excluded are the continental polar over land and continental polar/arctic over water modules, for which multilayered cloud samples are difficult to find). The results indicate that the FLC has an encouraging ability to distinguish between single-level and multilayered clouds.

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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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Yue Li
,
Gerald R. North
,
Ping Yang
, and
Bryan A. Baum

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) observations provide an unprecedented opportunity for studying cloud macrophysical (cloud-top pressure, temperature, height, and phase), microphysical (effective particle size), and optical (optical thickness) properties. Given the length of time these MODIS products have been available, it is found that the cloud products can provide a wealth of information about equatorial wave systems. In this study, more than six years of the MODIS cloud-top properties inferred from the Aqua MODIS observations are used to investigate equatorial waves. It is shown that the high-resolution daily gridded cloud-top temperature product can be used to quantitatively study convective clouds. Various modes of convectively coupled equatorial waves including Kelvin, n = 1 equatorial Rossby, mixed Rossby–gravity, n = 0 eastward inertial-gravity waves, and the Madden–Julian oscillation are identified on the basis of space–time spectral analysis. The application of spectral analysis to cirrus cloud optical thickness, retrieved from MODIS cirrus reflectance, confirms the convective signals at high altitudes. A cluster of Kelvin pulses is found to propagate eastward around the globe at a phase speed approximately 15 m s−1. The Madden–Julian oscillation propagates at a slower speed and is most prominent over the Indian–Pacific Oceans region. The consistency between the present results with those of previous studies demonstrates that the MODIS cloud-top property products are valuable for studying phenomena associated with atmospheric dynamics.

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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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Anthony J. Schreiner
,
Steven A. Ackerman
,
Bryan A. Baum
, and
Andrew K. Heidinger

Abstract

A technique using the Geostationary Operational Environmental Satellite (GOES) sounder radiance data has been developed to improve detection of low clouds and fog just after sunrise. The technique is based on a simple difference method using the shortwave (3.7 μm) and longwave (11.0 μm) window bands in the infrared range of the spectrum. The time period just after sunrise is noted for the difficulty in being able to correctly identify low clouds and fog over land. For the GOES sounder cloud product this difficulty is a result of the visible reflectance of the low clouds falling below the “cloud” threshold over land. By requiring the difference between the 3.7- and the 11.0-μm bands to be greater than 5.0 K, successful discrimination of low clouds and fog is found 85% of the time for 21 cases from 14 September 2005 to 6 March 2006 over the GOES-12 sounder domain. For these 21 clear and cloudy cases the solar zenith angle ranged from 87° to 77°; however, the range of solar zenith angles for cloudy cases was from 85° to 77°.

The success rate further improved to 95% (20 out of 21 cases) by including a difference threshold of 5.0 K between the 3.7- and 4.0-μm bands, requiring that the 11.0-μm band be greater than 260 K, and limiting the test to fields of view where the surface elevation is below 999 m. These final three limitations were needed to more successfully deal with cases involving snow cover and dead vegetation. To ensure that only the time period immediately after sunrise is included the solar zenith angle threshold for application of these tests is between 89° and 70°.

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