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This paper examines the mission success of a federally funded research center by using bibliometric methods that include quantitative, descriptive, and citation analyses. We developed a methodology to facilitate examination of patterns in research, publishing, and collaborations, quantification and categorization of research partners, classification of topics, historical and emerging areas of research, and publishing venues. These patterns over a 12-yr period are used to assess whether the institute is achieving its mission goals of 1) fostering collaborative research, 2) becoming a center of excellence, and 3) educating scientists and students. Our findings indicate that a self-study of publishing activities yields useful results about programmatic strengths and weaknesses. This could be a first step of a larger study of federal government research and programmatic evaluation. DOI:10.1175/2009BAMS2810.1

During 2002 and 2003, surveys of introductory meteorology students were conducted at the University of Georgia and the University of Wisconsin--Madison. These surveys asked which one question about weather and climate each student would most like to have answered in the class, as well as other demographic and educational information. The more than 750 responses that were obtained ran the gamut of meteorology and were not overwhelmingly focused on any one topic, including severe weather. Results from the two universities are nearly identical, with the exception of a greater awareness of climate issues at Wisconsin. Several topics that are most commonly noted by students, such as weather forecasting and atmospheric optics, are given inadequate treatment in many introductory meteorology textbooks and classes. The results of the surveys suggest that an instructor could use students' first-day responses to this kind of question to shape a syllabus that would incorporate student interests, while retaining educational integrity.

An assessment of the performance of the Geostationary Operational Environmental Satellite (GOES) sounder cloud-top pressure product is presented. GOES sounder cloud-top-height data were compared with values derived from a consensus cloud boundary dataset that utilizes data from a cloud lidar and a cloud radar located at the U.S. Department of Energy's (DOE's) Atmospheric Radiation Measurement (ARM) Program's Cloud and Radiation Test Bed (CART) site in Lamont, Oklahoma. Comparisons were performed from April 2000 to March 2002. A temporal filtering process was applied to the cloud lidar and cloud radar output so that a representative picture of the cloud field on the same spatial scale of the GOES sounder could be derived. Comparisons between the GOES sounder and ground-based cloud boundary measurements yielded a mean difference of 1772 m and a standard deviation of 1733 m. The difference between GOES cloud-top-height and ground-based retrievals is within [+ or -]500 m for 22% of the retrievals and within [+ or -]1500 m for 56% of the retrievals. These results are comparable to findings in similar studies that utilized the MODIS Airborne Simulator.

The theoretical basis is explored for inferring the microphysical properties of ice clouds from high spectral resolution infrared (IR) observations. Extensive radiative transfer simulations are carried out to address relevant issues. The single-scattering properties of individual ice crystals are computed from state-of-the-art light scattering computational methods and are subsequently averaged for 30 in situ particle size distributions and for four additional analytical Gamma size distributions. The nonsphericity of ice crystals is shown to have a significant impact on the radiative signatures in the IR spectrum. Furthermore, the errors associated with the use of the Henyey-Greenstein phase function can be larger than 1 K in terms of brightness temperature for large particle effective sizes (~ 80 [micro]m) at wavenumbers where the scattering of the IR radiation by ice crystals is not negligible. The simulations undertaken in this paper show that the slope of the IR brightness temperature spectrum between 790-960 [cm.sup.-1] is sensitive to the effective particle size. Furthermore, a strong sensitivity of the IR brightness temperature to cloud optical thickness is noted within the 1050-1250-[cm.sup.-1] region. Based on these spectral features, a technique is presented for the simultaneous retrieval of the visible optical thickness and effective particle size from high spectral resolution IR data for ice clouds. An error analysis shows that the uncertainties of the retrieved optical thickness and effective particle size have a small range of variation. The error for retrieving particle size in conjunction with an uncertainty of 5 K in cloud temperature, or a surface temperature uncertainty of 2.5 K, is less than 15%. The corresponding errors in the uncertainty of optical thickness are within 5% to 20%, depending on the value of cloud optical thickness. The applicability of the present retrieval technique is demonstrated using airborne high-resolution IR measurements obtained during two field campaigns. Index Terms--Ice Clouds, infrared spectrum, retrieval.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of five instruments aboard the Terra Earth Observing System (EOS) platform launched in December 1999. After achieving final orbit, MODIS began earth observations in late February 2000 and has been acquiring data since that time. The instrument is also being flown on the Aqua spacecraft, launched in May 2002. A comprehensive set of remote sensing algorithms for cloud detection and the retrieval of cloud physical and optical properties have been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate change studies, climate modeling, numerical weather prediction, as well as fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. We will describe the various algorithms being used for the remote sensing of cloud properties from MODIS data with an emphasis on the pixel-level retrievals (referred to as Level-2 products), with 1-km or 5-km spatial resolution at nadir. An example of each Level-2 cloud product from a common data granule (5 min of data) off the coast of South America will be discussed. Future efforts will also be mentioned. Relevant points related to the global gridded statistics products (Level-3) are highlighted though additional details are given in an accompanying paper in this issue. Index Terms--Clouds, meteorology, remote sensing, satellite applications, terrestrial atmosphere.

The Moderate Resolution Imaging Spectrora-diometer (MODIS) is an earth-viewing sensor that flies on the Earth Observing System Terra and Aqua satellites, launched in 1999 and 2002, respectively. MODIS scans a swath width of 2330 km that is sufficiently wide to provide nearly complete global coverage every two days from a polar-orbiting, sun-synchronous, platform at an altitude of 705 km. MODIS provides images in 36 spectral bands between 0.415 and 14.235 [micro]m with spatial resolutions of 250 m (two bands), 500 m (five bands), and 1000 m (29 bands). These bands have been carefully selected to enable advanced studies of land, ocean, and atmospheric properties. Twenty-six bands are used to derive atmospheric properties such as cloud mask, atmospheric profiles, aerosol properties, total precipitable water, and cloud properties. In this paper we describe each of these atmospheric data products, including characteristics of each of these products such as file size, spatial resolution used in producing the product, and data availability. Index Terms--Aerosols, Aqua, clouds, meteorology, remote sensing, satellite applications, Terra, terrestrial atmosphere, water vapor.

Global analyses of satellite spectral observations indicate the existence of negative brightness temperature differences between 11 and 6.7 [[micro]meter] ([BT.sub.11] - [BT.sub.6.7]) when cold scenes are viewed. Differences are typically greater than -5 K for the Tropics and midlatitudes but can be smaller than -15 K over high-altitude polar regions during winter. In July, more than 60% of the observations over the Antarctic Plateau had [BT.sub.11] - [BT.sub.6.7] < -5 K. In January, over Greenland, the frequency of occurrence is approximately 20%. Three factors are investigated that may contribute to these observed negative brightness temperature differences: 1) calibration errors, 2) nonuniform scenes within the field of view, and 3) physical properties of the observed phenomena. Calibration errors and nonuniform scenes may generate values of [BT.sub.11] - [BT.sub.6.7] that are less than zero; however, these differences are on the order of -2 K and, therefore, cannot fully explain the observations. A doubling and adding radiative transfer model is used to investigate the physical explanations of the negative differences. Simulations of satellite spectral observations for thick clouds produce negative differences that are comparable to those observed in the Tropics and midlatitudes. The magnitude of the differences is a function of cloud microphysics, cloud-top pressure, view angle, and the cloud optical thickness. The model simulations are also capable of producing large negative differences over high-altitude polar regions. Distinguishing clear and cloudy regions from satellite infrared radiances is a challenging problem in polar winter conditions. Brightness temperature differences between 11 and 6.7 [[micro]meter] provide a technique to separate cold, optically thick clouds from clear-sky conditions when strong radiation inversions exist at the surface. The presence of a cloud inhibits the development of this inversion and shields its detection using satellite radiance measurements. While physically reasonable, and in agreement with radiative transfer calculations, this technique has not been verified with ground nor with aircraft observations. Further evidence that the large negative values of [BT.sub.11] - [BT.sub.6.7] are associated with surface inversions is presented by comparing the satellite observations with surface temperature measurements from an Antarctica automated weather station.

Advanced Very High Resolution Radiometer (AVHRR) and Earth Radiation Budget Experiment (ERBE) data are used to obtain radiative energy budget details of oceanic, savanna and desert regions. The AVHRR spectral channels chosen are transparent to clear atmospheric conditions, enabling the study of surface and cloud-top conditions. The ERBE instruments are used to perform broad-band observations for climate studies.

Cloud and cloud attributes in the infrared can be identified using trispectral combination of observations at 8-, 11- and 12-micrometer bands. Cloudy and clear regions are indicated by positive 8-11-micrometer tightness temperature differences and near-zero or negative differences, respectively, as a result of the peaking of atmospheric ice and water vapor absorption in opposite halves of the window area. The absorption coefficient for ice rises less between 11 and 12 micrometer than between 8 and 11 micrometer.

During the period of 26 October 1989 through 6 December 1989 a unique complement of measurements was made at the University of Wisconsin - Madison to study the radiative properties of cirrus clouds. Simultaneous observations were obtained from a scanning lidar, two interferometers, a high spectral resolution lidar, geostationary and polar orbiting satellites, radiosonde launches, and a whole-sky imager. This paper describes the experiment, the instruments deployed, and, as an example, the data collected during one day of the experiment.

The influence of airborne dust on the radiative energy budget at the top of the atmosphere was determined using the Earth Radiation Budget Experiment data and model. Results show that the presence of the dust particlesat the top of the atmosphere has caused the rise of clear-sky shortwave radiative exitance (SWRE) over the ocean, while the longwave radiative exitance(LWRE) was reduced. However, the SWRE over the desert was not clearly measured by satellite readings, but the LWRE results over the desert featured a reduction similar to the ocean observations.

This paper investigates the relationship between high--spectral resolution infrared (IR) radiances and the microphysical and macrophysical properties of cirrus clouds. Through use of radiosonde measurements of the atmospheric state at the Department of Energy's Atmospheric Radiation Measurement Program site, high--spectral resolution IR radiances are calculated by combining trace gas absorption optical depths from a line-by-line radiative transfer model with the discrete ordinate radiative transfer (DISORT) method. The sensitivity of the high--spectral resolution IR radiances to particle size, ice-water path, cloud-top location, cloud thickness, and multilayered cloud conditions is estimated in a multitude of calculations. DISORT calculations and interferometer measurements of cirrus ice cloud between 700 and 1300 [cm.sup.-1] are compared for three different situations. The measurements were made with the High--Spectral Resolution Interferometer Sounder mounted on a National Aeronautics and Space Administration ER-2 aircraft flying at 20-km altitude during the Subsonic Aircraft Contrail and Cloud Effects Special Study (SUCCESS).

Two-category support vector machines (SVMs) have become very popular in the machine learning community for classification problems and have recently been shown to have good optimality properties for classification purposes. Treating multicategory problems as a series of binary problems is common in the SVM paradigm. However, this approach may fail under a variety of circumstances. The multicategory support vector machine (MSVM), which extends the binary SVM to the multicategory case in a symmetric way, and has good theoretical properties, has recently been proposed. The proposed MSVM in addition provides a unifying framework when there are either equal or unequal misclassification costs, and when there is a possibly nonrepresentative training set. Illustrated herein is the potential of the MSVM as an efficient cloud detection and classification algorithm for use in Earth Observing System models, which require knowledge of whether or not a radiance profile is cloud free. If the profile is not cloud free, it is valuable to have information concerning the type of cloud, for example, ice or water. The MSVM has been applied to simulated MODIS channel data to classify the radiance profiles as coming from clear sky, water clouds, or ice clouds, and the results are promising. It can be seen in simple examples, and application to Moderate Resolution Imaging Spectroradiometer (MODIS) observations, that the method is an improvement over channel-by-channel partitioning. It is believed that the MSVM will be a very useful tool for classification problems in atmospheric sciences. [ABSTRACT FROM AUTHOR]