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The Cloud Profiling Radar, the sole science instrument of the CloudSat Mission, is a 94-GHz nadir-looking radar that measures the power backscattered by hydrometeors (clouds and precipitation) as a function of distance from the radar. This instrument has been acquiring global time series of vertical cloud structures since June 2, 2006. In this paper, an overview of the radar performance and status, to date, is provided together with a description of the basic data products and the surface clutter rejection algorithm introduced for the Release 04 data product. Index Terms--A-train, clouds, CloudSat, radar.

Measurements are presented that were acquired from the National Aeronautics and Space Administration (NASA) DC-8 aircraft during an intensive 3-day study of Tropical Storm/Hurricane Humberto on 22, 23, and 24 September 2001. Particle size distributions, particle image information, vertical velocities, and single- and dual-wavelength Doppler radar observations were obtained during repeated sampling of the eyewall and outer eye regions. Eyewall sampling temperatures ranged from -22[degrees] to 57[degrees]C and peak updraft velocities from 4 to 15 m [s.sup.-1]. High concentrations of small ice particles, in the order 50 [cm.sup.-3] and above, were observed within and around the updrafts. Aggregates, some larger than 7 mm, dominated the larger sizes. The slope of the fitted exponential size distributions [lambda] was distinctly different close to the eye than outside of that region. Even at low temperatures, [lambda] was characteristic of warm temperature growth ([lambda] < 30 [cm.sup.-1]) close to the eye and characteristic of low temperature growth outside of it as well ([lambda] > 100 [cm.sup.-1]). The two modes found for A are shown to be consistent with observations from nonhurricane ice cloud layers formed through deep convection, but differ markedly from ice cloud layers generated in situ. It is shown that the median, mass-weighted, terminal velocities derived for the Humberto data and from the other datasets are primarily a function of A. Microphysical measurements and dual wavelength radar observations are used together to infer and interpret particle growth processes. Rain in the lower portions of the eyewall extended up to the 6- or 7-km level. In the outer eye regions, aggregation progressed downward from between 8.5 and 11.9 km to the melting layer, with some graupel noted in rainbands. Homogeneous ice nucleation is implicated in the high concentrations of small ice particles observed in the vicinity of the updrafts.

The threat of flooding from landfalling tropical cyclones is a function of the local variation in rain rate and rain accumulation. To date, these have been inferred from single-frequency radar reflectivity measurements. However, the Tropical Rainfall Measuring Mission experience has confirmed that one of the main difficulties in retrieving rain profiles using a single-frequency radar is the unknown raindrop size distribution (DSD). A dual-frequency radar such as the one planned for the upcoming Global Precipitation Measurement (GPM) core satellite is expected to help sort out at least part of this DSD-induced ambiguity. However, the signature of precipitation at 14 GHz does not differ greatly from its signature at 35 GHz (the GPM radar frequencies). To determine the extent of the vertical variability of the DSD in tropical systems and to quantify the effectiveness of a dual-frequency radar in resolving this ambiguity, several different models of DSD shape are considered and used to estimate the rain-rate and mean-diameter profiles from the measurements made by Jet Propulsion Laboratory's (JPL's) airborne second generation precipitation radar (PR-2) over Hurricanes Gabrielle and Humberto during the Fourth Convection and Moisture Experiment (CAMEX-4) in September 2001. It turns out that the vertical structures of the rain profiles retrieved from the same measurements under different DSD assumptions are similar, but the profiles themselves are quantitatively significantly different.

This study reports on the evolution of particle size distributions (PSDs) and habits as measured during slow, Lagrangian-type spiral descents through deep subtropical and tropical cloud layers in Florida, Brazil, and Kwajalein, Marshall Islands, most of which were precipitating. The objective of the flight patterns was to learn more about how the PSDs evolved in the vertical and to obtain information of the vertical structure of microphysical properties. New instrumentation yielding better information on the concentrations of particles in the size (D) range between 0.2 and 2 cm, as well as improved particle imagery, produced more comprehensive observations for tropical stratiform precipitation regions and anvils than have been available previously. Collocated radar observations provided additional information on the vertical structure of the cloud layers sampled. Most of the spirals began at cloud top, with temperatures (T) as low as -50[degrees]C, and ended at cloud base or below the melting layer (ML). The PSDs broadened from cloud top toward cloud base, with the largest particles increasing in size from several millimeters at cloud top, to 1 cm or larger toward cloud base. Some continued growth was noted in the upper part of the ML. Concentrations of particles less than 1 mm in size decreased with decreasing height. The result was a consistent change in the PSDs in the vertical. Similarly, systematic changes in the size dependence of the particle cross-sectional area was noted with decreasing height. Aggregation--as ascertained from both the changes in the PSDs and evolution of particle habits as observed in high detail with the cloud particle imager (CPI) probe--was responsible for these trends. The PSDs were generally well-represented by gamma distributions of the form N = [N.sub.0[GAMMA]][D.sup.mu][e.sup.-[lambda][GAMMA]]D that were fitted to the PSDs over 1-km horizontal intervals throughout the spirals. The intercept ([N.sub.0[GAMMA]]), slope ([[lambda].sub.[GAMMA]]), and dispersion ([mu]) values were derived for each PSD. Exponential curves (N = [N.sub.0][e.sup.-[lambda]D]; [mu] = 0) were also fitted to the distributions. The [[lambda].sub.[GAMMA]] values for given spirals varied systematically with temperature as did the values of [lambda] (exponential), and the data generally conformed to values found in previous studies involving exponential fits to size distributions in midlatitude frontal and cirrus layers. Considerable variability often noted in the PSD properties during the loops of individual spirals was manifested primarily in large changes in [N.sub.0[GAMMA]] and [N.sub.0], but [mu], [[lambda].sub.[GAMMA]], and [lambda] remained fairly stable. Temperature is not found to be the sole factor controlling [[lambda].sub.[GAMMA]] or [lambda], but is a primary one. Direct relationships were found between [[lambda].sub.[GAMMA]] and [N.sub.0[GAMMA]], or [[lambda].sub.[GAMMA]] and [mu], for the gamma distributions, and [lambda] and [N.sub.0] for the exponential. The latter relationship was not found as distinctly in earlier studies; observed PSDs in this study had better fidelity with less scatter. The [mu] values changed monotonically with T over the range of temperatures .and were directly related to [N.sub.0[GAMMA]] or [[lambda].sub.[GAMMA]], thereby reducing the number of variables in the PSD functional equation to two. In the upper part of the ML, [N.sub.0], and A continued to decrease, and in the lower part these values began to increase as the largest particles melted. General expressions relating various bulk microphysical, radar, and radiative-transfer-related variables to [N.sub.0[GAMMA]] and [[lambda].sub.[GAMMA]] were developed; they are useful for both tropical and midlatitude clouds. These relationships facilitate the specification of a number of bulk properties in cloud and climate models. The results presented in this paper apply best to temperatures between 0[degrees] and -40[degrees]C, for which the measured radar reflectivities fall in the range of 0 to 25 dB[Z.sub.e].

CloudSat is a satellite experiment designed to measure the vertical structure of clouds from space. The expected launch of CloudSat is planned for 2004, and once launched, CloudSat will orbit in formation as part of a constellation of satellites (the A-Train) that includes NASA's Aqua and Aura satellites, a NASA-CNES lidar satellite (CALIPSO), and a CNES satellite carrying a polarimeter (PARASOL). A unique feature that CloudSat brings to this constellation is the ability to fly a precise orbit enabling the fields of view of the CloudSat radar to be overlapped with the CALIPSO lidar footprint and the other measurements of the constellation. The precision and near simultaneity of this overlap creates a unique multisatellite observing system for studying the atmospheric processes essential to the hydrological cycle. The vertical profiles of cloud properties provided by CloudSat on the global scale fill a critical gap in the investigation of feedback mechanisms linking clouds to climate. Measuring these profiles requires a combination of active and passive instruments, and this will be achieved by combining the radar data of CloudSat with data from other active and passive sensors of the constellation. This paper describes the underpinning science and general overview of the mission, provides some idea of the expected products and anticipated application of these products, and the potential capability of the A-Train for cloud observations. Notably, the CloudSat mission is expected to stimulate new areas of research on clouds. The mission also provides an important opportunity to demonstrate active sensor technology for future scientific and tactical applications. The CloudSat mission is a partnership between NASA's JPL, the Canadian Space Agency, Colorado State University, the U.S. Air Force, and the U.S. Department of Energy. (Page 1771)

Concurrent measurements of sea-surface retroreflectance and associated wind velocity acquired with an airborne C[O.sub.2] Doppler lidar are described. These observations provide further insight into thermal infrared optical phenomenology of air-sea interface processes, contribute to a greater understanding of radiation transfer between the atmosphere and the hydrosphere, and enable improved models of wind-driven ocean-surface stress applicable to other remote sensing applications. In particular, we present lidar measurements of azimuthally anisotropic reflectance behavior and discuss the implications to current understanding of sea-surface optical properties. OCIS codes: 010.0010, 010.3640, 280.3640.

An approach has been developed that involves the fit of a combination of three simple scattering mechanisms to polarimetric SAR observations. The mechanisms are canopy scatter from a cloud of randomly oriented dipoles, even- or double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants and Bragg scatter from a moderately rough surface. This composite scattering model is used to describe the polarimetric backscatter from naturally occurring scatterers. The model is shown to describe the behavior of polarimetric backscatter from tropical rain forests quite well by applying it to data from NASA/Jet Propulsion Laboratory's (JPL's) airborne polarimetric synthetic aperture radar (AIRSAR) system. The model fit allows clear discrimination between flooded and nonflooded forest and between forested and deforested areas, for example. The model is also shown to be usable as a predictive tool to estimate the effects of forest inundation and disturbance on the fully polarimetric radar signature. An advantage of this model fit approach is that the scattering contributions from the three basic scattering mechanisms can be estimated for clusters of pixels in polarimetric SAR images. Furthermore, it is shown that the contributions of the three scattering mechanisms to the HH, HV, and VV backscatter can be calculated from the model fit. Finally, this model fit approach is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem.

The NASA/JPL Airborne Rain MApping Radar (ARMAR) was operated on the NASA DC-8 aircraft during TOGA COARE in early 1993. On 12 flights ARMAR observed stratiform precipitation associated with mesoscale convective systems. The statistics of 16 melting layer parameters, including maximum reflectivity, cooling rate, Doppler velocity, LDR, and the HH-VV correlation coefficient are presented and discussed.

This paper revisits the problem of finding a parametric form for the rain drop size distribution (DSD) which 1) is an appropriate model for tropical rainfall, and 2) involves statistically independent parameters. Using TOGA/COARE data, we derive a parametrization which meets these criteria. This new parametrization is an improvement on the one that was derived in [3], using TRMM ground truth data from Darwin, Australia. The new COARE data allows us to verify that the spatial variability of the two 'shape' parameters is relatively small, thus confirming that this parametrization should be particularly useful for remote sensing applications. We also derive new DSD-based radar-reflectivity-rain-rate power laws, whose coefficients are directly related to the shape parameters of the DSD. Perhaps most important, since the coefficients are independent of the rain-rate itself, and vary little spatially, the relations are ideally suited for rain retrieval algorithms. It should also prove straightforward to extend this method to the problems of estimating cloud hydrometeors from remote-sensing measurements.

An assessment using ERS-1 SAR data to differentiate methane source (wetland) and nonsource (nonwetland) areas was undertaken based on radar backscatter modeling and empirical observations of 24 scenes collected over Barrow, AK, in 1991 and 1992. Differences in backscatter between source and nonsource areas were dependent on surface hydrology and air temperature. Differential freezing of surface materials on daily to seasonal time scales greatly enhanced the separability of wetlands and nonwetlands with ERS-1 SAR. Radar return for nonwetlands decreased dramatically whereas backscatter from wetlands decreased little when freezing air temperatures coincided with the SAR overpass. Maximum separability between wetlands and nonwetlands, as determined from observed and modeled radar backscatter, were the result of changes in the dielectric constant of the plant and surface materials with phase change during freezing. This study has indicated the need to consider air temperature at the time of acquisition in selecting ERS-1 SAR scenes for differentiating methane source and nonsource areas.