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The U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) Program is deploying sensitive, millimeter-wave cloud radars at its Cloud and Radiation Test Bed (CART) sites in Oklahoma, Alaska, and the tropical western Pacific Ocean. The radars complement optical devices, including a Belfort or Vaisala laser ceilometer and a micropulse lidar, in providing a comprehensive source of information on the vertical distribution of hydrometeors overhead at the sites. An algorithm is described that combines data from these active remote sensors to produce an objective determination of hydrometeor height distributions and estimates of their radar reflectivities, vertical velocities, and Doppler spectral widths, which are optimized for accuracy. These data provide fundamental information for retrieving cloud microphysical properties and assessing the radiative effects of clouds on climate. The algorithm is applied to nine months of data from the CART site in Oklahoma for initial evaluation. Much of the algorithm's calculations deal with merging and optimizing data from the radar's four sequential operating modes, which have differing advantages and limitations, including problems resulting from range sidelobes, range aliasing, and coherent averaging. Two of the modes use advanced phase-coded pulse compression techniques to yield approximately 10 and 15 dB more sensitivity than is available from the two conventional pulse modes. Comparison of cloud-base heights from the Belfort ceilometer and the micropulse lidar confirms small biases found in earlier studies, but recent information about the ceilometer brings the agreement to within 20-30 m. Merged data of the radar's modes were found to miss approximately 5.9% of the clouds detected by the laser systems. Using data from only the radar's two less-sensitive conventional pulse modes would increase the missed detections to 22%-34%. A significant remaining problem is that the radar's lower-altitude data are often contaminated with echoes from nonhydrometeor targets, such as insects.

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[1] Black carbon is ubiquitous in the atmosphere and is the main anthropogenic absorbing particulate. Absorption by black carbon is thought to be comparable to the cooling associated with sulfate aerosols, although present-day satellites are incapable of obtaining this measurement, and model estimates are highly uncertain. More measurements of black carbon concentration are necessary for improving and validating transport and general circulation models. The Aerosol Robotics Network (AERONET) of 180 worldwide radiometers offers an opportunity to obtain these measurements. We use the Maxwell Garnett effective medium approximation to infer the column-averaged black carbon concentration and specific absorption of AERONET retrievals at 46 locations. The yearly averaged black carbon column concentrations exhibit the expected regional dependence, with remote island locations having values about an order of magnitude lower than the continental biomass burning locations. The yearly averaged black carbon specific absorption cross section is consistent with other measured values, 9.9 m 2 g À1 for 19,591 retrievals, but varies from 7.7 to 12.5 m 2 g À1. We attribute this variability to the details of the size distributions and the fraction of black carbon contained in the aerosol mixture. We also used the Maxwell Garnett equations to parameterize the imaginary refractive index with respect to the black carbon volume fraction, enabling simple but accurate absorption estimates for aerosol mixtures when the black carbon fraction and size distribution is known. The black carbon concentrations that we derive from AERONET measurements correctly describe the radiance field and represent an alternative to absorption optical thickness in the link between models and AERONET measurements.

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