Objective Determination of Cloud Heights and Radar Reflectivities Using a Combination of Active Remote Sensors at the ARM CART Sites

Abstract

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.

Intercomparison of model simulations of mixed‐phase clouds observed during the ARM Mixed‐Phase Arctic Cloud Experiment. II: Multilayer cloud

“…However, a small, mid-level low pressure system drifted into the M-PACE domain from the east that also promoted considerable moistening and cloudiness at mid-levels, in contrast to the case in Part I. The time evolution of cloudiness at Barrow (Figure 2) is illustrated by the cloud fraction derived from the Active Remotely-Sensed Cloud Locations (ARSCL) algorithm (Clothiaux et al, 2000). The deep, multilayered cloud system on 6 and 7 October consisted of a number of distinct liquid layers with ice crystals falling between the liquid layers as indicated by aircraft measurements (Figure 3).…”

Seasonal and interannual variations of top‐of‐atmosphere irradiance and cloud cover over polar regions derived from the CERES data set

“…The Atmospheric Radiation Measurement (ARM) program operates a millimeterwave cloud radar and two laser systems, a micro-pulse lidar and a Vaisala ceilometer, at Barrow, Alaska. The monthly mean CERES cloud amounts derived over the 1°Â 1°box that contains Barrow fall between the cloud radar and laser derived cloud fractions [Clothiaux et al, 2000] (Figure 4). The difference of cloud radar and laser-derived cloud occurrence is expected because lasers are the most sensitive sensors easily available for detecting cloud particles, where cloud radars fail to detect smallest cloud particles.…”

Assessing the performance of a prognostic and a diagnostic cloud scheme using single column model simulations of TWP–ICE

“…The Darwin ARM site has a suite of active remote sensing instruments that provide vertical cloud structure information. The Active Remotely Sensed Cloud Layers (ARSCL) data have been provided as part of the TWP-ICE validation dataset and give information on the location of cloud layers (see Clothiaux et al (2000) for details). The observations have been interpolated to the 38 model vertical levels and the horizontal winds have been used with a horizontal grid box size of 300 km to calculate the appropriate averaging time-scale as described by Protat et al (2010).…”