KAZR-ARSCL: the new ARSCL VAP for the Ka-band ARM zenith-pointing radar
Authors
Karen Lee Johnson — Brookhaven National Laboratory
David T. Troyan — Brookhaven National Laboratory
Pavlos Kollias — Stony Brook University
Heike Kalesse-Los — University of Leipzig
Edward Luke — Brookhaven National Laboratory
Scott Giangrande — Brookhaven National Laboratory
Michael Jensen — Brookhaven National Laboratory
Category
Cloud Properties
Description
With the installation of the new Ka-band ARM zenith-pointing radars (KAZRs) comes the need for a new value-added product (VAP) to make their measurements more readily usable by the scientific community. For over a decade, the ARSCL (Active Remote Sensing of Clouds) product has filled this need with respect to the previous generation of 35-GHz radars, the millimeter-wavelength cloud radars (MMCRs). Now that the MMCRs have been retired, the original ARSCL VAP will be retired following the completion of all historical processing.
As in the original ARSCL product, the KAZR-ARSCL VAP combines data from several active sensors that provide complementary measurements: the KAZR (with its various operating modes), the Vaisala ceilometer, and the micropulse lidar, via the micropulse lidar cloud mask VAP. In addition, it will incorporate rain gauge and radiosonde observations. The KAZRs, installed at all of ARM’s fixed sites as well as the second ARM Mobile Facility, are collecting observations with improved sensitivity, higher spatial and temporal resolution, and a more robust polarization mode. Taking advantage of these improvements, the new KAZR-ARSCL VAP has increased resolution relative to the original ARSCL, with 4-second time resolution and 30-meter height resolution. KAZR-ARSCL applies a correction for water vapor attenuation (which the original ARSCL did not), employs an improved mean Doppler velocity dealiasing technique, and offers greater accuracy in radar clutter detection. The KAZR-ARSCL VAP is expected to run autonomously, without the need for human intervention and corrections. This should result in far more timely data processing than was possible with the original ARSCL. KAZR-ARSCL will produce two primary datastreams: the complete data set with time-dependent cloud boundaries and best-estimate cloud bases as well as time-height fields of best-estimate radar moments, and a smaller datastream, consisting only of cloud boundaries and best-estimate cloud base.