Enhancing the ARSCL Product: Incorporating Doppler Spectra and Additional Instrumentation
Poster PDF
Authors
Karen Lee Johnson — Brookhaven National Laboratory
Scott Giangrande — Brookhaven National Laboratory
Edward Luke — Brookhaven National Laboratory
Christopher R Williams — University of Colorado, Boulder
Tami Fairless — Pacific Northwest National Laboratory
Pavlos Kollias — Stony Brook University
Category
ARM infrastructure
Description
The Active Remote Sensing of CLouds (ARSCL) Value-Added Product (VAP) provides cloud boundaries and best-estimates of Ka-band Zenith-pointing Radar (KAZR) moments. ARSCL is one of ARM’s most widely used products, combining observations from the KAZR, ceilometer, Micropulse Lidar (MPL), and Microwave Radiometer (MWR). Yet ARSCL can be further improved by leveraging data available in additional ARM datasets. A major focus of the ARSCL upgrade effort is improved discrimination of hydrometeors vs. insect/vegetative clutter and radar artifact returns. Also, at some sites, ARM Radar Wind Profilers (RWPs) offer the ability to ‘fill in’ regions of deep cloud which attenuate the KAZR signal within the cloud, preventing determination of cloud top. Improved detection of thin clouds is also a goal of this project. Raman Lidar observations, available at the Southern Great Plains (SGP) site, will be incorporated to provide better identification of thin cloud layers that ARSCL’s existing instrument set can miss.
Improved clutter and radar artifact identification within ARSCL is made possible using KAZR Doppler spectra, by means of inclusion of the soon-to-be-operational microARSCL product as an input. MicroARSCL provides statistical features of radar Doppler spectra, such as skewness, kurtosis and identification of multiple spectral peaks, as well as corrected radar reflectivity estimates and mean Doppler velocities associated with multiple spectral peaks, when present. It also includes a clutter flag based on characteristics of spectral shape. Additional efforts underway to make use of Doppler spectra to improve moments accuracy and identify non-hydrometeor returns will also be discussed, with the goal of establishing the best approaches to improve ARSCL’s best-estimate moments and clutter discrimination results and the most effective ways to communicate this information.
