An Evaluation of GOES Microphysical Property Retrievals at Anvil Regions of Deep Convection by Using MMCR and NEXRAD

Zhe Feng Pacific Northwest National Laboratory
Xiquan Dong University of North Dakota
Baike Xi University of North Dakota
Patrick Minnis NASA - Langley Research Center

Category: Cloud Properties

Working Group: Cloud Life Cycle

A new algorithm has been developed to perform automatic classification of deep convective systems by using data from U.S. DOE ARM measurements. The algorithm has been tested on major deep convective events of summer 2007 at the ARM SGP site. The major components of the data include ARSCL best estimated and precipitation mode reflectivity, Doppler velocity from MMCR, rain rate from rain gauges close to MMCR, and merged sounding temperature from value-added products. The classification is performed every minute, giving a total of seven classes, including shallow cumulus, convective, stratiform with bright band, stratiform without bright band, transition from precipitating to non-precipitating cloud, mixed-phase anvil, and ice anvil. The similar technique developed from ARM instruments is then applied to the scanning radars (NEXRAD). The NEXRAD data are first objectively analyzed onto a Cartesian grid, followed by a two-step precipitation-cloud classification. While the MMCR classification provides accurate time-series profiles for NEXRAD to separate precipitating and non-precipitating portion of the convective systems, the NEXRAD classification can provide a three-dimensional field of such systems. Direct time-series comparison between the MMCR and two nearby NEXRAD is performed to evaluate classification from NEXRAD. Applications of the NEXRAD classification to GOES satellite macrophysical and microphysical cloud property retrievals in regions of the mixed-phase anvil and ice anvil of deep convection will be shown to illustrate the potential of this technique to study continental deep convections.

This poster will be displayed at ASR Science Team Meeting.