Improving Lake-Effect Mixed Phase Cloud Detection over the Great Lakes

Improving lake-effect mixed phase cloud detection over the Great Lakes

Leila Gabrys^1,2, Michael Foster¹, Mark Kulie³, Marian Mateling², and Timothy Wagner¹

1. CIMSS, University of Wisconsin, Madison, WI, USA
2. Department of Atmospheric and Oceanic Sciences, University of Wisconsin Madison
3. NOAA/NESDIS/STAR, University of Wisconsin, Madison, WI, USA

Spatial limitations of WSR-88D radar pose difficulty in forecasting lake effect-induced snow events over the Great Lakes. The use of NOAA operational cloud products to detect mixed phase clouds, which commonly occur during lake-effect events, is helpful in assessing their temporal evolution. The CloudSat 2B-CLDCLASS-lidar is designed to classify clouds utilizing satellite-based radar and lidar, as well as categorize cloud layers into respective ice, mixed, and liquid phases. The GOES cloud detection algorithm uses a naive Bayesian methodology to identify clouds and their phase. A database of lake-effect events is created to assess where these products overlap, as well as where CloudSat passes over the water surface of the Great Lakes. Several additional GOES cloud properties are utilized in this analysis, such as cloud base height (CBH), cloud top height (CTH), particle effective radius (REFF), and cloud optical depth (OPD). In addition, an RGB GOES imagery recipe was generated to assess cloud type based on glaciation. This project analyzes the collocated data from both CloudSat 2B-CLDCLASS-lidar and GOES satellite products to evaluate cloud type identifications during lake-effect events, mixed phase cloud retrievals, cloud precipitation and microphysical processes, and synoptic influence. In this presentation, we will discuss results from case studies over the Great Lakes region using products to track the temporal evolution of lake-effect snow events.