A Numerical Modeling Perspective Utilizing 1-minute GOES-16 Data in Conjunction with Radar to Analyze Microphysical Properties of Clouds during the Convective Initiation (CI) Phase of Thunderstorms in the Southeast/Southern Great Plains of the United States

Continuing work will be presented in which GOES-16 data was analyzed for convective initiation (CI) events in the Southeast/Southern Great Plains of the United States. Over 50 CI cases were collected in which cumulus clouds were tracked manually in 1-minute super rapid scan GOES-16 data using the Man-computer Interactive Data Access System (McIDAS). Identifying cases proved to be somewhat challenging given that most of these thunderstorms are of the rudimentary pop-up variety in the spring/summertime in the Southeast, and since other GOES-16 mesoscale sectors would take precedence given the possibility of a more prolific severe storm event. GOES-16 provides better spatial and temporal resolution than the previous GOES installments in both the visible and IR channels. This work not only utilizes the super rapid scan (1-minute temporal resolution) data, but also radar fields to map both a top-down prospective of the CI process, as well as an in-cloud microphysical view.

This works expands on studies by Mecikalski et al. (2010, 2016), as well as Senf and Denke (2017), in which the CI process was studies through a satellite and radar-based perspective. In Mecikalski et al. (2010), 5-minute resolution Meteosat Second Generation (MSG) satellite data was used to detect CI over Germany/Northern France. A principle component analysis (PCA) was performed to find out which channels were the most important to the process of detecting CI, while the channel/channels differences that were identified in that study are utilized in this research. Mecikalski et al. (2016) demonstrated that using the 1-minute resolution data provided a copious amount of details regarding the cumulus cloud updrafts with short-term fluctuations in updraft velocity, acceleration, and glaciation. Senf and Denke (2017) then expanded on Mecikalski et al. (2016) by deriving relationships between cloud depth, growth, and glaciation properties from 5-minute Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the MSG satellite. They characterized the timing of formation of radar-derived moderate to heavy precipitation regarding the satellite-based growth and glaciation of convective cloud-top characteristics over central Europe that used a semi-automatic tracking algorithm. This present research is unique in that the data will have a higher temporal resolution (1 minute vs 5 minute), while also there are several additional spectral channels we examine here, as compared to the prior studies that used MSG SEVIRI data. A key question to be addressed is how much more details are provided by analyzing the 1 minute data as compared to the 5 minute data? In addition, results will be shown with the channel differences from the Mecikalski et al. (2010) to see if there are any substantial differences.

The modeling component of this study will utilize the Weather Research and Forecasting Model (WRF). A CI case will be chosen in the Southern Great Plains over the Atmospheric Radiation Measurement (ARM) facility. The goal here is to provide another layer of information to corroborate the radar and satellite observations. Obtaining estimates of cloud base information (e.g., cloud mass flux) will add in another dimension to understanding cloud behavior. A high-resolution WRF simulation will be ran, with the goal of simulating clouds as observed in GOES-16. Compare to the observations will be done to find links that could be used to fine-tune a microphysical/convective parameterization scheme.