2.4 Using Vertically Pointing Wind Profiling Lidars to Acquire Low-Level Thunderstorm Outflow Wind Characteristics

Monday, 22 October 2018: 12:00 PM
Pinnacle room (Stoweflake Mountain Resort )
W. Scott Gunter, Columbus State Univ., Columbus, GA

Knowledge of the low-level wind profile in and near thunderstorms can be useful to many aspects of both atmospheric science and wind engineering. As such, efforts to obtain field measurements of thunderstorm wind profiles have used both in situ anemometry mounted on tall towers and remote sensing platforms, such as mobile Doppler-radars and radar profilers, to primarily acquire horizontal wind speed and direction profiles and occasionally profiles of turbulence intensity and vertical velocity within thunderstorms. In additional to these instruments, wind profiling lidars are also able to provide wind speed and direction profiles of the lower boundary layer and have been used extensively in the wind energy industry. While the emphasis within the wind energy industry is typically placed on 10-minute wind statistics at hub height (nominally 80 m), many profiling lidar systems are capable of generating low-level wind profiles with high spatial and temporal resolution that contribute to the understanding of low-level thunderstorm outflow structure. This research seeks to explore the feasibility of using such data to obtain low-level (<100 m) wind profile information in and near thunderstorms. Data were acquired from a lidar calibration complex used by a wind energy company. Instrumentation within the complex includes a profiling lidar, a three-axis sonic anemometers mounted at 30 m, 2 cup anemometers mounted at 28 m, and a surface weather station capable of measuring wind speed, wind direction, temperature and relative humidity at 2 m. Several case studies will be presented in which strong thunderstorms passed over the complex while all sensors were operational and collecting data. For these datasets, horizontal and vertical wind speed profiles obtained by the lidar will be analyzed and compared to those previously documented in the literature. Additionally, the 30 m lidar data will be compared to the 30 m sonic anemometer data. While only 10-minute averaged sonic anemometer data are available for the comparison, other statistics, including the 10-minute standard deviation, maximum and minimum values, will provide insight into the ability of the two different platforms to respond to the rapidly changing thunderstorm environment. Lastly, profiles of vertical velocity will be analyzed from each event to investigate the structure of the outflow associated with each thunderstorm. Advantages and disadvantages of the wind profiling lidar thunderstorm data will finally be assessed and discussed in the context of other remotely sensed thunderstorm wind profiles.
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