Extracting 4-D Engineering-Relevant Wind Information from Research Radar Measurements

Historically, engineering-oriented characterization of low-level atmospheric winds has been dependent upon obtaining relevant measurements from anemometry, but recent research has shown promise in extracting relevant scales using radar measurements. Central to the success of these new analysis concepts was the proper correction of the acquired data fields to account for momentum advection and the implementation of a robust space-to-time conversion methodology. Combined, these facets allowed researchers to interpolate high resolution “tower-like” time histories from across the entire radar analysis domain, resulting in a more comprehensive evaluation of flow field evolution. However, the previous methodologies failed to properly characterize highly complex wind flows that contain wind speed and direction gradients. This study built upon these previous methodologies to better characterize complex wind flows, while also preserving engineering-relevant scales of motion. The new methodology was implemented on a wind record collected from a passing thunderstorm on 24 May 2020 at Reese Center, TX and included several sharp wind speed and direction changes. Validation of the resulting dual-Doppler-derived second-by-second projections was completed through comparison with available anemometry. The results highlight the significant advantages of using research radar information to bolster engineering perspectives.
The data provided within this publication includes the final 4-dimensional dataset that resulted from the new space-to-time methodology along with the raw binary SIGMET file format as output from a Vaisala digital radar receiver from both of Texas Tech University’s mobile Ka radars, Ka1 and Ka2, that were used for data collection. The raw SIGMET data are contained in directories labeled “raw_data” for the respective radar and the files are named according starting with the radar name (Ka1 or Ka2) followed by a date and time formatted as YYMMDDhhmmss. The raw files can be opened by numerous software applications and programming languages, such as MATLAB and Python. The final 4-dimensional data set contained within in the “4d_processed_dataset” features Network Common Deta Form (NetCDF) files that house the 3-dimensional dual-Doppler-derived wind speed and direction fields for each second of the time frame that was processed. These files are labeled as dates and times in the format YYYYMMDD_hhmmss. NetCDF files can be opened by numerous software applications and programming languages as well. The “deployment_notes.txt” file contains information for each radar truck while deployed, such as their GPS location, the direction the front of the trucks are facing, and the pitch and roll of the radar trucks. It also includes the truck relative sectors and elevation angles that each radar scanned for the duration of the event.