Wednesday, 10 January 2018: 11:15 AM
Room 15 (ACC) (Austin, Texas)
The Triton Sonic Wind Profiler Fleet is nearly 10 years old. Vaisala’s support and data hosting framework, SkyServe, enables us to evaluate the Triton Fleet’s performance rigorously. Summaries of data analysis of all Triton installations are presented here. The average sensor uptime is 98.5% and the 100m high-quality data recovery rates for Legacy and TPU Tritons are 85% and 93%, respectively. The 15% and 7% data recovery losses at 100m are due to primarily environmental drivers: atmospheric absorption of sound (α), ambient noise, poor acoustic reflectivity due to neutral atmospheric stability (CT2), snow accumulation on the reflector mirror (snow), speaker efficiency in cold climates (η), and battery health. For each installation, the effects of these drivers are quantified, and summary statistics presented. There are clear regional and seasonal trends for each of the drivers. α is most important at sites with low humidity, and varies seasonally, with highest impact during the summer. Noise is site-specific. In certain regions and seasons, insect noise overlaps the sodar’s bandwidth, requiring accurate characterization and filtering on a shot-to-shot basis. On farmland, depending on the season, ground level wind speeds may induce broadband noise from rustling crops, reducing sign-to-noise ratio, and reducing data recovery. CT2 is region- and season-specific and depends on the diurnal evolution of the boundary layer: transitions from stable to unstable atmospheres include a period of neutral stability with poor thermal structure at the sodar Bragg scattering scale. Snowy sites have a variety of challenges: snow accumulation on the mirror distorts and attenuates the sodar signal; from a full charge, the sodar can run for three weeks without sunlight, but then shuts itself off to preserve battery health; and, the piezoelectric tweeter’s efficiency is temperature-dependent, reducing the signal strength in cold climates. The TPU includes hardware improvements that reduce sensitivity to α, noise, and CT2 effects, as well as operating strategy optimization to improve η. Proper siting, and in some cases wind baffles, can significantly improve data recovery in the presence of ambient noise. Case studies including baffle design and performance are presented. Snowy-site data recovery and snow-event statistics are presented, and highlight the challenges of operating unattended, low-power remote sensors during wintertime. Continuous monitoring in winter months enables near-real-time snow melting, and quick resumption of high quality data capture after precipitation events. Tracking and adapting to these atmospheric drivers is the primary goal of Vaisala’s Support and R&D teams, and critical for maintaining high data capture for resource assessment (WRA) campaigns. For WRA professionals and banks engineers, understanding the nuances of sodar data recovery is critical for completing campaigns on time, and for having high confidence in the data quality.
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