The Hydrometeorology Testbed–West Legacy Observing Network: Supporting Research to Applications for Atmospheric Rivers and Beyond

• Published in: Atmosphere
• Main Authors: Andrea J. Ray, Allen B. White
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2019
• Subjects: hydrologic extremes; wind profiling radars; long-term observations; in situ observations; boundary layer processes; model verification; model validation; snow level; renewable energy; Arctic; hurricane; Meteorology. Climatology; QC851-999
• Online Access: https://doi.org/10.3390/atmos10090533; https://doaj.org/article/155f478251c346f3ab6ddb4b92e35608

An observing network has been established along the United States west coast that provides up to 20 years of observations to support early warning, preparedness and studies of atmospheric rivers (ARs). The Hydrometeorology Testbed−West Legacy Observing Network, a suite of upper air and surface observing instruments, is now an official National Oceanic and Atmospheric Administration (NOAA) observing system with real-time data access provided via publicly available websites. This regional network of wind profiling radars and co-located instruments also provides observations of boundary layer processes such as complex-terrain flows that are not well depicted in the current operational rawindsonde and radar networks, satellites, or in high-resolution models. Furthermore, wind profiling radars have been deployed ephemerally for projects or campaigns in other areas, some with long records of observations. Current research uses of the observing system data are described as well as experimental products and services being transitioned from research to operations and applications. We then explore other ways in which this network and data library provide valuable resources for the community beyond ARs, including evaluation of high-resolution numerical weather prediction models and diagnosis of systematic model errors. Other applications include studies of gap flows and other terrain-influenced processes, snow level, air quality, winds for renewable energy and the predictability of cloudiness for solar energy industry.