2.3 Observations of California’s Epic 2017 Water Year from Satellite and a 21st Century Ground-based Observing Network

Monday, 8 January 2018: 11:00 AM
Ballroom D (ACC) (Austin, Texas)
Allen B. White, NOAA, Boulder, CO; and P. J. Neiman, D. J. Gottas, G. A. Wick, M. Hughes, and L. S. Darby

During the 2017 Water Year (1 October 2016 – 30 September 2017), California was battered by a number of winter storms that caused record precipitation in parts of the State, numerous floods, mudslides, debris flows, and a major disruption to commerce and daily life. In February, 180,000 people were evacuated when an emergency spillway had to be used to lower the water level on Lake Oroville for the first time since the dam was installed in the 1960’s. In May, a massive landslide closed California’s scenic State Route 1 near Big Sur, and it remains closed as crews remove rocks and soil up to 40 feet deep in places. Some of the heavy precipitation events that occurred were the result of atmospheric rivers (ARs), narrow regions of enhanced water vapor transport in the warm sector of extratropical cyclones. ARs can be detected via satellites (e.g., the microwave sensors on the SSMI/S) and also are featured in the output generated from global and mesoscale numerical forecast models. When the ARs make landfall, California’s coastal and inland mountain ranges enhance precipitation through orographic processes. During the period 1 October 2016 to 31 May 2017, an AR made landfall somewhere along the California coast on 52 days, more than double the 20-year average, based on an analysis of integrated water vapor detected from satellite. We investigate the impacts of these AR and non-AR events using a network of instruments that are part of the NOAA Hydrometeorology Testbed and the California Department of Water Resources Enhanced Flood Response and Emergency Preparedness Program. The network consists of wind profiling radars, precipitation profiling radars, snow-level radars, soil moisture sensors, surface flux instruments, disdrometers, and rain gauges. The data from this network provide situational awareness to forecasters and allow them to determine how the tools they use for quantitative precipitation estimation (QPE) and quantitative precipitation forecasting (QPF) are performing. Researchers have used data from the network to improve our understanding of the underlying physical processes associated with ARs and orographic precipitation.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner