8A.5 Using an Ensemble Prediction System to Evaluate the Factors Influencing the Stationary Behavior of a Landfalling Atmospheric River over Central California

Tuesday, 30 January 2024: 5:30 PM
318/319 (The Baltimore Convention Center)
Ivan Leonel Fontanez, SUNY, Aguas Buenas, PR; Univ. at Albany, Albany, NY; and R. D. Torn

Atmospheric rivers (AR) are large-scale phenomena that transport water vapor from the tropics to the mid- and high latitudes. These ARs can be associated with substantial rainfall and snowfall along of west coast of the United States. During January 27-28, 2021, the Central California coast experienced a strong landfalling AR, which in turn produced heavy rainfall. One of the unique aspects of this event was the nearly stationary motion of the AR over an extended period of time over the Monterey and San Luis Obispo counties, producing intense precipitation and substantial flooding. This study analyzes some of the factors that were associated with the long-lasting AR over the Monterey and San Luis Obispo counties in some members of the West-WRF ensemble prediction system. These ensemble members were classified as wet and dry members based on the amount of precipitation over the central California coast. Preliminary results show that the wet members are characterized by a larger parent cyclone off the Pacific Northwest coast, with several smaller secondary cyclones that were embedded within it. This cyclone moves further south in the forecast and makes landfall near the Columbia River outlet, which allows this feature to interact with the AR. By contrast, the dry members show a more compact, and weaker parent cyclone, with fewer mesoscale cyclones. In addition, this cyclone remains further north, which limits the ability of these cyclones to interact with the AR. While a similar trough pattern to that observed in the wet members, the trough in the dry members is not strong. The differences in precipitation, integrated water vapor transport (IVT), and pressure will also be shown and compared with actual data.
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