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Terrain Trapped Airflows and Precipitation Variability during an Atmospheric...

Ryoo, J., Sen Chiao, R. Spackman, L. Iraci, F. M. Ralph, A. Martin, R. M. Dole, J. Marrero, E. Yates, T. P. Bui, J. Dean-Day, and C. Chang (2020), Terrain Trapped Airflows and Precipitation Variability during an Atmospheric River Event, J. Hydrometeorology, 21, 355-375, doi:10.1175/JHM-D-19-0040.1.

We examine thermodynamic and kinematic structures of terrain trapped airflows (TTAs) during an atmospheric river (AR) event impacting Northern California 10–11 March 2016 using Alpha Jet Atmospheric eXperiment (AJAX) aircraft data, in situ observations, and Weather and Research Forecasting (WRF) Model simulations. TTAs are identified by locally intensified low-level winds flowing parallel to the coastal ranges and having maxima over the near-coastal waters. Multiple mechanisms can produce TTAs, including terrain blocking and gap flows. The changes in winds can significantly alter the distribution, timing, and intensity of precipitation. We show here how different mechanisms producing TTAs evolve during this event and influence local precipitation variations. Three different periods are identified from the time-varying wind fields. During period 1 (P1), a TTA develops during synoptic-scale onshore flow that backs to southerly flow near the coast. This TTA occurs when the Froude number (Fr) is less than 1, suggesting low-level terrain blocking is the primary mechanism. During period 2 (P2), a Petaluma offshore gap flow develops, with flows turning parallel to the coast offshore and with Fr . 1. Periods P1 and P2 are associated with slightly more coastal than mountain precipitation. In period 3 (P3), the gap flow initiated during P2 merges with a pre-coldfrontal low-level jet (LLJ) and enhanced precipitation shifts to higher mountain regions. Dynamical mixing also becomes more important as the TTA becomes confluent with the approaching LLJ. The different mechanisms producing TTAs and their effects on precipitation pose challenges to observational and modeling systems needed to improve forecasts and early warnings of AR events.

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Atmospheric Composition