Connecting local‐scale heavy precipitation to large‐scale meteorological patterns over Portland, Oregon

• Published in: International journal of climatology 2020-09, Vol.40 (11), p.4763-4780
• Main Authors: Aragon, Christina M., Loikith, Paul C., McCullar, Nicholas, Mandilag, Arnel
• Format: Article
• Language: English
• Subjects: Atmospheric circulation; Atmospheric models; Circulation patterns; Climate models; Connecting; Convective precipitation; Cyclones; Diagnostic systems; Dynamic height; Environmental assessment; Geopotential; Geopotential height; Heavy precipitation; Precipitation; Precipitation days; Sea level; Sea level pressure; Seasonal variation; Storms; Summer; Troughs; Upper level troughs; Wind speed; Winter
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.6487

Identifying and characterizing the large‐scale meteorological patterns (LSMPs) associated with local‐scale heavy precipitation improve our understanding of the processes that drive these high‐impact phenomena. Focusing on Portland, Oregon, we identify and characterize the key LSMPs associated with heavy precipitation days, defined using a daily total and hourly intensity threshold. LSMPs are defined at the synoptic scale using sea level pressure, 500 hPa geopotential height (Z500), and 250 hPa wind speed concurrent with precipitation days between 1980 and 2016, to capture synoptic circulation at three diagnostic atmospheric levels. We employ the self‐organizing map (SOM) approach to group the LSMPs into clusters, spanning the full range of synoptic circulation patterns associated with heavy precipitation days across the seasonal cycle. Using an atmospheric river (AR) catalogue of events, we show that ARs are commonly associated with heavy precipitation days, especially in winter and fall; however, heavy precipitation can occur without an AR in all seasons. Spring and summer heavy precipitation days, which are less common than in the fall and winter, tend to be primarily associated with upper level troughs and localized convective precipitation, while in winter they are more commonly associated with a surface cyclone and more widespread, stratiform precipitation. Examination of two case studies, one occurring in summer and one in winter, supports the ability of the SOM approach to realistically capture key observed storm types. Methods developed here may be extensible to other locations and phenomena and results build an observational foundation for assessing impactful LSMPs in climate models. This work identifies and characterizes the large‐scale meteorological patterns associated with urban‐scale heavy precipitation using the self‐organizing maps approach to improve our understanding of the processes that drive these high‐impact phenomena. Using an atmospheric river (AR) identification catalogue, we show that ARs are commonly associated with heavy precipitation days, especially in winter and fall; however, heavy precipitation can occur without an AR in all seasons. Warm season heavy precipitation days, which are less common relative to the cool season, tend to be primarily associated with upper level troughs and localized convective precipitation, while in winter they are more commonly associated with a surface cyclone and more widespread, stratiform prec