Authors: Chris Zarzar*, North Carolina Central University
Topics: Climatology and Meteorology, Water Resources and Hydrology, Land Use and Land Cover Change
Keywords: precipitation, land-atmosphere, land cover, water distribution
Session Type: Virtual Guided Poster
Start / End Time: 3:05 PM / 4:20 PM
Room: Virtual 54
Presentation File: No File Uploaded
Daily precipitation data was analyzed to understand local-scale influences on precipitation patterns around the Triad region of North Carolina. The impacts of large metropolitan areas on precipitation have been well documented; however, the impacts of smaller urban areas and rural land cover boundaries on precipitation characteristics are less understood. Spatial analyses on 18-years (2003-2020) of 4-km National Center for Environmental Prediction (NCEP) Stage IV multi-sensor precipitation estimates provides insight into precipitation patterns around Central North Carolina urban areas. North Carolina cities analyzed include High Point, Winston-Salem, Greensboro, and Burlington. The analysis was put into both the context of traditional meteorological seasons and into a synoptic-scale air mass configuration to analyze potential precipitation patterns that present themselves only under the influence of a particular air mass regime. Significance of precipitation patterns was assessed by employing a local indicators of spatial association (LISA) analysis. This provides an assessment of statistically significant high and low precipitation clusters around each of the study areas. The outcomes from this study provide a foundation for numerical modeling efforts seeking to decipher whether precipitation clustering in Central North Carolina is the result of anthropogenic land cover modification or preexisting geological characteristics of the landscape. In addition, products generated by this study will be shared with local meteorologist to aid in forecasting efforts through maps of typical seasonal and air mass precipitation patterns around the study areas. It is expected that this information, combined with existing forecaster experience, will enhance forecaster precision in hyper-local precipitation forecasts.