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Nowcasting precipitation onset in Vancouver using CORALNet-UBC lidar imagery

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Title: Nowcasting precipitation onset in Vancouver using CORALNet-UBC lidar imagery
Author: Seagram, Annie F.
Issue Date: 2010-05-25
Citation: Seagram, Annie F. 2010. Nowcasting precipitation onset in Vancouver using CORALNet-UBC lidar imagery. Undergraduate Honours Thesis. Department of Earth and Ocean Sciences. University of British Columbia. http://hdl.handle.net/2429/25039
Abstract: In recent years, the application of ground-based LIDAR (LIght Detection And Ranging) to atmospheric observation and monitoring has become increasingly common. The UBC (University of British Columbia) CORALNet (Canadian Observational Research Aerosol Lidar Network) lidar dataset exhibits an interesting cloud signature on numerous daily plots from the1064/532 nm lidar imagery. The signature features a general downward sloping of cloud cover over time, ending in a precipitation event (marked by lidar shutoff by rain sensor). These Cloud LOwering Signatures (CLOSs) are unique in their shape, slope, and rate of decline (from first appearance to onset of precipitation). The focus of this study was to explore the real-time use of lidar imagery for nowcasting, by deriving a conceptual model based on CLOS characteristics. A synoptic map-typing procedure using sea-level pressure maps was applied to each of the 75 CLOSs catalogued from April, 2008, to September, 2009. Eight map classes were derived, most of which were associated with low pressure conditions. Additional temporal parameters from the lidar imagery were collected to produce statistically significant empirical models for each map class in order to predict the lead time to precipitation onset based on a linear rate of lowering of the cloud cover. In real time, the models accurately predicted the onset of precipitation, between 0.23 – 4.57 hrs of the actual lead time. However, these results were only preliminary due to the small dataset examined (n = 7). It is hoped that the results of this study may be used to nowcast precipitation onset with greater accuracy, and to serve as a precursor to automated methods that may exploit quantitative data as forecasting products.
Affiliation: Science, Faculty ofEarth and Ocean Sciences, Department of
URI: http://hdl.handle.net/2429/25039
Peer Review Status: Unreviewed
Scholarly Level: Undergraduate

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