Assimilation of satellite based rainfall estimates with the Canadian Precipitation Analysis

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dc.contributor.authorFriesen, Bruce
dc.contributor.examiningcommitteeStadnyk, Tricia (Civil Engineering) Stewart, Ronald (Environment and Geography) Fortin, Vincent (Environment Canada)en_US
dc.contributor.supervisorRasmussen, Peter (Civil Engineering)en_US
dc.date.accessioned2014-12-03T21:27:28Z
dc.date.available2014-12-03T21:27:28Z
dc.date.issued2014-12-03
dc.degree.disciplineCivil Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractThe Canadian Precipitation Analysis (CaPA) produces a gridded product by assimilating data from stations and the Global Environmental Multiscale (GEM) model. This project assesses the performance of the satellite based rainfall estimates for Canada, and the results of their assimilation with CaPA. The satellite based estimates considered are those from the Climate Prediction Center Morphing method (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Relative to the Second Generation of Daily Adjusted Precipitation for Canada (APC2), all satellite products are shown to generally underestimate rainfall, however convective events result in an overestimation. Skill scores show that the satellite products possess the most skill for eastern Canada and decreasingly so westward. When assimilated with CaPA, the satellite products express decreased skill for light rainfall and potential improvements for larger events. While central Canada experiences the greatest improvements, all regions benefit the most from June through August.en_US
dc.description.noteFebruary 2015en_US
dc.identifier.urihttp://hdl.handle.net/1993/30078
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectprecipitationen_US
dc.subjectassimilationen_US
dc.subjectsatelliteen_US
dc.titleAssimilation of satellite based rainfall estimates with the Canadian Precipitation Analysisen_US
dc.typemaster thesisen_US
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