title: Synoptic climatology of the long-distance dispersal of white pine blister rust. I. Development of an upper level synoptic classification.
abstract: This study developed a methodology to temporally classify large scale, upper level atmospheric conditions over North America, utilizing a newly-developed upper level synoptic classification (ULSC). Four meteorological variables: geopotential height, specific humidity, and u- and v-wind components, at the 500 hPa level over North America were obtained from the NCEP/NCAR Reanalysis Project dataset for the period 1965-1974. These data were subjected to principal components analysis to standardize and reduce the dataset, and then an average linkage clustering algorithm identified groups of observations with similar flow patterns. The procedure yielded 16 clusters. These flow patterns identified by the ULSC typify all patterns expected to be observed over the study area. Additionally, the resulting cluster calendar for the period 1965-1974 showed that the clusters are generally temporally continuous. Subsequent classification of additional observations through a z-score method produced acceptable results, indicating that additional observations may easily be incorporated into the ULSC calendar. The ULSC calendar of synoptic conditions can be used to identify situations that lead to periods of extreme weather, i.e., heat waves, flooding and droughts, and to explore long-distance dispersal of airborne particles and biota across North America.
authors.affiliation: Center for Climatic Research, Department of Geography, University of Delaware, Newark, DE 19716, USA. klfrank@udel.edu
authors.forename: K L
authors.initials: KL
authors.lastname: Frank
authors.forename: L S
authors.initials: LS
authors.lastname: Kalkstein
authors.forename: B W
authors.initials: BW
authors.lastname: Geils
authors.forename: H W
authors.initials: HW
authors.lastname: Thistle
mesh_terms: D001487:Basidiomycota / Q000302:isolation & purification*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D017753:Ecosystem; D015233:Models, Statistical; D009656:North America; D028223:Pinus / Q000382:microbiology*; D010935:Plant Diseases / Q000382:microbiology* / Q000706:statistics & numerical data*; D014887:Weather