An empirical approach for delineating fine scaled spatial patterns of freezing rain in the Appalachian region of the USA

Analyses of freezing rain in the United States are restricted to the use of a sparse network of first-order stations that provide hourly reports of we ather type. From this network, spatial analyses of freezing rain are limite d to broad scales. Terrain features, however, can strongly modify these pat terns at finer spatial scales. This is especially the case immediately east of the Appalachian Mountains, where cold air wedges commonly set up in con junction with the approach of cyclonic disturbances and precipitation. In t his study, an empirical approach is developed whereby daily climatological variables are used to predict mean annual freezing rain attributes over a d ense network of cooperative observation stations in the Appalachian region. Since freezing rain occurs when temperatures are at or below the freezing point, it is hypothesized that mean annual frequencies of daily high and lo w temperatures in a given range near 0 degrees C on days in which rain is o bserved (wet days) provide a surrogate for various annual attributes of fre ezing rain. In order to test this hypothesis, these mean annual temperature characteristics are correlated with the mean annual number of freezing rai n events (NFR) and hours of freezing rain (HFR) from a sample of first-orde r weather stations that observe freezing rain. Using the strongest identifi ed relationships, regression models are developed to predict HFR and NFR. O ver 80% of the variance in HFR and NFR is explained by the mean annual freq uencies of daily minimum temperature less than 0 and -2 degrees C, respecti vely, on wet days. These models are applied to daily climatological (e.g. t emperature. precipitation, and snowfall) summaries at selected cooperative observer sites in order to generate estimates of freezing rain occurrence. These estimations provide input for maps that better delineate spatial patt erns of freezing rain.