This study uses a gridded dataset of daily U.S. and Canadian surface observations from 1960–2000 to study regional spatial and temporal variability and trends in snow depth across North America. Analysis shows minimal change in North American snow depth through January, with regions of decreasing snow depths beginning in late January. These regional decreases grow in intensity and extent through March and into April, implying an earlier onset of spring melt. The region showing the greatest decreases in snow depth occurs in central Canada, along a line from the Yukon Territory in northwestern Canada to the Great Lakes region. The regional decreases in spring snow depth across central Canada are likely a result of more rapid melt of shallower winter snowpacks, evident through shallower snow cover (2–10 cm) during May and October and a decrease in extent of deeper snowpacks (>40cm) through March and April.

Numerical weather prediction ensembles are routinely used for operational weather forecasting. The members of these ensembles are individual simulations with either slightly perturbed initial conditions or different model parameterizations, or occasionally both. Multi-member ensemble output is usually large, multivariate, and challenging to interpret interactively. Forecast meteorologists are interested in understanding the uncertainties associated with numerical weather prediction; specifically variability between the ensemble members. Currently, visualization of ensemble members is mostly accomplished through spaghetti plots of a single mid-troposphere pressure surface height contour. In order to explore new uncertainty visualization methods, the Weather Research and Forecasting (WRF) model was used to create a 48-hour, 18 member parameterization ensemble of the 13 March 1993 "Superstorm". A tool was designed to interactively explore the ensemble uncertainty of three important weather variables: water-vapor mixing ratio, perturbation potential temperature, and perturbation pressure. Uncertainty was quantified using individual ensemble member standard deviation, inter-quartile range, and the width of the 95% confidence interval. Bootstrapping was employed to overcome the dependence on normality in the uncertainty metrics. A coordinated view of ribbon and glyph-based uncertainty visualization, spaghetti plots, iso-pressure colormaps, and data transect plots was provided to two meteorologists for expert evaluation. They found it useful in assessing uncertainty in the data, especially in finding outliers in the ensemble run and therefore avoiding the WRF parameterizations that lead to these outliers. Additionally, the meteorologists could identify spatial regions where the uncertainty was significantly high, allowing for identification of poorly simulated storm environments and physical interpretation of these model issues.

Abstract:A substantial decrease in snow cover extent (SCE) and snow depth over North America has been observed over the 1960-2000 period. One explanation for the changes in North American snow cover is a change in the frequency and/or intensity of snow ablation. This study uses a gridded dataset of United States and Canadian surface observations from 1960 to 2000 to examine patterns of snow ablation over North America. An ablation event is defined as an interdiurnal snow depth change exceeding a critical value. Results show a significant positive trend in the frequency of ablation events during March (p < 0.05) and a significant negative trend in May (p < 0.05), indicating an earlier onset of ablation. This pattern is consistent for ablation of varying intensity. Surface energy budget components and air mass frequencies are examined in relation to the observed trends in snow ablation. Changes in March ablation frequency were shown to be dominated by increases in the sensible heat flux. A higher frequency of dry moderate instead of moist polar air masses during high ablation years may explain the increase in sensible heat flux and ablation over the study period.

Microsatellite markers are quite popular due to their degree of polymorphism and efficiency; however, the utility of such markers for analysing allotetraploid species is often hampered by an inability to determine allele copy number for partial heterozygotes. tetrasat is a program that uses an iterative substitution process to account for all probable combinations of allele copy numbers in populations with partial heterozygote samples. The program subsequently calculates allele frequencies, and mean Hardy-Weinberg expected heterozygosity ( H E ), Shannon-Weiner Diversity Index ( H′ ′ ′ ′ ) and Nei's measure of population differentiation ( G ST ) are reported for each locus and population. Of equal importance is the calculation of statistical variability generated by the missing data and allele substitution process, which allows for assessment of the strength of conclusions drawn from the statistics.

This paper provides a meteorological overview of the 2015 mega‐heatwave (MHW) over Poland and compares the event with other MHWs in the region since World War II (up until 2015). A mega‐heatwave is defined as an event with at least 6 consecutive days with a maximum air temperature in excess of 30°C. These events are analysed here using observational meteorological data from ten major cities in Poland and neighbouring countries, along with 0.25° × 0.25° analysis fields from the Global Forecast System (GFS). Although the various events had different regional characteristics, in general they were caused by the inflow of a hot tropical air mass from the south, and were perpetuated by a high‐pressure system to the east of Poland and in some cases a low‐pressure system to the west. The 2015 MHW was the most extreme of the studied events, lasting from 9 days in Bialystok to 14 days in Warsaw and Poznan.