Jase Bernhardt

Preparing Geographers for Interdisciplinary Research: Graduate Training at the Interface of the Natural and Social Sciences

Recent debates about the state of geography raise valuable questions about how the discipline can and should change in response to shifting institutional realities. Focusing on the breadth and interdisciplinarity of geography, these discussions often overlook the role of pedagogy—particularly graduate training—in adapting the discipline to new institutional landscapes. Drawing on experiences as current and recent geography doctoral students, we identify institutional seedlings of opportunity that can be cultivated toward a spectrum of alternative doctoral training models. These alternatives offer significant opportunities to better prepare early-career geographers for success and to solidify geographys position as a leader in interdisciplinary research.

A comparison of daily temperature-averaging methods: Spatial variability and recent change for the CONUS

AbstractTraditionally, the daily average air temperature at a weather station is computed by taking the mean of two values, the maximum temperature (Tmax) and the minimum temperature (Tmin) over a 24-hour period. These values form the basis for numerous studies of long-term climatologies (e.g., 30-year normals) and recent temperature trends and changes. However, many first-order weather stations-- such as those at airports-- also record hourly temperature data. Using an average of the 24 hourly temperature readings to compute daily average temperature has been shown to provide a more precise and representative estimate of a given day’s temperature. This study assesses the spatial variability of the differences in these two methods of daily temperature averaging (i.e., [Tmax + Tmin]/2, average of 24 hourly temperature values) for 215 first-order weather stations across the conterminous United States (CONUS) the 30-year period 1981-2010. A statistically significant difference is shown between the two method...

Comparing daily temperature averaging methods: the role of surface and atmosphere variables in determining spatial and seasonal variability

The two main methods for determining the average daily near-surface air temperature, twice-daily averaging (i.e., [Tmax+Tmin]/2) and hourly averaging (i.e., the average of 24 hourly temperature measurements), typically show differences associated with the asymmetry of the daily temperature curve. To quantify the relative influence of several land surface and atmosphere variables on the two temperature averaging methods, we correlate data for 215 weather stations across the Contiguous United States (CONUS) for the period 1981–2010 with the differences between the two temperature-averaging methods. The variables are land use-land cover (LULC) type, soil moisture, snow cover, cloud cover, atmospheric moisture (i.e., specific humidity, dew point temperature), and precipitation. Multiple linear regression models explain the spatial and monthly variations in the difference between the two temperature-averaging methods. We find statistically significant correlations between both the land surface and atmosphere variables studied with the difference between temperature-averaging methods, especially for the extreme (i.e., summer, winter) seasons (adjusted R2 > 0.50). Models considering stations with certain LULC types, particularly forest and developed land, have adjusted R2 values > 0.70, indicating that both surface and atmosphere variables control the daily temperature curve and its asymmetry. This study improves our understanding of the role of surface and near-surface conditions in modifying thermal climates of the CONUS for a wide range of environments, and their likely importance as anthropogenic forcings—notably LULC changes and greenhouse gas emissions—continues.

Determining Regional Weather Patterns from a Historical Diary

AbstractPrior to the twentieth century, there was a dearth of official local weather and climate observations for much of the United States outside of major cities. Useful information can be gleaned, however, from primary accounts, such as historical diaries kept by farmers and others whose interests were tied to the land. Herman Smith, a farmer in west-central New York State, kept a detailed record of daily life, including weather characteristics such as temperature, precipitation, and wind, for his farm near Covert. Two full years of his diary, 1884 and 1886, were recently published and selected for study. Although typically not numeric data, the lexicon used in the diary to describe relative heat and cold allow Smith’s observations to be analyzed semiquantitatively in order to determine the weather experienced that year including factors affecting the growing season, as well as significant weather and climatic events. The analysis demonstrates that for Covert—located in an area of topographic variabili...

Subregion-Scale Hindcasting of Contrail Outbreaks, Utilizing Their Synoptic Climatology*

AbstractContrail statistical prediction methods are often location specific. To take advantage of the fact that the upper-tropospheric (UT) meteorological conditions that favor “clear-sky outbreaks” of persisting contrails, or contrail favored areas (CFAs), tend to be synoptic in scale, a visual UT-map technique to hindcast CFAs has been developed and tested for subregions of the contiguous United States (CONUS) that have high outbreak frequencies in midseason months (January, April, July, and October) of 2000–02. The method compares daily maps with the composite fields for outbreak days (CON) versus nonoutbreak days (NON), and those assessments are evaluated using standard skill measures. Binary logistic regression determines which UT variables are significant predictors, individually and in combination. The reproducibility of the outbreak hindcast results is tested on the same subregions for the corresponding months of 2008–09. The results confirm the importance of UT relative humidity and vertical-moti...