Abstract
Results of temperature measurements, which may be applied to inference of winter temperatures in data-sparse areas, are presented. The morning air temperatures during three winters were measured at 80 places in a 10 km × 30 km area along the Connecticut River. NOAA climatologies show this region to have complex spatial variation in mean minimum temperature. Frequency analysis techniques were applied to evaluate the differences in daily local temperature.
Temperature lapse or temperature inversion in the study area was inferred from the difference of surface temperature measurements 100 and 300 m above river level. The frequency of inferred temperature lapse and the inferred lapse rate diminished as snow cover increased. The frequency of inferred temperature inversion and inversion strength increased as snow cover increased. When more than 20 cm of snow covered the ground, an additional surface inversion was frequent in the layer less than 100 m above river level, and two-thirds of river level temperatures less than −20°C occurred concurrent with these conditions.
The daily temperature differences at the individual points, with respect to a defined point, were lognormally distributed. The magnitude and geometric standard deviation of temperature differences throughout the study area were larger on mornings when inversion was inferred. With respect to topography, temperature differences and the geometric standard deviation of temperature differences were smaller along flats or among basins than along or atop slopes on mornings when inversion was inferred.
It is proposed that some meteorologically prudent inferences of surface temperature and near-surface temperature lapse or temperature inversion can be made for similar data-sparse areas.
Corresponding author address: Dr. Austin W. Hogan, USA CRREL,72 Lyme Road, Hanover, NH 03755-1290.
