Spatial Standard Deviations Research Articles

Influence of spatial sampling and interpolation on estimates of air temperature change

When observed air temperatures are analyzed spatially, irregularly sampled data are usually interpolated in some fashion. As a result, methods of spatial analysis clearly play a role in determining the size and variability of estimated air temperature changes, both spatially and temporally. Through graphical and statistical analysis, 3 spherically based interpolation methods inversedistance weighting, triangulated surface patches, and thin-plate splines are evaluated and compared using air temperature anomaly data. Analysis of errors resulting from spatial interpolation provides information about the strengths and weaknesses of historical station networks. Analysis of differences between the 3 interpolation methods suggests that similar spatial patterns are produced, but with some regional disparities. Mean absolute differences between interpolation methods can be over 0.4 C for some sparse statlon networks and as low as O.l°C for dense station networks. When averaged spatially, however, the differences tend to offset one another, producing time series of terrestrial average air temperature anomalies that are largely independent of spatial interpolation method. Using cross validation to analyze spatial interpolation errors suggests that sparse station networks can produce nontrivial interpolation errors. Station networks from the late 1800s produce average interpolation errors of nearly 0.5C, errors that are similar in magnitude to spatial standard deviations of air temperature anomalies. Denser station networks, typical of the 1950s and 1960s, produce average interpolation errors as low as 0.2OC. While these regional interpolation errors do not appear to influence estimates of terrestrial average air temperature, they do raise additional concerns regarding our ability to detect small climatic signals at regional scales.

The Relation of Millimeter-Wavelength Backscatter to Surface Snow Properties

Although model results indicate that microwave backscatter and emission from snowcover should depend on dry-snow grain size and wet-snow surface roughness, there has been little or no experimental verification. In this paper, helicopter-mounted radar measure-ade ments of 94-GHz backscatter from snowcover, and ground-truth measurements of snow surface roughness, wetness, grain size, and porosity, are analyzed. For each of six polarization combinations, and separately for dry snow and wet snow, spatial mean values of the backscatter coefficient σ° are fit to linear combinations of the cosine of incidence angle and the snow variables, the latter in the order in which the explained variance in is most increased, provided the increase is significant. However, the significance of an included snow variable is considered questionable if the predicted response of to that variable is small compared with the spatial standard deviations of or (typically 4-5 dB). This is the case for dry-snow grain size, porosity, and for some polarization combinations, wetness. Only the response to wet-snow surface roughness is consistently comparable in magnitude to the standard deviations of σ°. Dry-snow surface roughness, wet-snow grain size, and for most polarization combinations, porosity, made no significant improvement to the explained variances. An examination of residuals shows that the linear model is adequate for the response to cosine of incidence angle and snow surface roughness.

New spatial sampling methodology for community noise

Prior to the performance of noise measurements, expected sound levels of a community or city are first predicted. These expected sound levels are utilized to develop sampling zones. Specific measurement locations are then randomly selected within each zone. The advantage of the methodology is that the noise zones are more homogeneous than those which would be associated with other survey methodologies (such as the 1-mile-square grid method). The results provide a higher resolution for noise maps than those associated with a square grid survey, and achieve a lower standard deviation associated with spatial acoustical variations. The lower standard-deviation results in considerably fewer sampling locations being required to achieve a predetermined survey accuracy. This, of course, results in a significant cost savings where surveys of large cities are considered. The methodology has been applied in surveys of the cities of Chattanooga, Tennessee and Torrance, California. Spatial standard deviations as low as 2.1 dB were achieved. A review of the results of 27 other major noise surveys indicates that standard deviations typically ranged from 3.5 to 10 dB. [Work sponsored by The City of Chattanooga, U. S. Environmental Protection Agency, and Wyle Laboratories.]

北半球における500mb等圧面高度および気温の標準偏差と気温との関係

北半球における500mb等圧面高度および気温の標準偏差と気温との関係