A Comparison of Spatial Interpolation Techniques in Temperature Estimation
First Author: Dr. Fred C. Collins Jr.
Affiliation: IBM Government Systems - Resource Management and Distributed Systems
Address: 6300 Diagonal Highway, 003F, Boulder Colorado 80301
Topic Area: GIS and Spatial Data Analysis
Authors: Fred C. Collins, Ph.D (IBM) and Paul V. Bolstad, Ph.D. (Univ. of Minnesota)
1.0 Introduction:
The increased awareness of government and industry to the potential benefits of geographic information systems (GIS) has been driven by an increase in the availability of digital spatial data and increased hardware and software capability. Along with thi s increased awareness, has been increased concern over the accuracy and precision of spatial data. GIS error begins with data collection and continues through data input, storage, manipulation, output, and interpretation of the results. Understanding th e source, nature, and extent of errors in GIS is the first step in a strategy for reducing error in GIS. This research is concerned with the spatial interpolation of meterological data as a preliminary step prior to use in landscape, regional, and global models or as layers in a GIS.
Spatially distributed estimates of meteorological data are becoming increasingly important as inputs to spatially explicit landscape, regional, and global models. Estimates of meteorological values such as temperature, precipitation, and evapotranspirati on rate are required for a number of landscape scale models, including those of regeneration, growth, and mortality of forest ecosystems. To calculate daily microclimate conditions in mountainous terrain, the model MT-CLIM requires minimum and maximum da ily temperature data as inputs (Running and Nemani, 1987). To compute forest evapotranspiration, landscape scale ecological models such as FOREST-BGC use spatially explicit meteorological inputs from models such as MT-CLIM (Band et. al., 1991). Accurate estimates of temperature are critical to the performance of the above models. In addition to those involved in temperature modeling, temperature prediction at unsampled sites is of interest t o individuals involved in fire management, resource management, and spraying or seeding operations.
Accurate measurements of temperature are also of interests to scientists studying the 'greenhouse effect' - global warming via the entrapment of longwave radiation due to certain gases such as carbon dioxide. While there is disagreement on the extent of global warming, most scientists estimate its effects between 0.50F to 1.00F (Handcock and Wallis, 1994). Clearly, even a small bias resulting from the interpolation method used would affect concl usions reached by scientists studying the greenhouse phenomena.
Accurate temperature estimates are critical in the calibration of satellite sensors. Satellite surface temperature retrieval in mountainous terrain is complicated by the high variability of occurring temperatures and complex terrain features. While sate llite surface temperature retrieval appears to be a promising technology, surface variations have been shown to bias temperature measurements upwards of 3.00C (Lipton, 1992). As bias is systematic, satellite deri ved temperature estimates calibrated with accurate ground truth, may offer cost effective temperature estimates where data are sparse.
Given a set of meteorological data, researchers are confronted with a variety of stochastic and deterministic interpolation methods to estimate meteorological variables at unsampled locations. Spatial interpolation is often an important first step in tak ing irregular point data and converting it for use in a GIS. Depending on the spatial attributes of the data, accuracies vary widely among different spatial interpolation methods (MacEachren and Davidson, 1987; and Rhind, 1975). The choice of spatial interpolator is especially important in mountainous regions where data collection are sparse and variables may change over short spatial scales.
While there have been comparisons of interpolation methods, few research efforts have been directed towards comparing the effectiveness of different spatial interpolators in predicting temperature (eg. Van Kuilenburg et. al. (1982); Dubrule (1983); Puente and Bras (1986); 空间插值技术在温度估计中的比较
联系:IBM Government Systems - Resource Management and Distributed Systems
主要研究范围: GIS and Spatial Data Analysis
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