Abstract

Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Initial correlation analyses revealed that data from 700 mb were sufficient to represent the upper-air or background climate. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%–95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. The R2's for the GHCN models ranged between 0,86 and 0.95, whereas the R2's for the COOP models ranged between 0.64 and 0.92. In addition, root-mean-square errors (rmse's) were over 3°C for GHCN models and over 2°C for COOP models for winter months, and near 2°C for GHCN models and near 1.5°C for COOP models for summer months. The results of this study—a large amount of explained variance and a relatively small rmse—indicate the usefulness of these models for predicting surface temperatures. Urban landscape data are incorporated into these models in Part II of this study to estimate the urban bias of surface ternperatures.

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