doi:10.3808/jei.200600079
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A Serially Complete U.S. Dataset of Temperature and Precipitation for Decision Support Systems

Z. Chen1, S. Goddard1*, K. G. Hubbard2, W. S. Sorensen2 and J. You2

  1. Department of Computer Science and Engineering, University of Nebraska-Lincoln Lincoln, NE 68588, USA
  2. High Plains Regional Climate Center 727 Hardin Hall, University of Nebraska-Lincoln Lincoln, NE 68583-0997, USA

*Corresponding author. Email: goddard@cse.unl.edu

Abstract


The effect of missing data can result in errors that exhibit temporal and spatial patterns in climatological and meteorological research applications. Many climate related tools perform best with a serially complete dataset (SCD). To support the National Agricultural Decision Support System (NADSS), a SCD with no missing data values for daily temperature and precipitation for the United States was developed using a self-calibrating data quality control (QC) library. The library performs two primary functions: identifies outliers and provides estimates to replace missing data values and outliers. This study presents the development of the SCD and the QC library in detail. An in-depth evaluation in terms of root mean square error (RMSE) and mean absolute error (MAE) for the SCD for the period of 1975-2004 is provided. The study shows an impressively low average RMSE in the range of 2.27 to 3.58°F for temperature and 0.07 to 0.23 inch for precipitation for the whole country for 30 years. The goal of this study is to enhance drought risk assessment and environmental risk analysis.

Keywords: Climate data, quality control, self-calibrate, serially complete dataset


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