**Statistical Quality Control**

**Chapter 1**

**Toward a Better Quality Control of Weather Data**

Previous studies have documented various QC tools for use with weather data (26; 4; 6; 25; 9; 3; 10; 16; 18). As a result, there has been good progress in the automated QC ofweather indices, especially the daily maximum/ minimum air temperature. The QC of precipitation is more dif‐ ficult than for temperature; this is due to the fact that the spatial and temporal variability of a variable (2) is related to the confidence in identifying outliers. Another approach to maintain‐ ing quality of data is to conduct intercomparisons of redundant measurements taken at a site. For example, the designers of the United States Climate Reference Network (USCRN) made it possible to compare between redundant measurements by specifying a rain gauge with multi‐ ple vibrating wires in order to avoid a single point of failure in the measurement process. In this case the three vibrating wires can be compared to determine whether or not the outputs are comparable and any outlying values can result in a site visit. CRN also includes three tempera‐

Generally identifying outliers involves tests designed to work on data from a single site (9) or tests designed to compare a station's data against the data from neighboring stations (16). Stat‐ istical decisions play a large role in quality control efforts but, increasingly there are rules intro‐ duced which depend upon the physical system involved. Examples of these are the testing of hourly solar radiation against the clear sky envelope (Allen, 1996; Geiger, et al., 2002) and the use of soil heat diffusion theory to determine soil temperature validity (Hu, et al., 2002). It is now realized that quality assurance (QA) is best suited when made a seamless process be‐ tween staff operating the quality control software at a centralized location where data is ingest‐

Quality assurance software consists of procedures or rules against which data are tested. Each procedure will either accept the data as being true or reject the data and label it as an

> © 2012 Hubbard et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Hubbard et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ed and technicians responsible for maintenance of sensors in the field (16; 10).

Kenneth Hubbard, Jinsheng You and

Additional information is available at the end of the chapter

ture sensors at each site for the purpose of comparison.

Martha Shulski

**1. Introduction**

http://dx.doi.org/10.5772/51632
