**Author details**

tem.The Climate Reference Network (CRN, Baker et al. 2004) is another example of the QC of high frequency data, which installs multiple sensors for each variable to guarantee the continuous operation of the weather station and thus the quality control can also rely on the multiple measurements of a single variable. This method is efficient to detect the instrumen‐ tal failures or other disturbances; however the cost of such a network may be prohibitive for non-research or operational networks. The authors of this chapter also carried out QC on a high temporal resolution dataset in the Beaufort and Chukchi Sea regions. Surface meteoro‐ logical data from more than 200 stations in a variety of observing networks and various stand-alone projects were obtained for the MMS Beaufort and Chukchi Seas Modeling Study (Phase II). Many stations have a relatively short period of record (i.e. less than 10 years).The traditional basic QC procedures were developed and tested for a daily data and found in need of improvement for the high temporal resolution data. In the modification, the time series of the maximum and the minimum were calculated from the high resolution data. The mean and standard deviation of the maximum and the minimum can then be calculated from the time series (e.g max and min temperatures) as the (ux, sx) and (un, sn), respectively. The equation (6) using (ux + f sx) and (un - f sn) forms limits defined by the upper limits of the maximum and lower limits of the minimum. The value falling outside the limits will be flag‐ ged as an outlier for further manual checking. Similarly, the diurnal change of a variable (e.g. temperature) was calculated from the high resolution (hourly or sub-hourly) data. The mean and standard deviation calculated from the diurnal changes will form the limits.

The traditional quality control methods were improved for examining the high temporal resolution data, to avoid intensive manual reviewing which is not timely or cost efficient. The identified problems in the dataset demonstrate that the improved methods did find con‐ siderable errors in the raw data including the time errors (e.g. month being great than 12). These newtools offer a dataset that, after manual checking of the flagged data, can be givin a statement of confidence. The level of confidence can be selected by the user, prior to QC.

The applied in-station limit tests can successfully identify outliers in the dataset. Howev‐ er, spatial tests based information from the neighboring stations is more robust in many cases and identifies errors or outliers in the dataset when strong correlation exists. The good relationship between the measurements at station pairs demonstrates that there is a potential opportunity to successfully apply the spatial regression test (SRT, 18) to the sta‐ tions which measure the same variables (i.e. air temperature orwind speed). The short term measurements at some stations may not be efficiently QC'ed with only the three methods described in this work. One example is the dew point measurements at the firstorder station Iultin-in-Chukot. More than 90 percent of the dew point measurements were flagged, because the parameters for QC'ing the variable used the state wide parameters

Quality control (QC) methods can never provide total proof that a data point is good or bad. Type I errors (false positives) or Type II errors (false negatives) can occur and result in labeling

which cannot reflect the microclimate of each station.

**10. Summary and Conclusions**

26 Practical Concepts of Quality Control

Kenneth Hubbard\* , Jinsheng You and Martha Shulski

\*Address all correspondence to: khubbard1@unl.edu

High Plains Regional Climate Center, University of Nebraska, Lincoln, NE, USA

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**Chapter 2**

**Applications of Control Charts Arima for**

The traditional methodology of Statistical Quality Control (SEQ) is based on a fundamental supposition that the process of the data is independent statisticaly, however, the data not al‐ ways are independent. When a process follows an adaptable model, or when the process is a

Drawing the process of data is extremely valuable, however, under such circumstances, there isn't any scientific reason to use the traditional techniques of statistical control of quality, be‐ cause it will induce erroneous conclusions and facilitate a safety absence that the process is under statistical control with flaw in the identification of systematic variation of the process. Thus, the theme here proposed is to investigate the acting and the adaptation of the tradi‐ tional use of the statistical control of process methods in no-stationary processes, and to dis‐

History of Quality Control is as old as the history of the industry itself. Before the Industrial Revolution, the quality was controlled by the vast experience of the artisans of the time, which guarantee product quality. The industrial system has suffered a new technical era, where the production process split complex operations into simple tasks that could be per‐ formed by workers with specific skills. Thus, the worker is no longer responsible for all

It is within this context that the inspection, which sought to separate the non-conforming items from the establishment of specifications and tolerances. A simple inspection did not

> © 2012 Russo 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,

© 2012 Russo 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.

distribution, and reproduction in any medium, provided the original work is properly cited.

cuss the use of time series methodologies to work with correlated observations.

product manufacturing, leaving the responsibility of only a part of it (Juran, 1993).

Suzana Leitão Russo, Maria Emilia Camargo and

Additional information is available at the end of the chapter

deterministic function, the data will be autocorrelated.

**Autocorrelated Data**

Jonas Pedro Fabris

**1. Introduction**

**2. Theorical Review**

http://dx.doi.org/10.5772/50990
