Abstract
Data quality control is a necessary component of any weather station network used for estimating reference evapotranspiration (ETo). The absence of a quality control program can result in poor quality ETo data that severely limits it usefulness for irrigation scheduling. Statistical quality control criteria are developed for assessing quality and reasonableness of hourly and daily weather data for the California Irrigation Management Information System (CIMIS) weather stations.
The quality control criteria, based on means (m) and standard deviations (s), are developed from historical CIMIS weather station data. Two statistical quality control limits, 3s and 2s upper control limit and lower control limit, are developed. The two control limits are integrated into existing data screening rules forming new CIMIS data quality control criteria. A new version of a control chart, time variant control chart is introduced. Statistical control charts have been widely used in the manufacturing industry for process mean or variability monitoring and quality control. Control limits developed herein are similar to those used in the manufacture of products. Unlike in manufacturing where one seeks to attain a state of statistical control, these control limits are used to identify data that fall outside the control limits. Such data are then flagged with a quality control flag.
INTRODUCTION
Recent improvements in automated weather station sensors and reference evapotranspiration (ETo) estimation techniques has made real time or near real time (ETo) readily available, which allows farmers to adopt ETo based water budget irrigation scheduling technique. The usefulness of ETo data, however, is dependent upon the quality of data used to estimate the ETo-solar radiation, air temperature, wind speed, and vapor pressure. Statistical quality control lends itself as a convenient means to screen some of these data.
Shewhart is credited for being the first to apply statistical methods to quality control. In......
