THE QUALITY-CONTROL OF LONG-TERM CLIMATOLOGICAL DATA USING OBJECTIVE DATA-ANALYSIS

One of the major concerns with detecting global climate change is the quality of the data. Climate data are extremely sensitive to errant va lues and outliers. Prior to analysis of these time series, it is impor tant to remove outliers in a methodical manner. This study provides st atistically derived bounds for the uncertainty associated with surface temperature and precipitation measurements and yields a baseline data set for validation of climate models as well as for a variety of other climatological uses. A two-step procedure using objective analysis wa s used to identify outliers. The first step was a temporal check that determines if a particular monthly value is consistent with other mont hly values for the same station. The second step utilizes six differen t spatial interpolation techniques to estimate each monthly time serie s. Each of the methods is ranked according to its respective correlati on coefficients with the actual time series, and the technique with th e highest correlation coefficient is chosen as the best estimator. For both temperature and precipitation, a multiple regression scheme was found to be the best estimator for the majority of records. Results fr om the two steps are merged, and a combined set of quality control fla gs are generated.