A Quality-Control and Bias-Correction Method Developed for Irregularly Spaced Time Series of Observational Pressure Data

Abstract

his paper presents a method to detect and correct occurring biases in observational mean sea level pressure (MSLP) data, which was developed within the Mesoscale Alpine Climate Dataset [MESOCLIM; i.e., 3-hourly MSLP, potential and equivalent potential temperature Vienna Enhanced Resolution Analysis (VERA) analyses for a 3000 km ? 3000 km area centered over the Alps during 1971?2005] project. There are many reasons for a change of a measurement site?s performance, for example, a change in the instrumentation, a slight modification of the site?s place or position, or a different way of data processing (pressure reduction). To get an estimate for these artificial influences in the data, deviations for each reporting station at each point of time were calculated, using a piecewise functional fitting approach that is based on a variational algorithm. In this algorithm first- and second-order spatial derivatives are minimized using the tested stations neighbor stations and furthermore their neighbors. The resulting time series of deviations for each station were then tested with a ?standard normal homogeneity test? to detect changes in the mean deviation. With the knowledge of these ?break points,? bias-correction estimates for each station were calculated. These correction estimates are constant between the detected break points because the method does not detect different slopes in trends. Application of these correction estimates yields in smoother fields and a more homogenous distribution of trends.