A quality control and bias correction method developed for irregularly spaced time-series of observational pressure- and temperature-data

摘要

Within the creation of the MESOCLIM-dataset (3 hourly MSLP, potential- and equivalent potential temperature VERA-analyses for a 3000 times 3000 km 2 area centered over the Alps from 1971-2005) a method to detect and correct occurring biases in the observational raw data had to be developed. The method is based on an automated variational algorithm that minimizes second spatial derivates in meteorological fields.