Received: 20 Jul 2017 – Accepted for review: 28 Nov 2017 – Discussion started: 21 Dec 2017
Abstract. Meteorological in situ observational data comes with a variety of errors and uncertainties. Any further usage of this data requires a sophisticated quality control to detect, quantify and possibly eliminate or at least to reduce errors and to increase the value of the information. It must be assumed, that each observational value Ψobs is contaminated by errors Ψerr so that the true state Ψtrue is not known. Different kinds of errors can be identified. Each of them has different characteristics and therefore has to be detected through appropriate methods. For years, various methods as a self consistency test, clustering and nearest neighbour techniques have been implemented in the complex quality control scheme of the Vienna Enhanced Resolution Analysis (VERA). Thereby former elaborations adressed the elimination and treatment of gross errors. In successioon the present investigation adresses the determination of stochastic and deterministic perturbations. In a first step we implemented the method to split up the observational value to smooth out the stochastic errors to the best and retain deterministic perturbations thereafter. Through controlled experiments on two dimensions the performance and limitations of the complex quality control scheme has been investigated. The treatment of errors and signals on different scales and the limit of the usability of this property is the main focus of the presented investigation. We highly recommend to use the method for data quality control within a high resolution model analysing spatially distributed data in highly complex terrain.
How to cite. Eibl, B. and Steinacker, R.: Treatment of deterministic perturbations and stochastic processes within a quality control scheme, Geosci. Instrum. Method. Data Syst. Discuss. [preprint], https://doi.org/10.5194/gi-2017-42, in review, 2017.
Meteorological observational data obtained in highly complex terrain has to be treated differently than it is usually done within state of the art quality control procedures. The main motivation and goal is to preserve as much information as possible in order to enhance the accuracy of analysis and forecast model output. It is recommended to use the method in highly complex terrain. Sufficient data density and coherent structures within data is required.
Meteorological observational data obtained in highly complex terrain has to be treated...