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

Sperka, Stefan; Steinacker, Reinhold

doi:10.1175/jtech-d-10-05046.1

Cited by 3 publications

(2 citation statements)

References 9 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Investigating the temporal variations in the deviations helped Sperka and Steinacker (2011) to develop a method for creating a homogenized 3-hourly MSL-pressure analysis data set. After determining breaks in the deviations time series, the measurements within these breaks are corrected by the bias calculated from the deviations.…”

Section: Usage Of Deviations Computed By Vera-qcmentioning

confidence: 99%

Innovations and applications of the VERA quality control

Mayer

Steiner

Steinacker

2012

Geosci. Instrum. Method. Data Syst.

View full text Add to dashboard Cite

Abstract. Quality control (QC) is seen today as an important scientific field to increase the value of observational data. Whereas most QC methods are linked to atmospheric modeling (being part of the data assimilation procedure), in this paper the focus is on the application of a model independent QC method based on data self consistency recently published: VERA-QC. A special challenge is the QC of data in complex terrain which requires special treatment in terms of data selection and data transformation. In this context, some special VERA-QC modules such as the consideration of significant elevation differences of adjacent stations or the consideration of transformed temperature values will be discussed. The system detects gross errors as well as biases and offers objective correction proposals (deviations) for each observation. The essential gross error detection is not only based on the statistical behavior of station specific deviations, but also on the rate of cost function reduction. Beside a two dimensional application, higher dimensionalities may also be chosen, for instance including the time coordinate. Applications and results are discussed for pressure, temperature as well as for precipitation data which needs, however, a very dense observation network. Real time application of VERA-QC allows the production of high quality fields of meteorological parameters, which can be used, e.g. for nowcasting as well as for model unbiased validation of prognostic models.

show abstract

Section: Usage Of Deviations Computed By Vera-qcmentioning

confidence: 99%

Innovations and applications of the VERA quality control

Mayer

Steiner

Steinacker

2012

Geosci. Instrum. Method. Data Syst.

View full text Add to dashboard Cite

show abstract

“…According to the methodology of Steinacker et al (2000), Sperka and Steinacker (2011) and , it must be assumed that each observational value Ψ obs is contaminated by errors Ψ err so that the true state Ψ true is not known.…”

mentioning

confidence: 99%

Treatment of deterministic perturbations and stochastic processes within a quality control scheme

Eibl

Steinacker

2017

Preprint

Self Cite

View full text Add to dashboard Cite

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 Ψ obs is contaminated by errors Ψ err so that the true state Ψ true 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 5 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 10 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.

show abstract

Innovations and applications of the VERA quality control

Mayer¹,

Steinacker²,

Steiner³

2012

Preprint

View full text Add to dashboard Cite

Quality Control (QC) is seen today as an important scientific field to increase the value of observational data. Whereas most QC methods are linked to atmospheric modelling (being part of the data assimilation procedure), in this paper the focus is on the application of a model independent QC method based on data self consistency recently published: VERA-QC. A special challenge is the QC of data in complex terrain which requires special treatment in terms of data selection and data transformation. In this context, some special VERA-QC modules such as the consideration of significant elevation differences of adjacent stations or the consideration of transformed temperature values will be discussed. The system detects gross errors as well as biases and offers objective correction proposals (deviations) for each observation. The essential gross error detection is not only based on the statistical behaviour of station specific deviations, but also on the rate of cost function reduction. Beside a two dimensional application, higher dimensionalities may also be chosen, for instance including the time coordinate. Applications and results are discussed for pressure, temperature and wind as well as for precipitation data which needs, however, a very dense observation network. Real time application of VERA-QC allows the production of high quality fields of meteorological parameters which can be used e.g. for nowcasting as well as for model unbiased validation of prognostic models

show abstract

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

Cited by 3 publications

References 9 publications

Innovations and applications of the VERA quality control

Innovations and applications of the VERA quality control

Treatment of deterministic perturbations and stochastic processes within a quality control scheme

Innovations and applications of the VERA quality control

Contact Info

Product

Resources

About