Predictors and grouping for bias correction of radiosonde temperature observations. Issue 20 (24th October 2015)

• Record Type: Journal Article
• Authors: Milan, M.; Haimberger, L.
• Is Part Of: Journal of geophysical research. Volume 120:Issue 20(2015:Nov.)
• Page Start: 10, 736
• Page End: 10, 766
• Publication Date: 2015-10-24
• Subjects: radiosonde temperature profiles -- bias -- Variational bias correction -- clusterization; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2015JD023635
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Due to various causes, artificial biases can be found in meteorological observations. The use of biased observations in the assimilation process leads to systematic errors in the resulting analysis unless special measures are taken during the data assimilation. One such method that avoids the assumption of unbiased input observations is the variational bias correction (VarBC), which is used successfully within the (European Centre for Medium‐range Weather Forecasts) operational system, mainly to deal with satellite radiance data. In VarBC the bias of the given observation is estimated using a linear predictor model based on a small number of predefined predictors and the corresponding unknown bias parameters. These are estimated together with the model state by including a bias term in the cost function of the variational analysis. The radiosonde temperature biases depend mainly on pressure, on solar elevation, and on the instrumentation used. The optimal choice of the grouping of radiosonde stations (to get larger samples) and of the bias models is not obvious. While the method should be used in a 4D‐VAR setting, its properties can be estimated off‐line with much less computational effort. In this paper different methods for the grouping and the bias model are investigated, both using and not using metadata. At the same time the statistics are compared with the output of two independent homogeneity adjustment algorithms. The major outcome of this work is, apart from the development of predictors model suitable for VarBC, the detection of the high variability in the bias using grouping based on metadata. Key Points: Radiosonde temperature profiles bias adjustments Predictors and groups for variational bias correction Groups based on clusterization