A new floating model level scheme for the assimilation of boundary‐layer top inversions: the univariate assimilation of temperature

Abstract

AbstractThe assimilation of observations with a forecast is often heavily influenced by the description of the error covariances associated with the forecast. When a temperature inversion is present at the top of the boundary layer (BL), a significant part of the forecast error may be described as a vertical positional error (as opposed to amplitude error normally dealt with in data assimilation). In these cases, failing to account for positional error explicitly is shown to result in an analysis for which the inversion structure is erroneously weakened and degraded.In this article, a new assimilation scheme is proposed to explicitly include the positional error associated with an inversion. This is done through the introduction of an extra control variable to allow position errors in the a priori to be treated simultaneously with the usual amplitude errors. This new scheme, referred to as the ‘floating BL scheme’, is applied to the one‐dimensional (vertical) variational assimilation of temperature. The floating BL scheme is tested with a series of idealised experiments and with real data from radiosondes.For each idealised experiment, the floating BL scheme gives an analysis which has the inversion structure and position in agreement with the truth, and outperforms the assimilation which accounts only for forecast amplitude error. When the floating BL scheme is used to assimilate a large sample of radiosonde data, its ability to give an analysis with an inversion height in better agreement with that observed is confirmed. However, it is found that the use of Gaussian statistics is an inappropriate description of the error statistics of the extra control variable. This problem is alleviated by incorporating a non‐Gaussian description of the new control variable in the new scheme. Anticipated challenges in implementing the scheme operationally are discussed towards the end of the article. Copyright © 2011 Royal Meteorological Society and British Crown Copyright, the Met Office