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

Abstract

The 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.

Exploring strategies for coupled 4D-Var data assimilation using an idealised atmosphere–ocean model

“…When the assimilation of observations attempts to correct this positional error, instead of shifting the profiles, it erroneously changes the structure of the temperature and salinity profiles so that the error in the analysis is actually increased compared to the background. This is an issue for all three coupling strategies and is a well-documented problem in the atmosphere when assimilating observations of the analogous boundary layer capping inversion (Fowler et al, 2012).…”

Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales

“…This method could be used to displace features in 3D, but Nehrkorn et al (2014) and other studies have applied this to displace fields only horizontally. Fowler et al (2012) on the other hand used a grid distortion method to shift the BL inversion vertically. In this scheme, a new parameter, a, is analysed (together with the usual amplitude adjustments), where a specifies how the usual model level heights (z i ) should be shifted to new heights (z i ):z i = z i + aD(z i ).…”

Towards operational use of aircraft‐derived observations: a case study at London Heathrow airport

“…For operational meteorology, modern radiosondes sample the atmosphere every second during ascent (World Meteorological Organization, 2014), which can take up to 2 hr. Typically, radiosondes are launched from fixed sites that are widely separated (approximately 100 km) and report at fixed times (usually 0000 and 1200 UTC) and so do not provide sufficient horizontal spatial or temporal resolution to capture the onset or duration of a temperature inversion (Fowler, 2010).…”

“…An elevated inversion in effect caps vertical movement and dispersion of atmospheric aerosols. This may affect the forecast conditions for solar insolation and the formation or persistence of fog and cloud (Fowler et al, ). It is suggested that T MACH observations could provide an additional source of information, albeit a qualitative source, on the vertical temperature profile that may otherwise be unknown, since the 0600 Herstmonceux radiosonde report is made only on demand (unlike the reports at 0000 and 1200).…”

“…Numerical weather prediction (NWP) forecasting fog and low visibility conditions is difficult since these require an accurate representation of orography, surface and boundary-layer fluxes, and inversions in the vertical temperature profile (Stull, 2000;Jacobs et al, 2008). Operational forecasting of temperature inversions depends on the availability of suitable observations (Roach et al, 1976;Jacobs et al, 2005;Fowler et al, 2011) to locate the inversion. For example, high-frequency reporting of vertical profiles of temperature and wind may provide extra information for use in NWP assimilation and nowcasting (Dance, 2004;Rennie et al, 2011;Simonin et al, 2014;Sun et al, 2014;Ballard et al, 2015;James and Benjamin, 2017).…”