Alison M. Fowler

An Evaluation of Boundary-Layer Depth, Inversion and Entrainment Parameters by Large-Eddy Simulation

Studies of entrainment across the top of the boundary layer rely to a great extent on identification of the boundary-layer top, inversion properties, entrainment-zone depth, and the temporal changes in all of these. A variety of definitions and techniques have been used to provide automated and objective estimates; however, direct comparisons between studies is made difficult by the lack of consistency in techniques. Here we compare boundary-layer depth, entrainment-zone thickness, and entrainment rate derived from several commonly used techniques applied to a common set of large-eddy simulations of the idealized, dry, convective boundary layer. We focus in particular on those techniques applicable to lidar backscatter measurements of boundary-layer structure. We find significant differences in all the quantities of interest, and further that the behaviour as functions of common scaling parameters, such as convective Richardson number, also differ, sometimes dramatically. The discretization of the possible values of some quantities imposed by the vertical grid is found to affect some of the results even when changes to model resolution does not affect the entrainment rate or scaling behaviour. This is a particular problem where entrainment parameters are derived from a single mean profile (e.g. the buoyancy-flux profile), but not where they are derived from the statistical properties of large numbers of individual profiles (e.g. the probability distribution of the local boundary-layer top at each model grid point).

Observation impact in data assimilation: the effect of non-Gaussian observation error

Data assimilation methods which avoid the assumption of Gaussian error statistics are being developed for geoscience applications. We investigate how the relaxation of the Gaussian assumption affects the impact observations have within the assimilation process. The effect of non-Gaussian observation error (described by the likelihood) is compared to previously published work studying the effect of a non-Gaussian prior. The observation impact is measured in three ways: the sensitivity of the analysis to the observations, the mutual information, and the relative entropy. These three measures have all been studied in the case of Gaussian data assimilation and, in this case, have a known analytical form. It is shown that the analysis sensitivity can also be derived analytically when at least one of the prior or likelihood is Gaussian. This derivation shows an interesting asymmetry in the relationship between analysis sensitivity and analysis error covariance when the two different sources of non-Gaussian structure are considered (likelihood vs. prior). This is illustrated for a simple scalar case and used to infer the effect of the non-Gaussian structure on mutual information and relative entropy, which are more natural choices of metric in non-Gaussian data assimilation. It is concluded that approximating non-Gaussian error distributions as Gaussian can give significantly erroneous estimates of observation impact. The degree of the error depends not only on the nature of the non-Gaussian structure, but also on the metric used to measure the observation impact and the source of the non-Gaussian structure.

An Idealized Study of Coupled Atmosphere–Ocean 4D-Var in the Presence of Model Error

AbstractAtmosphere-only and ocean-only variational data assimilation (DA) schemes are able to use window lengths that are optimal for the error growth rate, nonlinearity, and observation density of the respective systems. Typical window lengths are 6–12 h for the atmosphere and 2–10 days for the ocean. However, in the implementation of coupled DA schemes it has been necessary to match the window length of the ocean to that of the atmosphere, which may potentially sacrifice the accuracy of the ocean analysis in order to provide a more balanced coupled state. This paper investigates how extending the window length in the presence of model error affects both the analysis of the coupled state and the initialized forecast when using coupled DA with differing degrees of coupling.Results are illustrated using an idealized single-column model of the coupled atmosphere–ocean system. It is found that the analysis error from an uncoupled DA scheme can be smaller than that from a coupled analysis at the initial time,...

A Sampling Method for Quantifying the Information Content of IASI Channels

AbstractThere is a vast amount of information about the atmosphere available from instruments on board satellites. One example is the Infrared Atmospheric Sounding Interferometer (IASI) instrument, which measures radiances emitted from Earth’s atmosphere and surface in 8461 channels. It is difficult to transmit, store, and assimilate such a large amount of data. A practical solution to this has been to select a subset of a few hundred channels based on those that contain the most useful information.Different measures of information content for objective channel selection have been suggested for application to variational data assimilation. These include mutual information and the degrees of freedom for signal. To date, the calculation of these measures of information content has been based on the linear theory that is at the heart of operational variational data assimilation. However, the retrieval of information about the atmosphere from the satellite radiances can be highly nonlinear.Here, a sampling me...

Characterising the background errors for the boundary-layer capping inversion

The one-dimensional variational assimilation of vertical temperature information in the presence of a boundary-layer capping inversion is studied. For an optimal analysis of the vertical temperature profile, an accurate representation of the back - ground error covariances is essential. The background error covariances are highly flow-dependent due to the variability in the presence, structure and height of the boundary-layer capping inversion. Flow-dependent estimates of the background error covariances are shown by studying the spread in an ensemble of forecasts. A forecast of the temperature profile (used as a background state) may have a significant error in the position of the capping inversion with respect to observa- tions. It is shown that the assimilation of observations may weaken the inversion structure in the analysis if only magnitude errors are accounted for as is the case for traditional data assimilation methods used for operational weather prediction. The positional error is treated explicitly here in a new data assimilation scheme to reduce positional error, in addition to the traditional framework to reduce magni- tude error. The distribution of the positional error of the background inversion is estimated for use with the new scheme.