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This work tackles the problem of the automated detection of the atmospheric boundary layer (BL) height h, from aerosol lidar/ceilometer observations. A new method, the Bayesian selective method (BSM), is presented. It implements a Bayesian statistical inference procedure which combines in a statistically optimal way different sources of information. Firstly, atmospheric stratification boundaries are located from discontinuities in the ceilometer backscattered signal. The BSM then identifies the discontinuity edge that has the highest probability to effectively mark the BL height. Information from the contemporaneous physical boundary layer model simulations and a climatological dataset of BL height evolution are combined in the assimilation framework to assist this choice.The BSM algorithm has been tested for 4 months of continuous ceilometer measurements collected during the BASE:ALFA project, and is shown to realistically diagnose the BL depth evolution in many different weather conditions. A standard one-dimensional processing of the ceilometer signal without the a priori support of the dynamical and climatological BL models often fails to correctly detect h, with the greatest inaccuracies occurring at night-time when residual layers can generate very strong signals, which are then classified by an automated application of the gradient or of the wavelet analysis as the most probable BL height. The BSM approach instead carries information on the low climatological probability to find elevated BL depths at night and penalizes the selection of these points. Moreover, this method is able to correctly convey information along the temporal dimension, thus filling data gaps using earlier and subsequent ceilometer information for the retrieval.

Three diverse methods of initializing soil moisture and temperature in limited-area numerical weather prediction models are compared and assessed through the use of nonstandard surface observations to identify the approach that best combines ease of implementation, improvement in forecast skill, and realistic estimations of soil parameters. The first method initializes the limited-area model soil prognostic variables by a simple interpolation from a parent global model that is used to provide the lateral boundary conditions for the forecasts, thus ensuring that the limited-area model's soil field cannot evolve far from the host model. The second method uses the soil properties generated by a previous limited-area model forecast, allowing the soil moisture to evolve over time to a new equilibrium consistent with the regional model's hydrological cycle. The third method implements a new local soil moisture variational analysis system that uses screen-level temperature to adjust the soil water content, allowing the use of high-resolution station data that may be available to a regional meteorological service.The methods are tested in a suite of short-term weather forecasts performed with the Consortium for Small Scale Modeling (COSMO) model over the period September-November 2008, using the ECMWF Integrated Forecast System (IFS) model to provide the lateral boundary conditions. Extensive comparisons to observations show that substantial improvements in forecast skills are achievable with improved soil temperature initialization while a smaller additional benefit in the prediction of surface fluxes is possible with the soil moisture analysis. The analysis suggests that keeping the model prognostic variables close to equilibrium with the soil state, especially for temperature, is more relevant than correcting the soil moisture initial values. In particular, if a local soil analysis system is not available, it seems preferable to adopt an ''open loop'' strategy rather than the interpolation from the host global model analysis. This appears to be especially true for the COSMO model in its current operational configuration since the soil-vegetation-atmosphere transfer (SVAT) scheme of the ECMWF global host model and that of COSMO are radically diverse.

The energy budget at the surface is strongly influenced by the presence of vegetation, which alters the partitioning of thermal energy between sensible and latent heat fluxes. Despite its relevance, numerical weather prediction (NWP) systems often use only two parameters to describe the vegetation cover: the fractional area of vegetation occupying a given pixel and the leaf area index (LAI). In this study, the Consortium for Small-Scale Modelling (COSMO) limited-area forecast model is used to investigate the sensitivity of regional predictions to LAI assumptions over the Italian peninsula. Three different approaches are compared: a space-and time-invariant LAI dataset, a LAI specification based on Coordination of Information on the Environment (CORINE) land classes, and a Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-retrieved dataset. The three approaches resolve increasingly higher moments both in time and space of LAI probability density functions. Forecast scores employing the three datasets can therefore be used to assess the required degree of accuracy needed for this parameter. The MODIS dataset is the only one able to capture the expected vegetative cycle that is typical of the Mediterranean ecosystem and noticeably improves the 850-hPa temperature and humidity forecast scores up to 172 h forecast time. This suggests that accounting for LAI temporal and spatial variability could potentially improve the prevision of lower-level variables. Nevertheless, model biases of 2-m screen temperatures are not substantially reduced by the more detailed LAI specification when comparisons to synoptic observing stations are performed. Using long-term measurements collected by the CarboEurope project, a detailed verification of sensible and latent heat flux predictions is also presented. It shows that the desirable positive impact arising from a better LAI specification is nullified by the large uncertainties in the initialization of the soil moisture, which remains a crucial parameter for the reduction of screen-level biases.

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