Catálogo de publicaciones - libros

Skillful low visibility forecasts are essential for air-traffic managers to effectively regulate traffic and to optimize air-traffic control at international airports. For this purpose, the COBEL-ISBA local numerical forecast system has been implemented at Paris CDG international airport. This local approach is robust owing to the assimilation of detailed local observations. However, even with dedicated observations and initialization, uncertainties remain in both initial conditions and mesoscale forcings. The goal of the research presented here is to address the sensitivity of COBEL-ISBA forecast to initial conditions and mesoscale forcings during the winter season 2002–2003. The main sources of uncertainty on the forecasts has been studied. A budget strategy is applied during the winter season to quantily COBEL-ISBA sensitivity. This study is the first step toward building a local ensemble prediction system based on COBEL-ISBA. The conclusions of this work point out the potential for COBEL-ISBA ensemble forecasting and quantify sources of uncertainty that lead to dispersion.

Three cases of widespread sea fog in Lunenburg Bay, Nova Scotia were used to evaluate the suitability of operational regional GEM forecast fields for inferring advection fog occurrences. Verification scores suggest that the objective analyses contain significant departures from observations that will affect model accuracy, given the sensitivity of fog condensation microphysics. Dew point depression (ES) scores show larger differences compared to temperature, with both influenced by surface characteristics. For objective analyses and GEM forecasts ES >2C seems to match fog satellite images better than the physical threshold ES ≤0 C. In addition the GEM forecasts show a general tendency towards drier conditions near the surface, therefore reconfiguring GEM to better represent condensations in the boundary layer is proposed.

Operations at Central-Spanish airports are often, especially in winter, affected by visibility reduction. The (INM), the Spanish Weather Service, has developed a single-column model (SCM) in order to improve short-term forecasts of fog, visibility and low-clouds. The SCM, called H1D, is a one-dimensional version of the HIRLAM limited-area model. It is operationally run for three airports in the region: Madrid-Barajas, Almagro and Albacete-Los Llanos. Since SCMs cannot deal with horizontal heterogeneity, the terms that depend on the horizontal structure of the atmosphere are estimated from the outputs of the three-dimensional (3-D) model and introduced into the SCM as external forcings. The systematic analysis of the meteorological situations has evidenced the existence of a close relationship between fog formation and the presence of drainage winds in the region. Since the 3-D model does not have the necessary resolution to correctly simulate the main features of the drainage flow caused by the complex topography in the proximity of Madrid-Barajas, it cannot provide the SCM with the correct forcings. This problem has been partially overcome through the introduction of a module that, under certain conditions, substitutes the values computed from the 3-D model outputs by others that are based on a conceptual model of the phenomenon and have been empirically derived from climatological knowledge. This module improves the H1D verification scores for the basic meteorological variables—wind, temperature and humidity—and reduces the false alarm rate in fog forecast.

Weather charts depicting the spatial distribution of various meteorological parameters constitute an indispensable pictorial tool for meteorologists, in diagnosing and forecasting synoptic conditions and the associated weather. The purpose of the present research is to investigate whether training artificial neural networks can be employed in the objective identification of synoptic patterns on weather charts. In order to achieve this, the daily analyses at 0000UTC for 1996 were employed. The respective data consist of the grid-point values of the geopotential height of the 500 hPa isobaric level in the atmosphere. A uniform grid-point spacing of 2.5°×2.5° is used and the geographical area covered by the investigation lies between 25°N and 65°N and between 20°W and 50°E, covering Europe, the Middle East and the Northern African Coast. An unsupervised learning self-organizing feature map algorithm, namely the Kohonen’s algorithm, was employed. The input consists of the grid-point data described above and the output is the synoptic class which each day belongs to. The results referred to in this study employ the generation of 15 and 20 synoptic classes (more classes have been investigated but the results are not reported here). The results indicate that the present technique produced a satisfactory classification of the synoptic patterns over the geographical region mentioned above. Also, it is revealed that the classification performed in this study exhibits a strong seasonal relationship.

Statistical methods are widely applied in visibility forecasting. In this article, further improvements are explored by extending the standard probabilistic neural network approach. The first approach is to use several models to obtain an averaged output, instead of just selecting the overall best one, while the second approach is to use deterministic neural networks to make input variables for the probabilistic neural network. These approaches are extensively tested at two sites and seen to improve upon the standard approach, although the improvements for one of the sites were not found to be of statistical significance.

Forecasters need climatological forecasting tools because of limitations of numerical weather prediction models. In this article, using Finnish SYNOP observations and ERA-40 model reanalysis data, low visibility cases are studied using subjective and objective analysis techniques. For the objective analysis, we used an AutoClass clustering algorithm, concentrating on three Finnish airports, namely, the Rovaniemi in northern Finland, Kauhava in western Finland, and Maarianhamina in southwest Finland. These airports represent different climatological conditions. Results suggested that combining of subjective analysis with an objective analysis, e.g., clustering algorithms such as the AutoClass method, can be used to construct climatological guides for forecasters. Some higher level subjective “meta-clustering” was used to make the results physically more reasonable and easier to interpret by the forecasters.

The intent of this study is to develop a better understanding of the behavior of late spring through early fall marine layer stratus and fog at Vandenberg Air Force Base, which accounts for a majority of aviation forecasting difficulties. The main objective was to use () study as a starting point to evaluate synoptic and mesoscale processes involved, and identify specific meteorological parameters that affected the behavior of marine layer stratus and fog. After identifying those parameters, the study evaluates how well the various weather models forecast them. The main conclusion of this study is that weak upper-air dynamic features work with boundary layer motions to influence marine layer behavior. It highlights the importance of correctly forecasting the surface temperature by showing how it ties directly to the wind field. That wind field, modified by the local terrain, establishes the low-level convergence and divergence pattern and the resulting marine layer cloud thicknesses and visibilities.