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APPLICATION OF NON-HYDROSTATIC NUMERICAL MODELS DATA AND METEOROLOGICAL SATELLITES IMAGES TO INVESTIGATION OF AIRCRAFT FLIGHT CONDITIONS WITHIN CLOUDS SYSTEMS

The paper presents research results concerning developing software for determining optimum aircraft flight routes in severe weather. The application uses COAMPS mesoscale model data and MSG satellite images. The analyses concentrate mainly on forecasted fields of clouds and hazardous for aviation atmospheric phenomena turbulence and icing. Gradient characteristics of the atmospheric state including frontogenetic function, temperature coefficient of fronts location and vorticity are used for investigating creation, development and dissipation of frontal zones, cloud systems and precipitation. The charts of physical properties in their gradient form enhance the standard analysis. The software is designed for operational use and for theoretical research of atmospheric phenomena’ dynamics. It enables to look for and investigate quantitative relations between cloud systems observed on satellite images and in the meteorological elements fields. Numerous options of processing the mesoscale materials enable to use satellite images and charts of various combinations of meteorological fields within one session. Transformation of satellite images and COAMPS products to uniform cartographic projections facilitates simultaneous interpretation of various data, including nonstandard ones, e.g. vertical profiles illustrating evolution of atmospheric conditions and various forms of spatial and temporal distributions of meteorological elements fields. This increases effectiveness of weather forecasting for optimizing flight time and route. The application includes programs for investigating the impact of mesoscale models parameterization on forecasts. These programs enable to compare numerically generated cloud fields with those observed in satellite images. The program will be developed by including orbiting satellites images, meteorological radar network data and new generation models data. * Corresponding author.