Andras Horanyi

Application of P-Grade Development Environment in Meteorology

The main objective of a meteorological nowcasting system is to analyse and predict in ultra short range those weather phenomena, which might be dangerous for life and property. The Hungarian Meteorological Service developed a nowcasting system, called MEANDER and its most computational intensive calculations have been parallelised by the help of P-GRADE graphical programming environment. In order to demonstrate the efficient application of P-GRADE in real-size problems we give an overview on the parallelisation of MEANDER system using the P-GRADE environment at the different stages of parallel program development; design, debugging and performance analysis.

Sensitivity of High-Resolution Forecasts Using the Adjoint Technique at the 10-km Scale

This paper provides an experimental framework designed to assess the performance and the evolution of the diabatic Aire Limitee Adaptation Dynamique Developpement International (ALADIN) adjoint model at 10-km grid size. Numerical experiments are carried out with the goal of evaluating the adjoint model solutions and the benefit of employing a complex linearized physical parameterization package in the gradient computation. Sensitivity studies with respect to initial conditions at high resolution on real meteorological events are performed. Numerical results obtained in the gradient computations show that, at high resolution, a strong nonlinear flow over complex orography might be a potential source of numerical instability in the absence of a robust dissipative physics employed in the adjoint model. Also, the scheme of the linearized large-scale precipitation is a source of noise in precipitating areas. The results on one particular case suggest that on the one hand the adjoint model is able to capture the dynamically sensitive area, but on the other hand the subsequent sensitivity forecast is more sensitive to the sign and the amplitude of the initial state perturbation rather than the structure of the gradient field.

The ARPEGE/ALADIN limited area ensemble prediction system : the impact of global targeted singular vectors

In this paper the present state of the establishment of a LAMEPS (Limited Area Ensemble Prediction System) system for Central Europe is described. Sensitivity studies were performed in order to explore whether or not it is possible to optimize the existing ARPEGE global ensemble system for Central Europe by changing the optimization area and optimization time used for the global singular vector computations. With this purpose several optimization areas and times were defined and tested through case studies and longer test periods. Verification results show that the proper choice of the singular vector optimization domain and optimization time can increase the spread and on average improves the skill of the ensemble for the Central European area. This conclusion was found to be valid for the global forecasts and the limited area predictions (i.e. the simple downscaling of the global model) as well. The experiments also demonstrate that the dynamical downscaling in itself does not result in significantly better skill. Therefore the computation of mesoscale initial perturbations might be desirable for a more efficient short-range ensemble system. Along this line research started in the field of singular vectors computed inside the limited area model ALADIN.

Demonstration of P-GRADE Job-Mode for the Grid

The P-GRADE job execution mode will be demonstrated on a small Grid containing 3 clusters from Budapest and London. The first demonstration illustrates the Grid execution of a parallel meteorology application. The parallel program will be on-line monitored remotely in the Grid and locally visualized on the submitting machine. The second demonstration will use a parallel traffic simulation program developed in P-GRADE to show the usage of the P-GRADE job mode for Grid execution. The parallel program will be check-pointed and migrated to another cluster of the Grid. On-line job and execution monitoring will be demonstrated.

Ensemble Methods in Meteorological Modelling

Numerical modelling is a continuously developing discipline in meteorology, which provides meteorological forecasts and climate change projections based on the numerical solutions of the set of equations describing the processes in the atmosphere and the related spheres. The progress in numerical weather prediction (NWP) and climate modelling has been enormous in the last few decades thanks to the improved theoretical understanding of the meteorological processes, the growing number of observations and the increasing available computer power. In spite of the steady progress, meteorological forecasts cannot be fully perfect due to the intrinsic characteristics of the atmosphere and the climate system. Weather forecast uncertainties exist in initial conditions and in the model formulations themselves and evolve rapidly with lead time. In climate change projections the initial conditions have negligible role, but the internal climate variability and the unknown future evolution of the anthropogenic activity are additional sources of uncertainties. Since they cannot be avoided (just minimized), their representation and quantification are essential tasks both in numerical weather prediction and climate research. Currently the only feasible way to challenge this problem is the ensemble approach, which delivers probabilistic information and attributes uncertainty information to the numerical weather forecasts and climate projections. This additional uncertainty estimation is a valuable bonus for the users and can be efficiently applied in decision-making.