Abstract
The results of a sensitivity analysis based on COSMO-LM (COnsortium for Small-Scale MOdeling—Lokal Model) simulations driven by ECMWF-IFS (European Centre for Medium-Range Weather Forecasts—Integrated Forecasting System). global data over a domain located in southern Italy are presented. Simulations have been performed at very high resolution (about 1 km). The main aim of this study is to individuate the most sensitive physical and numerical parameters of the model configuration, comparing a set of 18 simulations in terms of temperature and precipitation against ground observations. The parameters that result in having more influence for a proper representation of temperature and precipitation fields are the heat resistance length of laminar layer (which accounts for the high complexity of the interaction of the atmosphere with the underlying surface) and the minimal diffusion coefficient for heat. Temperature values are strongly influenced also by the vertical variation of critical relative humidity. An optimized tuning of these parameters allows COSMO-LM to improve the representation of simulated main features of this area, with significant bias reductions.
Highlights
Numerical weather prediction (NWP) models are powerful tools of weather forecasting that employ a set of equations describing the flow of fluids
The results of sensitivity experiments performed with the COSMO-LM model at very high resolution, over a domain located in southern Italy, are presented
The main aim of this work was to establish a hierarchy regarding the parameter sensitivity that could be useful in order to apply more advanced optimization techniques, such as the ones based on the application of meta-models
Summary
Numerical weather prediction (NWP) models are powerful tools of weather forecasting that employ a set of equations describing the flow of fluids. These equations are translated into computer codes and by using governing equations, numerical methods, parameterizations of other physical processes combined with initial and boundary conditions. Are currently used in order to get detailed information over a geographic area of interest. They are driven by global models (GM) with the specific goal of providing atmospheric variables at very high temporal and spatial resolution. Jarvinen et al [3] developed a theory to use the existing ensemble prediction infrastructures
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