A commonly noted problem in the simulation of warm season convection in the North American monsoon region has been the inability of atmospheric models at the meso- scales (10 s to 100 s of kilometers) to simulate organized convection, principally mesoscale convective systems. With the use of convective parameterization, high precipitation biases in model simulations are typically observed over the peaks of mountain ranges. To address this issue, the Kain-Fritsch (KF) cumulus parameterization scheme has been modified with new diagnostic equations to compute the updraft velocity, the convective available potential energy closure assumption, and the convective trigger function. The scheme has been adapted for use in the Weather Research and Forecasting (WRF). A numerical weather prediction-type simulation is conducted for the North American Monsoon Experiment Intensive Observing Period 2 and a regional climate simulation is performed, by dynamically downscaling. In both of these applications, there are notable improvements in the WRF model-simulated precipitation due to the better representation of organized, propagating convection. The use of the modified KF scheme for atmospheric model simulations may provide a more computationally economical alternative to improve the representation of organized convection, as compared to convective-permitting simulations at the kilometer scale or a super-parameterization approach.

Non disponible. / Aerosol particles are ubiquitous in the Earth’s atmosphere. Although a minor constituent of the atmosphere, the aerosol particles are linked to visibility reduction, adverse health effects and heat balance of the Earth. The secondary aerosols which are formed in the atmosphere from the gaseous phase : precursor gases become particles by nucleation and condensation (Seinfeld and Pandis, 1998) represents the largest source in a number concentration of atmospheric particles. The chemical reactions can play an important role by turning high volatility gases into species with low vapor pressure and thus high saturation ratio, i.e. creating favorable conditions for particulate matter formation. In this work the CHIMERE chemical transport model is used to ameliorate our understanding of the governing processes for aerosol formation and to investigate its capability to reproduce the mass and number concentrations and temporal evolution of the aerosols particles at high altitudes (as for example Puy de Dome research station), and in particular, evaluate its capacity to simulate the formation of new particles due to nucleation. For the studied cases it was investigated the impact of : a fine resolution topographical database on the accuracy of simulation of dynamical parameters at high altitude, of the use of different emissions databases in the accuracy of gas-phase and aerosol concentration predictions, what is the most adequate nucleation parameterization scheme for simulating new particle formation at high altitude and what is the influence of the choice of the primary particle size distribution on the prediction of new particle formation. Also the ability of the different theories to reproduce the occurrence or lack of a nucleation event is evaluated.

Global climate models (GCM) have been used for nearly two decades now as a tool to investigate and analyze past, present, and future weather and climate. Even though the first several generations of climate models were very simple, today's models are very sophisticated. They use complex parameterization schemes to approximate many nonlinear physical fields. In these models, the resolution and time steps can be set to be as small or as large as desired. In either case, the model generates over 100 atmospheric variables and 20 land surface variables that can be reported daily or monthly. The Community Atmospheric Model Version Two global climate model spends over sixty percent of the time computing shortwave and longwave parameterization schemes. Our goal is to replace its shortwave scheme with empirical methods and show that accuracy of the tropospheric variables is not compromised when using these empirical methods. We found that an autoregressive moving average (ARMA) model can be used to simulate the solar radiation at the top of the model atmosphere. However, the calculated insolation value is only valid for one particular grid point. To simulate the radiation over the entire globe, many ARMA models need to be determined. We also found that large 4-10-10-1 neural networks can be used to simulate the solar radiation to within 2 W m-2. However, much smaller and manageable neural networks can be used to simulate the complete solar insolation term if the neural network only simulates the residual after the annual and diurnal cycles and removed from the field (referred to as the - method). By using the neural network in the - method and by setting the eccentricity term to a constant, we were able to cut the models processing of the solar insolation by at least a factor of four.