Processos de remoção de material particulado atmosférico: uma modelagem numérica de estudo de casos

Below cloud scavenging processes have been investigated considering a numerical simulation, local atmospheric conditions and particulate matter (PM) concentrations, at different sites in Germany. The below cloud scavenging model has been coupled with bulk particulate matter counter TSI (Trust Portacounter dataset, consisting of the variability prediction of the particulate air concentrations during chosen rain events. The TSI samples and meteorological parameters were obtained during three winter Campaigns: at Deuselbach, March 1994, consisting in three different events; Sylt, April 1994 and; Freiburg, March 1995. The results show a good agreement between modeled and observed air concentrations, emphasizing the quality of the conceptual model used in the below cloud scavenging numerical modeling. The results between modeled and observed data have also presented high square Pearson coefficient correlations over 0.7 and significant, except the Freiburg Campaign event. The differences between numerical simulations and observed dataset are explained by the wind direction changes and, perhaps, the absence of advection mass terms inside the modeling. These results validate previous works based on the same conceptual model.Os processos de remoção atmosféricos foram investigados em simulação numérica, levando-se em conta as condições atmosféricas locais e a concentração de matéria particulada (MP) em diferentes lugares na Alemanha. Um modelo de remoção foi desenvolvido com intuito de predizer a variabilidade da concentração no ar da matéria particulada observada por um contador de partículas totais TSI, durante eventos de precipitação. As amostras de TSI, bem como os dados meteorológicos, foram coletadas em três Campanhas em Deuselbach, em três diferentes eventos; Sylt e Freiburg, respectivamente durante os meses de março de 1994, abril de 1994 e março de 1995. Os resultados mostram boa concordância entre os dados modelados e observados, com altos coeficientes de correlação quadráticos acima de 0.70, dentro da significância, exceto na Campanha de Freiburg. As diferenças apresentadas são devidas a alterações significativas da direção do vento e, talvez, devido à ausência de advecção de massa dentro do modelo. Estes resultados auxiliam a validação de trabalhos prévios baseados no mesmo modelo conceitual

Influence of turbulence on the drop growth in warm clouds, Part II: sensitivity studies with a spectral bin microphysics and a Lagrangian cloud model

Raindrops in warm clouds grow faster than predicted by classical cloud models. One of the possible reasons for this discrepancy is the influence of cloud turbulence on the coagulation process. In Part I (Siewert et al., 2014) of this paper series, a turbulent collision kernel has been derived from wind tunnel experiments and direct numerical simulations (DNS). Here we use this new collision kernel to investigate the influence of turbulence on coagulation and rain formation using two models of different complexity: a one-dimensional model called RAINSHAFT (height as coordinate) with cloud microphysics treated by a spectral bin model (BIN) and a large-eddy simulation (LES) model with cloud microphysics treated by Lagrangian particles (a so called Lagrangian Cloud Model, LCM). Simulations are performed for the case of no turbulence and for two situations with moderate and with extremely strong turbulence. The idealized 0- and 1-dimensional runs show, that large drops grow faster in the case turbulence is taken into account in the cloud microphysics, as was also found by earlier investigations of other groups. For moderate turbulence intensity, the acceleration is only weak, while it is more significant for strong turbulence. From the model intercomparison it turns out, that the BIN model produced large drops much faster than the LCM, independent of turbulence intensity. The differences are larger than those due to a variation in turbulence intensities. The diverging rate of formation of large drops is due to the use of different growth models for the coagulation process, i.e. the quasi-stochastic model in the spectral BIN model and the continuous growth model in LCM. From the results of this model intercomparison it is concluded, that the coagulation process has to be improved in future versions of the LCM. The LES-LCM model was also applied to the simulation of a single 3-D cumulus cloud. It turned out, that the effect of turbulence on drop formation was even smaller as the turbulence within the cloud was weaker than prescribed in the idealized cases. In summary, the use of the new turbulent collision kernel derived in Part I does enhance rain formation under typical turbulence conditions found in natural clouds but the effect is not very striking. © 2015 The authors.DFG/SPP/1276DFG/ET 8/14DFG/WA 1334/8DFG/BE 2081/1

Model results, link to archive file

Raindrops in warm clouds grow faster than predicted by classical cloud models. One of the possible reasons for this discrepancy is the influence of cloud turbulence on the coagulation process. In Part I (Siewert et al., 2014, doi:10.1127/0941-2948/2014/0566) of this paper series, a turbulent collision kernel has been derived from wind tunnel experiments and direct numerical simulations (DNS). Here we use this new collision kernel to investigate the influence of turbulence on coagulation and rain formation using two models of different complexity: a one-dimensional model called RAINSHAFT (height as coordinate) with cloud microphysics treated by a spectral bin model (BIN) and a large-eddy simulation (LES) model with cloud microphysics treated by Lagrangian particles (a so called Lagrangian Cloud Model, LCM). Simulations are performed for the case of no turbulence and for two situations with moderate and with extremely strong turbulence. The idealized 0- and 1-dimensional runs show, that large drops grow faster in the case turbulence is taken into account in the cloud microphysics, as was also found by earlier investigations of other groups. For moderate turbulence intensity, the acceleration is only weak, while it is more significant for strong turbulence. From the model intercomparison it turns out, that the BIN model produced large drops much faster than the LCM, independent of turbulence intensity. The differences are larger than those due to a variation in turbulence intensities. The diverging rate of formation of large drops is due to the use of different growth models for the coagulation process, i.e. the quasi-stochastic model in the spectral BIN model and the continuous growth model in LCM. From the results of this model intercomparison it is concluded, that the coagulation process has to be improved in future versions of the LCM. The LES-LCM model was also applied to the simulation of a single 3-D cumulus cloud. It turned out, that the effect of turbulence on drop formation was even smaller as the turbulence within the cloud was weaker than prescribed in the idealized cases. In summary, the use of the new turbulent collision kernel derived in Part I does enhance rain formation under typical turbulence conditions found in natural clouds but the effect is not very striking