Articles | Volume 14
02 May 2017
 | 02 May 2017

LES study of microphysical variability bias in shallow cumulus

Yefim Kogan

Abstract. Subgrid-scale (SGS) variability of cloud microphysical variables over the mesoscale numerical weather prediction (NWP) model has been evaluated by means of joint probability distribution functions (JPDFs). The latter were obtained using dynamically balanced Large Eddy Simulation (LES) model dataset from a case of marine trade cumulus initialized with soundings from Rain in Cumulus Over the Ocean (RICO) field project. Bias in autoconversion and accretion rates from different formulations of the JPDFs was analyzed. Approximating the 2-D PDF using a generic (fixed-in-time), but variable-in-height JPDFs give an acceptable level of accuracy, whereas neglecting the SGS variability altogether results in a substantial underestimate of the grid-mean total conversion rate and producing negative bias in rain water. Nevertheless the total effect on rain formation may be uncertain in the long run due to the fact that the negative bias in rain water may be counterbalanced by the positive bias in cloud water. Consequently, the overall effect of SGS neglect needs to be investigated in direct simulations with a NWP model.

Short summary
Exact description of cloud microphysical processes is essential for providing accurate weather forecasts. The paper evaluates errors of different methods to account for variability of cloud microphysical parameters, such as cloud water. rain water, and cloud drop concentration. It is found that neglecting cloud variability results in a substantial underestimate of rain development in the short run. Nevertheless the total effect on rain development in the long run may be uncertain due to the fac