LES study of microphysical variability bias in shallow cumulus

Yefim Kogan

doi:10.5194/asr-14-103-2017

Abstract

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.

Highlights

Formulation of microphysical processes in meso and large scale models requires accounting for subgrid-scale (SGS) variability; its neglect can lead to substantial bias in calculations of microphysical process rates (Pincus and Klein, 2000; Larson et al, 2001, 2012; Kogan and Mechem, 2014, KM14 hereafter, Nelson et al, 2016)
We consider two joint probability distribution functions (JPDFs) which enter the expressions for microphysical autoconversion and accretion rates
The first JPDF is used for calculation of autoconversion; it depends on cloud-drop number concentration, Nc and cloud water mixing ratio, qc

Summary

Introduction

Formulation of microphysical processes in meso and large scale models requires accounting for subgrid-scale (SGS) variability; its neglect can lead to substantial bias in calculations of microphysical process rates (Pincus and Klein, 2000; Larson et al, 2001, 2012; Kogan and Mechem, 2014, KM14 hereafter, Nelson et al, 2016). SGS microphysical variability can be represented using probability distribution functions (PDFs). The CLUBB approach uses theoretical considerations and a number of a-priori assumptions about the shape of the distributions, obtained from LES output and aircraft observations. KM14 use a series of Large Eddy Simulations (LES) of trade wind shallow cumulus based on the RICO field campaign (Rauber et al, 2007). These clouds are one of the most prevalent cloud types on Earth and play an important role in establishing the thermodynamic structure of the lower atmosphere in the trade latitudes. In KM14 the absolute errors of various PDF formulations were evaluated; the goal of this paper is to evaluate the errors in more detail and analyze how they may affect precipitation formation at different stages of cloud and rain formation

Objectives

Results

Conclusion