Evaluation of the Plant–Craig stochastic convection scheme (v2.0) in the ensemble forecasting system MOGREPS-R (24 km) based on the Unified Model (v7.3)

Abstract

Abstract. The Plant-Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic scheme only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant-Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant-Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model

“…As suggested in Palmer (2001Palmer ( , 2012, more realistic statistics of the impacts of subgrid convective clouds should be derived by simulating them as random samples from probability distributions conditioned on the grid-scale state so that the influences of different individual realizations are introduced in the convection parameterization. In this regard, much effort in the past 2 decades has been made to develop stochastic convection schemes (e.g., Neelin, 2000, 2002;Plant and Craig, 2008;Khouider et al, 2010;Sakradzija et al, 2015). Among these schemes, Plant and Craig (2008) (PC08 hereafter) developed a stochastic deep convection parameterization under a framework based on statistical mechanics (Cohen and Craig, 2006;Craig and Cohen, 2006) for noninteracting convective clouds in statistical equilibrium using cloud-resolving model (CRM) simulations.…”

Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection

“…Plant and Craig () created a new stochastic convection scheme based on adaptation of the plume closure of the Kain‐Fritsch convection scheme to allow the number and size of clouds to randomly vary in a grid box. By comparison with a standard deep parameterization, the Plant‐Craig scheme improves the prediction of rainfall of light and medium intensities over large areas (Keane et al, ). Bright and Mullen () increased the probabilistic skill and spread for ensemble forecasts by applying a stochastic perturbation to the trigger function of the Kain‐Fritsch parameterization scheme.…”

Global climate impacts of stochastic deep convection parameterization in the NCARCAM5

“…The local fluctuations around the large‐scale equilibrium state are reflected by those clouds randomly initialized from the PDF. A stochastic convective parameterization scheme was later formally developed by Plant and Craig [], and applied in numerical weather predication (NWP) models [ Groenemeijer and Craig , ; Keane et al ., ] and a GCM under aquaplanet setting [ Keane et al ., ]. Sakradzija et al .…”