Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection

Abstract

Stochastic parameterizations are increasingly becoming skillful in representing unresolved atmospheric processes for global climate models. The stochastic multicloud model, used to simulate the life cycle of the three most common cloud types (cumulus congestus, deep convective, and stratiform) in tropical convective systems, is one example. In this model, these clouds interact with each other and with their environment according to intuitive‐probabilistic rules determined by a set of predictors, depending on the large‐scale atmospheric state and a set of transition time scale parameters. Here we use a Bayesian statistical method to infer these parameters from radar data. The Bayesian approach is applied to precipitation data collected by the Shared Mobile Atmospheric Research and Teaching Radar truck‐mounted C‐band radar located in the Maldives archipelago, while the corresponding large‐scale predictors were derived from meteorological soundings taken during the Dynamics of the Madden‐Julian Oscillation field campaign. The transition time scales were inferred from three different phases of the Madden‐Julian Oscillation (suppressed, initiation, and active) and compared with previous studies. The performance of the stochastic multicloud model is also assessed, in a stand‐alone mode, where the cloud model is forced directly by the observed predictors without feedback into the environmental variables. The results showed a wide spread in the inferred parameter values due in part to the lack of the desired sensitivity of the model to the predictors and the shortness of the training periods that did not include both active and suppressed convection phases simultaneously. Nonetheless, the resemblance of the stand‐alone simulated cloud fraction time series to the radar data is encouraging.

A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds

“…Recent efforts to bridge the gap include studies by Peters et al (2017), Cardoso-Bihlo et al (2019), Hagos et al (2018), for example. The present study may be considered as an extension of Hagos et al (2018) in which the use of observational radar data was complemented by insights from corresponding convection-permitting simulations.…”

Evaluation of Mean State in NCEP Climate Forecast System (Version 2) Simulation Using a Stochastic Multicloud Model Calibrated With DYNAMO RADAR Data

“…(2016) and used in CFSv2 (Goswami et al., 2017b). In the present study, the transition times are calculated from the initiation phase of the MJO event of DYNAMO observational data‐sets (Cardoso‐Bihlo et al., 2019) by applying the Bayesian inference technique by De La Chevrotière et al. (2014, 2016).…”

“…Cardoso‐Bihlo et al. (2019) recently introduced and reassessed a refinement of the SMCM by introducing new dynamical predictors such as convective inhibition and vertical subsidence and the previously used mid‐level moisture and CAPE. They then extended the Bayesian inference algorithm of De La Chevrotière et al.…”

“…In this work, we use the DYNAMO transition time scales of Cardoso‐Bihlo et al. (2019) in CFSv2‐SMCM (hereafter SMCM‐DYNAMO). After replacing the tuning parameters in SMCM‐DYNAMO, we have run the model for 25 years and compared the results with the pre‐existing revised, simplified Arakawa–Schubert (RSAS as in Ganai et al., 2015) cumulus scheme and with the SMCM implemented in CFSv2 (SMCM‐CTRL as in Goswami et al., 2017b).…”

Toward a Stochastic Relaxation for the Quasi‐Equilibrium Theory of Cumulus Parameterization: Multicloud Instability, Multiple Equilibria, and Chaotic Dynamics

“…While in the present study both h and U 0 in and are used as fixed parameters, in an actual parameterization they depend explicitly on key large‐scale dynamical and thermodynamical quantities that are known to directly influence the organization of convection on various scales. Many meteorological parameters such as CAPE, convective inhibition (CIN), midlevel moisture, vertical shear, orography, and land‐sea thermal contrast can be used to define the quantities h and U (Bergemann et al, ; Cardoso‐Bihlo et al, ; Khouider, ). The effect of vertical shear and (unresolved) land‐sea contrast can be used to break the symmetry in U and allow self organization and propagation of mesoscale systems at the unresolved scales.…”