An Atmospheric Instability Perturbation Approach for Ensemble Forecasts and Its Application in Heavy Rain Cases

Abstract

An ensemble perturbation approach focusing on Atmospheric Instability Perturbation was proposed. This approach perturbs diagnostics quantifying atmospheric instability and calculates corresponding model state perturbations through a data assimilation-like procedure, with flexibility enhanced through the numerical estimation of derivatives of diagnostic equations. The amplitude perturbation of moist potential vorticity (MPV) measuring convective (MPV1) and baroclinic instability (MPV2) is investigated. Flow-dependent characteristics of MPV amplitude perturbations are observed through single-point tests, with the MPV2 perturbation enhancing the temperature gradient in the baroclinic instability area. For 10 heavy rain cases in Eastern China during the summer of 2019, the ensemble using the combination of a positive MPV2 amplitude perturbation and a negative MPV1 amplitude perturbation outperforms the ensemble with the downscaled Global Ensemble Forecast System (GEFS) perturbations. This superiority of MPV perturbations is attributed to their ability to capture more precipitation events through enhancing the instability environment, which is conducive to both convection initialization and precipitation intensity. However, the MPV perturbations contribute less to the heavy rain probability forecast skill and reliability, because more false alarms are produced. The experimental results also indicate the necessity of cycle perturbation of MPV during forecasting, as the forecast model may underestimate instability after the initial condition perturbation impact diminishes. Considering that all types of model state perturbations adjust atmospheric instability, with most instability adjustments being secondary outcomes, the results of MPV amplitude perturbations highlight the effectiveness of directly perturbing atmospheric instability in ensemble precipitation forecasting.