Plume Model Assessment of Tropical Convection Biases in Weather Forecast Systems: Application to the NOAA Unified Forecast System

Abstract We develop a diagnostic framework for assessing systematic biases in tropical precipitation to support the improvement of weather forecast systems. The approach is demonstrated through its application to a suite of global models associated with various stages of National Oceanic and Atmospheric Administration’s (NOAA’s) Unified Forecast System (UFS) development. The diagnostics are based on observed relationships between precipitation and lower-tropospheric buoyancy, estimated offline using a plume model. This buoyancy metric serves as a proxy for convective instability, incorporating the effects of dry air entrainment, a key factor in tropical convection. Among the models examined, tropical convection biases primarily arise during two convective regimes: the transition from shallow to deep convection, involving cumulus congestus clouds, and periods of widespread deep convection, dominated by mesoscale convective systems (MCSs). When large-scale conditions favor enhanced cumulus congestus activity in observations, the NOAA models analyzed here tend to overproduce precipitation and develop a dry bias in the lower troposphere. This leads to rapid stabilization of the convective environment compared to observations, suppressing the frequency of highly convectively unstable conditions that typically support active MCS development. During periods when large-scale conditions favor MCS activity in observations, the models often underpredict precipitation, partly due to this artificially stabilized model environment. Systematic coupled biases in precipitation, humidity, and lower-tropospheric buoyancy emerge rapidly, within less than a day, and persist over longer time scales. Building on previous applications to other models, these results underscore the value of plume model diagnostics as a powerful tool for evaluating how convection scheme modifications influence tropical precipitation biases, providing actionable insights that can directly inform operational model development. Significance Statement Weather and climate models routinely struggle in accurately simulating how tropical convection interacts with its environment, leading to errors in model forecasts. In the current study, we evaluate the coupling between convection and its large-scale thermodynamic environment in a suite of National Oceanic and Atmospheric Administration (NOAA) models to show that coupled errors develop rapidly in model precipitation and thermodynamic instability. We find that models produce too much rainfall during the early stages of convective development when enhanced cumulus congestus activity is observed and produce too little rainfall during later stages of convective development when mesoscale convective systems are frequently observed. The increased rainfall is further associated with drying out the lower levels of the atmosphere, which reduces the thermodynamic instability and amount of energy available for subsequent convection. We hypothesize that the increased stability of the atmosphere due to increased rainfall in the early stages of convective development persists and contributes to decreased rainfall amounts in subsequent stages of convection in the model.