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Deep convection is one of the most important atmospheric transport mechanisms and associated with various severe weather phenomena. Manifestations of deep convection in the atmosphere are composed of a recurring fundamental building block, called cell, which evolves through a characteristic lifecycle. Despite its importance, no simple, physically consistent quantitative lifecycle model exists that correctly reproduces remote-sensing observations. Based on the standard conceptual model, we develop an analytic minimal model of the convection lifecycle in the form of coupled reaction rules. This reaction scheme is equivalent to a system of coupled nonlinear differential equations that qualitatively agree with the empirically known dynamics. In order to demonstrate quantitative agreement of our convection-lifecycle model, we construct a representative random sample of satellite and radar observations of deep-convection events over Germany. In particular, we introduce a conditional sampling strategy based on an objective timescale criterion to remove events not covered by our model from the random sample. We show that by this procedure, a definite and recurrent temporal signature of the convection lifecycle can be identified. Our model closely reproduces the principal characteristics while essentially staying within one standard deviation of the mean signature. Next to the definite relevance of our model for nowcasting, the approach may be useful for convection parametrisations., Comment: 13 pages, 6 figures. This work has been submitted to Journal of the Atmospheric Sciences. Copyright in this work may be transferred without further notice

Thunderstorms have significant social and economic impacts due to heavy precipitation, hail, lightning, and strong winds, necessitating reliable forecasts. Thunderstorm forecasts based on numerical weather prediction (NWP) often rely on single-level surrogate predictors, like convective available potential energy and precipitation rate, derived from vertical profiles of three-dimensional atmospheric variables. In this study, we develop SALAMA 1D, a deep neural network that directly infers the probability of thunderstorm occurrence from vertical profiles of ten atmospheric variables, bypassing single-level predictors. By training the model on convection-permitting NWP forecasts, we allow SALAMA 1D to flexibly identify convective patterns, with the goal of enhancing forecast accuracy. The model's architecture is physically motivated: sparse connections encourage interactions at similar height levels, while a shuffling mechanism prevents the model from learning non-physical patterns tied to the vertical grid. SALAMA 1D is trained over Central Europe with lightning observations as the ground truth. Comparative analysis against a baseline machine learning model that uses single-level predictors shows SALAMA 1D's superior skill across various metrics and lead times of up to at least 11 hours. Moreover, increasing the number of forecasts used to compile the training set improves skill, even when training set size is kept constant. Sensitivity analysis using saliency maps indicates that the model reconstructs environmental lapse rates and rediscovers patterns consistent with established theoretical understandings, such as positive buoyancy, convective inhibition, and ice particle formation near the tropopause, while ruling out thunderstorm occurrence based on the absence of mid-level graupel and cloud cover., Comment: 14 pages, 8 figures, 2 tables. This work has been submitted to Artificial Intelligence for the Earth Systems. Copyright in this work may be transferred without further notice

The general epidemic process (GEP), also known as susceptible-infected-recovered model (SIR), describes how an epidemic spreads within a population of susceptible individuals who acquire permanent immunization upon recovery. This model exhibits a second-order absorbing state phase transition, commonly studied assuming immobile healthy individuals. We investigate the impact of mobility on disease spreading near the extinction threshold by introducing two generalizations of GEP, where the mobility of susceptible and recovered individuals is examined independently. In both cases, including mobility violates GEP's rapidity reversal symmetry and alters the number of absorbing states. The critical dynamics of the models are analyzed through a perturbative renormalization group approach and large-scale stochastic simulations using a Gillespie algorithm. The renormalization group analysis predicts both models to belong to the same novel universality class describing the critical dynamics of epidemic spreading when the infected individuals interact with a diffusive species and gain immunization upon recovery. At the associated renormalization group fixed point, the immobile species decouples from the dynamics of the infected species, dominated by the coupling with the diffusive species. Numerical simulations in two dimensions affirm our renormalization group results by identifying the same set of critical exponents for both models. Violation of the rapidity reversal symmetry is confirmed by breaking the associated hyperscaling relation. Our study underscores the significance of mobility in shaping population spreading dynamics near the extinction threshold., Comment: These authors contributed equally. B Nettuno and D Toffenetti performed the analytical work, C Metzl and L Weigand did the numerical one. 48 pages, 12 figures