Mesoscale Convective Systems (MCS) are common over Europe and can produce severe weather, including extreme precipitation, which can lead to flash floods. The few studies analyzing the climatological characteristics of MCS over Europe are either based on only few years of data or focus on limited sub-areas. Using the recent Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) satellite precipitation climatology, we identify and track MCS for 16 years over Europe. We devise a spatial filter and track cells according to the overlap of filtered rain patches between consecutive time steps. By fitting an ellipse to these patches, we determine their overall shape and orientation. To distinguish convective rain patches we condition on lightning data, thus reducing potential identification errors. We analyze this new European MCS climatology to characterize MCS rainfall properties: MCS overall occur most frequently over the Mediterranean and Atlantic during fall and winter, whereas during summer, they concentrate over the continent. Typically, more than half of seasonal precipitation can be attributed to MCS, and their contribution to extreme precipitation is even greater, often exceeding 70\%. MCS over the continent display a clear diurnal cycle peaking during the afternoon, and some continental areas even show a second, nocturnal peak. The MCS diurnal cycle for coastal and oceanic regions is more variable. Selecting sub-areas, we find that the spatio-temporal distribution of MCS precipitation throughout the year can be well explained by the spatio-temporal distribution of specific environmental variables, namely (sea) surface temperature, fronts occurrence and convective instability.

Recent observations and modeling increasingly reveal the key role of cold pools in organizing the convective cloud field. Several methods for detecting cold pools in simulations exist, but are usually based on buoyancy fields and fall short in reliably identifying the active gust front. The current algorithm, termed CoolDeTA, aims to detect and track cold pools along with their active gust fronts and the “offspring” rain cells generated nearby. We show how CoolDeTA can reconstruct cold pool family trees. Using it allows us to contrast RCE and diurnal cycle cold pool dynamics, as well as cases with vertical wind shear and without. The results suggest a conceptual model where cold pool triggering of children rain cells follows a simple birth rate, which is proportional to a cold pool’s gust front length. The proportionality factor depends on the ambient atmospheric stability and is lower for RCE, in line with marginal stability as traditionally ascribed to the moist adiabat. In the diurnal case, where ambient stability is lower, the birth rate thus becomes substantially higher, in line with periodic insolation forcing — resulting in essentially run-away mesoscale excitations generated by a single parent rain cell and its cold pool.

Convective cold pools (CPs) are known to mediate the interaction between convective rain cells and thereby help organize thunderstorm clusters, in particular mesoscale convective systems and extreme rainfall events. Unfortunately, the observational detection of CPs on a large scale has so far been hampered by the lack of relevant large-scale nearsurface data. Unlike numerical studies, where high-resolution near-surface fields of relevant quantities such as virtual temperature and winds are available and frequently used to detect cold pools, observational studies mainly identify CPs based on surface time series. Since research vessels or weather stations measure these time series locally, the characterization of cold pools from observations is limited to regional or station-based studies. To eventually enable studies on a global scale, we here develop and evaluate a methodology for the detection of CPs that relies only on data that (i) is globally available and (ii) has high spatio-temporal resolution. We trained convolutional neural networks to segment CPs in cloud and rainfall fields from high-resolution cloud resolving simulation output. Such data is not only available from simulations, but also from geostationary satellites that fulfill both (i) and (ii). The networks make use of a U-Net architecture, a common choice for image segmentation due to its strength in learning spatial correlations at different scales. Based on cloud and rainfall fields only, the trained networks systematically identify CP pixels in the simulation output. Our methodology may thus open for reliable global CP detection from space-borne sensors. As it also provides information on the spatial extent and the relative positioning of CPs over time, our method may offer new insight into the role of CPs in convective organization.

Observations and simulations have found convective cold pools to trigger and organize subsequent updrafts by modifying near-surface temperature and moisture as well as by lifting air parcels at the outflow boundaries. We study the causality between cold pools and subsequent deep convection in an idealized large-eddy simulation by tracking colliding outflow boundaries preceding hundreds of deep convection events. When outflow boundaries collide, their common front position remains immobile, whereas the internal cold pool dynamics continues for hours. We analyze how this dynamics “funnels” moisture from a relatively large volume into a narrow convergence zone. We quantify moisture convergence and separate the contribution from surface fluxes, finding that it plays a secondary role. Our results highlight that dynamical effects are crucial in triggering new convection, even in radiative-convective equilibrium. However, it is the moisture convergence resulting from this dynamics that moistens the atmosphere aloft and ultimately permits deep convection.

Tropical convection is known to self-organize under the diurnal cycle, yet is also subject to large scale convergence. In a suite of idealized numerical experiments we mimic Earth’s tropical circulation, to probe the cross talk between inherent circulation eigenmodes and the convective diurnal cycle — which generally are characterized by incommensurate oscillatory frequencies. The tropics are caricatured by a doubly-periodic domain with spatially constant surface temperature $T_S(x,y,t)$ in the “zonal” ($x$) but decreasing $T_S$ in the “meridional” dimension. Temporally, we contrast constant $T_S(x,y,t)=T_S(x,y)$ with diurnally varying $T_S(x,y,t)=T_S(x,y,t+\tau_d)$, with $\tau_d=1\,$day. We find that the diurnal forcing by no means dominates the precipitation power spectrum. Rather, the intrinsic circulation period $\tau_i$ drives temporal precipitation patterns for large and small domains. At intermediate domain sizes, where intrinsic frequencies approximately match the diurnal one, i.e., $\tau_i\approx \tau_d$, the diurnal cycle is amplified and, substantially increasing the precipitation amplitude.

It is well-recognized that triggering of convective cells through cold pools is key to the organization of convection. Yet, numerous studies have found that both the characterization and parameterization of these effects in numerical models is cumbersome - in part due to the lack of numerical convergence, $\Delta x\rightarrow 0$, achieved in typical cloud-resolving simulators. Through a comprehensive numerical convergence study we systematically approach the $\Delta x \rightarrow 0$ limit in a set of idealized large-eddy simulations capturing key cold pool processes: free propagation, frontal collision and incident merging of gust fronts. We characterize at which $\Delta x$ convergence is achieved for physically relevant quantities, namely accumulated upwards water mass fluxes, leading front vortical rates, tropospheric moistening and group velocity. The understanding gained from this analysis lays the groundwork to develop robust subgrid models for CP dynamics able to sustain their growth and combat artificial numerical dissipation and dispersion.