This study investigates the hydrometeor development and response to cloud droplet number concentration (CDNC) perturbations in convection-permitting model configurations. We present results from a real-data simulation of deep convection in the Congo basin, an idealised supercell case, and a warm-rain large-eddy simulation (LES). In each case we compare two frequently used double-moment bulk microphysics schemes and investigate the response to CDNC perturbations. We find that the variability among the two schemes, including the response to aerosol, differs widely between these cases. In all cases, differences in the simulated cloud morphology and precipitation are found to be significantly greater between the microphysics schemes than due to CDNC perturbations within each scheme. Further, we show that the response of the hydrometeors to CDNC perturbations differs strongly not only between microphysics schemes, but the inter-scheme variability also differs between cases of convection. Sensitivity tests show that the representation of autoconversion is the dominant factor that drives differences in rain production between the microphysics schemes in the idealised precipitating shallow cumulus case and in a subregion of the Congo basin simulations dominated by liquid-phase processes. In this region, rain mass is also shown to be relatively insensitive to the radiative effects of an overlying layer of ice-phase cloud. The conversion of cloud ice to snow is the process responsible for differences in cold cloud bias between the schemes in the Congo. In the idealised supercell case, thermodynamic impacts on the storm system using different microphysics parameterisations can equal those due to aerosol effects. These results highlight the large uncertainty in cloud and precipitation responses to aerosol in convection-permitting simulations and have important implications not only for process studies of aerosol–convection interaction, but also for global modelling studies of aerosol indirect effects. These results indicate the continuing need for tighter observational constraints of cloud processes and response to aerosol in a range of meteorological regimes.

Satellite studies of aerosol–cloud interactions usually make use of retrievals of both aerosol and cloud properties, but these retrievals are rarely spatially co-located. While it is possible to retrieve aerosol properties above clouds under certain circumstances, aerosol properties are usually only retrieved in cloud-free scenes. Generally, the smaller spatial variability of aerosols compared to clouds reduces the importance of this sampling difference. However, as precipitation generates an increase in spatial variability of aerosols, the imperfect co-location of aerosol and cloud property retrievals may lead to changes in observed aerosol–cloud–precipitation relationships in precipitating environments. In this work, we use a regional-scale model, satellite observations and reanalysis data to investigate how the non-coincidence of aerosol, cloud and precipitation retrievals affects correlations between them. We show that the difference in the aerosol optical depth (AOD)–precipitation relationship between general circulation models (GCMs) and satellite observations can be explained by the wet scavenging of aerosol. Using observations of the development of precipitation from cloud regimes, we show how the influence of wet scavenging can obscure possible aerosol influences on precipitation from convective clouds. This obscuring of aerosol–cloud–precipitation interactions by wet scavenging suggests that even if GCMs contained a perfect representation of aerosol influences on convective clouds, the difficulty of separating the "clear-sky" aerosol from the "all-sky" aerosol in GCMs may prevent them from reproducing the correlations seen in satellite data.

The impact of aerosols on ice- and mixed-phase processes in deep convective clouds remains highly uncertain, and the wide range of interacting microphysical processes is still poorly understood. To understand these processes, we analyse diagnostic output of all individual microphysical process rates for two bulk microphysics schemes in the Weather and Research Forecasting model (WRF). We investigate the response of individual processes to changes in aerosol conditions and the propagation of perturbations through the microphysics all the way to the macrophysical development of the convective clouds. We perform simulations for two different cases of idealised supercells using two double-moment bulk microphysics schemes and a bin microphysics scheme. The simulations cover a comprehensive range of values for cloud droplet number concentration (CDNC) and cloud condensation nuclei (CCN) concentration as a proxy for aerosol effects on convective clouds. We have developed a new cloud tracking algorithm to analyse the morphology and time evolution of individually tracked convective cells in the simulations and their response to the aerosol perturbations. This analysis confirms an expected decrease in warm rain formation processes due to autoconversion and accretion for more polluted conditions. There is no evidence of a significant increase in the total amount of latent heat, as changes to the individual components of the integrated latent heating in the cloud compensate each other. The latent heating from freezing and riming processes is shifted to a higher altitude in the cloud, but there is no significant change to the integrated latent heat from freezing. Different choices in the treatment of deposition and sublimation processes between the microphysics schemes lead to strong differences including feedbacks onto condensation and evaporation. These changes in the microphysical processes explain some of the response in cloud mass and the altitude of the cloud centre of gravity. However, there remain some contrasts in the development of the bulk cloud parameters between the microphysics schemes and the two simulated cases.

The variability of convective cloud spans a wide range of temporal and spatial scales and is of fundamental importance for global weather and climate systems. Datasets from geostationary satellite instruments such as the Spinning Enhanced Visible and Infrared Imager (SEVIRI) provide high-time-resolution observations across a large area. In this study we use data from SEVIRI to quantify the diurnal cycle of cloud top temperature within the instrument's field of view and discuss these results in relation to retrieval biases. We evaluate SEVIRI cloud top temperatures from the new CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) dataset against Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Results show a mean bias of +0.44 K with a standard deviation of 11.7 K, which is in agreement with previous validation studies. Analysis of the spatio-temporal distribution of these errors shows that absolute retrieval biases vary from less than 5 K over the southeast Atlantic Ocean up to 30 K over central Africa at night. Night- and daytime retrieval biases can also differ by up to 30 K in some areas, potentially contributing to biases in the estimated amplitude of the diurnal cycle. This illustrates the importance of considering spatial and diurnal variations in retrieval errors when using the CLAAS-2 dataset. Keeping these biases in mind, we quantify the seasonal, diurnal, and spatial variation of cloud top temperature across SEVIRI's field of view using the CLAAS-2 dataset. By comparing the mean diurnal cycle of cloud top temperature with the retrieval bias, we find that diurnal variations in the retrieval bias can be small but are often of the same order of magnitude as the amplitude of the observed diurnal cycle, indicating that in some regions the diurnal cycle apparent in the observations may be significantly impacted by diurnal variability in the accuracy of the retrieval. We show that the CLAAS-2 dataset can measure the diurnal cycle of cloud tops accurately in regions of stratiform cloud such as the southeast Atlantic Ocean and Europe, where cloud top temperature retrieval biases are small and exhibit limited spatial and temporal variability. Quantifying the diurnal cycle over the tropics and regions of desert is more difficult, as retrieval biases are larger and display significant diurnal variability. CLAAS-2 cloud top temperature data are found to be of limited skill in measuring the diurnal cycle accurately over desert regions. In tropical regions such as central Africa, the diurnal cycle can be described by the CLAAS-2 data to some extent, although retrieval biases appear to reduce the amplitude of the real diurnal cycle of cloud top temperatures. This is the first study to relate the diurnal variations in SEVIRI retrieval bias to observed diurnal cycles in cloud top temperature. Our results may be of interest to those in the observation and modelling communities when using cloud top properties data from SEVIRI, particularly for studies considering the diurnal cycle of convection.

Focussing on the major cities of Brisbane, Sydney, and Melbourne in southeast Australia, this study seeks to determine the environmental factors that distinguish between heavy rainfall events (HREs) and extreme rainfall events (EREs). Using daily rain gauge observations, HREs and EREs are defined for each domain based, respectively, on the 95th and 99th percentiles of wet‐day rainfall for the period 1979–2018. K‐means clustering is applied to mean sea‐level pressure, data from ERA5 to obtain a set of representative large‐scale circulation patterns associated with these events. Composite synoptic maps, mean vertical profiles, and a series of column‐integrated diagnostics are then examined for each cluster and used to compare the environmental characteristics of HREs and EREs. For all three cities, HREs are associated with an upper‐level trough to the west, with large‐scale ascent, positive column water vapour (CWV) anomalies, and strong moisture transport over the analysis domain. For Brisbane and Sydney, the clusters are characterised by a coastal trough/low with moist onshore flow from the Tasman and Coral Seas. For Melbourne, circulation patterns are more distinct, with clusters characterised by a front, a cut‐off low, and an inland trough. Compared with HREs, EREs show a more amplified upper‐level trough, with stronger vertical motion and larger CWV anomalies over the analysis domain. In Brisbane and Sydney, EREs also feature stronger and deeper onshore flow, promoting enhanced moisture transport. A diagnostic termed upward vapour transport, which combines the key ingredients of high CWV and large‐scale ascent, is shown to discriminate well between HREs and EREs in all three domains. In contrast, surface temperature, which is frequently linked to rainfall extremes via Clausius–Clapeyron scaling, shows significant overlap between the different event categories, particularly for Brisbane and Sydney.

This paper presents incorporation of the microphysical piggybacking into the Weather Research and Forecasting (WRF) model. Microphysical piggybacking is to run a single simulation applying two microphysical schemes, the first scheme driving the simulation and the second piggybacking this simulated flow. “Driving the simulation” means that the simulated microphysical processes, affect the cloud buoyancy and thus force the simulated flow. In contrast, the piggybacking variables are advected by the simulated flow and undergo microphysical transformation, but they do not affect the simulated flow (like in prescribed flow—kinematic—simulations). The two sets of variables (driver and piggybacker) include temperature, water vapor mixing ratio, and all microphysical variables. We provide details of implementing piggybacking into the WRF model, illustrate its applications, and demonstrate the benefits of this methodology in two idealized three‐dimensional cases: (a) a squall line case applying two microphysics schemes, the Thompson bulk microphysics scheme and the University of Pécs/NCAR bin (UPNB) scheme. The piggybacking simulations revealed that the microphysics‐dynamics interaction plays a more important role than the pure microphysical size sorting effect in the transition zone formation. (b) A case of daytime shallow‐to‐deep convective development over land. This case uses the UPNB scheme and contrasts convection developing in environments with either pristine or polluted cloud condensation nuclei (CCN). The piggybacking results indicated that the increase of cloud cover and decrease of supersaturation are mainly associated with the microphysical effect of increasing CCN while the change of precipitation on the ground is also influenced by microphysics‐dynamics interactions.