Organized deep convection plays a critical role in the global water cycle and drives extreme precipitation events in tropical and mid-latitude regions. However, simulating deep convection remains challenging for modern weather forecasts and climate models due to the complex interactions of processes from microscales to mesoscales. Recent models with kilometer-scale (km-scale) horizontal grid spacings (Δx) offer notable improvements in simulating deep convection compared to coarser-resolution models. Still, deficiencies in representing key physical processes, such as entrainment, lead to systematic biases. Additionally, evaluating model outputs using process-oriented observational data remains difficult. In this study, we present an ensemble of MCS simulations with Δx spanning the deep convective grey zone (Δx from 12 km to 125 m) in the Southern Great Plains of the U.S. and the Amazon Basin. Comparing these simulations with Atmospheric Radiation Measurement (ARM) wind profiler observations, we find greater Δx sensitivity in the Amazon Basin compared to the Great Plains. Convective drafts converge structurally at sub-kilometer scales, but some discrepancies, such as too-deep up- and downdrafts and too-weak peak downdrafts in both regions or too-strong updrafts in Amazonian storms remain. Overall, we observe higher Δx sensitivity in the tropics, including an artificial buildup in vertical velocities at five times the Δx, suggesting a need for Δx≤250 m. Nevertheless, bulk convergence - agreement of storm average statistics - is achievable with km-scale simulations within a ±10 % error margin, with Δx=1 km providing a good balance between accuracy and computational cost.

Mesoscale convective systems (MCSs) are an important component of our hydrologic cycle as they produce prolific rainfall in the tropics and mid-latitudes. Recent advancements in high-resolution modeling show promise in representing MCSs in regional climate simulations. However, how well do these models represent the complex interactions between convective dynamics and microphysics in MCSs remain unknown. In this study, we take advantage of observations collected during the Midlatitude Continental Convective Cloud (MC3E) experiment to evaluate multi-scale aspects of MCSs in convection-permitting WRF model. We conducted three sets of month-long simulations with Morrison and P3 (1-ice and 2-ice categories) microphysics, respectively, at 1.8 km grid-spacing over the Southern Great Plains. MCSs in observations and simulations were tracked using a newly developed FLEXTRKR algorithm. About 15-20 MCSs were identified in the simulations, consistent with observations. All three simulations underestimate observed monthly total precipitation which are primarily from MCSs, suggesting the biases might be caused by large-scale forcings rather than microphysics. All simulated MCSs overestimate convective area and precipitation amount but underestimate stratiform rain area and precipitation. Simulated MCS convective updraft intensities are comparable with radar retrievals for moderate depths of convective cores, but are too strong for deep cores. The two P3 simulations have smaller mean ice mass aloft but more frequent heavy convective rain rate at the surface than the simulation with Morrison, agreeing better with observations (Figure 1). Simulated stratiform area ice mass in the upper troposphere are generally larger than radar retrievals, but the P3 2-ice category has relatively smaller bias. We will also use polarimetric radar 3-D rain water retrieval to further evaluate the vertical evolution of rainfall to explain differences in simulated surface precipitation.

Mesoscale convective systems (MCSs) are the main source of precipitation in the tropics and parts of the mid-latitudes and are responsible for high-impact weather worldwide. Studies showed that deficiencies in simulating mid-latitude MCSs in state-of-the-art climate models can be alleviated by kilometer-scale models. However, whether these models can also improve tropical MCSs and weather we can find model settings that perform well in both regions is understudied. We take advantage of high-quality MCS observations collected over the Atmospheric Radiation Measurement (ARM) facilities in the U.S. Southern Great Plains (SGP) and the Amazon basin near Manaus (MAO) to evaluate a perturbed physics ensemble of simulated MCSs with 4\,km horizontal grid spacing. A new model evaluation method is developed that enables to distinguish biases stemming from spatiotemporal displacements of MCSs from biases in their reflectivity and cloud shield. Amazon MCSs are similarly well simulated across these evaluation metrics than SGP MCSs despite the challenges anticipated from weaker large-scale forcing in the tropics. Generally, SGP MCSs are more sensitive to the choice of model microphysics, while Amazon cases are more sensitive to the planetary boundary layer (PBL) scheme. Although our tested model physics combinations had strengths and weaknesses, combinations that performed well for SGP simulations result in worse results in the Amazon basin and vice versa. However, we identified model settings that perform well at both locations, which include the Thompson and Morrison microphysics coupled with the Yonsei University (YSU) PBL scheme and the Thompson scheme coupled with the Mellorâ\euro“Yamadaâ\euro“Janjic (MYJ) PBL scheme.