A new methodology is developed to construct a global (60°S–60°N) long‐term (2000–2019) high‐resolution (∼10‐km h) mesoscale convective system (MCS) database by tracking MCS jointly using geostationary satellite infrared brightness temperature (T_b) and precipitation feature (PF) characteristics from the Integrated Multi‐satellitE Retrievals for GPM precipitation data sets. Independent validation shows that the satellite‐based MCS data set is able to reproduce important MCS statistics derived from ground‐based radar network observations in the United States and China. We show that by carefully considering key PF characteristics in addition to T_b signatures, the new method significantly improves upon previous T_b‐only methods in detecting MCSs in the midlatitudes for all seasons. Results show that MCSs account for over 50% of annual total rainfall across most of the tropical belt and in selected regions of the midlatitudes, with a strong seasonality over many regions of the globe. The tracking database allows Lagrangian aspects such as MCS lifetime and translational speed and direction to be analyzed. The longest‐lived MCSs preferentially occur over the subtropical oceans. The land MCSs have higher cloud‐tops associated with more intense convection, and oceanic MCSs have much higher rainfall production. While MCSs are observed in many regions of the globe, there are fundamental differences in their dynamic and thermodynamic structures that warrant a better understanding of processes that control their evolution. This global database provides significant opportunities for observational and modeling studies of MCSs, their characteristics, and roles in regional and global water and energy cycles, as well as their hydrologic and other impacts.

Realistic simulation of the Earth's mean-state climate remains a major challenge, and yet it is crucial for predicting the climate system in transition. Deficiencies in models' process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections and incorrect attribution of the physical mechanisms governing past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and long-standing biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it is possible to achieve improvements to the historical evolution of surface temperature over EAMv1 and that precise knowledge of global mean ERFs is not enough to constrain historical or future climate change. Cloud feedbacks are also significantly reduced in the recalibrated model, suggesting that there would be a lower climate sensitivity when it is run as part of the fully coupled E3SM. This study also compares results from incremental changes to cloud microphysics, turbulent mixing, deep convection, and subgrid effects to understand how assumptions in the representation of these processes affect different aspects of the simulated atmosphere as well as its response to forcings. We conclude that the spectral composition and geographical distribution of the ERFs and cloud feedback, as well as the fidelity of the simulated base climate state, are important for constraining the climate in the past and future.

On 15 February 2022, the city of Petrópolis in the highlands of the state of Rio de Janeiro, Brazil, received an unusually high volume of rain within 3 h (258 mm), generated by a strongly invigorated mesoscale convective system. It resulted in flash floods and subsequent landslides that caused the deadliest landslide disaster recorded in Petrópolis, with 231 fatalities. In this paper, we analyzed the root causes and the key triggering factors of this landslide disaster by assessing the spatial relationship of landslide occurrence with various environmental factors. Rainfall data were retrieved from 1977 to 2022 (a combination of ground weather stations and the Climate Hazards Group InfraRed Precipitation – CHIRPS). Remotely sensed data were used to map the landslide scars, soil moisture, terrain attributes, line-of-sight displacement (land surface deformation), and urban sprawling (1985–2020). The results showed that the average monthly rainfall for February 2022 was 200 mm, the heaviest recorded in Petrópolis since 1932. Heavy rainfall was also recorded mostly in regions where the landslide occurred, according to analyses of the rainfall spatial distribution. As for terrain, 23 % of slopes between 45–60^∘ had landslide occurrences and east-facing slopes appeared to be the most conducive for landslides as they recorded landslide occurrences of about 9 % to 11 %. Regarding the soil moisture, higher variability was found in the lower altitude (842 m) where the residential area is concentrated. Based on our land deformation assessment, the area is geologically stable, and the landslide occurred only in the thin layer at the surface. Out of the 1700 buildings found in the region of interest, 1021 are on the slope between 20 to 45^∘ and about 60 houses were directly affected by the landslides. As such, we conclude that the heavy rainfall was not the only cause responsible for the catastrophic event of 15 February 2022; a combination of unplanned urban growth on slopes between 45–60^∘, removal of vegetation, and the absence of inspection were also expressive driving forces of this disaster.

The rain parameters derived from the laser OTT second‐generation Particle Size Velocity (Parsivel²), the two‐dimensional video disdrometer (2DVD), and DZZ5 tipping‐bucket rain gauge (RG) during the Integrative Monsoon Frontal Rainfall Experiment (IMFRE) in summer 2018 are compared. The total rainfall amounts observed by Parsivel, 2DVD, and RG during IMFRE were 178.07, 176.76, and 182.5 mm, while their total rainy hours were 113, 113, and 90. The mean D_m (mass‐weighted mean diameter) and LWC (liquid water content) values derived from Parsivel and 2DVD were 1.03 and 1.01 mm and 0.247 and 0.223 g m⁻³. The rainy samples from six Parsivel sites over the middle reaches of the Yangtze River during the 2016–2018 Meiyu seasons have been collected and analyzed. The occurrence frequencies for rain rates (RR) < 5 and >10 mm hr⁻¹ were 83.9% and 7.4%, respectively, but they contributed 30.5% and 52.1% of the total accumulated rainfall. Compared with the results over the lower reaches of the Yangtze River, the total accumulated rainfall percentages for RR < 5 mm hr⁻¹ and RR > 10 mm hr⁻¹ observed at XN site were higher and lower, respectively. The stratiform rain (SR) raindrop size increases with RR over both the middle and lower reaches, whereas the N_w values over the middle reaches are much higher. Opposite to the SR results, the convective rain (CR) raindrops from this study are larger, while their N_w values are similar to one another. For gamma‐type‐size distribution, the μ − λ and Z − R relations are strongly dependent on geographical location.

Located on the south-facing slope of the Himalayas, Nepal receives intense, long-lasting precipitation during the Asian summer monsoon, making Nepal one of the most susceptible countries to flood and landslide hazards in the region. However, sparse gauging and irregular measurement constrain the vulnerability assessments of floods and landslides, which rely highly on the accuracy of precipitation. Therefore, this study evaluates the performance of Satellite-based Precipitation Products (SPPs) in the Himalayas region by comparing different datasets and identifying the best alternative of gauge-based precipitation for hydro-meteorological applications. We compared eight SPPs using statistical metrics and then used the Multi-Criteria Decision-Making (MCDM) technique to rank them. Secondly, we assessed the hydrological utility of SPPs by simulating them through the GR4J hydrological model. We found a high POD (0.60–0.80) for all SPPs except CHIRPS and PERSIANN; however, a high CC (0.20–0.40) only for CHIRPS, IMERG_Final, and CMORPH. Based on MCDM, CMORPH and IMERG_Final rank first and second. While SPPs could not simulate daily discharge (NSE < 0.28), they performed better for monthly streamflow (NSE > 0.54). Overall, this study recommends CMORPH and IMERG_Final and improves the understanding of data quality to better manage hydrological disasters in the data-sparse Himalayas. This study framework can also be used in other Himalayan regions to systematically rank and identify the most suitable datasets for hydro-meteorological applications.

Six deep convective systems (DCSs) with a total of 5589 five‐second samples and a range of temperatures from −41°C to 0°C during the Midlatitude Continental Convective Clouds Experiment (MC3E) were selected to investigate the ice cloud microphysical properties of DCSs over the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. The ice cloud measurements of the DCS cases were made by the University of North Dakota Citation II research aircraft, and the ice cloud properties were derived through the following processes. First, the instances of supercooled liquid water in the ice‐dominated cloud layers of DCSs have been eliminated using multisensor detection, including the Rosemount Icing Detector, King and Cloud Droplet Probes, as well as 2DC and Cloud Imaging Probe images. Then the Nevzorov‐measured ice water contents (IWCs) at maximum diameter D_max < 4000 µm are used as the best estimation to determine a new mass‐dimensional relationship. Finally, the newly derived mass‐dimensional relationship (a = 0.00365, b = 2.1) has been applied to a full spectrum of particle size distributions (PSDs, 120–30,000 µm) constructed from both 2DC and High‐Volume Precipitation Spectrometer measurements to calculate the best‐estimated IWCs of DCSs during MC3E. The averages of the total number concentrations (N_t), median mass diameter (D_m), maximum diameter (D_max), and IWC from six selected cases are 0.035 cm⁻³, 1666 µm, 8841 µm, and 0.45 g m⁻³, respectively. The gamma‐type‐size distributions are then generated matching the observed PSDs (120–30,000 µm), and the fitted gamma parameters are compared with the observed PSDs through multimoment assessments including first moment (D_m), third moment (IWC), and sixth moment (equivalent radar reflectivity, Z_e). For application of observed PSDs to the remote sensing community, a series of empirical relationships between fitted parameters and Z_e values has been derived, and the bullet rosette ice crystal backscattering relationship has been suggested for ground‐based remote sensing.