connections to environmental factors. From November 2023 to January 2024, the Democratic Republic of the Congo and Congo-Brazzaville experienced their worst flooding in 60 years (Davies, 2024;Joachim et al., 2024). We apply satellite observations and MPAS-A to analyze MCSs from November 21 to 25, 2023. We then apply the Tracking Algorithm for Mesoscale Convective Systems (TAMS; Núñez Ocasio et al., 2020b;Núñez Ocasio & Moon, 2024) to objectively identify, track, and classify MCSs over the Congo Basin. TAMS offers capabilities comparable to other tracking methods but stands out due to its unique tracking approach, MCS classification scheme, and consideration of background flow. Once MCS tracks are obtained, we focus on long-lasting (over 15 hours) MCS events and use MPAS-A to quantify the role of key environmental factors in MCS developments. The remainder of the paper is organized as follows.
Data and methodology are described in Section 2. Section 3.1 discusses Congo Basin rainfall and MCS statistics. Section 3.2 evaluates model's ability to simulate mesoscale convective processes.
The role of vertical wind shear and the AEJ is discussed in Section 3.3. A summary and additional discussion are provided in Section 4.
We use hourly data to examine Congo Basin MCSs for the period of November 21-25, 2023. The National Centers for Environmental Prediction (NCEP)/Climate Prediction Center (CPC) L3 Half Hourly 4km Global Merged infrared (IR) Tb data (Janowiak et al., 2017) is used to detect MCSs. This Tb data has been widely used in tracking MCSs, especially over tropical regions (e.g., Feng et al., 2021Feng et al., , 2023aFeng et al., , 2025;;Chen et al., 2023;Prein et al., 2024;Muetzelfeldt et al., 2025). Due to the sparsity of gauge networks over the Congo Basin in recent decades (Nicholson et al., 2018), we will mainly use satellite precipitation data. Since MCS detection requires high-resolution, sub-daily data, we analyze precipitation associated with identified MCSs using half-hourly precipitation data from the CPC Morphing Technique (CMORPH) Climate Data Record (CDR) with an 8 km horizontal resolution (Xie et al., 2019) and the Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) Final Precipitation with a 0.1° horizontal resolution (Huffman et al., 2023).
In addition to hourly data, daily precipitation data are used to analyze daily and monthly precipitation over the Congo Basin. These datasets include CMORPH CDR (0.25° horizontal resolution, 1998-2024) (Xie et al., 2019), GPM IMERG Final Precipitation (0.1° horizontal resolution, 2001-2024) (Huffman et al., 2023), and the CPC Global Unified Gauge-Based Analysis of Daily Precipitation (0.5° horizontal resolution, 1979-2024) (Chen et al., 2008).
Additionally, daily runoff data are obtained from the Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model L4 (0.25° horizontal resolution, 2004-2024) (Li et al., 2020).
MPAS-A is a global cloud-resolving model representing a new category of atmospheric models and is a participant in DYAMOND, the first intercomparison project of such models (Stevens et al., 2019). It solves non-hydrostatic equations using kilometer-scale global meshes and simulates deep convection explicitly (Skamarock et al., 2012;Satoh et al., 2019). In this study, we use MPAS-A version 8 modified to output variables at 27 isobaric levels (Núñez Ocasio et al., https://doi.org/10.5194/egusphere-2025-3591 Preprint. Discussion started: 25 August 2025 c Author(s) 2025. CC BY 4.0 License.
). We use a variable-resolution, 60-3 km global mesh, and the Congo Basin is roughly within the 3 km domain (Fig. 1a). The model uses 55 vertical levels up to ~30 km height.
The model physics follows the standard mesoscale-reference suite incorporating the new scale-aware Tiedtke convection scheme, which is newly added to MPAS-A version 8 and suitable for a convection-permitting mesh with grid spacing below 10 km (Wang, 2022). The scale-aware Tiedtke scheme effectively reduces deep convection by decreasing the convective portion of total surface precipitation, and also ensures smooth handling of convection across mesh transition zones when applied to a variable mesh in MPAS-A (Wang, 2022). The other schemes of the physics suite include the WSM6 microphysics scheme (Hong & Lim, 2006), the Noah land surface scheme (Niu et al., 2011), the YSU boundary layer scheme (Hong et al., 2006), the Monin-Obukhov surface layer scheme (Jiménez et al., 2012), the RRTMG shortwave and longwave radiation scheme (Iacono et al., 2008), and the Xu-Randall subgrid cloud fraction scheme (Xu & Randall, 1996).
We use the European Centre for Medium-Range Weather Forecasts (ECMWF) fifthgeneration reanalysis (ERA5; Hersbach et al., 2020), including a complete set of atmospheric pressure level and surface variables, to initialize model simulations. The experiments begin at 1200 UTC on November 21, 2023, running for four days, with the first six hours discarded for spin-up.
The model produces hourly outputs at 27 isobaric levels. In addition to the primary simulation discussed above, we have conducted additional simulations to ensure that our conclusions were not influenced by model stochasticity. In the sensitivity tests, random perturbations were added to the 1000-hPa potential temperature field following a Gaussian distribution with a standard deviation of 0.6 K, as in Núñez Ocasio et al. (2024). Since the results of the sensitivity tests closely resemble those of the unperturbed simulation, we present only the model simulation without random perturbations in this study.
The Congo Basin MCS tracking is conducted objectively using the latest version of TAMS, an open-source, Python-based package for tracking and classifying MCSs (Núñez Ocasio & Moon, 2024). Following the MCS tracking algorithm described in Núñez Ocasio et al. (2020b) and Núñez Ocasio & Moon (2024), hourly Tb contours are used to identify cloud elements (CEs), selecting 235 K regions that contain embedded 219 K areas of at least 4,000 km 2 . In MPAS-A, Tb is derived from the model's outgoing longwave radiation output following the method of Yang & Slingo (2001). MCS tracks are determined by linking CEs from the current time step to those from the previous step based on maximum CE polygon overlap. Once an MCS is identified and tracked, its complete trajectory is analyzed, allowing its entire lifespan to be assigned to a single classification.
Table 1 shows the MCS classification criteria (e.g., Maddox, 1980;Evans & Shemo, 1996;Tsakraklides & Evans, 2003;Núñez Ocasio et al., 2020b) used in TAMS, which includes four categories: mesoscale convective complex (MCC), convective cloud cluster (CCC), disorganized long-lived (DLL), and disorganized short-lived (DSL). TAMS outputs include the latitude and longitude centroids of 219 K and 235 K regions for each MCS at every time step, along with additional statistics such as area, duration, and mean precipitation for each identified MCS. 210 3 Results 211 3.1 Congo Basin rainfall and MCS statistics 214 resolution from 3 km to 40 km. The pink box represents the Congo Basin (10°S-5°N, 15°E-30°E), 215 which roughly has the resolution of 3 km. (b) The elevation (m) of the Congo Basin (within the 216 dashed box). Blue lines indicate rivers. (c) Climatology of the monthly mean basin-averaged 217 precipitation (mm/day) for three observed datasets. The climatological period for each dataset 218 corresponds to its available data range, as detailed in Section 2.1. 219 220 https://doi.org/10.5194/egusphere-2025-3591 Preprint. Discussion started: 25 August 2025 c Author(s) 2025. CC BY 4.0 License.
The Congo Basin spans a vast area across Central Africa, with elevation varying significantly. It primarily consists of lowlands in the west, central, and northern regions, surrounded by higher elevations in the south and east (Fig. 1b). It is home to a complex river system, dominated by the Congo River, the second largest in the world by discharge. The seasonal cycle of Congo Basin domain-averaged precipitation exhibits a biannual pattern, with the first peak occurring in March-April and the second in October-November (Fig. 1c). The satellite data generally show a higher daily precipitation rate compared to gauge-based data, a discrepancy that has been widely studied in comparisons with gauging station networks (e.g., Hughes, 2006). This biannual cycle is recognized as a key characteristic of Congo Basin rainfall (e.g., Washington et al., 2013;Pokam et al., 2014;Dyer et al., 2017), though it may be more pronounced south of the equator (Nicholson, 2022).
Between November 2023 and January 2024, the Democratic Republic of the Congo and Congo-Brazzaville experienced their worst flooding in 60 years (Davies, 2024;Joachim et al., 2024). MCSs account for approximately 80% of the total rainfall in the Congo Basin (e.g., Mohr et al., 1999;Nicholson, 2022;Andrews et al., 2024), this study focuses on the period mentioned above to improve the process-level understanding of MCSs in this region.
https://doi.org/10.5194/egusphere-2025-3591 Preprint. Discussion started: 25 August 2025 c Author(s) 2025. CC BY 4.0 License. Following the method in Section 2, we apply TAMS to objectively identify, track, and classify MCSs. Figure 3a displays all identified MCSs that passed through the Congo Basin during the study period in observations. For simplicity, only the initial and final locations are shown.
Consistent with previous studies (Jackson et al., 2009;Laing et al., 2011;Hartman, 2020), most
MCSs propagate westward, with a substantial number originating in the lee side of the high terrain of the Great Rift Valley. Following the strictest criteria in Table 1,
In this section, we evaluate how well MPAS-A simulates MCSs by conducting simulations for November 21-25, 2023, as described in Section 2. We do not expect the model to capture every individual MCS track in MPAS-A, instead our goal is to assess whether long-duration or longtrack MCS events and the overall MCS statistics are realistically represented. The model successfully simulates the general westward propagation of MCSs, but the number of MCC cases is lower than in observations (Fig. 3b). Notably, the long-track MCC case over the southern basin (referred to as MCC-south) is well captured in terms of spatial location. Figure 3c-d compares MCS characteristics between observations and model simulations. The MCS duration is well simulated, particularly for DSL, DLL, and CCC (Fig. 3c). MCCs typically last over 15 hours, with one observed case lasting 27 hours. The two simulated MCCs last 15 and 20 hours, respectively, consistent with observations. Compared to observations, the model overestimates rainfall for all four categories (Fig. 3d), likely due to stronger simulated convection and increased moisture convergence, consistent with previous studies (e.g., Raghavendra et al., 2022;Feng et al., 2023bFeng et al., , 2025)).
We compare the spatial distribution of the observed MCC-south case with that from MPAS-A simulations. The observed Tb shows that MCC-south originates from the Great Rift Valley (Fig. 4a), propagates westward during the late afternoon and evening (local time UTC +01:00 or +02:00, Fig. 4b-c), and weakens in the following morning (Fig. 4d). The mean area of the observed MCC-south (averaged across all time steps) is 6.7×10 5 km 2 , covering nearly 30% of the Congo Basin, and the mean area enclosed by the 219 K contours is 3.2×10 5 km 2 , approximately half of the total MCC area. The simulated MCC-south originates from the Great Rift Valley, though slightly farther south, and propagates westward across the Congo Basin (Fig. 4e-h). While the simulated Tb is more scattered and the mean MCC area is 12% smaller than observed, the model still successfully captures the timing and general location of MCC-south. To better understand the evolution of Tb and associated rainfall, we present the Hovmöller diagram along the MCC track. Figure 5a illustrates the observed Tb evolution for MCC-south, which originates near 29°E, featuring three centers (Tb < 210 K) along its path, excluding the one east of 30°E, as it does not belong to MCC-south. The simulated Tb evolution shares a similar pattern with observations, but during the initial stage, Tb magnitudes are smaller, and the Tb pattern appears more scattered (Fig. 5b). The simulated MCC strengthens (Tb decreases) significantly during the mid-stage of MCC-south (around 16Z), warranting further analyses to investigate the underlying mechanisms. While rainfall from the two observations generally aligns
with Tb from NCEP/CPC data, CMORPH shows lower rainfall in the early-mid stages and higher rainfall in the later stage (Fig. 5c). Consistent with Fig. 3d, the simulated rainfall magnitudes exceed those in observations (Fig. 5d). The high rainfall magnitudes in cloud-resolving models have also been observed in previous studies (e.g., Raghavendra et al., 2022;Feng et al., 2023bFeng et al., , 2025)). Despite these differences, the results above suggest that MPAS-A shows promise in simulating long-lived, long-track MCC over the Congo Basin. Next, we utilize the model to further understand mesoscale convective processes by comparing two representative MCC cases, MCC-south and another MCC case over the northern basin, referred to as MCC-north (Fig. 3b). MCC-north originates during the evening over the northeastern boundary of the Congo Basin, where the elevation ranges from approximately 700 to 1000 m (Fig. 6a). The system then propagates westward through midnight (Fig. 6b) and weakens over lowlands, where elevations drop to 400-700 m or lower, by early morning (Fig. 6c). The mean area of the simulated MCC-north is about 22% of that of MCC-south, with a shorter lifecycle of 15 hours compared to 20 hours for MCC-south. Additionally, its moving distance is approximately 35% of that of MCC-south. Overall, MCC-north is smaller in size, shorter in duration, and less extensive in movement compared to MCC-south.
where 𝐿 𝑣 is latent heat of vaporization, 𝑞 is specific humidity, 𝑐 𝑝 is specific heat capacity at constant pressure, 𝑇 is air temperature, and 𝛷 is geopotential. Figure 7c shows that MSE decreases with height for both MCC cases, indicating a convectively unstable environment favorable for MCC developments. MCC-south exhibits higher MSE from 850 hPa to the mid-troposphere, suggesting greater potential for convection and stronger moist updrafts.
In this section, we examine dynamic factors driving the different evolutions of the two MCC cases. Although previous studies have explored the relationship between MCSs over the Congo Basin or equatorial Africa and dynamic factors such as vertical wind shear and AEJ (Nguyen & Duvel, 2008;Jackson et al., 2009;Laing et al., 2011), they primarily relied on low-resolution reanalysis data from a climate perspective without quantitative analyses based on MCS tracks.
Weather-focused investigations, such as quantifying the influence of these dynamic factors on MCS developments along its track, require cloud-resolving model simulations (Laing et al., 2011).
During the mid-stage (16Z) of MCC-south, its convection, linked to low-tropospheric convergence (blue shading) and updrafts (red contours), is concentrated over elevated terrain (Fig. 8a), with a cold pool region behind it (Fig. 9a). Westerly mountain-valley breezes ahead of the MCC lift moisture-rich air from the lower troposphere, providing abundant latent energy to fuel the system. As the MCC propagates into the valley, convergence intensifies from the surface to the mid-troposphere, accompanied by enhanced vertical wind shear between the lower and midtroposphere ahead of the center (Fig. 8b) and a narrow cold pool region trailing the MCC center (Fig. 9b). The vertical wind shear ahead of the MCC-south track is generally easterly. For MCCnorth, however, the system reaches the lowland forest region during its mid-stage (21Z), where the lower troposphere is dominated by divergence (orange shading), inhibiting the upward transport of low-level moisture to the mid-troposphere (Fig. 8c). The cold pool behind the MCC center extends broadly in the zonal direction (from 26°E to 29°E, ~300 km), with temperatures dropping by up to 4 K (291 K vs 295 K) (Fig. 9c). Compared to MCC-south, lower-level westerly winds are absent ahead of the MCC-north, resulting in generally weaker easterly wind shear (Fig. 8c-d); the cold pool is generally stronger and extends broader zonally in MCC-north (Fig. 9c-d). According to Rotunno et al. (1988), that is RKW theory, the weaker vertical wind shear may result from an overly strong cold pool, characterized by greater intensity and broader horizontal extent in this case, causing the convective system to tilt upshear and limiting MCC developments (e.g., Schumacher & Rasmussen, 2020;Kirshbaum et al., 2025). In contrast, MCC-south features a more favorable balance between vertical wind shear and cold pool strength, approaching the "optimal state" for MCC developments during the mid-stage.
We further quantify how vertical wind shear influences MCC evolution by using a simple linear regression model (e.g., Zhao & Fu, 2022):
where 𝑇𝑏 is brightness temperature at the MCC center, ∇𝑈 𝑥 is vertical zonal wind shear (𝑈 600 -𝑈 875 for MCC-south and 𝑈 600 -𝑈 900 for MCC-north) with the longitudinal distance (𝑥; i.e., degree) relative to the MCC center, and 𝛼 and 𝛽 are regression coefficients. We calculate coefficient of determination (𝑅 2 = 1 -𝑅𝑆𝑆/𝑇𝑆𝑆, where 𝑅𝑆𝑆 is sum of squares of residuals and 𝑇𝑆𝑆 is total sum of squares) to assess how much of the variance in Tb can be explained by wind shear. Figure 10a shows that vertical wind shear located approximately 1.5° to 4° ahead of the MCC-south center explains between 35% and 65% of the total variance in Tb, with the largest explained variance around 2° west of the MCC center. This result suggests that vertical wind shear ahead of the MCC-south center plays a key role in modulating convection, explaining up to 65% of the Tb variance, and that this favorable environment may promote the formation of a gust front ahead of the MCC center (Schumacher & Rasmussen, 2020). Strong convection (Tb < 220 K) is associated with easterly wind shear exceeding 10 m/s (Fig. 10b). In contrast, for MCC-north, the wind shear that significantly explains Tb variance (~60%) is confined near the center of the MCC, with a sharp decline beyond 1° west of the center (Fig. 10c), indicating that the generation of convective cells is primarily close to cold pool. During several timesteps along the track, MCCnorth exhibits weak or even westerly shear (positive values), despite persistent easterly flow in the mid-troposphere along the MCC track (Fig. 10d). This suggests that the lower troposphere experiences relatively stronger easterly winds than the mid-level. Therefore, compared to MCC-north, the longer duration and longer track of MCC-south are likely associated with a more favorable pre-existing wind shear structure extending up to ~400 km ahead of the system. According to the RKW theory, this shear environment can achieve an "optimal" balance with the cold pool, effectively interacting with its outflow to lift warm, moist air, sustain updrafts, and promote the continued development and forward propagation of the MCC. These differences between the two MCC cases highlight the role of latitude and topography in modulating the impacts of environmental factors on MCS developments.
Finally, we investigate the linkage between the AEJ and the vertical wind shear associated with the two MCC cases. The AEJ system, consisting of both a northern and a southern branch (AEJ-N and AEJ-S), can promote mid-tropospheric moisture convergence within the right entrance region of the jet, thereby enhancing rainfall (Uccellini and Johnson, 1979;Jackson et al., 2009). Figure 11a shows zonal winds at 650 hPa averaged over the study period. The AEJ centers are typically identified by zonal wind speeds exceeding 6 m/s, and their approximate locations in this figure are consistent with previous studies (e.g., Jackson et al., 2009). MCC-north and MCC-
This study investigates mesoscale convective processes over the Congo Basin using MPAS-A. MCSs are objectively identified, tracked, and classified using the TAMS algorithm for the period of November 21-25, 2023, during which severe flooding was observed across the region. The model captures key MCS characteristics, including their general westward propagation and typical lifespans, although it overestimates rainfall-an issue attributed to known model biases (e.g., Raghavendra et al., 2022;Feng et al., 2023bFeng et al., , 2025)). Among the identified MCSs, one longlived and long-track MCC is well represented in terms of its timing and location, demonstrating the model's capability to simulate MCS in the Congo Basin.
Two MCC cases-one over the southern mountainous region (MCC-south) and the other over the northern lowland forests (MCC-north)-are compared to examine the distinct environmental factors influencing MCC developments. Overall, MCC-south is larger in size, longer in duration, and moves over a longer distance than MCC-north. These differences are accompanied by higher CAPE and greater low-to mid-level MSE in MCC-south. The contrasting behavior is particularly evident during the mid-stage of each system: MCC-south shows enhanced convection associated with strong lower-to mid-level convergence and sustained updrafts, whereas MCC-north exhibits decreased convection, linked to low-level divergence. These differences reflect the role of vertical wind shear in MCC developments and its potential balance with the cold pool. MCC-south is associated with a more favorable pre-existing shear structure extending up to ~400 km ahead of the system, which explains up to 65% of the Tb variance, along with a moderate cold pool that provides a supportive environment for MCC developments according to RKW theory. In contrast, MCC-north is primarily influenced by wind shear near the system center and a stronger cold pool characterized by a temperature drop of up to 4 K and a zonal extent of ~300 km, both of which may inhibit MCC developments. Finally, while AEJ supports favorable shear (correlation > 0.7), an intensified jet may suppress low-level westerlies and weaken convection.
Compared to previous studies that examined the relationship between Congo Basin MCSs and environmental factors without quantitative investigations based on MCS tracks (e.g., Laing & Fritsch, 1993;Nguyen & Duvel, 2008;Jackson et al., 2009;Laing et al., 2011;Hartman, 2020;Mba et al., 2022;Nicholson, 2022;Kigotsi et al., 2022;Solimine et al., 2022;Andrews et al., 2024), this study provides a process-level view of the initiation, development globally. As a follow-up of this study, we will explicitly examine the influence of large-scale atmospheric dynamic fields and land surface conditions (especially the role of land-atmosphere interaction) on MCSs over the Congo Basin through numerical experiments by MPAS-A (e.g., Koster et al., 2004;Imamovic et al., 2017;Núñez Ocasio et al., 2024). The insights gained have the potential to inform broader applications in other regions, particularly those lacking dense gauge networks. Ultimately, this work advocates for the use of state-of-the-art global cloud-resolving models to advance our understanding of MCSs, given their significant role in driving weather extremes and associated hazards with direct implications for socio-economic stability and human well-being.