Convective Events Research Articles

Development of Vertical Radar Reflectivity Profiles Based on Lightning Density Using the Geostationary Lightning Mapper Dataset in the Subtropical Region of Brazil

The study aims to develop vertical radar reflectivity profiles based on lightning density data from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in the subtropical region of Brazil. The primary objective is to improve the assimilation of lightning data in numerical weather prediction models. The methodology involves the analysis of polarimetric radar data from Chapecó-SC and Jaraguari-MS, spanning from January 2019 to December 2023, and their correlation with lightning data from the GLM. Radar reflectivity profiles were created for different lightning density classes, categorized into six classes based on geometric progression. Results show a significant relationship between lightning activity and radar reflectivity, with distinct profiles for convective and stratiform events. These findings demonstrate the potential of using GLM data to enhance short-term weather forecasting, particularly for severe weather events. The study concludes that the integration of GLM data into weather models can lead to more accurate predictions of intense precipitation events, contributing to better preparedness and response strategies.

Comparison of the Morrison and WDM6 Microphysics Schemes in the WRF Model for a Convective Precipitation Event in Guangdong, China, Through the Analysis of Polarimetric Radar Data

Numerical weather prediction (NWP) models are indispensable for studying severe convective weather events. Research demonstrates that the outcomes of convective precipitation simulations are profoundly influenced by the choice between single or double-moment schemes for ice precipitation particles and the categorization of rimed ice. The advancement of dual-polarization radar has enriched the comparative validation of these simulations. This study simulated a convective event in Guangdong, China, from May 7 to 8, 2017, employing two bulk microphysical schemes (Morrison and WDM6) in the WRF v4.2 model. Each scheme was divided into two versions: one representing rimed ice particles as graupel (Mor_G, WDM6_G) and the other as hail (Mor_H, WDM6_H). The simulation results indicated negligible differences between the rimed ice set as graupel or hail particles, for both schemes. However, the Morrison schemes (Mor_G, Mor_H) depicted a more accurate raindrop size distribution below the 0 °C height level. A further analysis suggested that disparities between the Morrison and WDM6 schemes could be attributed to the intercept parameter (N0) setting for snow and graupel/hail in WDM6 scheme. The prescribed snow and graupel/hail N0 of WDM6 scheme might influence the melting processes, leading to a higher number concentration but a reduced mass-weighted diameter of raindrops. Reducing the intercept parameter for snow and graupel/hail in the WDM6 scheme could potentially enhance the simulation of convective precipitation. Conversely, the increase in N0 might deteriorate the precipitation simulation performance of the WDM6_G scheme, whereas the WDM6_H scheme exhibits minimal sensitivity to such changes.

The Evolution of the 2021 Seacor Power Tragedy in Coastal Louisiana

Abstract On 13 April 2021, a mesoscale convective system (MCS) swept across the southeastern Louisiana coast, capsizing the 39-m Seacor Power roughly 7 km from shore and leaving 13 mariners drowned or missing. In addition to the severe straight-line winds that sank the vessel, sustained surface winds > 20 m s−1 behind the leading convection persisted well after the main convective band, inhibiting search and rescue efforts. Though complete historical fatality statistics are unavailable, the 13 deaths associated with this event likely represent one of the deadliest severe convective weather events in modern U.S. maritime history. This analysis integrates in situ, remotely sensed, and reanalysis datasets to reconstruct the 2021 Seacor Power accident as well as ascertain its depiction in day-of operational convection-allowing model (CAM) guidance. Results suggest that the MCS formed along an unanalyzed coastal boundary and developed a strong mesohigh to the east of the wreck as it moved offshore. The resulting zonally oriented pressure gradient directed stiff easterly winds over the wreck for several hours, even as the squall line had propagated well away from the coast. This multihour period of severe weather along the Louisiana coast was relatively well resolved by morning-of CAM guidance, providing optimism that future such events may be anticipated with the lead times required by vulnerable seacraft to reach safe harbor. Future severe convective weather watches containing marine zones might include a “marine” section detailing the potential sea conditions, analogous to the “aviation” section in current severe weather watches.

Inter-comparison of GNSS- IPWV with ERA-5 IPWV and monitoring of convective events over the Indian region

The present study deals with (i) An Inter-comparison study of ground based Global Navigation Satellite System (GNSS) derived Integrated Precipitable Water Vapour (IPWV) with 5th generation global climate reanalysis data of European Centre for Medium Range Weather Forecast (ERA-5) (ii) IPWV thresholds of GNSS data (iii) case studies of IPWV analysis. It is found that both the datasets (GNSS and ERA-5) are strongly correlated & the correlation coefficient ranging between 0.97 and 0.99. Monthly Thresholds of IPWV (MTI) are generated with 2017 -2020 data sets from Indian GNSS station having continuous data and found very useful input as value addition to know the possibility of building up /decaying of convection in day to day daily forecast issued to the public. Case studies shows an increase of IPWV, 3 to 4 hour prior to the occurrence of convective event around the GNSS station and found very useful for nowcasting. Therefore, utilization of IPWV (model as wells GNSS based) have incremented value in enriching the understanding about the weather event and its forecasting. This supplement information along with other analysis products (model, surface, upper air, satellite or radar etc) can support further in appropriate decision making to the forecasters, decision makers and the end users of the society.

Storms and convection on Uranus and Neptune: Impact of methane abundance revealed by a 3D cloud-resolving model

Context. Uranus and Neptune have atmospheres dominated by molecular hydrogen and helium. In the upper troposphere (between 0.1 and 10 bar), methane is the third main molecule, and it condenses, yielding a vertical gradient in CH4 . As this condensable species is heavier than H2 and He, the resulting change in mean molecular weight due to condensation serves as a factor countering convection, which is traditionally considered as governed by temperature only. This change in mean molecular weight makes both dry and moist convection more difficult to start. As observations also show latitudinal variations in methane abundance, one can expect different vertical gradients from one latitude to another. Aims. In this paper, we investigate the impact of this vertical gradient of methane and the different shapes it can take, including on the atmospheric regimes and especially on the formation and inhibition of moist convective storms in the troposphere of ice giants. Methods. We developed a 3D cloud-resolving model to simulate convective processes at the required scale. This model is nonhydrostatic and includes the effect of the mean molecular weight variations associated with condensation. Results. Using our simulations, we conclude that typical velocities of dry convection in the deep atmosphere are rather low (on the order of 1 m/s) but sufficient to sustain upward methane transport and that moist convection at the methane condensation level is strongly inhibited. Previous studies derived an analytical criterion on the methane vapor amount above which moist convection should be inhibited in saturated environments. In ice giants, this criterion yields a critical methane abundance of 1.2% at 80 K (this corresponds approximately to the 1 bar level). We first validated this analytical criterion numerically. We then showed that this critical methane abundance governs the inhibition and formation of moist convective storms, and we conclude that the intensity and intermittency of these storms should depend on the methane abundance and saturation. In the regions where CH4 exceeds this critical abundance in the deep atmosphere (at the equator and the middle latitudes on Uranus and at all latitudes on Neptune), a stable layer almost entirely saturated with methane develops at the condensation level. In this layer, moist convection is inhibited, ensuring stability. Only weak moist convective events can occur above this layer, where methane abundance becomes lower than the critical value. The inhibition of moist convection prevents strong drying and maintains high relative humidity, which favors the frequency of these events. In the regions where CH4 remains below this critical abundance in the deep atmosphere (possibly at the poles on Uranus), there is no such layer. More powerful storms can form, but they are also a bit rarer. Conclusions. In ice giants, dry convection is weak, and moist convection is strongly inhibited. However, when enough methane is transported upward, through dry convection and turbulent diffusion, sporadic moist convective storms can form. These storms should be more frequent on Neptune than on Uranus because of Neptune’s internal heat flow and larger methane abundance. Our results can explain the observed sporadicity of clouds in ice giants and help guide future observations that can test the conclusions of this work.

Comparing the interactions between particulate matter and cloud properties over two populated cities in Texas using WRF-Chem fine-resolution modeling

Accurate modeling of aerosol-cloud interactions is essential for reliable weather and air quality simulations, given their significant impact on precipitation patterns, cloud dynamics, and aerosol distributions. This study employed the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to examine the impact of enhanced meteorological simulations, achieved through advanced microphysics parameterization supported by data assimilation techniques, on air quality across Texas on August 19 and 20, 2022. We tested four distinct configurations: (1) the Morrison two-moment bulk microphysics scheme, (2) Morrison's with observation nudging, (3) the Spectral Bin Microphysics (SBM), and (4) SBM with observation nudging. While the SBM scheme is known for its detailed representation of aerosol-cloud interactions, our focus was on how improvements in meteorological accuracy translate to more precise air quality simulations. Our findings demonstrated a progressive improvement in simulation accuracy, starting with the Morrison's scheme and further enhanced by adopting the SBM scheme, complemented by incorporating observation nudging. Specifically, the combination of the SBM scheme and the nudging substantially enhanced the model's ability to capture convective precipitation events, as shown by better alignment with NEXRAD radar reflectivity, with R increasing from −0.21 to 0.82, IOA from 0.10 to 0.87, and NMB decreasing from 99% to 34% in Houston. The enhanced meteorology translated into more accurate PM2.5 concentration simulations, particularly through the more accurate representation of aerosol washout during precipitation events. In Houston, the SBM scheme with nudging improved the model's PM2.5 simulations significantly, with NMB decreasing from −20% to 5% and IOA improving from 0.43 to 0.61. In San Antonio, improvements were also notable, with NMB improved from −27% to −22%, R increased from 0.48 to 0.82, and IOA increased from 0.66 to 0.86. Our results underscore the crucial role of accurate meteorological simulations in refining our understanding of aerosol behaviors in relation to precipitation patterns, directly enhancing the reliability and effectiveness of air quality modeling.

Intensification and Changing Spatial Extent of Heavy Rainfall in Urban Areas

AbstractUrban areas have been shown to impact rainfall by altering both its intensity and spatial structure at sub‐hourly and sub‐kilometer scales. However, there is currently no clear understanding of the precise pattern of change and the mechanisms that drive these changes. Since the hydrological response in urban areas is highly sensitive to such rainfall properties, understanding these changes is critical to improving our ability to assess urban flood risk. We use 7 years of high‐resolution weather radar data (4‐ or 5‐min and 1 km) to analyze changes in patterns of rainfall intensity, spatial structure, and storm evolution across eight urban areas within Europe and the United States. The use of the same methodology across the different cities enables a consistent comparison among them. We track convective rainfall events using a storm tracking algorithm and assess changes in rainfall properties in the upwind, center, and downwind regions of each city. We also investigate changes in the frequency of storm initiations, terminations, splitting, and merging. Our results show that urban areas act to intensify rainfall—mostly over them, but sometimes on their peripheries. Overall, larger cities tend to show the largest rainfall enhancements. Our findings highlight that rainfall spatial structure is altered over the urban core; usually resulting in more spatially concentrated rainfall. We also observe increased storm initiations over most cities and increased storm splitting over one. Given that demographic projections show that future urban population will increase, our results point toward an increased future flood risk in growing cities.

AN ANALYSIS OF WIND SPEED VARIATIONS INSIDE AND OUTSIDE OF THE ATMOSPHERIC BOUNDARY LAYER DURING CONVECTIVE STORMS USING SODAR AND RADIOSONDE DATA

This paper analyzes the mean wind speed variations inside the atmospheric boundary layer and in the free troposphere based on data from vertical sounding of the atmosphere using SODAR and radiosonde data. The variation of the mean wind speed inside and outside of the atmospheric boundary layer were analyzed using data collected during warnings of convective storms, but also 12 hours before and after the alerts. The resulting data are important in understanding the way the wind behaves in the atmospheric boundary layer and the free troposphere before, during and after convective events.

Extreme Convective Gusts in the Contiguous USA

Most damage to buildings across the contiguous United States of America (USA) is caused by gusts in convective events associated with thunderstorms. Design rules for structures to resist these events rely on the integrity of meteorological observations and the methods of assessment. These issues were addressed for the US Automated Surface Observation System (ASOS) in six preliminary studies published in 2022 and 2023, allowing this present study to focus on the analysis and reporting of gust events observed between 2000 and 2023 at 642 well-exposed ASOS stations distributed across the contiguous USA. It has been recently recognized that the response of buildings to convective gusts, which are non-stationary transient events, differs in character from the response to the locally stationary atmospheric boundary gusts, requiring gust events to be classified and assessed by type. This study sorts the mixture of all observed gust events exceeding 20 kn, but excluding contributions from hurricanes and tropical storms, into five classes of valid meteorological types and two classes of invalid artefacts. The valid classes are individually fitted to optimal sub-asymptotic models through extreme value analysis. Classes are recombined into a joint mixture model and compared with current design rules.

Initiation, organizational modes and environmental conditions of severe convective wind events during the warm season over North China

Initiation, organizational modes and environmental conditions of severe convective wind events during the warm season over North China

Evaluating synthetic aperture radar surface winds for convective squalls in the Gulf of Mexico

AbstractThe detection and quantification of strong sea surface winds, reaching up to 25 m·s−1, whether associated with deep convection aloft or not, have been extensively discussed in previous studies. This method involves the combined observation of the same event from both low‐orbit altitude and geostationary (GEO) satellites. Strong surface winds observed by the Sentinel‐1 C‐band synthetic aperture radar (SAR) are robustly associated with deep convective clouds detected by GEO infrared sensors. The current paper aims to generalize the previous assessment of several convective wind events by collecting a larger dataset and comparing these data to in‐situ wind observations. To achieve this, we evaluated wind speeds retrieved from Sentinel‐1 SAR images against corresponding in‐situ wind measurements from all active buoys/stations in the Gulf of Mexico. Significant agreement between satellite‐based winds and in‐situ data was achieved, particularly for wind speeds exceeding 3 m·s−1, with even better agreement for wind speeds over 10 m·s−1. From this dataset, three specific convective cases were extracted to illustrate various stages of convective squall events: before, during, and after the occurrence of a squall peak. In each case, comparison with in‐situ measurements showed that SAR‐estimated wind speeds closely matched observed speeds, including the peak convective winds, which were estimated at 18.90 m·s−1 and measured at 20.69 m·s−1. Furthermore, combining these findings with GOES‐16 sequential images illustrates the temporal and spatial similarity between deep convection areas, estimated strong sea surface wind patterns, and measured wind speeds.

A Radar-Based 10-Year Climatology of Convective Snow Events in Central Pennsylvania

Abstract Convective snow (CS) presents a significant hazard to motorists and is one of the leading causes of weather-related fatalities on Pennsylvania roadways. Thus, understanding environmental factors promoting CS formation and organization is critical for providing relevant and accurate information to those impacted. Prior research has been limited, mainly focusing on frontal CS bands often called “snow squalls”; thus, these studies do not account for the diversity of CS organizational modes that is frequently observed, highlighting a need for a robust climatology of broader CS events. To identify such events, a novel, radar-based CS detection algorithm was developed and applied to WSR-88D radar data from 10 cold seasons in central Pennsylvania, during which 159 cases were identified. Distinct convective organization modes were identified: linear (frontal) snow squalls, single cells, multicells, and streamer bands. Each algorithm-flagged radar scan containing CS was manually classified as one of these modes. Interestingly, the most-studied frontal mode only occurred <5% of the time, whereas multicellular modes dominated CS occurrence. Using the times associated with each CS mode, synoptic and local environmental information from model analyses was investigated. Key characteristics of CS environments compared to null cases include a 500-hPa trough in the vicinity, lower-tropospheric conditional instability, and sufficient moisture. Environments favorable for the different CS modes featured statistically significant differences in the 500-hPa trough axis position, surface-based CAPE, and the unstable layer depth, among others. These results provide insights into forecasting CS mode, explicitly presented in a forecasting decision tree. Significance Statement Convective snow events such as snow squalls are a leading cause of weather-related deaths on Pennsylvania roads. Research into these events is limited, thus negatively impacting forecast skill. To better understand convective snow event frequency of occurrence, inter- and intra-annual variability, and their supporting environments, we performed a 10-yr radar-based climatology of these events. We report the results of this climatology and on the statistically significant differences in their supporting environments. The latter are used to propose a forecasting framework for convective snow, which may improve the predictability of convective snow in an operational setting.

Decrease in MJO Predictability Following Indo–Pacific Warm Pool Expansion

AbstractThe characteristics of the Madden–Julian oscillation (MJO) have changed and are projected to continue changing with the expansion of the Indo–Pacific warm pool, which is the Earth's largest region of warm sea surface temperatures (SSTs). However, the likelihood of a change in MJO predictability following warm pool expansion remains unaddressed. Therefore, this study investigated the effect of warm pool expansion on MJO variability and predictability using the highly idealized aquaplanet configuration of Community Earth System Model 2 (CESM2). By expanding the warm pool in the Indo–Pacific, MJO‐like waves become more regionally confined, short‐lived convective events with weaker magnitude and less robust eastward propagating signals, possibly due to stronger zonal SST gradients and wider meridional widths of the warm pool. Perfect‐model ensemble forecast experiments revealed that the MJO predictability decreased by approximately 5 days, the forecast error proliferated, and the signal rapidly reduced following warm pool expansion.

Intensity-Duration-Frequency Curves for Manicaragua city, Cuba

The Intensity-Duration-Frequency (IDF) curves are a way to visualize and represent extreme hydrometeorological rainfall events. In this article, an analysis of convective rainfall events recorded at the La Piedra Meteorological Station, Villa Clara, Cuba, was conducted. To develop IDF curves, the 2006- 2019 time series was analyzed. A partial duration series was generated, including intervals from 20 minutes to 4320 minutes, subjected to an outlier detection process. The series was divided into two categories: one for durations ≤ 720 minutes and another for durations > 720 minutes. The resulting series underwent nonparametric tests to assess their independence, randomness, homogeneity, and seasonality. Subsequently, they were fitted to the Generalized Pareto probability distribution and to a parametric equation of the Montana model, and then the curves were plotted for return periods of 10, 50 and 100. The Montana model led to obtaining correlation coefficients greater than 0.90 compared to the other methods used, significantly improving the quality of the fit in both categories. This research provides information to understand and plan the management of intense climatic phenomena and adequate risk management in an area where such studies are lacking, facilitating access to crucial data essential in the design and execution of hydraulic engineering projects in the region.

Downstream and upstream effects of urban chains on precipitation in Beijing

A summer convective rainfall event in Beijing on June 26, 2018 was investigated though a series of numerical simulations. The system moved from northwest to southeast, passing through Yanqing (YQ), Changping (CP), and the central urban area (CUA) of Beijing. The combined influences of the multiple urban areas and the mechanisms of the mutual impacts of upstream and downstream cities on precipitation were investigated. Results showed that the urban surfaces led to an increase in near-surface temperature, a decrease in lower-level wind speed, and airflow triggered convergence and updrafts. The resulting upward moisture transport and latent heat release in the cloud were promoted during the subsequent precipitation. The thermal and dynamic impacts of CUA and CP urban areas propagated downstream along the low-level southeasterly flow, resulting in increased temperatures, strengthened updrafts, and latent heat release in the cloud along YQ-CP-CUA region. The urban effects propagated downstream under the southeasterly wind, leading to enhanced precipitation over YQ and CP. The YQ also, however, contributed to the increased precipitation in CUA. The “city chain” comprised of YQ, CP, and CUA thus promoted the progression and advancement of the precipitation system. This study highlights the importance of the correct identification of the “upwind” and “downwind” directions when studying urban impacts on precipitation.

Bibliometric Analysis of Weather Radar Research from 1945 to 2024: Formations, Developments, and Trends.

In the development of meteorological detection technology and services, weather radar undoubtedly plays a pivotal role, especially in the monitoring and early warning of severe convective weather events, where it serves an irreplaceable function. This research delves into the landscape of weather radar research from 1945 to 2024, employing scientometric methods to investigate 13,981 publications from the Web of Science (WoS) core collection database. This study aims to unravel, for the first time, the foundational structures shaping the knowledge domain of weather radar over an 80-year period, exploring general features, collaboration, co-citation, and keyword co-occurrence. Key findings reveal a significant surge in both publications and citations post-1990, peaking in 2022 with 1083 publications and 13832 citations, signaling sustained growth and interest in the field after a period of stagnation. The United States, China, and European countries emerge as key drivers of weather radar research, with robust international collaboration playing a pivotal role in the field's rapid evolution. Analysis uncovers 30 distinct co-citation clusters, showcasing the progression of weather radar knowledge structures. Notably, deep learning emerges as a dynamic cluster, garnering attention and yielding substantial outcomes in contemporary research efforts. Over eight decades, the focus of weather radar investigations has transitioned from hardware and software enhancements to Artificial Intelligence (AI) technology integration and multifunctional applications across diverse scenarios. This study identifies four key areas for future research: leveraging AI technology, advancing all-weather observation techniques, enhancing system refinement, and fostering networked collaborative observation technologies. This research endeavors to support academics by offering an in-depth comprehension of the progression of weather radar research. The findings can be a valuable resource for scholars in efficiently locating pertinent publications and journals. Furthermore, policymakers can rely on the insights gleaned from this study as a well-organized reference point.

Residual Spatiotemporal Convolutional Neural Network Based on Multisource Fusion Data for Approaching Precipitation Forecasting

Approaching precipitation forecast refers to the prediction of precipitation within a short time scale, which is usually regarded as a spatiotemporal sequence prediction problem based on radar echo maps. However, due to its reliance on single-image prediction, it lacks good capture of sudden severe convective events and physical constraints, which may lead to prediction ambiguities and issues such as false alarms and missed alarms. Therefore, this study dynamically combines meteorological elements from surface observations with upper-air reanalysis data to establish complex nonlinear relationships among meteorological variables based on multisource data. We design a Residual Spatiotemporal Convolutional Network (ResSTConvNet) specifically for this purpose. In this model, data fusion is achieved through the channel attention mechanism, which assigns weights to different channels. Feature extraction is conducted through simultaneous three-dimensional and two-dimensional convolution operations using a pure convolutional structure, allowing the learning of spatiotemporal feature information. Finally, feature fitting is accomplished through residual connections, enhancing the model’s predictive capability. Furthermore, we evaluate the performance of our model in 0–3 h forecasting. The results show that compared with baseline methods, this network exhibits significantly better performance in predicting heavy rainfall. Moreover, as the forecast lead time increases, the spatial features of the forecast results from our network are richer than those of other baseline models, leading to more accurate predictions of precipitation intensity and coverage area.

Rapid Restratification Processes Control Mixed Layer Turbulence and Phytoplankton Growth in a Deep Convection Region

AbstractThe Gulf of Lion, Northwestern Mediterranean Sea, is one of few oceanic regions where deep convection occurs. We investigate the restratification following a convection event using measurements from an ocean glider equipped with turbulence microstructure sensors. This unique combination of instruments provides a high‐resolution description of the mixed layer with regard to turbulence, stratification and chlorophyll. We observe a rapid restratification process that proceeds over a timescale of days to one week. We find that restratification exerts a leading order control on surface mixed layer turbulence variability, as abrupt changes in turbulence dissipation rates are associated with the formation of near‐surface stratification. The near‐surface formation of stratification occurs through both the diurnal variability in surface buoyancy fluxes and through lateral advective processes. We conclude that daily near‐surface processes that influence stratification control mixed layer turbulence levels, and thus the phytoplankton response in the critical transition period to spring bloom.

Post‐processing output from ensembles with and without parametrised convection, to create accurate, blended, high‐fidelity rainfall forecasts

AbstractFlash flooding is a significant societal problem, but related precipitation forecasts are often poor. To address, one can try to use output from convection‐parametrising (global) ensembles, post‐processed to forecast at point‐scale, or convection‐resolving limited area ensembles. The new methodology described here combines both. We apply “ecPoint‐rainfall” post‐processing to the ECMWF global ensemble. Alongside we use 2.2 km COSMO LAM ensemble output (centred on Italy), and also post‐process that, using a scale‐selective neighbourhood approach to compensate for insufficient members and to preserve consistently forecast local details. The two resulting scale‐compatible components then undergo lead‐time‐weighted blending, to create the final probabilistic 6 h rainfall forecasts. Product creation for forecasters, in this way, constituted the “Italy Flash Flood use case” within the EU‐funded MISTRAL project; real‐time delivery of open access products is ongoing. One year of verification shows that, of the five components (2 raw, 2 post‐processed and blended), ecPoint is the most skilful. The post‐processed COSMO ensemble adds most value to summer convective events in the evening, when the global model has an underprediction bias. In two typical heavy rainfall case studies we observed underestimation of the largest point totals in the raw ECMWF ensemble, and overestimation in the raw COSMO ensemble. However, ecPoint elevated the ECMWF maxima and highlighted best the most affected areas and merged products seemed to be the most skilful of all. Even though our LAM post‐processing does not include (or arguably need) bias‐correction, this study still provides a unique blueprint for successfully combining ensemble rainfall forecasts from global and LAM systems around the world. It also has important implications for forecast products as global ensembles move ever closer to having convection‐permitting resolution.

Recent changes of the dissolved oxygen distribution in the deep convection cell of the southern Adriatic Sea

The dynamics of dissolved oxygen in the ocean are of crucial importance for understanding marine ecosystems, with influences ranging from exchange with the atmosphere to biological processes and ocean circulation. In this study, we focus on the southern Adriatic Sea, an essential component of the Eastern Mediterranean “conveyor belt”, to investigate long-term oxygen dynamics and its driving factors. We use cross-platform datasets from 2013 to 2020, including remote sensing data, model reanalysis and in-situ observations from Argo floats, ocean gliders and ship-based measurements. Our analysis investigate the interplay of physical, biological and atmospheric forcing that drive oxygen variability. The distribution of dissolved oxygen in the southern Adriatic Sea is influenced by vertical mixing, advection of water masses and ecosystem dynamics. In the surface layer, the variability of dissolved oxygen is triggered by annual primary production and deep convection events. The dynamics in the intermediate and the deep layers are instead primarily influenced by physical processes, such as the vertical mixing and the water masses inflow from the adjacent sub-basins, which is driven by the periodic reversals of northern Ionian Gyre circulation. In particular our study reveals that the water masses advective dynamics driving the increase and decrease of dissolved oxygen have drastically changed in recent years. The highest dissolved oxygen concentrations are currently observed during the northern Ionian Gyre anticyclonic phase, while they have been previously documented during the cyclonic phase. This change appears to be connected with the significant increase in salinity observed in the southern Adriatic Sea in the same period and contributes to a better understanding of the processes that determine oxygen distribution in the Eastern Mediterranean basin.