An Optimal Selection Method for Object-Based Thunderstorms Using Numerical Models

The representation of deep moist convection and organized convective systems in numerical modeling of the atmosphere is pivotal to model fidelity. Although kilometer-scale global models better capture large-scale oscillations and convection features that traditional general circulation models struggle to represent, past studies have reported notable differences in their simulations of convective organization, especially over the tropics. However, untangling the controlling factors of convective organization remains challenging, since physical processes associated with deep moist convection are intrinsically multiscale and deeply intertwined. To address this challenge, this study develops a cumulus parameterization tailored for kilometer-scale models, aiming to enable the modulation and systematic testing of multiscale interactions associated with deep moist convection.The cumulus parameterization developed in this study employs Arakawa's unified parameterization to represent the interactions of unresolved deep moist convection with its environmental flow, including the explicitly simulated convection. The underlying parameterizability of unresolved deep moist convection follows the notion of convective quasi-equilibrium, while a complementary closure is employed to predict the fractional area covered by cumulus updrafts and adjust the local vertical eddy transports accordingly. A representation of stochasticity and convection memory is introduced by coupling our parameterization with a cellular automaton. Empirical values and physical assumptions are used to establish our parameterization as a prototype subject to designing systematic experiments of specific process representation in the future.Idealized experiments of tropical maritime deep convection at a horizontal grid spacing of 3 km demonstrate that employing our parameterization in convection-permitting simulations modulates deep convective system features while generally retaining the profile of total vertical energy transport associated with deep convection. Unresolved deep convection dominates the vertical energy transport in the early stages of deep convective systems, and its contribution gradually weakens as systems develop. Meanwhile, detrainment from unresolved deep convection leads to the early occurrence of cumulus congestus and cumulonimbus. In comparison with the ordinary convection-permitting simulation, our parameterization exhibits a pronounced congestus mode in the vertical velocity profile of mature convective systems, partially due to the enhanced dry static stability. Overall, simulations with our parameterization exhibit fewer and larger short-lived convective systems. Further investigation into the source of the uncertainty in convective organization is warranted.

Comprehensive properties of non-penetrating and penetrating deep convection precipitation in summer over the Tibetan Plateau derived from GPM observations

The Indian mesosphere–stratosphere–troposphere (MST) radar measurements during the passage of 60 convective systems are used to study the vertical air velocity (w) characteristics of tropical convection. The up- and downdraft cores and various stages/types of convection (shallow, deep, and decaying) are discerned from radar time–intensity maps of w. The characteristics of cores (speed, size, orientation, vertical extent, gravity wave activity, etc.) at different stages of convection are discussed with the help of three case studies. The cores stratified based on the type of convection are mostly erect in nature in all types of convective systems, except for deep updraft cores. A considerable percentage (35%) of deep updraft cores show inclined structure with elevation angles as low as 0°–20°. The variation of the horizontal wind field with height and the internal dynamics of mesoscale convective systems (MCSs) are thought to be responsible for this geometry. Further, the vertical extent of draft cores is limited in all types of convection, except for deep updraft cores. About 77% of deep updraft cores have a vertical extent greater than 10 km and ∼23% of these cores reach an altitude of 16 km. The size (overpass time) of the core shows an increasing trend with altitude up to 10–12 km and then decreases. Among different types of convection, the size of core is larger for deep updraft cores and smaller for shallow updraft cores. The variation of w distribution with height is different for different convection categories. The mode (and also the mean) of the distribution shows low-level descent (below 3 km) and mid–high-level ascent in shallow and deep convection categories, while nearly uniform distribution is seen in decaying convection. Strong updrafts are seen in deep convective systems in the upper troposphere (of the order of 15–20 m s−1), followed by shallow and decaying systems, while downdrafts are generally weaker in all types of convection. The variability (within the cores and also with altitude) and the number of data points are larger in updraft cores than in downdraft cores corresponding to shallow and deep convection. Contrasting the composite w profile at Gadanki with those obtained elsewhere revealed interesting features: the absence of subsidence at higher levels, the presence of low-level subsidence, a single ascent peak in the middle troposphere, etc. Further, the magnitude of composite w derived from wind profiler measurements is larger than that obtained with other techniques.

Realistically representing deep atmospheric convection is important for accurate numerical weather and climate simulations. However, parameterizing where and when deep convection occurs (“triggering”) is a well-known source of model uncertainty. Most triggers parameterize convection deterministically, without considering the uncertainty in the convective state as a stochastic process. In this study, we develop a machine learning model, a random forest, that predicts the probability of deep convection, and then apply clustering of Shapley additive explanations (SHAP) values, an explainable machine learning method, to characterize the uncertainty of convective events. The model uses observed large-scale atmospheric variables from the Atmospheric Radiation Measurement constrained variational analysis dataset over the Southern Great Plains, United States. The analysis of feature importance shows which mechanisms driving convection are most important, with large-scale vertical velocity providing the highest predictive power for more certain, or easier to predict, convective events, followed by the dynamic generation rate of dilute convective available potential energy. Predictions of uncertain, or harder to predict, convective events instead rely more on other features such as precipitable water or low-level temperature. The model outperforms conventional convective triggers. This suggests that probabilistic machine learning models can be used as stochastic parameterizations to improve the occurrence of convection in weather and climate models in the future. Significance Statement Convective storms, which produce clouds and precipitation, are difficult to represent in models since they occur at scales smaller than a model grid box. The purpose of this study is to better understand why convection is sometimes easier or harder to predict with certainty. This is important because predicting where and when convection occurs in atmospheric models affects the energy, moisture, and momentum processes in these models, which is known to lead to errors in weather forecasts and climate projections. This work highlights the importance of representing uncertainty in processes like convection.

Troposphere to stratosphere exchange is generally driven by deep convection capable of overshooting tropospheric materials contributing to stratospheric chemistry. The La Plata Basin region in South America is known for organized deep convection and mesoscale convective systems. This study employs the Weather Research and Forecasting model to simulate deep convection during the RELAMPAGO field campaign in Argentina. This work investigates upper troposphere – lower stratosphere (UTLS) thermodynamics, specifically double tropopause events, and identifies lower stratospheric hydration related to deep convection. Results show that lower stratospheric hydration occurred during two organized convective types, a mesoscale convective complex (MCC) and squall line, which coincided with strong low level jet moisture transport. However, the lower stratosphere was not hydrated during discrete cells. While UTLS moisture was present in all three convective types, during the discrete cell, ice and water vapor were mixed, inhibiting net positive buoyancy and the transport of tropospheric material aloft. During the MCC and squall line events, UTLS moisture was stratified. A dry layer in the tropopause was collocated with an ice layer where net positive buoyancy contributed to stratospheric hydration as high as 20 km.

This study revisited the association of African easterly waves (AEWs) to Atlantic tropical cyclone (TC) development using weather states (WSs) from the International Satellite Cloud Climatology Project, National Hurricane Center best track hurricane data (HURDAT2), and reanalysis products. The WS data are used as a proxy for two different types of deep convection. This study covers July–October 1984–2009. Statistical analysis based on HURDAT2 and objectively tracked AEWs has shown that a small fraction (~20%) of the AEWs that propagate from Africa serve as TC precursors. About 80% of the AEWs from the continent were non-developing. As in the past work, our study showed an important difference between developing and non-developing AEWs. Composites based on developing AEWs revealed well-organized large scale deep convection (one type, composed of mesoscale systems and thick anvil clouds) is tightly coupled to the AEW trough, while scattered, less well-organized deep convection (second type, isolated cumulonimbus and cumulus congestus clouds) dominated a large area downstream of the developing AEW trough. Developing AEWs propagate westwards while strengthening. In contrast, non-developing AEWs showed that the peak well-organized deep convection is located either behind (to the east of) or far ahead (to the west) of the AEW trough (peaks values are not in close proximity). Moreover, well-organized deep convections associated with non-developing AEWs were weaker than those associated with developing AEWs. The results indicated that convective activity ahead of the non-developing AEWs is weak. Positive relative humidity (RH) anomalies dominate the area around AEWs and downstream over the main TC development region. In contrast, negative RH dominated the main TC development region ahead of non-developing AEWs, suggesting an unfavorable environment downstream of the AEWs. The results also showed that developing AEWs maintained stronger features in the lower and middle troposphere, while non-developing AEWs exhibited weaker structures, in agreement with past work.

The Madden-Julian Oscillation (MJO) is a pattern of intense rainfall and associated planetary-scale circulations in the tropical atmosphere, with a recurrence interval of 30-90 days. Although the MJO was first discovered 40 years ago, it is still a challenge to simulate the MJO in general circulation models (GCMs), and even with simple models it is difficult to agree on the basic mechanisms. This deficiency is mainly due to our poor understanding of moist convection—deep cumulus clouds and thunderstorms, which occur at scales that are smaller than the resolution elements of the GCMs. Moist convection is the most important mechanism for transporting energy from the ocean to the atmosphere. Success in simulating the MJO will improve our understanding of moist convection and thereby improve weather and climate forecasting. We address this fundamental subject by analyzing observational datasets, constructing a hierarchy of numerical models, and developing theories. Parameters of the models are taken from observation, and the simulated MJO fits the data without further adjustments. The major findings include: 1) the MJO may be an ensemble of convection events linked together by small-scale high-frequency inertia-gravity waves; 2) the eastward propagation of the MJO is determined by the difference between the eastward and westward phase speeds of the waves; 3) the planetary scale of the MJO is the length over which temperature anomalies can be effectively smoothed by gravity waves; 4) the strength of the MJO increases with the typical strength of convection, which increases in a warming climate; 5) the horizontal scale of the MJO increases with the spatial frequency of convection; and 6) triggered convection, where potential energy accumulates until a threshold is reached, is important in simulating the MJO. Our findings challenge previous paradigms, which consider the MJO as a large-scale mode, and point to ways for improving the climate models.

Observations show substantial variations of the intensity of tropical and/or summertime deep convection on land that are not explained by standard measures of convective instability. One feature that distinguishes land surfaces is their heterogeneity. The possible importance of this is investigated here by calculating the response of a nonrotating atmosphere to localized, transient surface heating using both the linearized equations of motion and a cloud-resolving configuration of the Weather Research and Forecasting (WRF) numerical model with moist physics, each in 2D. Both models predict that the depth of the resulting surface heat low near storm center will be greatest for a particular horizontal scale of heating. The linear model reveals that this is a resonant scale determined by the product of the environmental buoyancy frequency, characteristic heating time scale, and thickness of the thermal boundary layer, and the resonance occurs when the aspect ratio of the applied heating matches the ratio of vertical and horizontal wavenumbers demanded by the dispersion relation for buoyancy (gravity) waves. For realistic conditions, the resonant horizontal scale is roughly 50 km. The numerical model indicates that other measures of convective intensity, such as updraft speed and storm height, are largely controlled by the depth of the heat low, despite the presence of conditional instability and the vigorous growth of moist convective plumes. Predictions here agree with reported observations of storm severity over islands of different sizes. These findings may help explain why observed geographical variations in storm intensity defy parcel theory and indicate that phenomena often attributed to parcel entrainment may instead be due largely to storm-scale dynamical constraints.

The focus of this paper is on the problem of airline schedule recovery when airspace capacity is limited by convective weather. The problem is represented in a simulation, the Boeing National Flow Model (NFM), and an analysis is conducted to measure the benefits of increased automation and improved weather forecasting. In particular, an analysis is undertaken based upon all scheduled traffic and the actual convective weather experienced on specific good and bad weather days for the U.S. National Airspace System (NAS) in the year 2007. The analysis quantifies the benefits associated with a collaborative flow management concept involving distributed airline schedule recovery. The concept permits airline users to optimize or re-plan their own schedules given an assigned and equitable share of forecast system capacities. The methodology can be used to evaluate alternative convective weather forecasting products, including both polygonal forecasts (such as the CCFP), along with gridded probabilistic forecasts. The methodology can also utilize the actual weather, typically available in snapshots every 5-6 minutes, as a gridded 0/1 “perfect” forecast. The analysis was conducted and is reported on in two major phases. In both phases of the study we allowed the use of strategies for ground delay and flight cancellation but did not allo w for re-routing. The first phase of the analysis was an initial attempt to study the relative value of alternative forecasts and levels of planner automation and utilized multiple forecasting products in their original form. In this phase, we discovered a systematic difference (i.e., bias) in overall coverage between the forecast and actual weather. The forecasts contained much higher levels of weather coverage, leading to re-planning responses that were considerably over-reactive. These findings led to a forecast calibration effort at NOAA that served to adjust both the forecast and actual weather coverage to greatly reduce or eliminate the bias. In the second phase of the study we undertook to compare multiple calibrated forecasting products as well as alternative levels of planner automation.

The mesoscale structure of a mature polar low was studied on the basis of high‐resolution airborne measurements and numerical modelling. A polar low was measured by light detection and ranging (lidar) and dropsonde observations over the Norwegian Sea on 3 and 4 March 2008. Lidar observations provided cross‐sections of water‐vapour mixing ratio, backscatter ratio and horizontal wind speed around the polar low and through its centre. Mesoscale structures, such as shallow convection in a cold‐air outbreak, a dry intrusion in the eye‐like centre of the cyclone and deep convection surrounding it could be identified. Numerical simulations were performed with the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and a high‐resolution, polar version of the Weather Research and Forecasting (WRF) model. WRF simulations reproduced these structures and showed that the polar low had a warm, upper‐level core with descending motions. The eye‐like centre had a diameter of about 100–150 km and was characterized by rather stable stratification, horizontally constant potential temperatures and calm winds. Beyond the centre, wind speeds increased rapidly. The observed radial wind and temperature profiles support previous idealized simulations. Several WRF sensitivity tests showed the influence of the initialization time and sensible and latent heat fluxes from the surface on the simulated polar‐low development. The polar‐low simulations were more accurate in runs starting at the mature stage. Heat fluxes from the surface were important for the polar‐low energetics, especially at the final stages. Copyright © 2011 Royal Meteorological Society

In the field of Atmospheric Physics, the recent half century was characterized by the development of Dynamical Meteorology, along with its applications in weather forecasting and climate science for the troposphere and stratosphere (the altitude range from about 0 to 50 km). This process was stimulated by the use of comprehensive numerical models combined with new measurement and data assimilation techniques. Exploration of the upper atmosphere (at altitudes above about 120 km) and space was mainly fostered by many dedicated satellites and by novel ground-based instruments. The atmospheric layer between these two heights, nowadays known as the mesosphere/lower thermosphere (MLT, from 50 to 120 km), received comparatively little attention. Although being interesting in its own right because of various fluid dynamical and other physical curiosities, it is only in recent years that the MLT region has become to be recognized as the missing piece in the jigsaw puzzle that connects weather and climate to processes occurring in the upper atmosphere and space. The proper physical description of the MLT region is particularly challenging because it requires the combination of different physical disciplines. The knowledge acquired and the methods used in Meteorology have to be extended by concepts that have been developed for the particular processes operating in the MLT and the upper atmosphere. Amongst those challenges, we mention the proper description of the following:

Weather forecasting plays a crucial role in various sectors such as agriculture, aviation, transportation, and disaster management. Accurate and timely weather predictions are essential for making informed decisions and mitigating potential risks. This project presents a comparative study of different Application Programming Interfaces (APIs) for weather data retrieval and their effectiveness in weather forecasting. The objective is to evaluate the performance, reliability, and accuracy of various APIs in providing weather information for accurate forecasting. This project presents the development of a sophisticated weather forecasting application using the Python Django framework. In response to the growing demand for accurate and accessible weather information, this application provides users with timely updates on current weather conditions, temperature variations, and detailed forecasts for the upcoming six hours. The application dynamically adapts its interface to reflect hourly changes in weather patterns, ensuring that users receive up-to-date and reliable information at all times. One of the key features of this application is its user-friendly interface, which allows users to conveniently search for weather data anytime and anywhere. Whether planning a trip, scheduling outdoor activities, or simply staying informed about local weather conditions, users can rely on this application to deliver precise and relevant information tailored to their needs. Furthermore, the application enhances user experience by providing comprehensive coverage of global weather data. Users can easily access weather information for any location worldwide, enabling them to plan travel itineraries, explore new destinations, and make informed decisions about their future holiday plans.

More observations of vertical mass transport in deep convection are needed to improve dynamical understanding of detrainment processes and for verification of transport models. A methodology for using radar reflectivity as a direct observation of vertical transport of mass from the boundary layer to the upper troposphere and lower stratosphere is investigated, and the “level of maximum detrainment” (LMD) is proposed. The case investigated is the 26 January 1999 squall line from the Tropical Rainfall Measuring Mission Large‐Scale Biosphere‐Atmosphere field campaign. Echo top heights and dual‐Doppler derived divergence profiles are used to define the mass detrainment range. Over 10% of anvil echo tops occurred above the sounding‐derived level of neutral buoyancy of 15.4 km during the mature stage of the storm, and convective tops reached above 18 km. Anvil ice water content, with a simple correction for ice fall speed, is found to be a good proxy for both the LMD, which for the storm analyzed is 11.25 km, and for the detrainment range of 6 to 17 km. More cases need to be analyzed to confirm the strength of this methodology, but the case study presented shows a strong correlation between anvil properties determined from radar reflectivity and the mass detrainment profile. Thus, radar reflectivity can be used as an indicator of the LMD to test model convective and transport parameterizations.

The interaction between humans and the environment is crucial for the survival of ecosystems and human societies. Understanding the impact of human activities, particularly carbon dioxide (CO2) emissions, on climate change is essential for developing effective mitigation strategies. This study investigates the role of artificial intelligence (AI) in addressing climate change by enhancing disaster prediction, optimizing energy consumption, and reducing emissions. AI applications in weather forecasting and disaster management provide accurate, real-time data, enabling proactive responses to extreme weather events and natural disasters. Additionally, AI-driven energy optimization can support the transition to renewable energy sources and improve efficiency in industrial and urban systems. Complementary strategies, such as reforestation and sustainable resource management, further contribute to emission reduction. The findings emphasize that technological solutions alone are insufficient; coordinated global efforts and policy implementation are critical to achieve sustainable outcomes. By integrating AI with ecological and societal measures, it is possible to mitigate the adverse effects of climate change and enhance resilience to environmental challenges. This research underscores the transformative potential of AI in climate action, highlighting that innovative technological interventions, when combined with sustainable practices, can significantly improve global climate mitigation efforts. The study provides evidence that leveraging AI in conjunction with human-driven environmental strategies offers a practical pathway toward a sustainable and resilient future.

<strong class="journal-contentHeaderColor">Abstract.</strong> The intertropical convergence zone (ITCZ) is a key circulation and precipitation feature in the tropics. There has been a large spread in the representation of the ITCZ in global weather and climate models for a long time, the reasons for which remain unclear. This manuscript presents a novel approach with which we disentangle different physical processes responsible for the changeable behavior of the ITCZ in numerical models. The diagnostic tool is based on a conceptual framework developed by Emanuel (2019) and allows for physically consistent estimates of convective mass flux and precipitation efficiency for simulations with explicit and parameterized convection. We apply our diagnostics to a set of tropical aquachannel experiments using the ICOsahedral Nonhydrostatic (ICON) model with horizontal grid resolution of 13 km and with various representations of deep and shallow convection. The channel length corresponds to the Earth's circumference and has rigid walls at 30&deg; N/S. Zonally symmetric sea surface temperatures are prescribed. All four runs share overall similar rainfall patterns and dynamical structures. They simulate an ITCZ at the equator coinciding with the ascending branch of the Hadley circulation, descending branches at 15&deg; N/S with subtropical jets and easterly trade wind belts straddling the ITCZ. Differences are largest between runs with and without parameterized deep convection. With explicit deep convection, rainfall in the ITCZ increases by 35 % and the Hadley circulation as well as surface winds become stronger. Our diagnostic framework reveals that boundary-layer quasi-equilibrium is a key to physically understanding those differences. The stronger surface horizontal winds with explicit deep convection essentially enhance surface enthalpy fluxes and thus perturb quasi-equilibrium in the boundary layer. This is balanced by increasing convective downdraft mass flux that carries low moist static energy from the lower troposphere into the boundary layer. The downdraft strength is proportional to convective updraft mass flux, which is closely linked to rainfall, since &ndash; somewhat surprisingly &ndash; the convective treatment does not appear to influence precipitation efficiency significantly. Changes in radiative cooling are largely compensated by changes in dry stability, leading to little impact on rainfall. The results highlight the utility of our diagnostics to pinpoint processes important for rainfall differences between models, suggesting applicability for global climate model intercomparison projects.