arXiv subjects

Funing Li

Publications and source records attributed to Funing Li.

6 recordsLinked to original sources

arXiv2026

Future Amplification of Moist Weather Extremes in the Midlatitudes

Moist heatwaves and convective storms frequently co-occur, posing compound risks. Although historically concentrated in the tropics, these moist weather extremes are projected to intensify substantially towards the midlatitudes, with regions downstream of major highland terrains, including northeastern Asia and eastern North America, emerging as hotspots of future change. Yet their physical drivers remain uncertain. Here we show that the intensification of concurrent moist heat and convection extremes in the midlatitudes is tightly constrained by changes in low-level atmospheric inversions. Specifically, we find that amplified warming over western highlands is transported downstream by prevailing westerlies, strengthening low-level thermal inversions and raising the attainable maxima of moist heat and convection. Targeted model experiments confirm the critical role of orographically elevated heating in driving these extremes. Our results reveal a mechanistic pathway for compound extremes and highlight low-level inversions as a key factor for emerging midlatitude risks of moist heat and severe weather under climate change.

physics.ao-ph↗

arXiv2025

Topology-Aware and Highly Generalizable Deep Reinforcement Learning for Efficient Retrieval in Multi-Deep Storage Systems

In modern industrial and logistics environments, the rapid expansion of fast delivery services has heightened the demand for storage systems that combine high efficiency with increased density. Multi-deep autonomous vehicle storage and retrieval systems (AVS/RS) present a viable solution for achieving greater storage density. However, these systems encounter significant challenges during retrieval operations due to lane blockages. A conventional approach to mitigate this issue involves storing items with homogeneous characteristics in a single lane, but this strategy restricts the flexibility and adaptability of multi-deep storage systems. In this study, we propose a deep reinforcement learning-based framework to address the retrieval problem in multi-deep storage systems with heterogeneous item configurations. Each item is associated with a specific due date, and the objective is to minimize total tardiness. To effectively capture the system's topology, we introduce a graph-based state representation that integrates both item attributes and the local topological structure of the multi-deep warehouse. To process this representation, we design a novel neural network architecture that combines a Graph Neural Network (GNN) with a Transformer model. The GNN encodes topological and item-specific information into embeddings for all directly accessible items, while the Transformer maps these embeddings into global priority assignments. The Transformer's strong generalization capability further allows our approach to be applied to storage systems with diverse layouts. Extensive numerical experiments, including comparisons with heuristic methods, demonstrate the superiority of the proposed neural network architecture and the effectiveness of the trained agent in optimizing retrieval tardiness.

cs.LG↗

arXiv2025

Atmospheric stability sets maximum moist heat and convection in the midlatitudes

Extreme near-surface moist heat and severe convective storms are among the leading causes of weather-related damages worldwide. Here, we show that episodes of extreme moist heat and severe convection frequently co-occur across midlatitude land regions, and develop a theoretical framework that links their maximum potential intensities to preexisting low-level energy inversions. By accounting for the stored-energy nature of midlatitude severe convection, where moist heat and atmospheric instability accumulate before convection initiates, our work advances the understanding of convective constraints on extreme heat events. The theory identifies low-level inversions as a critical factor shaping compound extreme heat and convective weather risks, and offers a pathway for improving the modeling and future projection of these events.

physics.ao-ph↗

arXiv2023

Future changes in the vertical structure of severe convective storm environments over the U.S. central Great Plains

The effect of warming on severe convective storm potential is commonly explained in terms of changes in vertically-integrated ("bulk") environmental parameters, such as CAPE and 0--6 km shear. However, such events are known to depend on details of the vertical structure of the thermodynamic and kinematic environment that can change independently of these bulk parameters. This work examines how warming may affect the complete vertical structure of these environments for fixed ranges of values of high CAPE and bulk shear, using data over the central Great Plains from two high-performing climate models. Temperature profiles warm relatively uniformly with height, with a slight decrease in free tropospheric lapse rate, and the tropopause shifts upwards at constant temperature. The boundary layer becomes slightly drier (-2--4\% relative humidity) while the free troposphere becomes slightly moister (+2--3\%). Moist static energy (MSE) increases relatively uniformly with height with slightly larger increase within the boundary layer. Moist static energy deficit increases slightly above 4 km altitude. Wind shear and storm-relative helicity increase within the lowest 1.5 km associated with stronger hodograph curvature. Changes are broadly consistent between the two models despite differing biases relative to ERA5. The increased low-level shear and SRH suggests an increased potential for severe thunderstorms and tornadoes, while the slight increase in free tropospheric MSE deficit (enhanced entrainment) and decrease in boundary layer relative humidity (higher LCL) may oppose these effects. Evaluation of the net response of severe convective storm outcomes cannot be ascertained here but could be explored in simulation experiments.

physics.ao-ph↗

arXiv2020

The role of elevated terrain and the Gulf of Mexico in the production of severe local storm environments over North America

The prevailing conceptual model for the production of severe local storm (SLS) environments over North America asserts that upstream elevated terrain and the Gulf of Mexico are both essential to their formation. This work tests this hypothesis using two prescribed-ocean climate model experiments with North American topography removed or the Gulf of Mexico converted to land and analyzes how SLS environments and associated synoptic-scale drivers (southerly Great Plains low-level jets, drylines, elevated mixed layers, and extratropical cyclones) change relative to a control historical run. Overall, SLS environments depend strongly on upstream elevated terrain but weakly on the Gulf of Mexico. Removing elevated terrain substantially reduces SLS environments especially over the continental interior due to broad reductions in both thermodynamic and kinematic parameters, leaving a more zonally-uniform residual distribution that is maximized near the Gulf coast and decays toward the continental interior. This response is associated with a strong reduction in synoptic-scale drivers and a cooler and drier mean-state atmosphere. Replacing the Gulf of Mexico with land modestly reduces SLS environments thermodynamically over the Great Plains and increases them kinematically over the eastern U.S, shifting the primary local maximum eastward into Illinois; it also eliminates the secondary, smaller local maximum over southern Texas. This response is associated with modest changes in synoptic-scale drivers and a warmer and drier lower-tropospheric mean state. These experiments provide insight into the role of elevated terrain and the Gulf of Mexico in modifying the spatial distribution and seasonality of SLS environments.

physics.ao-ph↗

arXiv2020

Climatology of severe local storm environments and synoptic-scale features over North America in ERA5 reanalysis and CAM6 simulation

Severe local storm (SLS) activity is known to occur within specific thermodynamic and kinematic environments. These environments are commonly associated with key synoptic-scale features--including southerly Great Plains low-level jets, drylines, elevated mixed layers, and extratropical cyclones--that link the large-scale climate to SLS environments. This work analyzes spatiotemporal distributions of both the environmental parameters and synoptic-scale features in ERA5 reanalysis and in Community Atmosphere Model version 6 (CAM6) during 1980--2014 over North America. Compared to radiosondes, ERA5 successfully reproduces SLS environments, with strong spatiotemporal correlations and low biases, especially over the Great Plains. Both ERA5 and CAM6 reproduce the climatology of SLS environments over the central United States as well as its strong seasonal and diurnal cycles. ERA5 and CAM6 also reproduce the climatological occurrence of the synoptic-scale features, with the distribution pattern similar to that of SLS environments. Compared to ERA5, CAM6 exhibits a high bias in Convective Available Potential Energy over the eastern United States primarily due to a high bias in surface moisture, and to a lesser extent, storm-relative helicity due to enhanced low-level winds. Composite analysis indicates consistent synoptic anomaly patterns favorable for significant SLS environments over much of the eastern half of the United States in both ERA5 and CAM6, though the pattern differs for the southeastern United States. Overall, results indicate that both ERA5 and CAM6 are capable of reproducing SLS environments as well as the synoptic-scale features and transient events that generate them.

physics.ao-ph↗