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In response to north-south pressure gradients set by the annual march of the Sun, a cross-equatorial flow that turns to become a low-level Somali Jet at around 10°N is established in the lower troposphere over the Indian Ocean. This flow plays a fundamental role in the Indian monsoon. A mechanistic understanding of drivers of this flow is lacking. Here, we present a seasonal-mean analysis of the kinetic energy (KE) budget of the low-level flow using high spatiotemporal resolution ERA5 reanalysis to identify sources and sinks of KE. We find that the largest KE generation occurs around East African orography where the Somali Jet forms while significant KE is also generated over the Western Ghats and the Madagascar Island (“hot spots”). These regions are distant from core monsoon precipitation regions, suggesting that local circulations driven by condensation do not directly produce the bulk of KE during monsoons. A unique KE balance supports the generation of the Somali Jet, with KE generation balanced by nonlinear KE advection as it forms. Over oceans, KE generation occurs mainly due to cross-isobaric meridional winds in the boundary layer (BL). In contrast, over the East African highlands and Western Ghats, KE generation maximizes just above the BL and mainly occurs due to the interaction of flow with orography. We propose a simple decomposition of lower tropospheric KE generation into contributions from surface pressure, orography, and free-tropospheric gradients that corroborate the important role played by surface pressure gradients once adjusted for effects of orography., Atmospheric Remote Sensing

The frequency and severity of floods is increasing in many countries, and hospitals play an essential role in protecting the community from their potentially devastating impacts. However, many hospitals are vulnerable to flooding having been designed and located without these risks in mind. In such situations, evacuation of vulnerable patients to safer alternative healthcare facilities might be the most reasonable response. However, there has been little research into hospital evacuation, and to address this research gap, this paper presents an innovative hospital evacuation model that can facilitate the optimal scheduling and sequencing of hospital evacuation under indeterministic flooding conditions. The proposed model combines a flood simulator and a mathematical model. Assuming the high levels of complexity of a realistic hospital evacuation system, a BendersDecomposition approach is developed to cope with large size problems. A case study in New South Wales (NSW), Australia, is used to illustrate the proposed model, and results confirm that the model can generate effective plans to evacuate hospitals in the shortest time possible in flood emergency conditions. The findings of this study contribute new theoretical and practical insights to a growing body of knowledge about disaster response strategies in hospitals and have implications for health service management and health policymakers in countries where health infrastructures are vulnerable to increasing climate change risks.

Recent studies proposed parking reservation schemes in a bi-modal transport network to manage parking competition and traffic congestion. This study proposes a new flexible parking reservation scheme, under which commuters’ reservation can expire and those arriving later than the reservation expiration time can retain his reservation by paying additional late-for-reservation fees. Time-varying late-for-reservation fees are also examined. The proposed reservation scheme is more flexible and practical than those in the literature. Moreover, we found that, when compared to reservation scheme in the literature, the total social cost can be further reduced by an appropriately designed flexible reservation scheme in this paper. We also analytically and numerically quantify the efficiency gain of the proposed flexible parking reservation scheme.

This study develops day-to-day models to explore how unreliability of public transit service affects the day-to-day evolution of travel choices. We consider two dynamical processes that incorporate transit service unreliability, i.e., travelers' learning and perception updating process (LPUP) and proportional-switch adjustment process (PSAP). The conditions for existence, uniqueness, and stability of the fixed point of each model are analytically derived. These conditions are then examined using real-world public transit data from the Greater Sydney area. We find that with some aggregations, the system stability conditions are satisfied in both models. The observed weighted average flow change between two successive days is around 6.5% over the observation period, which may reflect the system stochasticity rather than instability. Among a series of empirical findings, it is noteworthy that in the Sydney case, the value of service schedule delay is around 3.27 times that of in-vehicle time.

It is well known that subtropical shallow convection transports heat and water vapor upwards from the surface. It is less clear if it also transports horizontal momentum upwards to significantly affect the trade winds in which it is embedded. We utilize unique multiday large-eddy simulations run over the tropical Atlantic with ICON-LEM to investigate the character of shallow convective momentum transport (CMT). For a typical trade-wind profile during boreal winter, CMT acts as an apparent friction to decelerate the north-easterly flow. This effect maximizes below the cloud base while in the cloud layer, friction is very small, although present over a relatively deep layer. In the cloud layer, the zonal component of the momentum flux is counter-gradient and penetrates deeper than reported in traditional shallow cumulus LES cases. The transport through conditionally sampled convective updrafts and downdrafts explains a weak friction effect, but not the counter-gradient flux near the cloud tops. The analysis of the momentum flux budget reveals that, in the cloud layer, the counter-gradient flux is driven by convectively triggered nonhydrostatic pressure-gradients and horizontal circulations surrounding the clouds. A model set-up with large domain size and realistic boundary conditions is necessary to resolve these effects., Atmospheric Remote Sensing

Motivated by the abundance of low clouds in the subtropics, where the easterly trade winds prevail, we study the role of shallow convection in the momentum budget of the trades. To this end, we use ICON-LEM hindcasts run over the North Atlantic for 12 days corresponding to the NARVAL1 (winter) and NARVAL2 (summer) flight campaigns. The simulation protocol consists of several nested domains, and we focus on the inner domains (≈100 × 100 km2) which have been run at resolutions of 150–600 m and are forced by analysis data, thus exhibiting realistic conditions. Combined, the resolved advection and the subgrid stresses decelerate the easterly flow over a frictional layer that balances the prevailing geostrophic wind forcing. Irrespective of the horizontal resolution, this layer is about 2 km deep in the strong winter trades and 1 km in summer, as winds and geostrophic forcing weaken and cloudiness reduces. The unresolved processes are strongest near the surface and are well captured by traditional K-diffusion theory, but convective-scale motions which are not considered in K-diffusion theory contribute the most in the upper part of the mixed layer and are strongest just below cloud base. The results point out that convection in the mixed layer – the roots of trade-wind cumuli and subcloud-layer circulations – play an important role in slowing down easterly flow below cloud base (but little in the cloud layer itself), which helps make the zonal wind jet more distinct. Most of the friction within the clouds and near the wind jet stems from smaller-scale turbulence stresses., Atmospheric Remote Sensing

The frequency and intensity of both human-made and natural disasters are predicted to increase, and hospitals play a critically important role in reducing injury and mortality rates. However, there is increasing evidence that many hospitals are vulnerable to disasters, and more effective strategies are needed to enable the safe evacuation of patients to alternative healthcare centres. Transport planning is central to this process, but there has been no systematic and critical review of the research on this critically important challenge. This means there is no collective synthesis of this literature for hospital managers, policymakers, and researchers to refer to in addressing these important vulnerabilities. This paper reports the findings of a critical literature review to contribute practical insights for health facilities planning and management decision-making in a context where both the likelihood and consequences of natural disasters are increasing in many countries.

Autonomous vehicles (AVs) are being extensively tested on public roads in several states in the USA, such as California, Florida, Nevada, and Texas. AV utilization is expected to increase into the future, given rapid advancement and development in sensing and navigation technologies. This will eventually lead to a decline in human driving. AVs are generally believed to mitigate crash frequency, although the repercussion of AVs on crash severity is ambiguous. For the data-driven and transparent deployment of AVs in California, the California Department of Motor Vehicles (CA DMV) commissioned AV manufacturers to draft and publish reports on disengagements and crashes. This study performed a comprehensive assessment of CA DMV data from 2014 to 2019 from a safety standpoint, and some trends were discerned. The results show that decrement in automated disengagements does not necessarily imply an improvement in AV technology. Contributing factors to the crash severity of an AV are not clearly defined. To further understand crash severity in AVs, the features and issues with data are identified and discussed using different machine learning techniques. The CA DMV accident report data were utilized to develop a variety of crash AV severity models focusing on the injury for all crash typologies. Performance metrics were discussed, and the bagging classifier model exhibited the best performance among different candidate models. Additionally, the study identified potential issues with the CA DMV data reporting protocol, which is imperative to share with the research community. Recommendations are provided to enhance the existing reports and append new domains