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The radius of maximum wind R max , an important parameter in tropical cyclone (TC) ocean surface wind structure, is currently resolved by only a few sensors so that, in most cases, it is estimated subjectively or via crude statistical models. Recently, a semiempirical model relying on an outer wind radius, intensity, and latitude was fit to best-track data. In this study we revise this semiempirical model and discuss its physical basis. While intensity and latitude are taken from best-track data, R max observations from high-resolution (3 km) spaceborne synthetic aperture radar (SAR) and wind radii from an intercalibrated dataset of medium-resolution radiometers and scatterometers are considered to revise the model coefficients. The new version of the model is then applied to the period 2010–20 and yields R max reanalyses and trends that are more accurate than best-track data. SAR measurements corroborate that fundamental conservation principles constrain the radial wind structure on average, endorsing the physical basis of the model. Observations highlight that departures from the average conservation situation are mainly explained by wind profile shape variations, confirming the model’s physical basis, which further shows that radial inflow, boundary layer depth, and drag coefficient also play roles. Physical understanding will benefit from improved observations of the near-core region from accumulated SAR observations and future missions. In the meantime, the revised model offers an efficient tool to provide guidance on R max when a radiometer or scatterometer observation is available, for either operations or reanalysis purposes.

Several methods have been proposed to characterize the complex interactions in turbulent wakes, especially for flows with strong cyclic dynamics. This paper introduces the concept of Fourier-averaged Navier–Stokes (FANS) equations as a framework to obtain direct insights into the dynamics of complex coherent wake interactions. The method simplifies the interpretations of flow physics by identifying terms contributing to momentum transport at different time scales. The method also allows for direct interpretation of nonlinear interactions of the terms in the Navier–Stokes equations. By analysing well-known cases, the characteristics of FANS are evaluated. Particularly, we focus on physical interpretation of the terms as they relate to the interactions between modes at different time scales. Through comparison with established physics and other methods, FANS is shown to provide insight into the transfer of momentum between modes by extracting information about the contributing pressure, convective and diffusive forces. The FANS equations provide a simply calculated and more directly interpretable set of equations to analyse flow physics by leveraging momentum conservation principles and Fourier analysis. By representing the velocity as a Fourier series in time, for example, the triadic model interactions are apparent from the governing equations. The method is shown to be applicable to flows with complex cyclic waveforms, including broadband spectral energy distributions.

Cloud and convection strongly modulate atmospheric energy budgets, but the latter's responses often vary across timescales because of complex interactions between fast and slow processes. Here, based on atmospheric model simulations at intermediate state between weather and climate timescales, we investigate how the responses in the global‐mean atmospheric energy budgets evolve over time after simultaneously perturbing various cloud‐scale processes. We find that the responses in radiative and sensible heat fluxes converge much more rapidly compared to condensation heat associated with precipitation, which is attributed to the compensating feedback effects of precipitation on longwave cooling and shortwave heating. Because of energy conservation, uncertainty in long‐term precipitation simulations can be substantially reduced by constraining the fast processes of radiative and sensible heat fluxes. These findings can help economize on computational resources required for model tuning and serve as a crucial link between the convective‐scale and equilibrium‐state outcomes within the model. Plain Language Summary Cloud and convection occurring on short timescales can interact with processes that require much longer time to respond to small perturbations in the climate system, making it extremely difficult to understand the sources of uncertainty in climate modeling. Here, with an atmospheric model, we investigate how the simulated global‐mean atmospheric energy budgets gradually evolve over time when various cloud‐scale processes are simultaneously perturbed. We find that the responses in radiative and sensible heat fluxes converge much more rapidly compared to condensation heat associated with precipitation. As a result, a substantial reduction in uncertainty regarding long‐term precipitation simulations can be achieved by constraining the radiative and sensible heat fluxes at shorter timescales, in accordance with energy conservation principles. Our results can help modelers economize on computational resources required for model tuning and serve as a crucial link between the model physics parameterization community and the model application community. Key Points The responses in radiative and sensible heat fluxes converge much more rapidly compared to precipitation The rapid radiative response is attributed to the compensating feedback effects of precipitation on longwave cooling and shortwave heating Constraining the fast processes of radiative and sensible heat fluxes can alleviate uncertainty in long‐term precipitation simulations

We investigate jumping of sessile droplets from a solid surface in ambient oil using modulated electrowetting actuation. We focus on the case in which the electrowetting effect is activated to cause droplet spreading and then deactivated exactly at the moment the droplet reaches its maximum deformation. By systematically varying the control parameters such as the droplet radius, liquid viscosity and applied voltage, we provide detailed characterisation of the resulting behaviours including a comprehensive phase diagram separating detachment from non-detachment behaviours, as well as how the detach velocity and detach time, i.e. duration leading to detachment, depend on the control parameters. We then construct a theoretical model predicting the detachment condition using energy conservation principles. We finally validate our theoretical analysis by experimental data obtained in the explored ranges of the control parameters.

An ecosystem approach is the only way to conserve habitats and the enormous number of species. The related area-based Aichi biodiversity target of the convention on biological diversity aims to conserve at least 17% of terrestrial environment by 2020. This is the first regional study to recognize a network of key habitats to achieve conservation goals. It is essential to have a spatial framework by creating the indicator using existing remote sensing-based data. In this work, conservation principles were integrated at the ecosystem level covering irreplaceability and vulnerability along with representativeness. Forest persistence, ecosystem rarity, forest intactness, landscape-level ecosystem, biomass carbon stocks, and biological richness were among the biological criteria used to analyze ecosystem irreplaceability. The proxies used for ecosystem vulnerability are high fragmentation, fire hotspots and proximity to disturbance factors. A unique value is assigned to each individual pixel in the prioritization map. Overall representation of habitat coverage in protected area network of South Asian countries indicates under-representation of several forest types with less than 17% coverage. The overlay of the priority areas proposes that there is a possibility of conserving many species with the notification of protected areas. This study demonstrates the conservation priorities by identifying key habitats based on multiple conservation principles.

Vertical mixing parameterizations in ocean models are formulated on the basis of the physical principles that govern turbulent mixing. However, many parameterizations include ad hoc components that are not well constrained by theory or data. One such component is the eddy diffusivity model, where vertical turbulent fluxes of a quantity are parameterized from a variable eddy diffusion coefficient and the mean vertical gradient of the quantity. In this work, we improve a parameterization of vertical mixing in the ocean surface boundary layer by enhancing its eddy diffusivity model using data‐driven methods, specifically neural networks. The neural networks are designed to take extrinsic and intrinsic forcing parameters as input to predict the eddy diffusivity profile and are trained using output data from a second moment closure turbulent mixing scheme. The modified vertical mixing scheme predicts the eddy diffusivity profile through online inference of neural networks and maintains the conservation principles of the standard ocean model equations, which is particularly important for its targeted use in climate simulations. We describe the development and stable implementation of neural networks in an ocean general circulation model and demonstrate that the enhanced scheme outperforms its predecessor by reducing biases in the mixed‐layer depth and upper ocean stratification. Our results demonstrate the potential for data‐driven physics‐aware parameterizations to improve global climate models. Plain Language Summary The upper region of the ocean is highly energetic and is responsible for transferring mass, energy and biogeochemical tracers between the atmosphere and the deeper regions of the ocean. This transport takes place because of turbulent swirling motions, which are found to be of varying sizes. Climate models cannot represent all of these motions because smaller‐scale swirls are complex and require additional computational resources. As we cannot neglect those small swirls, we try to approximate their effects on larger‐scale motions using mathematical models. These models have a few ad hoc or empirical assumptions that lead to uncertainty when these climate models are used to project the future climate. To reduce this uncertainty, we augment an existing model of turbulent swirling process with machine learning, which replaces some ad hoc approximations with data‐driven neural networks. Neural networks can learn those missing processes more accurately than a traditional physics‐based model. The neural networks are shown to improve physics in climate simulations. Although we only touch on one component in an ocean climate model, this approach can be replicated to improve any other component that was using ad hoc assumptions and replace them with data‐driven models using techniques from machine learning. Key Points We improve a parameterization of vertical mixing in the ocean surface boundary layer using neural networks Neural networks are trained to predict the diffusivity of second moment closure and maintain energetic constraints of the original parameterization The improved scheme reduces biases of mixed layer depth and thermocline in an atmospherically forced ocean model

Hydrological modeling, leveraging mathematical formulations to represent the hydrological cycle, is a pivotal tool in representing the spatiotemporal dynamics and distribution patterns inherent in hydrology. These models serve a dual purpose: they validate theoretical robustness and applicability via observational data and project future trends, thereby bridging the understanding and prediction of natural processes. In rapid advancements in computational methodologies and the continuous evolution of observational and experimental techniques, the development of numerical hydrological models based on physically-based surface-subsurface process coupling have accelerated. Anchored in micro-scale conservation principles and physical equations, these models employ numerical techniques to integrate surface and subsurface hydrodynamics, thus replicating the macro-scale hydrological responses of watersheds. Numerical hydrological models have emerged as a leading and predominant trend in hydrological modeling due to their explicit representation of physical processes, heightened by their spatiotemporal resolution and reliance on interdisciplinary integration. This article focuses on the theoretical foundation of surface-subsurface numerical hydrological models. It includes a comparative and analytical discussion of leading numerical hydrological models, encompassing model architecture, numerical solution strategies, spatial representation, and coupling algorithms. Additionally, this paper contrasts these models with traditional hydrological models, thereby delineating the relative merits, drawbacks, and future directions of numerical hydrological modeling.

Contemporary design within a historical context is a special topic of architectural conservation that needs considerations. New designs should be reversible and compatible with existing; however it needs to be legible and distinguishable as well. Creating the compatibility between new and the old is a challenging process. The originality of the heritage should be preserved while adding another layer and value to the heritage. The international preservation standards and charters provide guideline for contemporary designs in historical settings. The aim of the study is to examine the selected field study by following these principles. Eleftheria square in Cyprus that is designed by Zaha Hadid Architects is the main study area of the paper. Eleftheria square is a public space located in Nicosia, Southern Cyprus that design has recently completed. It includes design of a moat, which is located next to the historic city walls and designing a bridge that connect the historic part of the city with the new development area. Although the project brought a new life to the neglected part of the city, the design approaches of the project should be examined through conservation principles. Within the scope of the study, the completed project has been examined through preservation principles, which is suggested in selected standards and charters.

Cultural routes are a composite set of heritage sites that refer to historical routes of human communication. As key products of cultural tourism, they provide visitors with rich cultural experiences across regions. We systematically review reports and studies related to the tourism development of 38 cultural route cases worldwide, with a special focus on their distribution, typology, planning patterns, and tools for cultural tourism. We summarized eight tools and found some differences in how often these eight tools are used by the different types of routes and different planning patterns for route tourism. This study also developed an evaluation system based on the conservation principles of cultural routes to determine how different tourism tools affect the conservation and development of historical regions. Although tourism decision-makers have made numerous efforts to protect and develop cultural routes, there are still many problems and challenges in the process of tourism development along cultural routes. We conclude the paper by making recommendations for decision-makers and researchers concerning future route tourism planning and study.

This paper provides a comprehensive derivation of the total energy equations for the atmospheric components of Earth System Models (ESMs). The assumptions and approximations made in this derivation are motivated and discussed. In particular, it is emphasized that closing the energy budget is conceptually challenging and hard to achieve in practice without resorting to ad hoc fixers. As a concrete example, the energy budget terms are diagnosed in a realistic climate simulation using a global atmosphere model. The largest total energy errors in this example are spurious dynamical core energy dissipation, thermodynamic inconsistencies (e.g., coupling parameterizations with the host model) and missing processes/terms associated with falling precipitation and evaporation (e.g., enthalpy flux between components). The latter two errors are not, in general, reduced by increasing horizontal resolution. They are due to incomplete thermodynamic and dynamic formulations. Future research directions are proposed to reconcile and improve thermodynamics formulations and conservation principles. Plain Language Summary Earth System Models (ESMs) have numerous total energy budget errors. This article establishes the governing total energy equations for large‐scale ESMs and assesses the energy budget errors in real‐world simulations in a widely used climate model. To move towards a closed energy budget in ESMs, further research on total energy conserving discretizations (in the dynamical core), unified thermodynamics (through thermodynamic potentials/conserved variables) and missing processes is paramount. This research is especially important since some of the energy budget errors will not improve with higher spatial resolution and may even get worse. Key Points Closing total energy budgets in Earth System Models without ad hoc fixers is a monumental task Largest errors are from missing processes/terms, thermodynamic inconsistencies and dynamical core Further research is needed on conservative discretizations, unified thermodynamics and missing processes