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Our paper presents the chaotic behaviour of atmospheric systems with fractional order by using Bernoulli wavelet collocation technique. This technique has not yet been implemented on this model. The study have analysed the dynamics of three climate components—green house gases, temperature and permafrost thaw. To determine the simplicity and efficiency of the developed technique, numerical results have obtained. The combination of graphs and tables shows the stability and efficacy of the developed strategy. Based on the obtained results, we have draw the conclusion that this method offers superior accuracy and efficiency to other methods such as Adam Bashforth's method, Forward Euler's method and fde 12 solver method. This study have reveals the effectiveness of the numerical technique as well as the effect of the non integer derivative on atmospheric models. All calculations have been done using a mathematical program called Matlab. Environmental science can be better understood through these studies. [ABSTRACT FROM AUTHOR]

Subgrid‐scale processes, such as atmospheric gravity waves (GWs), play a pivotal role in shaping the Earth's climate but cannot be explicitly resolved in climate models due to limitations on resolution. Instead, subgrid‐scale parameterizations are used to capture their effects. Recently, machine learning (ML) has emerged as a promising approach to learn parameterizations. In this study, we explore uncertainties associated with a ML parameterization for atmospheric GWs. Focusing on the uncertainties in the training process (parametric uncertainty), we use an ensemble of neural networks to emulate an existing GW parameterization. We estimate both offline uncertainties in raw NN output and online uncertainties in climate model output, after the neural networks are coupled. We find that online parametric uncertainty contributes a significant source of uncertainty in climate model output that must be considered when introducing NN parameterizations. This uncertainty quantification provides valuable insights into the reliability and robustness of ML‐based GW parameterizations, thus advancing our understanding of their potential applications in climate modeling. Plain Language Summary: Climate models are unable to resolve processes that vary on length and time scales smaller than the model resolution and timestep. For example, atmospheric gravity waves (GWs), which are waves created when winds encounter disturbances to the flow, such as mountains, convection and fronts, can have wavelengths smaller than the spacing between grid cells. Climate models use "parameterizations" to capture the effect of these processes. Machine learning based parameterizations are becoming popular because they can learn relationships purely from data. However, we do not have a good understanding of the uncertainties introduced through machine learning parameterizations. This study estimates uncertainties associated with training a neural network (NN) GW parameterization. We explore uncertainties in the NN output, as well as the uncertainties in the climate model output, when the NN is used for the GW parameterization. Key Points: Using ensembles of neural networks, we learn parametric uncertainties associated with an emulator of a gravity wave parameterizationWhen coupled to the climate model, the ensemble of neural networks reveals increased climate variabilityParametric uncertainty dominates the Quasi‐Biennial Oscillation statistics, although polar vortex properties remain robust to parameters [ABSTRACT FROM AUTHOR]

As part of a project aimed at exploring the use of next-generation high-performance computing technologies for numerical weather prediction, we have ported two physics modules from the Common Community Physics Package (CCPP) to Graphics Processing Unit (GPU) and obtained accelerations of up to 10× relative to a comparable multi-core CPU. The physics parameterizations accelerated in this work are the aerosol-aware Thompson microphysics (TH) scheme and the Grell–Freitas (GF) cumulus convection scheme. Microphysics schemes are among the most time-consuming physics parameterizations, second to only radiative process schemes, and our results show better acceleration for the TH scheme than the GF scheme. Multi-GPU implementations of the schemes show acceptable weak scaling in a single node with 8 GPUs, and perfect weak scaling on multiple nodes using one GPU per node. The lack of inter-node communication for column physics parameterizations contributes to their scalability, however, physics parameterizations are run along with dynamics, so the overall multi-GPU performance is often governed by the latter. In the context of optimizing CCPP physics modules, our observations underscore that the extensive use of automatic arrays within inner subroutines hampers GPU performance due to serialized memory allocations. We have used the OpenACC directive programming language for this work because it allows for easy porting of large amounts of code and makes code maintenance more manageable compared to low-level languages like CUDA and OpenCL. [ABSTRACT FROM AUTHOR]

Copyright of Laser Technology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Atmospheric drag is an important influencing factor in precise orbit determination and the prediction of low-orbit space debris. It has received widespread attention. Currently, calculating atmospheric drag mainly relies on different atmospheric density models. This experiment was designed to explore the impact of different atmospheric density models on the orbit prediction of space debris. In the experiment, satellite laser ranging data published by the ILRS (International Laser Ranging Service) were used as the basis for the precise orbit determination for space debris. The prediction error of space debris orbits at different orbital heights using different atmospheric density models was used as a criterion to evaluate the impact of atmospheric density models on the determination of space-target orbits. Eight atmospheric density models, DTM78, DTM94, DTM2000, J71, RJ71, JB2006, MSIS86, and NRLMSISE00, were compared in the experiment. The experimental results indicated that the DTM2000 atmospheric density model is best for determining and predicting the orbits of LEO (low-Earth-orbit) targets. [ABSTRACT FROM AUTHOR]

In a data-driven context, bionic polarization navigation requires a mass of skylight polarization pattern data with diversity, complete ground truth, and scene information. However, acquiring such data in urban environments, where bionic polarization navigation is widely utilized, remains challenging. In this paper, we proposed a virtual-real-fusion framework of the skylight polarization pattern simulator and provided a data preparation method complementing the existing pure simulation or measurement method. The framework consists of a virtual part simulating the ground truth of skylight polarization pattern, a real part measuring scene information, and a fusion part fusing information of the first two parts according to the imaging projection relationship. To illustrate the framework, we constructed a simulator instance adapted to the urban environment and clear weather and verified it in 174 urban scenes. The results showed that the simulator can provide a mass of diverse urban skylight polarization pattern data with scene information and complete ground truth based on a few practical measurements. Moreover, we released a dataset based on the results and opened our code to facilitate researchers preparing and adapting their datasets to their research targets. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of a new method for retrieving the parameters of the atmospheric boundary layer in hurricanes. This method is based on the approximation of the upper parabolic part of the wind speed profile and the retrieval of the lower logarithmic part. Based on the logarithmic part, the friction velocity, near-surface wind speed and the aerodynamic drag coefficient are obtained. The obtained data are used for the verification of the modeling data in the WRF-ARW model. The case of the Irma hurricane is studied. Different configurations of the model are tested, which differ in the use of physical parameterizations. The difference of wind profiles in various sectors of the hurricane is studied. [ABSTRACT FROM AUTHOR]

The evaluation results of wind energy resources directly affect the economic benefits and healthy development of wind farms. Therefore, a high-resolution wind resource assessment method for wind farms based on mesoscale atmospheric model and CFD technology is studied to accurately simulate relevant data of wind resources and improve the assessment effect. The mesoscale WRF numerical model is used to solve the regional data of wind farms and obtain the mesoscale meteorological analysis data. According to the solution results of the mesoscale atmospheric model, the wind speed profile is established, the boundary conditions and initial conditions are extracted, and the CFD micro scale model is input to obtain the wind speed and wind speed frequency at the height of the fan impeller. In order to improve the accuracy of numerical simulation of micro scale CFD model, the large eddy simulation method is used to simulate the operation of wind farms. Complete theoretical power generation evaluation based on wind speed, wind speed frequency and generator power. The experimental results show that this method can accurately simulate wind resources and accurately evaluate the theoretical power generation of wind farms. The wind farm is rated as Level 3, and the wind frequency is mainly between 4 and 10 m/s. This method can ensure that the wind farm can generate electricity all year round without damaging the wind speed. [ABSTRACT FROM AUTHOR]

Cities have a significant impact on the environment, forming microclimatic features such as urban heat island, an increase in the intensity of convective weather events, etc. Numerical models of the atmosphere with an integrated block that describes the interaction between the urbanized surface and the atmosphere—urban parameterization—are good at reproducing the meteorological features of the urban environment. Reviews of urban parameterizations are mostly outdated, and recent ones do not fully cover aspects of the methods used in the models to describe physical processes. This paper is dedicated to updating information on urban parameterizations, comparing the approaches used in them to describe physical processes, and forming proposals for their improvement. Based on the most common urban parameterizations of various levels of complexity, the main groups of physical processes describing "urban surface-atmosphere" interaction are identified. They are the surface energy balance, radiation heat transfer, surface moisture balance, turbulent heat and moisture exchange in the urban canopy, anthropogenic influence on heat and moisture fluxes, radiation, and turbulent interaction with urban vegetation. The main approaches to the parameterization of physical processes are defined within each block. Modern trends in the development of urban parameterizations are highlighted: (1) over the past 10 years, parameterizations have become more complicated due to the addition of the building energy model, a three-dimensional structure of urban vegetation, and vertical resolution when calculating turbulent fluxes; (2) at the same time, not much attention is paid to revising the original empirical formulas, often obtained on the basis of single field or laboratory experiments. Ways to improve urban parameterizations are proposed by clarifying the basic dependencies used mainly in the calculation of turbulent fluxes, particularly using the results of highly detailed large-eddy simulation modeling, which, with growing computational power, is increasingly used to simulate explicit heat transfer between the atmosphere and individual elements of the urban environment. [ABSTRACT FROM AUTHOR]

In the Adriatic Sea, the sea surface wind forecasts are often underestimated, with detrimental effects on the accuracy of sea level and storm surge predictions. Among the various causes, this mainly depends on the meteorological forcing of the wind. In this paper, we try to improve an existing numerical method, called "wind bias mitigation", which relies on scatterometer wind observations to determine a multiplicative factor Δ w , whose application to the model wind reduces its inaccuracy with respect to the scatterometer wind. Following four different mathematical approaches, we formulate and discuss seven new expressions of the multiplicative factor. The eight different expressions of the bias mitigation factor, the original one and the seven formulated in this study, are assessed with the aid of four datasets of real sea surface wind events in a variety of sea level conditions in the northern Adriatic Sea, several of which gave rise to high water events in the Venice Lagoon. The statistical analysis shows that some of the seven new formulations of the wind bias mitigation factor are able to lower the model-scatterometer bias with respect to the original formulation. For some other of the seven new formulations, the absolute bias, with respect to scatterometer, of the mitigated model wind field, results lower than that supplied by the unmodified model wind field in 81% of the considered storm surge events in the area of interest, against the 73% of the original formulation of the wind bias mitigation. This represents an 11% improvement in the bias mitigation process, with respect to the original formulation. The best performing of the seven new wind bias mitigation factors, that based on the linear least square regression of the squared wind speed ( L L S R E ), has been implemented in the operational sea level forecast chain of the Tide Forecast and Early Warning Centre of the Venice Municipality (CPSM), to provide support to the operation of the MO.SE. barriers in Venice. [ABSTRACT FROM AUTHOR]

In this paper we derive a fourth order two step Runge-Kutta method with four stages, for additively partitioned systems of ordinary differential equations. Our main objective is that it will be useful as a horizontally explicit and vertically implicit (HEVI) method for atmospheric models. In our method the diagonal coefficients in the implicit part are all equal, and the HEVI-stability properties seem to be excellent. Further, the accuracies obtained for two simple test problems, used with different resolutions and integration intervals, considerably surpass those of a third order one step Runge-Kutta method also with four stages, used for comparison. [ABSTRACT FROM AUTHOR]

It is very well known that parameter tuning can have a major impact on the performance of Global Climate Models. However, parameter tuning is not the only implementation detail that can drastically affect model performance and the representation of various phenomena in models. "Minor‐looking treatments" often exert a critical control on model performance; they include lower and upper limits of physical variables, thresholds of variables that control the enabling or disabling of a specific process, whether two schemes can work together or only one scheme works exclusively, and numerical methods including the order of calling various physics schemes. The impacts of such minor‐looking treatments are sometimes comparable to or even larger than those obtained by introducing advanced parameterizations based on theory and observation. Because the importance of these treatments is often overlooked and not discussed in the literature, we comprehensively summarize examples of various minor‐looking treatments that can considerably affect model performance. Plain Language Summary: Global climate models (GCMs) are used for climate simulations, including global warming simulations. Global climate models (GCMs) simulate various physical processes such as convection, clouds, radiation, and turbulence. A lot of effort is devoted to improving the representation of these physical processes by introducing more sophisticated or more complicated and detailed processes. The values of parameters in such processes significantly affect the model performance, and many modelers carefully set such parameter values. However, other factors can also affect model performance. The lower or upper limits specified in the detailed implementation of such processes, threshold values for switching on a certain effect, how to calculate each effect, vertical resolutions, and so on can significantly affect the model performance. We call these "minor‐looking treatments" and show a wide range of examples in this paper. Key Points: Minor‐looking treatments crucially determine the performance of global climate modelsThey include lower limits, upper limits, thresholds for enabling physical processes, and numerical methodsMinor‐looking treatments should be documented in detail, shared, and discussed more in the climate community [ABSTRACT FROM AUTHOR]

Determining physical process parameters in atmospheric models is critical to obtaining accurate weather and climate simulations; estimating optimal parameters is essential for reducing model error. Recently, automatic parameter estimation using the ensemble Kalman filter (EnKF) has been tested instead of conventional manual parameter tuning. To maintain uncertainty for the parameters to be estimated and avoid filter divergence in EnKF-based methods, some inflation techniques should be applied to parameter ensemble spread (ES). When ES is kept constant through the estimation using an inflation technique, the precision and convergence speed of the estimation vary depending on the ES assigned to estimated parameters. However, there is debate over how to determine an appropriate constant ES for estimated parameters in terms of precision and convergence speed. This study examined the dependence of precision and convergence speed of an estimated parameter on the ES to establish a reliable and efficient method for EnKF-based parameter estimation. This was carried out by conducting idealized experiments targeting a parameter in a cloud microphysics scheme. In the experiments, there was a threshold value for ES where any smaller values did not result in any further improvements to the estimation precision, which enabled the determination of the optimal ES in terms of precision. On the other hand, the convergence speed accelerates monotonically as ES increases. To generalize the precision and convergence speed, we approximated the time series of parameter estimation with a first-order autoregression (AR(1)) model. We demonstrated that the precision and convergence speed may be quantified by two parameters from the AR(1) model: the autoregressive parameter and the amplitude of random perturbation. As the ES increases, the autoregressive parameter decreases, while the random perturbation amplitude increases. The estimation precision was determined based on the balance between the two values. The AR(1) approximation provides quantitative guidelines to determine the optimal ES for the precision and convergence speed of the EnKF-based parameter estimation. [ABSTRACT FROM AUTHOR]

Recently, machine learning techniques have been employed to develop improved models for predicting surface‐layer fluxes of momentum, heat and moisture based on field observations. Herein we explore refinement to these models, in particular artificial neural networks (NN), and investigate their applicability within an atmospheric large‐eddy simulation model as an alternative to the widely adopted standard of Monin − Obukhov (MO) similarity theory. Atmospheric boundary layer (ABL) simulations under different stability conditions are carried out for a variety of scenarios of increasing complexity, from dry steady neutral boundary layers to moist diurnal cycle. Simulations using the NN models result in predicted flux differences with respect to the corresponding MO simulations that are consistent with NN model skill in predicting the tower observations. These differences lead to notable modifications of mean and turbulence quantities throughout the ABL. While these NN models provide an alternative to improve upon MO, it is demonstrated that thorough scrutiny in design and evaluation is necessary to prevent unphysical predictions and establish a robust and generalizable parameterization. Design aspects considered include input feature engineering, applicability under inputs obtained from different heights, biased predictions due to climatological fingerprints, and sensitivity to the choice of activation function. In this context, it is shown how an atmospheric model can contribute toward efficiently investigating these relevant aspects, including expanding the training data set to generalize the NN model to a range of surface roughness values. Finally, we outline remaining challenges to be addressed toward developing a universal parameterization for surface‐layer fluxes using machine learning techniques. Key Points: A generalized neural network (NN) inference algorithm is implemented within the FastEddy® large‐eddy simulation modelApplicability of an observations‐based NN parameterization for surface fluxes is explored, showing potential to improve upon simpler modelsThorough scrutiny in design and evaluation is necessary to ensure robust and generalizable machine learning parameterizations [ABSTRACT FROM AUTHOR]

The Community Atmosphere Model (CAM) has been ported, redesigned, and scaled to the full system of the Sunway TaihuLight, and provides peta-scale climate modeling performance. Based on a novel domain decomposition method, we have fully optimized the complete model code by using both OpenACC refactoring and more aggressive and finer-grained Athread approaches. The Athread approach enables us to achieve exceptional memory control and usage, efficient vectorization, and sophisticated utilization of the thread-level communication mechanism. We have also further refined the load-balance behaviors towards ultra-large-scale numerical simulation. By combining all these novelties, we achieved a simulation speed of 7.2 and 25.6 simulation-year-per-day (SYPD) for global 25-km and 100-km resolution, respectively (1.2- to 2.2-fold improvements over previous efforts), and a sustainable double-precision performance of 3.3 PFlops for a 750-m global simulation when using 10075000 cores. [ABSTRACT FROM AUTHOR]

In recent years, Free Space Optics (FSO) communication provides attractive bandwidth enhancement with unlicensed bands worldwide spectrum. However, the link capacity and availability are the major concern in the different atmospheric conditions. The reliability of the link is highly dependent on weather conditions that attenuate the signal strength. In the tropical region specifically in Borneo Sarawak, rain is the dominant weather that act as the limiting factor to FSO performance. Hence, this study highlights on the impact of rain attenuation to the performance of FSO communication system. The finding is based on atmospheric model and performed through the simulation of OptiSystem software. The design of parameters in the simulation involves iteration of wavelengths, size of transmitter and receiver aperture and beam divergence angle. The simulation result shows longer wavelength 1550nm is much better than 785nm. The 0.25m of receiver aperture and transmitter aperture at 0.05m can reduce the loss. Narrow beam divergence angle can reduce the power consumption and maintain the high power at receiver. The analysis indicates that proposed FSO communication system is capable to withstand the attenuation from rain weather. [ABSTRACT FROM AUTHOR]

Recent work on the Intermediate eXperimental Vehicle (IXV) re-entry simulation in hypersonic rarefied regime highlighted several numerical and experimental key aspects for a satisfactory reconstruction of its aerothermodynamic flight data. Indeed, among the investigated sources of discrepancies, the atmospheric model uncertainty on the density estimation appeared as one of the major sources of error for the heat flux calculation. The initial investigation was based on the NRLMSISE-00 model of 2000 which was recently updated in the NRLMSIS 2.0 version of 2020. This last version incorporates major formulation changes and new measurements yielding significantly smaller densities for altitudes below 100 km. The present work therefore focuses on the impact of improving our knowledge of the atmospheric environment to reduce the error related to the numerical reconstruction of the heat flux. [ABSTRACT FROM AUTHOR]

This work added semi-Lagrangian convected air particles to the Intermediate Complexity Atmospheric Research (ICAR) model. The ICAR model is a simplified atmospheric model using quasi-dynamical downscaling to gain performance over more traditional atmospheric models. The ICAR model uses Fortran coarrays to split the domain amongst images and handle the halo region communication of the image's boundary regions. The newly implemented convected air particles use trilinear interpolation to compute initial properties from the Eulerian domain and calculate humidity and buoyancy forces as the model runs. This paper investigated the performance cost and scaling attributes of executing unsaturated and saturated air particles versus the original particle-less model. An in-depth analysis was done on the communication patterns and performance of the semi-Lagrangian air particles, as well as the performance cost of a variety of initial conditions such as wind speed and saturation mixing ratios. This study found that given a linear increase in the number of particles communicated, there is an initial decrease in performance, but that it then levels out, indicating that over the runtime of the model, there is an initial cost of particle communication, but that the computational benefits quickly offset it. The study provided insight into the number of processors required to amortize the additional computational cost of the air particles. [ABSTRACT FROM AUTHOR]

In response to severe haze pollution, the Chinese government has announced a series of policies focusing on controlling emissions from coal consumption. "Ultra-low emission" (ULE) technologies have the potential to dramatically reduce emissions from coalfired power plants, and have been deployed at some facilities in recent years. This paper estimated the potential environmental benefits of the widespread adoption of ULE in the Jing-Jin-Ji Region. Atmospheric modeling scenarios were analyzed for three cases: a "standard" scenario assuming no ULE deployment, a "best case" scenario assuming complete adoption of ULE across all power plants in the region, and a "natural gas" scenario, assuming emissions factors consistent with natural gas-fired power generation. The simulations show that the widespread adoption of ULE technologies can be an effective and economically competitive option for reducing the impacts of coal-fired power generation on air quality. [ABSTRACT FROM AUTHOR]

Atmospheric radiation is the main driver of weather and climate, yet due to a complicated absorption spectrum, the precise treatment of radiative transfer in numerical weather and climate models is computationally unfeasible. Radiation parameterizations need to maximize computational efficiency as well as accuracy, and for predicting the future climate many greenhouse gases need to be included. In this work, neural networks (NNs) were developed to replace the gas optics computations in a modern radiation scheme (RTE+RRTMGP) by using carefully constructed models and training data. The NNs, implemented in Fortran and utilizing BLAS for batched inference, are faster by a factor of 1–6, depending on the software and hardware platforms. We combined the accelerated gas optics with a refactored radiative transfer solver, resulting in clear‐sky longwave (shortwave) fluxes being 3.5 (1.8) faster to compute on an Intel platform. The accuracy, evaluated with benchmark line‐by‐line computations across a large range of atmospheric conditions, is very similar to the original scheme with errors in heating rates and top‐of‐atmosphere radiative forcings typically below 0.1 K day−1 and 0.5 W m−2, respectively. These results show that targeted machine learning, code restructuring techniques, and the use of numerical libraries can yield material gains in efficiency while retaining accuracy. Plain Language Summary: Solar and terrestrial radiation interact with Earth's atmosphere, surface, and clouds and provide the energy which drives climate and weather. Simulating these radiative flows in climate and weather models is crucial and can also be very time‐consuming. One possible way to model radiative effects more efficiently is to use neural networks or similar machine learning algorithms, but predictions are not guaranteed to be realistic because such models do not use physical equations. Here we investigate using neural networks to replace only one part of traditional radiation code, where the optical properties of the atmosphere are computed. We have found that this approach can be several times faster, while still being accurate in various situations, such as simulating future climate. Key Points: Neural networks (NNs) were trained to predict the optical properties of the gaseous atmosphereTraining data were generated with a recently developed radiation scheme for dynamical models (RRTMGP)RRTMGP‐NN is roughly 3 times faster than the reference code and has a similar accuracy, also in future climate scenarios [ABSTRACT FROM AUTHOR]

Convergence testing is a common practice in the development of dynamical cores of atmospheric models but is not as often exercised for the parameterization of subgrid physics. An earlier study revealed that the stratiform cloud parameterizations in several predecessors of the Energy Exascale Earth System Model (E3SM) showed strong time step sensitivity and slower‐than‐expected convergence when the model's time step was systematically refined. In this work, a simplified atmosphere model is configured that consists of the spectral‐element dynamical core of the E3SM atmosphere model coupled with a large‐scale condensation parameterization based on commonly used assumptions. This simplified model also resembles E3SM and its predecessors in the numerical implementation of process coupling and shows poor time step convergence in short ensemble tests. We present a formal error analysis to reveal the expected time step convergence rate and the conditions for obtaining such convergence. Numerical experiments are conducted to investigate the root causes of convergence problems. We show that revisions in the process coupling and closure assumption help to improve convergence in short simulations using the simplified model; the same revisions applied to a full atmosphere model lead to significant changes in the simulated long‐term climate. This work demonstrates that causes of convergence issues in atmospheric simulations can be understood by combining analyses from physical and mathematical perspectives. Addressing convergence issues can help to obtain a discrete model that is more consistent with the intended representation of the physical phenomena. Plain Language Summary: Computer codes that simulate the time evolution of a physical system produce errors in the results due to the finite step sizes used to advance the calculations in time. These errors are expected to decrease as the time steps are shortened, at a rate determined by the characteristics of the equations and numerical methods. An earlier study revealed that the error reduction in several predecessors of the Energy Exascale Earth System Model (E3SM) was at a rate slower than expected. This study creates a simplified configuration of those models and investigates the causes of the unexpected behavior. We show that slow error reduction can be understood and improved by combining analyses from physical and mathematical perspectives. The required code modifications can lead to significant changes in the simulated long‐term behavior of a full‐fledged climate model. Furthermore, ensuring a proper rate of error reduction can help to obtain a computer code that is more consistent with the intended representation of the corresponding physical system. Key Points: Poor numerical convergence is seen in a simplified model that resembles design features of a full‐fledged climate modelRevisions in process coupling and closure assumption help improve convergence and avoid nonphysical behaviorThese revisions also affect long‐term climate in the corresponding full model [ABSTRACT FROM AUTHOR]

Sulfur‐water chemistry plays an important role in the middle atmosphere of Venus. Ground‐based observations have found that simultaneously observed SO2 and H2O at ~64 km vary with time and are temporally anticorrelated. To understand these observations, we explore the sulfur‐water chemical system using a one‐dimensional chemistry‐diffusion model. We find that SO2 and H2O mixing ratios above the clouds are highly dependent on mixing ratios of the two species at the middle cloud top (58 km). The behavior of sulfur‐water chemical system can be classified into three regimes, but there is no abrupt transition among these regimes. In particular, there is no bifurcation behavior as previously claimed. We also find that the SO2 self‐shielding effect causes H2O above the clouds to respond to the middle cloud top in a nonmonotonic fashion. Through comparison with observations, we find that mixing ratio variations at the middle cloud top can explain the observed variability of SO2 and H2O. The sulfur‐water chemistry in the middle atmosphere is responsible for the H2O‐SO2 anticorrelation at 64 km. Eddy transport change alone cannot explain the variations of both species. These results imply that variations of species abundance in the middle atmosphere are significantly influenced by the lower atmospheric processes. Continued ground‐based measurements of the coevolution of SO2 and H2O above the clouds and new spacecraft missions will be crucial for uncovering the complicated processes underlying the interaction among the lower atmosphere, the clouds, and the middle atmosphere of Venus. Plain Language Summary: Sulfur chemistry composes one important chemical cycle in the Venusian atmosphere. Sulfur dioxide, the most abundant sulfur species, is transported from the lower atmosphere (below the clouds) to the middle atmosphere. On the dayside, sulfur dioxide is dissociated by ultraviolet light and forms various sulfur‐bearing species. These species, like polysulfur and sulfuric acid, are critical for the formation of Venus' haze and the sulfuric acid clouds. Sulfur dioxide is observed to vary by orders of magnitude, though mechanisms underlying those variations remain elusive. In this work, we use a one‐dimensional photochemical model to explain the coevolution of sulfur dioxide and water from ground‐based observations. We find that sulfur chemistry and variations inside the clouds are two important factors affecting temporal variations of sulfur dioxide and water. Our study highlights the importance of the interaction among the lower atmosphere, the clouds, and the middle atmosphere on Venus. Key Points: We found that there is no bifurcation behavior in the sulfur‐water chemical system as previously claimedThe observed SO2‐H2O anticorrelation can be explained by the sulfur‐water chemistry with mixing ratio variations at the middle cloud topThis suggests that the observed temporal variations of SO2 and H2O are linked to the lower atmospheric processes [ABSTRACT FROM AUTHOR]

A new methodology is proposed for computing the city-descriptive parameters for the TERRA_URB scheme in the COSMO model. It is based on the GIS analysis of OpenStreetMap data, Sentinel-2 satellite images, and global land cover data. A set of parameters is obtained for Moscow megacity on a grid with the spacing of 500 m. The dataset is promising for high-resolution numerical weather prediction within the COSMO-Ru system and for urban climate research. [ABSTRACT FROM AUTHOR]

The study presents the new system for operational short-range numerical weather prediction with the grid spacing of 1 km for the Moscow region that considers the features of urbanized surface and is based on the COSMO-Ru1M model configuration. This system is implemented in the Hydrometcenter of Russia. The results of the trial testing of the optimum model configuration for the Moscow region using observations from the dense network of weather stations and MTP-5 temperature profilers are presented. High prediction capabilities of the new forecasting system are demonstrated. The approaches to the minimization of the time of calculations for the technology chain implemented at the Roshydromet supercomputer are described. A case study of modeling with the grid spacing of 500 m versus 1000 m for summer convective weather events in the Moscow region has been analyzed. [ABSTRACT FROM AUTHOR]

The climate of stratosphere was simulated with a spectral element model under two different vertical level distributions, one had 28 levels with 0.3 hPa at the top of the atmosphere and another 66 levels with 4.5×10-6 hPa at the top boundary. Simulation results were validated against ERA-Interim reanalysis dataset from ECMWF (European Center for Medium-Range Weather Forecasts) and the influence of vertical level distributions on the numerical simulation of stratospheric temporal mean state had been analyzed. It is shown that the lift of upper boundary can reduce the temperature error above the height of 100 hPa level significantly. It can also improve the simulation of seasonal variation of zonal mean zonal wind on 50 hPa in the northern high latitudes. In both configurations, cold biases of 10-year averaged zonal mean of temperature are mainly discerned at levels around 75 hPa at low latitudes and levels around 175 hPa at high southern latitudes. The simulated seasonal variation of westerly at 50 hPa in the northern hemisphere with 66-layer model coincides with results of ERA-Interim better. [ABSTRACT FROM AUTHOR]

The Energy Exascale Earth System Model Atmosphere Model version 1, the atmospheric component of the Department of Energy's Energy Exascale Earth System Model is described. The model began as a fork of the well‐known Community Atmosphere Model, but it has evolved in new ways, and coding, performance, resolution, physical processes (primarily cloud and aerosols formulations), testing and development procedures now differ significantly. Vertical resolution was increased (from 30 to 72 layers), and the model top extended to 60 km (~0.1 hPa). A simple ozone photochemistry predicts stratospheric ozone, and the model now supports increased and more realistic variability in the upper troposphere and stratosphere. An optional improved treatment of light‐absorbing particle deposition to snowpack and ice is available, and stronger connections with Earth system biogeochemistry can be used for some science problems. Satellite and ground‐based cloud and aerosol simulators were implemented to facilitate evaluation of clouds, aerosols, and aerosol‐cloud interactions. Higher horizontal and vertical resolution, increased complexity, and more predicted and transported variables have increased the model computational cost and changed the simulations considerably. These changes required development of alternate strategies for tuning and evaluation as it was not feasible to "brute force" tune the high‐resolution configurations, so short‐term hindcasts, perturbed parameter ensemble simulations, and regionally refined simulations provided guidance on tuning and parameterization sensitivity to higher resolution. A brief overview of the model and model climate is provided. Model fidelity has generally improved compared to its predecessors and the CMIP5 generation of climate models. Plain Language Summary: This study provides an overview of a new computer model of the Earth's atmosphere that is used as one component of the Department of Energy's latest Earth system model. The model can be used to help understand past, present, and future changes in Earth's behavior as the system responds to changes in atmospheric composition (like pollution and greenhouse gases), land, and water use and to explore how the atmosphere interacts with other components of the Earth system (ocean, land, biology, etc.). Physical, chemical, and biogeochemical processes treated within the atmospheric model are described, and pointers to previous and recent work are listed to provide additional information. The model is compared to present‐day observations and evaluated for some important tests that provide information about what could happen to clouds and the environment as changes occur. Strengths and weaknesses of the model are listed, as well as opportunities for future work. Key Points: A brief description and evaluation is provided for the atmospheric component of the Department of Energy's Energy Exascale Earth System ModelModel fidelity has generally improved compared to predecessors and models participating in past international model evaluationsStrengths and weaknesses of the model, as well as opportunities for future work, are described [ABSTRACT FROM AUTHOR]

Simulating waves using numerical wave models provide essential wave information for navigational safety and coastal protection applications. Accuracy of such simulations depends mainly on the accuracy of the forcing wind fields, which are influenced by the wind fields’ spatial and temporal resolutions. In this study, the sensitivity of a SWAN-based Red Sea wave model to spatial and temporal resolutions of forcing wind fields was investigated. The sensitivity analysis showed that forcing the wave model with wind fields of low spatial and/or temporal resolutions will affect the quality of wave model outputs, not only in terms of integrated wave parameters, but also in terms of the overall wave energy distribution in both frequency and directional domains. This study suggests that the spatial resolution of the forcing wind filed plays more significant role than the temporal resolution on the quality of the wave model predictions. [ABSTRACT FROM AUTHOR]

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We present the acceleration of an IMplicit–EXplicit (IMEX) nonhydrostatic atmospheric model on manycore processors such as graphic processing units (GPUs) and Intel's Many Integrated Core (MIC) architecture. IMEX time integration methods sidestep the constraint imposed by the Courant–Friedrichs–Lewy condition on explicit methods through corrective implicit solves within each time step. In this work, we implement and evaluate the performance of IMEX on manycore processors relative to explicit methods. Using 3D-IMEX at Courant number C = 15, we obtained a speedup of about 4× relative to an explicit time stepping method run with the maximum allowable C = 1. Moreover, the unconditional stability of IMEX with respect to the fast waves means the speedup can increase significantly with the Courant number as long as the accuracy of the resulting solution is acceptable. We show a speedup of 100× at C = 150 using 1D-IMEX to demonstrate this point. Several improvements on the IMEX procedure were necessary in order to outperform our results with explicit methods: (a) reducing the number of degrees of freedom of the IMEX formulation by forming the Schur complement, (b) formulating a horizontally explicit vertically implicit 1D-IMEX scheme that has a lower workload and better scalability than 3D-IMEX, (c) using high-order polynomial preconditioners to reduce the condition number of the resulting system, and (d) using a direct solver for the 1D-IMEX method by performing and storing LU factorizations once to obtain a constant cost for any Courant number. Without all of these improvements, explicit time integration methods turned out to be difficult to beat. We discuss in detail the IMEX infrastructure required for formulating and implementing efficient methods on manycore processors. Several parametric studies are conducted to demonstrate the gain from each of the abovementioned improvements. Finally, we validate our results with standard benchmark problems in numerical weather prediction and evaluate the performance and scalability of the IMEX method using up to 4192 GPUs and 16 Knights Landing processors. [ABSTRACT FROM AUTHOR]

We present a Graphics Processing Unit (GPU)-accelerated nodal discontinuous Galerkin method for the solution of the three-dimensional Euler equations that govern the motion and thermodynamic state of the atmosphere. Acceleration of the dynamical core of atmospheric models plays an important practical role in not only getting daily forecasts faster, but also in obtaining more accurate (high resolution) results within a given simulation time limit. We use algorithms suitable for the single instruction multiple thread architecture of GPUs to accelerate our model by two orders of magnitude relative to one core of a CPU. Tests on one node of the Titan supercomputer show a speedup of up to 15 times using the K20X GPU as compared to that on the 16-core AMD Opteron CPU. The scalability of the multi-GPU implementation is tested using 16,384 GPUs, which resulted in a weak scaling efficiency of about 90%. Finally, the accuracy and performance of our GPU implementation is verified using several benchmark problems representative of different scales of atmospheric dynamics. [ABSTRACT FROM AUTHOR]

Limited‐area ensemble predictions can be sensitive to the specification of lateral boundary conditions, which are often built by subsampling larger ensembles. Using the operational PEARP and AROME‐EPS ensembles, we compare several subsampling methods, including random selection, representative members, and a new selection method. The tests show that the algorithms used for the clustering and the member selection have a significant impact on the resulting ensembles. Clustering‐based methods are shown to outperform random subsampling, mostly (but not only) because they change the ensemble spread. Cluster sizes can be highly variable, which can complicate ensemble interpretation. We present a subsampling algorithm that has little impact on performance scores, but better preserves ensemble spread and produces nearly equally likely members by limiting cluster size variability. Limited‐area ensemble predictions can be sensitive to the specification of lateral boundary conditions, which are often built by subsampling larger ensembles. Using the operational PEARP and AROME‐EPS ensembles, we compare several subsampling methods, including random selection, representative members, and a new selection method. [ABSTRACT FROM AUTHOR]

It is common to treat the parametrisation of the atmospheric boundary layer, as well as other physics packages, separately from the inviscid dynamics within weather/climate models. In the case of the boundary layer in particular, there is then an issue with the application of the boundary conditions which are different, such as no‐slip and free‐slip. This article proposes a consistent and tightly coupled approach that involves incorporating the boundary‐layer terms within the semi‐implicit treatment of the dynamics and resulting Helmholtz problem. Cross‐section of the velocity components (u,v,w) after 1 day for a constant viscosity. [ABSTRACT FROM AUTHOR]

ABSTRACT Ha, T.; Yoon, J.-S., and Heo, K.-Y., 2018. Numerical simulations of a meteotsunami using both atmospheric and phase-resolving wave models in the Yellow Sea. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 786–790. Coconut Creek (Florida), ISSN 0749-0208. During the past decades, meteotsunamis have occurred all over the world oceans and their destructive long waves have threatened the local community near the coastal areas. Among them, meteorological tsunamis occurred in the Yellow Sea have brought unexpected casualties and property damage to the local community on the western coast of the Korean Peninsula in 2007 and 2008. The events have attracted the attention of many engineers and scientists because abrupt extreme waves have attacked several coasts and ports even under fine weather conditions. There have been many researches to identify underlying processes of meteotsunamis and our understanding has substantially improved. Nevertheless, many open issues still remain unresolved. Although it is well known that Proudman resonance plays a dominant role in meteotsunami-related atmosphere-ocean energy transfer, detailed amplification processes of meteotsunamis influenced by the local bathymetry have not been sufficiently identified yet. In this study, numerical experiments were conducted to identify the generation and amplification mechanisms of the meteotsunami occurred in the Yellow Sea. In general, a small-scale meteotsunami, like the events occurred in the Yellow Sea, should be analyzed using a high-resolution modeling system because water motions can be affected by local terrain. To achieve the objective, high-resolution atmospheric modelling was conducted to reproduce the atmospheric pressure disturbances observed in the Yellow Sea and, then the generation and propagation of the meteotsunami over real topographies was simulated using a phase-resolving wave model. Both an atmospheric model, Weather Research and Forecasting Model (WRF), and a shoallow water equations model, COMCOT, were employed to simulate the generation and transformation of the meteotsunami, respectively. The numerical results were first compared with the available observational data during the event to validate the modelling system, and then used to identify the generation and amplification mechanism of the meteotsunami in the Yellow Sea. [ABSTRACT FROM AUTHOR]

Ha, T.; Yoon, J.-S., and Heo, K.-Y., 2018. Numerical simulations of a meteotsunami using both atmospheric and phase-resolving wave models in the Yellow Sea. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 786–790. Coconut Creek (Florida), ISSN 0749-0208. During the past decades, meteotsunamis have occurred all over the world oceans and their destructive long waves have threatened the local community near the coastal areas. Among them, meteorological tsunamis occurred in the Yellow Sea have brought unexpected casualties and property damage to the local community on the western coast of the Korean Peninsula in 2007 and 2008. The events have attracted the attention of many engineers and scientists because abrupt extreme waves have attacked several coasts and ports even under fine weather conditions. There have been many researches to identify underlying processes of meteotsunamis and our understanding has substantially improved. Nevertheless, many open issues still remain unresolved. Although it is well known that Proudman resonance plays a dominant role in meteotsunami-related atmosphere-ocean energy transfer, detailed amplification processes of meteotsunamis influenced by the local bathymetry have not been sufficiently identified yet. In this study, numerical experiments were conducted to identify the generation and amplification mechanisms of the meteotsunami occurred in the Yellow Sea. In general, a small-scale meteotsunami, like the events occurred in the Yellow Sea, should be analyzed using a high-resolution modeling system because water motions can be affected by local terrain. To achieve the objective, high-resolution atmospheric modelling was conducted to reproduce the atmospheric pressure disturbances observed in the Yellow Sea and, then the generation and propagation of the meteotsunami over real topographies was simulated using a phase-resolving wave model. Both an atmospheric model, Weather Research and Forecasting Model (WRF), and a shoallow water equations model, COMCOT, were employed to simulate the generation and transformation of the meteotsunami, respectively. The numerical results were first compared with the available observational data during the event to validate the modelling system, and then used to identify the generation and amplification mechanism of the meteotsunami in the Yellow Sea. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciência e Natura is the property of Revista Ciencia e Natura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Ciência e Natura is the property of Revista Ciencia e Natura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently there are many international microbarograph networks for high-resolution recording of wave pressure variations on the Earth,s surface. This arouses interest in wave propagation in the atmosphere generated by atmospheric pressure variations. A full system of nonlinear hydrodynamic equations for atmospheric gases with lower boundary conditions in the form of wavelike pressure variations on the Earth,s surface is considered. Since the wave amplitudes near the Earth,s surface are small, linearized equations are used in the analysis of well-posedness of the problem. With the help of a wave energy functional method, it is shown that in the non-dissipative case the solution to the boundary value problem is uniquely determined by the variable pressure field on the Earth,s surface. The corresponding dissipative problem is well-posed if, in addition to the pressure field, appropriate conditions on the velocity and temperature on the Earth,s surface are given. In the case of an isothermal atmosphere, the problem admits analytical solutions that are harmonic in the variables x and t. A good agreement between the numerical and analytical solutions is obtained. The study shows that the temperature and density can rapidly vary at the lower boundary of the boundary value problem. An example of solving the three-dimensional problem with variable pressure on the Earth,s surface taken from experimental observations is given. The developed algorithms and computer programs can be used to simulate atmospheric waves generated by pressure variations on the Earth,s surface. [ABSTRACT FROM AUTHOR]

The main stages are considered of the process of Roshydromet forecast technologies modernization that started in the 1990s, especially those related to the use of supercomputers for operational numerical weather prediction (NWP) and to the development of supercomputer technologies for NWP with different lead times. Some outcomes of the modernization are presented. [ABSTRACT FROM AUTHOR]

Low and high clouds of shallow extent, especially stratocumulus and even more so for high-level cirrus clouds that reside where vertical resolution is particularly coarse, are poorly represented in large-scale models such as global climate models and weather forecasting models. This adversely affects, among others, estimation of cloud feedbacks for climate prediction and weather forecasts. Here we address vertical resolution as a reason for the failure of these models to adequately represent shallow clouds. We introduce a new methodology, the Framework for Improvement by Vertical Enhancement (FIVE). FIVE computes selected processes on a one-dimensional vertical grid with local high resolution in the boundary layer and near the tropopause. In addition to the host model, variables on the locally high-resolution grid are predicted in parallel so that high-resolution information is retained. By exchanging tendencies with one another, the host model and high-resolution field are always synchronized. The methodology is demonstrated for drizzling stratocumulus capped by a sharp inversion. First, FIVE is applied to a single-column model to identify the cause of biases associated with computing an assigned process at low resolution. Second, a two-dimensional regional model coupled with FIVE is shown to produce results comparable to those performed with high vertical resolution. FIVE is thus expected to represent low clouds more realistically and hence reduce the low-cloud bias in large-scale models. Finally, we propose a number of methods that will be developed and tested to further optimize FIVE. [ABSTRACT FROM AUTHOR]

The AROME-EPS convection-permitting ensemble prediction system has been evaluated over the HyMeX-SOP1 period. Objective verification scores are computed using dense observing networks prepared for the HyMeX experiment. In probabilistic terms, the AROME-EPS ensemble performs better than the AROME-France deterministic prediction system, and a state-of-the-art ensemble at a lower resolution. The strengths and weaknesses of AROME-EPS are discussed. Here, impact experiments are used to study perturbation schemes for the initial conditions and the model surface. Both have a significant effect on the ensemble performance. The interactions between the perturbations of lateral boundaries, initial conditions and surface perturbations are studied. The consistency between initial and lateral perturbations is found to be unimportant from a meteorological point of view. Ensemble data assimilation is not as effective as a simpler surface perturbation scheme, and it is noted that both approaches could be usefully combined. [ABSTRACT FROM AUTHOR]

A discontinuous Galerkin nonhydrostatic atmospheric model is used for two-dimensional and three-dimensional simulations. There is a wide range of timescales to be dealt with. To do so, two different implicit/explicit time discretizations are implemented. A stabilization, based upon a reduced-order discretization of the gravity term, is introduced to ensure the balance between pressure and gravity effects. While not affecting significantly the convergence properties of the scheme, this approach allows the simulation of anisotropic flows without generating spurious oscillations, as it happens for a classical discontinuous Galerkin discretization. This approach is shown to be less diffusive than usual spatial filters. A stability analysis demonstrates that the use of this modified scheme discards the instability associated with the usual discretization. Validation against analytical solutions is performed, confirming the good convergence and stability properties of the scheme. Numerical results demonstrate the attractivity of the discontinuous Galerkin method with implicit/explicit time integration for large-scale atmospheric flows. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

On the evening of 9 March 2014, a bright fireball was observed in many places in South Korea, the energy associated with the event was also recorded at local seismic and regional infrasound stations. Using impulsive seismic signals recorded at 19 stations, we calculated the possible extent of the sonic source of the fireball by assuming point- and line-source models, which might be physically linked to either a point-source explosion in the atmosphere or shock waves radiating from the fireball's trail, respectively. By comparing the observed seismic arrival times with those predicted by both models, a line-source model resulted in better solution than a point-source model. It means that shock waves along the meteor's high-speed passage through the atmosphere probably acted as the dominant source of the seismic signals. The parameters of the fireball's sonic trajectory, after incorporating a realistic sound velocity model of the atmosphere, are estimated to be an azimuth arrival angle of 313.5° clockwise from the north and an elevation arrival angle of 44.5° above the Earth's surface, with a time of 11:04:51 UTC at which the trajectory meets the Earth's surface. The estimated ground impact point is about 2 km offset from locations where meteorites associated with the fireball were found. The line-source model also explained the large azimuthal deviation of infrasound waves observed at arrays in the vicinity of the trajectory. From the infrasound measurements, the kinetic local-source energy generating the sound was estimated to be approximately 0.8 tons TNT, based on semi-empirical relations for the point- and line-source models. [ABSTRACT FROM AUTHOR]

Ha, T.; Park, C.; Yoon, J., and Heo, K.-Y., 2016. Synoptic study on forecasting large swell waves along the eastern coast of Korea using the operational system. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 1102 - 1106. Coconut Creek (Florida), ISSN 0749-0208. In recent years, the eastern coast of Korea has been experiencing frequent large swell waves during winter. These waves, occasionally higher than 3 m, rarely occurred until recently and now cause severe casualties, damage to coastal structures, and extensive beach erosion along the eastern coast of the country. Therefore, the urgency for appropriately predicting large swell waves affecting these areas has increased. A convenient forecasting source is a numerical wave prediction system running in an operational mode. Since 2009, Korea Institute of Ocean Science and Technology (KIOST) has been developing and improving Korea Operational Oceanographic System (KOOS), funded by the Ministry of Oceans and Fisheries (MOF). The project aims at developing an integrated operational oceanographic system providing nowcasts and forecasts of ocean information around the Korean Peninsula. This study introduces an operational wave forecasting system using high-resolution multigrids, developed for its implementation in KOOS. A series of numerical experiments were conducted on previous cases of large swell waves along the eastern coast of Korea, using weather hindcasting products of KOOS. The new system was evaluated by comparing the obtained numerical results with data from the previous wave forecasting system and available observations. The wave forecasting system proposed yielded good qualitative and quantitative predictions for large swell waves in different locations. Furthermore, the obtained numerical results highly agree with available observational data. [ABSTRACT FROM AUTHOR]

A method to solve the three-dimensional compressible Navier-Stokes equations on the sphere is suggested, based on a stereographic projection with a high-order mapping of the elements from the stereographic space to the sphere. The projection is slightly modified, in order to take into account the domain thickness without introducing any approximation about the aspect ratio (deep-atmosphere). In a discontinuous Galerkin framework, the elements alongside the equator are exactly represented using a nonpolynomial geometry, in order to avoid the numerical issues associated with the seam connecting the two hemispheres. This is an crucial point, necessary to avoid mass loss and spurious deviations of the velocity. The resulting model is validated on idealized three-dimensional atmospheric test cases on the sphere, demonstrating the good convergence properties of the scheme, its mass conservation, and its satisfactory behavior in terms of accuracy and low numerical dissipation. A simulation is performed on a variable resolution unstructured grid, producing accurate results despite a substantial reduction of the number of elements. [ABSTRACT FROM AUTHOR]

Motivated by findings that energetically consistent subgrid dissipation schemes can improve eddy-permitting ocean simulations, this work investigates the impact of the subgrid dissipation scheme on low-resolution atmospheric dynamical cores. A kinetic energy-conserving dissipation scheme is implemented in the model adding a negative viscosity term that injects back into the eddy field the kinetic energy dissipated by horizontal hyperdiffusion. The kinetic energy-conserving scheme enhances numerical convergence when horizontal resolution is changed with fixed vertical resolution and gives superior low-resolution results. Improvements are most obvious for eddy kinetic energy but also found in other fields, particularly with strong or little scale-selective horizontal hyperdiffusion. One advantage of the kinetic energy-conserving scheme is that it reduces the sensitivity of the model to changes in the subgrid dissipation rate, providing more robust results. [ABSTRACT FROM AUTHOR]

Coastlines, shoals, and reefs are some of the most dynamic and constantly changing regions of the globe. The emergence of high-resolution satellites with new spectral channels, such as the WorldView-2, increases the amount of data available, thereby improving the determination of coastal management parameters. Water-leaving radiance is very difficult to determine accurately, since it is often small compared to the reflected radiance from other sources such as atmospheric and water surface scattering. Hence, the atmospheric correction has proven to be a very important step in the processing of high-resolution images for coastal applications. On the other hand, specular reflection of solar radiation on nonflat water surfaces is a serious confounding factor for bathymetry and for obtaining the seafloor albedo with high precision in shallow-water environments. This paper describes, at first, an optimal atmospheric correction model, as well as an improved algorithm for sunglint removal based on combined physical and image processing techniques. Then, using the corrected multispectral data, an efficient multichannel physics-based algorithm has been implemented, which is capable of solving through optimization the radiative transfer model of seawater for bathymetry retrieval, unmixing the water intrinsic optical properties, depth, and seafloor albedo contributions. Finally, for the mapping of benthic features, a supervised classification methodology has been implemented, combining seafloor-type normalized indexes and support vector machine techniques. Results of atmospheric correction, remote bathymetry, and benthic habitat mapping of shallow-water environments have been validated with in situ data and available bionomic profiles providing excellent accuracy. [ABSTRACT FROM PUBLISHER]

Automatic infrasound detection is analyzed with infrasonic data from the western US for the time period of November 2010 to October 2012. Data from nine University of Utah Seismograph Stations (UUSS) infrasonic arrays are supplemented by three additional arrays in Nevada, operated by Southern Methodist University (SMU). In this study, the detection procedure is based on an adaptive F-detector (AFD) that accounts for both correlated and uncorrelated noise by capturing the time-varying background noise conditions. The adaptation of the detector, characterized by time varying linear remapping of the F-distribution (C-value), depends on the background noise level at the arrays including weather conditions with seasonal variations and local site effects. The infrasound detection catalog consists of 580,177 signals, depending on time and space as well as data availability at individual stations. Based on the Ground-to-Space (G2S) specifications, the number of detections is shown to be correlated with seasonal variations in the stratospheric winds, illustrating the time varying nature of infrasound propagation. These seasonal variations of infrasound detections include the number of detections, correlation value, occurrences in time, phase velocity, and azimuth. The majority of events occur during working hours, Monday through Friday, suggesting a human cause. Based on the automatic detection results, we reviewed the waveforms generated from the known repeated sources such as Dugway Testing Ground (DTG) and Utah Test and Training Range (UTTR) in Utah, and New Bomb (NB) in Nevada. In order to validate the automatic infrasound detection catalog, we document the celerities of automatic infrasound arrivals during the summer and winter using the seismic origin times of NB explosions. Infrasound propagation is dependent on the seasonal variation of wind conditions as well as source location and array distribution. [ABSTRACT FROM AUTHOR]

A set of numerical experiments designed to analyze the oceanic forcing in spring show that the combined forcing of cold (warm) El Niño (La Niña) phases in the Niño4 region and sea surface temperature anomalies (SSTA) in the westerly drifts region would result in abnormally enhanced NorthEast Cold Vortex (NECV) activities in early summer. In spring, the central equatorial Pacific El Niño phase and westerly drift SSTA forcing would lead to the retreat of non-adiabatic waves, inducing elliptic low-frequency anomalies of tropical air flows. This would enhance the anomalous cyclone-anticyclone-cyclone-anticyclone low-frequency wave train that propagates from the tropics to the extratropics and further to the mid-high latitudes, constituting a major physical mechanism that contributes to the early summer circulation anomalies in the subtropics and in the North Pacific mid-high latitudes. The central equatorial Pacific La Niña forcing in the spring would, on the one hand, induce teleconnection anomalies of high pressure from the Sea of Okhotsk to the Sea of Japan in early summer, and on the other hand indirectly trigger a positive low-frequency East Asia-Pacific teleconnection (EAP) wave train in the lower troposphere. [ABSTRACT FROM AUTHOR]

This work describes the two-way coupling performed between the regional atmospheric model Brazilian Regional Atmospheric Modeling System (BRAMS) and the hydrological model MGB-IPH. As a first step of the atmosphere-hydrology coupling, only the water balance variables were coupled. Differences in temporal and spatial scales between MGH-IPH and BRAMS were analyzed. By default, MGB-IPH has a daily time step whereas BRAMS uses smaller time steps. Thus, accumulated rainfall values from BRAMS were used to feed MGB-IPH. On the other hand, daily values of evapotranspiration from MGB-IPH were provided to BRAMS. This procedure was assumed as a daily loop in the simulations. Differences in spatial scales were avoided by using the same grid size (10 × 10 km) in both models, in such a way that neither upscaling nor downscaling was necessary. The coupled system was tested for the Rio Grande basin situated in south-eastern Brazil by comparing results from BRAMS with results from the coupled system for the same period, with the same input data. Outputs from the runs were compared to water vapor satellite images. The results from the coupled model tests indicated that its predictions of rainfall distribution were more accurate than BRAMS. [ABSTRACT FROM AUTHOR]