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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]

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]

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]

It is well known that there are several vertical flight phases for the aircraft in the flight. The cruise flight phase takes most of the flight time, flight distance and fuel consumption, so it is important and necessary to develop the flight management system(FMS) vertical trajectory prediction algorithm for the cruise flight phase, aiming to improve the economy, comfort and safety of the flight. In order to meet the versatility of the vertical trajectory prediction algorithm between different types of aircraft in the cruise phase and improve the accuracy and reliability of the vertical trajectory prediction, the algorithm of vertical trajectory prediction for cruise flight phase is proposed in this paper. Firstly, the cruise speed profile is calculated, and the realistic atmospheric model of prediction is constructed. Then the cruise fuel flow data is calculated based on the aircraft model of first-principles, and the vertical trajectory prediction for the cruise phase is given by designing the vertical trajectory prediction algorithm logic. Finally, the effectiveness and accuracy of the algorithm are verified by ground simulation and air flight test. The results show that the vertical trajectory prediction algorithm of FMS cruise phase based on the aircraft model of firstprinciples can predict the cruise trajectory of the aircraft, and the prediction accuracy error is less than 1%.

Abstract 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.

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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]

Abstract 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.

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.

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]

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.

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]

Irrigation is the process of artificially providing water to agricultural lands in order to provide crops with the necessary water supply to ensure or foster the growth of the plants. However, its implications reach beyond the agro-economic aspect as irrigation affects the soil-land-atmosphere interactions and thus influences the water and energy cycles in the Earth system. Past studies have shown how through these interactions, an increase in soil moisture due to irrigation also affects the atmospheric state and its dynamics. Thus, the lack of representation of irrigation in numerical Earth system models—be it for reanalysis, weather forecasting or climate prediction—can lead to significant errors and biases in various parameters of the system including but not limited to surface temperature and precipitation. In this study, we aim to summarize and discuss currently available irrigation parameterizations across different numerical models. This provides a reference framework to understand the impact of irrigation on the various components of Earth system models. Specifically, we discuss the impact of these parameterizations in the context of their spatio-temporal scale representation and point out the benefits and limitations of the various approaches. In fact, most of the parameterizations use irrigation as a direct modification of soil moisture with just a few implementations add irrigation as a form of surface water. While the former method might be suitable for coarse spatio-temporal scales, the latter better resembles the range of employed irrigation techniques. From the analysis, we find that not only the method or the spatio-temporal scales but the actual amount of water used is of great importance to the response of the Earth system model.

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.

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.

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]

In California offshore waters, sustained northwesterly winds have been identified as a key energy resource that could contribute substantially to California’s renewable energy mandate. It is these winds that drive upwelling, which is responsible for much of the primary productivity that sustains one of the richest ecosystems on the planet. The goal of this study is to quantify changes in wind fields at the sea surface as the result of offshore wind turbine deployments by use of an atmospheric model. Modeled wind fields from this study will drive an ocean circulation model. The Weather Research and Forecasting model was implemented on a regional scale along the U.S. west coast, with a higher resolution nest along the California continental shelf. Simulated arrays of offshore wind turbines were placed within call areas for wind farm development offshore of Central and Northern California. At full build-out, it was found that wind speeds at 10 m height are reduced by approximately 5%, with wakes extending approximately 200 km downwind of the nominated lease block areas. The length scale of wind speed reductions was found to be several times the internal Rossby radius of deformation, the spatial scale at which rotationally-influenced ocean circulation processes such as upwelling occur.

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 Δw, 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 (LLSRE), 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.

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]