Parameterization Of Processes Research Articles

Algorithmic Differentiation for Sensitivity Analysis in Cloud Microphysics

AbstractThe role of clouds for radiative transfer, precipitation formation, and their interaction with atmospheric dynamics depends strongly on cloud microphysics. The parameterization of cloud microphysical processes in weather and climate models is a well‐known source of uncertainties. Hence, robust quantification of this uncertainty is mandatory. Sensitivity analysis to date has typically investigated only a few model parameters. We propose algorithmic differentiation (AD) as a tool to detect the magnitude and timing at which a model state variable is sensitive to any of the hundreds of uncertain model parameters in the cloud microphysics parameterization. AD increases the computational cost by roughly a third in our simulations. We explore this methodology as the example of warm conveyor belt trajectories, that is, air parcels rising rapidly from the planetary boundary layer to the upper troposphere in the vicinity of an extratropical cyclone. Based on the information of derivatives with respect to the uncertain parameters, the ten parameters contributing most to uncertainty are selected. These uncertain parameters are mostly related to the representation of hydrometeor diameter and fall velocity, the activation of cloud condensation nuclei, and heterogeneous freezing. We demonstrate the meaningfulness of the AD‐estimated sensitivities by comparing the AD results with ensemble simulations spawned at different points along the trajectories, where different parameter settings are used in the various ensemble members. The ranking of the most important parameters from these ensemble simulations is consistent with the results from AD. Thus, AD is a helpful tool for selecting parameters contributing most to cloud microphysics uncertainty.

Hierarchical bead materials multi-property design for wire-feed laser additive manufacturing

Wire-feed laser additive manufacturing has gained attention for years with its promises of high-level automation, reducing materials waste, overall costs, and large-scale volume production. However, printing material with multiple desired bead properties is a great challenge due to the high cost of experimental data and trial and error methods and the interdependent correlations among the properties. This study proposes a comprehensive multi-property integrated design framework based on learning from experiments data to enable quality control of part (bead) surface finish, geometry and microstructural properties. The printed bead process – microstructure – geometry relationships and process features' importance scores are investigated. The overall bead quality (smooth, ripple, and failed), geometry (e.g. bead height, width, fusion zone depth, fusion zone area), and microstructures characteristics are simultaneously optimized to obtain the optimal processing window. Specifically, the desirable property thresholds are defined experimentally, and the microstructures and geometries of printed beads under various process combinations have been predicted by machine learning models and visualized in a 3D contour space. With these predictions, the process parameters are optimized by passing through a set of filters to filter out undesired process candidates from a process searching space. The validation results highlight that the process parameterizations optimized by this framework can speed up the manufacturing of materials with the desired bead multi-performance.

Parametric Modelling in Furniture Design A Case Study: Two Door Wardrope

In the process of designing and modeling industrial products, computer technologies are used intensively in the furniture sector, as in every sector. In the past traditionally produced furniture, today enables many different stages to be handled at the same time, through the calculation of variable parameters enabled by computer aided design Thus, designers can make all kinds of changes on their design in a short time. In this paper a parametric approach for rapid assembly design is presented. Rhinoceros software and Grasshopper plugin used for the parameterization process of components and Microsoft Excel used for implementation design. Furniture manufacturers, who will use the parametric design approach, will be able to respond quickly to the changing demands of their customers and will stand out among their competitors. 
 As a case study, the author has chosen the wardrobe type furniture with several shelves, hanger section, drawer/s has two doors.

Energetics and Mixing of Stratified, Rotating Flow over Abyssal Hills

Abstract One of the proposed mechanisms for energy loss in the ocean is through dissipation of internal waves, in particular above rough topography where internal lee waves are generated. Rates of dissipation and diapycnal mixing are often estimated using linear internal wave generation theory and a constant value for mixing efficiency. However, previous oceanographic measurements found that nonlinear dynamics may be important close to topography. To investigate the role of nonlinear interactions, we conduct idealized 3D direct numerical simulations (DNS) of steady flow over 1D topography and vary the topographic height, which correlates to the degree of flow nonlinearity. We analyze the spatial distribution of energy transfer rates between internal waves and the nongeostrophic portion of the time-mean flow, and of dissipation and diapycnal mixing rates. In our simulations with taller, more nonlinear topographies, energy transfer rates are similar to previously unexplained oceanographic observations near topography: internal waves gain energy from time-mean flow through horizontal straining and lose energy through vertical shearing. In the tall topography simulations, buoyancy fluxes also play a significant role, consistent with observations but contrary to linear wave theory, suggesting that quasigeostrophy-based approximations and linear theory may not hold in some regions above rough topography. Both dissipation and mixing rates increase with topographic height, but their vertical distributions differ between topographic regimes. As such, the vertical profile of mixing efficiency is different for linear and nonlinear topographic regimes, which may need to be incorporated into parameterizations of small-scale processes in models and estimates of ocean energy loss.

Limitations of Separate Cloud and Rain Categories in Parameterizing Collision‐Coalescence for Bulk Microphysics Schemes

AbstractWarm rain collision‐coalescence has been persistently difficult to parameterize in bulk microphysics schemes. We use a flexible bulk microphysics scheme with bin scheme process parameterizations, called AMP, to investigate reasons for the difficulty. AMP is configured in a variety of ways to mimic bulk schemes and is compared to simulations with the bin scheme upon which AMP is built. We find that an important limitation in traditional bulk schemes is the use of separate cloud and rain categories. When the drop size distribution is instead represented by a continuous distribution, the simulation of cloud‐to‐rain conversion is substantially improved. We also find large sensitivity to the threshold size to distinguish cloud and rain in traditional schemes; substantial improvement is found by decreasing the threshold from 40 to 25 μm. Neither the use of an assumed functional form for the size distribution nor the choice of predicted distribution moments has a large impact on the ability of AMP to simulate rain production. When predicting four total moments of the liquid drop size distribution, either with a traditional two‐category, two‐moment scheme with a reduced size threshold, or a four‐moment single‐category scheme, errors in the evolution of mass and the cloud size distribution are similar, but the single‐category scheme has a substantially better representation of the rain size distribution. Optimal moment combinations for the single‐category approach are investigated and appear to be linked more to the information content they provide for constraining the size distributions than to their correlation with collision‐coalescence rates.

Hybrid manufacturing of complex components: Full methodology including laser metal deposition (LMD) module development, cladding geometry estimation and case study validation

To optimize and satisfy current industrial requirements, during the last decade new alternatives to conventional manufacturing processes are implemented into conventional machines, leading to multitasking machines. Especially hybrid machines combining additive and subtractive technologies (AM/SM), have become a potential solution for manufacturing and repairing operations in terms of material waste reduction, time consumption and flexibility. Nevertheless, this technology has implications for the machine and the auxiliary elements as well as other challenges: digitalization, process parameterization or CAD/CAM solutions. Thereby, this work proposes a new methodology for hybrid manufacturing systems, a programmed interface to interact between additive and subtractive technologies within the same environment. The developed application-programming interface (API) offers a CAM module oriented to AM with appropriate laser metal deposition (LMD) parameters, with three different options of strategy programming: Planar LMD, 3-axis LMD and 5-axis LMD. Additionally, the value of this work stems from the implementation of an algorithm to estimate the cladding geometry, so, the full resulting geometry can be considered as the new blank for SM. A height measuring laser sensor was implemented in the LMD machine to obtain the real height of the generated clad, critical for the next machining step. Finally, to validate the methodology, a blisk made of Hastelloy®X was built-up on Inconel®718 with LMD and milled to the final size. Dimensional deviation was measured after each process.

Modelling and Optimisation of Laser-Structured Battery Electrodes.

An electrochemical multi-scale model framework for the simulation of arbitrarily three-dimensional structured electrodes for lithium-ion batteries is presented. For the parameterisation, the electrodes are structured via laser ablation, and the model is fit to four different, experimentally electrochemically tested cells. The parameterised model is used to optimise the parameters of three different pattern designs, namely linear, gridwise, and pinhole geometries. The simulations are performed via a finite element implementation in two and three dimensions. The presented model is well suited to depict the experimental cells, and the virtual optimisation delivers optimal geometrical parameters for different C-rates based on the respective discharge capacities. These virtually optimised cells will help in the reduction of prototyping cost and speed up production process parameterisation.

An Extended Target Tracking Algorithm Based on Gaussian Surface Fitting

Although the existing algorithms have done a lot of progress in extended target tracking (ETT), most algorithms are limited by the assumption of the target shape, and there are problems with parameter calculations. To solve the problems above, an extended target tracking algorithm based on level set and Gaussian Surface fitting (GSF), is proposed. Different from the existing extended target tracking algorithms, the proposed algorithm uses a level set to implicitly describe the evolution of the estimated shape, eliminates the parameterization process, and uses the GSF to construct the measurement source spatial distribution function (MSSDF) of the extended target. The proposed algorithm can obtain the estimated shape of the extended target without knowing the prior shape and size of the target. The simulation results verify the effectiveness and reliability of the proposed algorithm.

Environmental and Biophysical Effects of the Bowen Ratio over Typical Farmland Ecosystems in the Loess Plateau

The Bowen ratio (β) comprehensively reflects physical characteristics of the land-surface climate. In this study, eddy covariance systems installed at Dingxi and Qingyang were used to conduct energy distribution measurements and observations characteristic of semi-arid and semi-humid farmland ecosystems on the China Loess Plateau. We studied mechanisms by which eco-environmental factors influence β. Additionally, we investigated responses of physiological and ecological factors to water and heat exchange under seasonally dry and wet conditions within each farmland ecosystem. Our results showed that sensible heat flux in the semi-arid farmland was the main consumer of available energy. In the semi-humid area, latent heat flux in summer had the dominant role in energy distribution (mean β 0.71). The β in the semi-arid region was 1.5 times higher than that in the semi-humid region during the growing season. β increased with an increase in the vapor pressure deficit (VPD) and ground–air temperature difference (Ts − Ta), and decreased significantly with an increase in effective precipitation and soil moisture. The change in β with environmental factors was more clear-cut in semi-arid areas than in semi-humid areas. The Priestley–Taylor coefficient (α) and β satisfied a power function law in the growing season. There was a strong correlation between them, with the coefficients of determination for semi-humid and semi-arid areas being 0.62 and 0.72, respectively. β decreased with an increase in the normalized difference vegetative index (NDVI), with this phenomenon being more obvious in the semi-humid zone (R2 = 0.40). β responded more rapidly to NDVI in the semi-arid area than in the semi-humid area. There was a negative exponential relationship between canopy stomatal conductance (Gs) and β, which displayed a stronger declining trend with the increase in Gs in the semi-arid area than in the semi-humid area. This study provides an important reference for the determination of land-surface characteristics of semi-arid and semi-humid farmland ecosystems on the Loess Plateau and for improving parameterization of land-surface processes.

Assessing the Impact of Cumulus Parameterization Schemes on Simulated Summer Wind Speed over Mainland China

Wind speed is an important meteorological parameter, whose simulation is influenced by various physical process parameterizations. However, the impact of cumulus parameterization schemes (CPSs) on wind speed simulation at the climate scale has not been sufficiently investigated in previous studies. Using the Advanced Research version of the Weather Research and Forecasting model (ARWv3) and hydrostatic wind speed change equation, we assessed the effects of four CPSs on a 10 m wind speed simulation over mainland China in the summer of 2003. In general, different CPSs can reproduce the wind speed distribution. Meanwhile, the sensitivity of wind speed simulation to CPSs was found to be the highest in East and southern China, followed by the Tibetan Plateau, and then Northwest China. We found that the main physical processes influencing wind speed (i.e., the pressure gradient (PRE), diffusion (DFN), and convection (CON) terms) vary greatly with sub-regions. CPSs mainly affect the secondary CON that regulates the balance between the dominant terms PRE and DFN, and also has a significant effect on PRE. For example, for CON, the difference index (DIF) between the Kain–Fritsch (KF) and previous KF (pKF) CPSs is larger than 20%, corresponding to a PRE DIF of about 14%. The term of local wind speed change (Vt) is significantly more sensitive to the CPSs than the other terms with a DIF of 283% over the Tibetan Plateau, suggesting high CPS sensitivity of the simulated wind speed. In addition, we explained the causes of the CPS-induced sensitivities. This work helps understand the Weather Research and Forecasting model (WRF) performance and emphasizes the importance of the CPS choice in simulating/forecasting wind speed.

AMOC Recent and Future Trends: A Crucial Role for Oceanic Resolution and Greenland Melting?

The Atlantic Meridional Overturning Circulation (AMOC) is a crucial element of the Earth climate. It is a complex circulation system difficult to monitor and to model. There is considerable debate regarding its evolution over the last century as well as large uncertainty about its fate at the end of this century. We depict here the progress since the IPCC SROCC report, offering an update of its chapter 6.7. We also show new results from a high-resolution ocean model and a CMIP6 model to investigate the impact of Greenland Ice Sheet (GrIS) melting, a key uncertainty for past and future AMOC changes. The ocean-only simulation at 1/24° resolution in the Arctic-North Atlantic Ocean performed over the period 2004–2016 indicates that the spread of the Greenland freshwater runoff toward the center of the Labrador Sea, where oceanic convection occurs, seems larger in this model than in a CMIP6 model. Potential explanations are related to the model spatial resolution and the representation of mesoscale processes, which more realistically transport the freshwater released around the shelves and, through eddies, provides strong lateral exchanges between the fine-scale boundary current and the convective basin in the Labrador Sea. The larger freshening of the Labrador Sea in the high-resolution model then strongly affects deep convection activity. In the simulation including GrIS melting, the AMOC weakens by about 2 Sv after only 13 years, far more strongly than what is found in the CMIP6 model. This difference raises serious concerns on the ability of CMIP6 models to correctly assess the potential impact of GrIS melting on the AMOC changes over the last few decades as well as on its future fate. To gain confidence in the GrIS freshwater impacts on climate simulations and therefore in AMOC projections, urgent progress should be made on the parameterization of mesoscale processes in ocean models.

Evaluating the influence of different environmental water allocation schemes on the water level of a typical shallow lake in semiarid regions: From the perspective of an integrated modeling approach

Many lakes in semiarid regions around the world rely on environmental water allocation to maintain the health of the lake ecosystem. However, under changing environments, the competition for water resources between human society and natural ecosystems has intensified. How to manage environmental water allocation more reasonably and precisely has become an important issue. The largest lake on the North China Plain, Baiyangdian Lake (BYDL), is a typical lake facing such challenges. To provide feasible strategies for sustainable water allocation to BYDL, with the proper parameterization of hydrological processes, this study developed a 10-day temporal scale lake water level prediction model to quantify how environmental water allocation regulates the BYDL water level under different hydroclimatic conditions. Evaluation of model performance revealed that environmental water allocation rather than natural climatic periodicity dominates the variation in the BYDL water level. The model structure could be further improved with consideration of more detailed observations of both the surface runoff entering BYDL and the water area beneath the canopy of the reeds in BYDL. Analysis of 72 model simulation scenarios indicated that water allocations from multiple sources are indispensable and that the water resources that guarantee maintaining the BYDL water level within the ecologically suitable range vary substantially under different hydroclimatic conditions. More elaborate allocation plans are required both to improve the water quality and health of the aquatic ecosystem of BYDL and to reduce the risk of flooding. The findings from this study are valuable for guiding the implementation of environmental water allocations to lakes in semiarid regions worldwide.

Developing a Plume‐in‐Grid Model for Plume Evolution in the Stratosphere

AbstractStratospheric emissions from aircraft or rockets are important sources of chemical perturbations. Small‐radius high‐aspect‐ratio plumes from stratospheric emissions are smaller than global Eulerian models' grid cells. To help global Eulerian models resolve subgrid plumes in the stratosphere, a Lagrangian plume model, comprising a Lagrangian trajectory model and an adaptive‐grid plume model with a sequence of plume cross‐section representations (from a highly resolved 2‐D grid to a simplified 1‐D grid based on a tradeoff between the accuracy and computational cost), is created and embedded into a global Eulerian (i.e., GEOS‐Chem) model to establish a multiscale Plume‐in‐Grid (PiG) model. We compare this PiG model to the GEOS‐Chem model based on a 1‐month simulation of continuous inert tracer emissions by aircraft in the stratosphere. In the PiG results, the final injected tracer is more concentrated and approximately 1/3 of the tracer is at concentrations 2–4 orders of magnitude larger compared to the GEOS‐Chem results. The entropy of injected tracer in the PiG results is 6% lower than the GEOS‐Chem results, indicating less tracer mixing. The total product mass from a hypothetical second‐order process (applied to the injected tracer) in the PiG results is 2 orders of magnitude larger than the GEOS‐Chem results. Increasing the GEOS‐Chem model's horizontal resolution 4‐fold is insufficient to resolve this product difference, while requiring over seven times the computational resources of the PiG model. This paper describes the PiG model framework and parameterization of plume physical processes. Chemical and aerosol processes will be introduced in the future.

Deep Learning for Improving Numerical Weather Prediction of Heavy Rainfall

AbstractThe accurate prediction of rainfall, and in particular of the heaviest rainfall events, remains challenging for numerical weather prediction (NWP) models. This may be due to subgrid‐scale parameterizations of processes that play a crucial role in the multi‐scale dynamics generating rainfall, as well as the strongly intermittent nature and the highly skewed, non‐Gaussian distribution of rainfall. Here we show that a U‐Net‐based deep neural network can learn heavy rainfall events from a NWP ensemble. A frequency‐based weighting of the loss function is proposed to enable the learning of heavy rainfall events in the distributions' tails. We apply our framework in a post‐processing step to correct for errors in the model‐predicted rainfall. Our method yields a much more accurate representation of relative rainfall frequencies and improves the forecast skill of heavy rainfall events by factors ranging from two to above six, depending on the event magnitude.

Soil microorganisms regulate extracellular enzyme production to maximize their growth rate

Soil carbon cycling and ecosystem functioning can strongly depend on how microbial communities regulate their metabolism and adapt to changing environmental conditions to improve their fitness. Investing in extracellular enzymes is an important strategy for the acquisition of resources, but the principle behind the trade-offs between enzyme production and growth is not entirely clear. Here we show that the enzyme production rate per unit biomass may be regulated in order to maximize the biomass specific growth rate. Based on this optimality hypothesis, we derive mathematical expressions for the biomass specific enzyme production rate and the microbial carbon use efficiency, and verify them with experimental observations. As a result of this analysis, we also find that the optimal enzyme production rate decays hyperbolically with the soil organic carbon content. We then show that integrating the optimal extracellular enzyme production into soil microbial carbon models may change considerably soil carbon projections under global warming, underscoring the need to improve parameterization of microbial processes.

Spatial parameterization of attachment processes in molecular motor-cargo systems

Intracellular transport is conducted largely by molecular motor proteins which process along cytoskeletal filaments, from which they can attach or detach. We describe an analytical framework to characterize motor attachment or reattachment rates to microtubules as a function of the physical and geometric properties of the motor, the cargo to which it is attached, and possibly a second motor attached to the same cargo and a microtubule. The biophysical model is coarse-grained at the level of the macromolecular motors and formulated in terms of stochastic differential equations, allowing for rotation of the cargo and nonlinear force laws for the motor-cargo tether.

Implementing a New Rubber Plant Functional Type in the Community Land Model (CLM5) Improves Accuracy of Carbon and Water Flux Estimation

Rubber plantations are an economically viable land-use type that occupies large swathes of land in Southeast Asia that have undergone conversion from native forest to intensive plantation forestry. Such land-use change has a strong impact on carbon, energy, and water fluxes in ecosystems, and uncertainties exist in the modeling of future land-use change impacts on these fluxes due to the scarcity of measured data and poor representation of key biogeochemical processes. In this current modeling effort, we utilized the Community Land Model Version 5 (CLM5) to simulate a rubber plant functional type (PFT) by comparing the baseline parameter values of tropical evergreen PFT and tropical deciduous PFT with a newly developed rubber PFT (focused on the parameterization and modification of phenology and allocation processes) based on site-level observations of a rubber clone in Indonesia. We found that the baseline tropical evergreen and baseline tropical deciduous functions and parameterizations in CLM5 poorly simulate the leaf area index, carbon dynamics, and water fluxes of rubber plantations. The newly developed rubber PFT and parametrizations (CLM-rubber) showed that daylength could be used as a universal trigger for defoliation and refoliation of rubber plantations. CLM-rubber was able to predict seasonal patterns of latex yield reasonably well, despite highly variable tapping periods across Southeast Asia. Further, model comparisons indicated that CLM-rubber can simulate carbon and energy fluxes similar to the existing rubber model simulations available in the literature. Our modeling results indicate that CLM-rubber can be applied in Southeast Asia to examine variations in carbon and water fluxes for rubber plantations and assess how rubber-related land-use changes in the tropics feedback to climate through carbon and water cycling.

ENHANCEMENT OF GENERIC CODE CLONE DETECTION MODEL FOR PYTHON APPLICATION

Identical code fragments in different locations are recognized as code clones. There are four native terminologies of code clones concluded as Type-1, Type-2, Type-3 and Type-4. Code clones can be identified using various approaches and models. Generic Code Clone Detection (GCCD) model was created to detect all four terminologies of code clones through five processes. A prototype has been developed to detect code clones in Java programming language that starts with Pre-processing Transformation, Parameterization, Categorization and ends with the Match Detection process. Hence, this work targeted to enhance the prototype using a GCCD model to identify all clone types in Python language. Enhancements are done in the Pre-processing process and parameterization process of the GCCD model to fit the Python language criteria. Results are improved by finding the best constant value and suitable weightage according to Python language. Proposed enhancement results of the Python language clone detection in GCCD model imply that Public as the weightage indicator and def as the best constant value.

ENHANCEMENT OF GENERIC CODE CLONE DETECTION MODEL FOR PYTHON APPLICATION

Identical code fragments in different locations are recognized as code clones. There are four native terminologies of code clones concluded as Type-1, Type-2, Type-3 and Type-4. Code clones can be identified using various approaches and models. Generic Code Clone Detection (GCCD) model was created to detect all four terminologies of code clones through five processes. A prototype has been developed to detect code clones in Java programming language that starts with Pre-processing Transformation, Parameterization, Categorization and ends with the Match Detection process. Hence, this work targeted to enhance the prototype using a GCCD model to identify all clone types in Python language. Enhancements are done in the Pre-processing process and parameterization process of the GCCD model to fit the Python language criteria. Results are improved by finding the best constant value and suitable weightage according to Python language. Proposed enhancement results of the Python language clone detection in GCCD model imply that Public as the weightage indicator and def as the best constant value.

Influence of Planetary Boundary Layer (PBL) Parameterizations in the Weather Research and Forecasting (WRF) Model on the Retrieval of Surface Meteorological Variables over the Kenyan Highlands

Regional climate models (RCMs) are crucial for climate studies and may be an alternative source of meteorological data in data-scarce regions. However, the effectiveness of the numerical weather prediction (NWP) models applied in RCMs is hampered by the parameterization of unresolved physical processes in the model. A major source of uncertainties in NWP models is the parameterization of the planetary boundary layer (PBL). This study evaluates the influence of seven PBL parameterization schemes in the Weather Research and Forecasting (WRF) model on the retrieval of four meteorological variables over the Kenyan highlands. The seven PBL schemes consist of four local schemes: the Mellor-Yamada-Janjic (MYJ), Mellor-Yamada-Nakanishi-Niino (MYNN), Bougeault-Lacarrere (BouLac), quasinormal scale elimination (QNSE), and three nonlocal schemes: asymmetrical convective model version 2 (ACM2), Shin and Hong (SHIN) and Yonsei University (YSU). The forcing data for the WRF model was obtained from the fifth generation of the European ReAnalysis (ERA5) dataset. The results were validated against observational data from the Trans-African Hydro-Meteorological Observatory (TAHMO). WRF was found to simulate surface meteorological variables with spatial details coherent with the complex topography within the Kenyan highlands, irrespective of the PBL scheme. A comparison between 2-meter temperature (T2) derived from the YSU scheme and T2 from the land component of ERA5 (ERA5-Land) indicates that surface meteorological variables derived from WRF are better suited for applications over the Kenyan highlands. The choice of the PBL scheme was found to primarily influence the simulation of the 10-meter wind speed (WS10) and rainfall as opposed to T2 and the 2-meter relative humidity (RH2). The insensitivity of the 2-meter variables to the choice of the PBL scheme is attributed to the influence of the surface layer parameterization near the surface. Results from the rainfall simulation indicate that the YSU scheme provides a more realistic depiction of PBL dynamics within the study area. Hence, the YSU scheme is best suited for simulating surface meteorological variables over the Kenyan highlands.