Sentences with phrase «model physics process»

However, model physics process representations that are supposed to account for the eddy moisture transport effects on convection significantly underestimate them compared to simulations that explicitly resolved eddy moisture transport without using convective representations.

Not exact matches

A process model is a relational model, drawing on the data of physics and biology, maintaining that we do indeed live in an interconnected universe where everything relates to everything else.

Nevertheless, as we have seen, there is a small but growing number of scientists, both in physics and biology, who operate with a relational model, who see some correspondence between the constructs of the mind and reality itself, however inexact, and who also see the possibility of restoring the experience of meaning if the non-human natural world is perceived as dynamic, creative, full of life and purpose, whom process thinkers have engaged in conversation; together they have attempted to explore new visions of reality better suited for adaptation to the urgent needs of the contemporary world.

Simulations of gas flows on large scales, as well as the physics of small - scale processes, support this model for DCBH formation.

Computer simulations have become a useful part of mathematical modelling of many natural systems in physics, chemistry and biology, human systems in economics, psychology, and social science and in the process of engineering new technology, to gain insight into the operation of those systems.

Ultracold molecules provide the possibility to investigate fundamental chemical processes or to explore physics beyond the standard model of particle physics.

This simple statistical physics model is believed to describe many contact processes in nature such as the spreading of forest fires or of an epidemic in a population.

So our approach is to apply geophysical research to supercomputer simulations and accurately model the underlying physics of these processes.»

The new model incorporates recent findings gathered from related research efforts and simplifies the physics involved so computers can process the program more quickly.

To predict hail storms, or weather in general, scientists have developed mathematically based physics models of the atmosphere and the complex processes within, and computer codes that represent these physical processes on a grid consisting of millions of points.

«Aside from this technological solution, we are working on modeling the physics behind the process.

A simplified reduced - order model was also developed to rapidly and accurately describe the physics of the self - folding process.

Writing in the New Journal of Physics, the researchers from Shaanxi Normal University in China, the Robert Koch Institute, and Humboldt University, Germany, present an extension of the traditional SIS (Susceptible - Infected - Susceptible) model used for modelling single contagion processes.

Propose numerical experiments aiming to refine numerical techniques and the formulation of atmospheric physics processes, boundary layer processes and land surface processes in models.

The project may involve the following topics: — Interaction of the solar wind with magnetised and unmagnetised planets — Space weather forecasts — Numerical (HPC) and analytical modelling of MHD wave processes and jets in solar and astrophysical plasma — MHD wave observations and solar magneto - seismology — Application of advanced data analysis to solar system science — Physics of collisionless shocks (including planetary and interplanetary shocks)-- Analysis of multi-point measurements made by space missions, e.g Cluster (ESA), THEMIS (NASA), MMS (NASA)

In plasma boundary physics, models require integration of multiple physical processes that cover a wide range of overlapping spatial and temporal scales, from the hot, confined pedestal zone with sharp gradients, to the cooler unconfined edge and divertor plasma, and finally to the first few microns of the wall itself.

By using modern imaging, image analysis and modelling techniques, we will develop an integrated approach to perform experiments in soil physics, bio-chemistry, to reconstruct soil structure in 3D and to model soil processes.

WCRP - JSC / CAS WGNE promotes co-ordinated numerical experimentation for validating model results, observed atmospheric properties, exploring the natural and forced variability and predictability of the atmosphere, (e.g. the Atmospheric Model Intercomparison Project, AMIP), as well as studies aimed at refining numerical techniques, and the formulation of atmospheric physics procemodel results, observed atmospheric properties, exploring the natural and forced variability and predictability of the atmosphere, (e.g. the Atmospheric Model Intercomparison Project, AMIP), as well as studies aimed at refining numerical techniques, and the formulation of atmospheric physics proceModel Intercomparison Project, AMIP), as well as studies aimed at refining numerical techniques, and the formulation of atmospheric physics processes.

The computer models in use are not, by necessity, direct calculations of all basic physics but rely upon empirical approximations for many of the smaller scale processes of the oceans and atmosphere.

Thus, parameterizations, efficient ways to represent complex processes in climate models, have been constructed to capture the essence of cloud physics at a much lower computational cost.

Chosen through a peer review process, ALCC projects cover a wide range of research areas, including energy efficiency, renewable energy, physics, climate modeling, and materials science.

-- Matt Trask, a secondary senior physics teacher is having students make musical instruments to learn about wavelength — Kelly Skehill using design and software for students to apply calculus models to create new pop bottle designs — teachers are using technology to capture assessment information during the learning process — one particular good example at PJ Elementary — Kindergarten teacher (Sonia Southam) using iPads to engage parents and transform communication by capturing daily learning and sharing immediately via email with parents — the creation of Gleneagles Learning Lab Open House to showcase the process of learning — a teacher created Social Dynamics course for students with high functioning autism — the creation of an outdoor learning program for grades 6/7 students at Bowen Island Community School (Scott Slater created) that has students blending in - class and outdoor learning experiences

While models contain a lot of physics, they don't contain many small - scale processes that more specialised groups (of atmospheric chemists, or coastal oceanographers for instance) might worry about a lot.

While some of the equations in climate models are based on the laws of physics, many key processes in the model are only approximated and are not directly related to physical laws.

There are uncertainties in parts of the general circulation models used to forecast future climate, but thousands of scientists have made meticulous efforts to make sure that the processes are based on observations of basic physics, laboratory measurements, and sound theoretical calculations.

No, Alan, the modeler will not decide on a «value» at all, but will instead model the processes using the best possible physics available.

However, the fact that climate models are not an instance of curve - fitting, but are instead built upon a solid (albeit cummulative) foundation of physics, the incorporation of physics to describe processes in one area tend to lead to a progressive tightening of the fit in others.

The weakening of the Walker circulation arises in these models from processes that are fundamentally different from those of El Nià ± o — and is present in both mixed - layer and full - ocean coupled models, so is not dependent on the models» ability to represent Kelvin waves (by the way, most of the IPCC - AR4 models have sufficient oceanic resolution to represent Kelvin waves and the physics behind them is quite simple — so of all the model deficiencies to focus on this one seems a little odd).

Now on to the interpretive part of that paragraphs: yes, to match real earth climate data, if a) you have a given model (say GISS II) with coded physics, with parameterization for physcial processes.

In particular, a strong case has been made that climatological processes exhibit time - series structures that are not well represented by an AR (1) model, either, and that a richer, fractal (scaling), model might correspond better to both the physics and the observed data (see Koutsoyiannis, here).

The meeting will mainly cover the following themes, but can include other topics related to understanding and modelling the atmosphere: ● Surface drag and momentum transport: orographic drag, convective momentum transport ● Processes relevant for polar prediction: stable boundary layers, mixed - phase clouds ● Shallow and deep convection: stochasticity, scale - awareness, organization, grey zone issues ● Clouds and circulation feedbacks: boundary - layer clouds, CFMIP, cirrus ● Microphysics and aerosol - cloud interactions: microphysical observations, parameterization, process studies on aerosol - cloud interactions ● Radiation: circulation coupling; interaction between radiation and clouds ● Land - atmosphere interactions: Role of land processes (snow, soil moisture, soil temperature, and vegetation) in sub-seasonal to seasonal (S2S) prediction ● Physics - dynamics coupling: numerical methods, scale - separation and grey - zone, thermodynamic consistency ● Next generation model development: the challenge of exascale, dynamical core developments, regional refinement, super-parametrization ● High Impact and Extreme Weather: role of convective scale models; ensembles; relevant challenges for model deProcesses relevant for polar prediction: stable boundary layers, mixed - phase clouds ● Shallow and deep convection: stochasticity, scale - awareness, organization, grey zone issues ● Clouds and circulation feedbacks: boundary - layer clouds, CFMIP, cirrus ● Microphysics and aerosol - cloud interactions: microphysical observations, parameterization, process studies on aerosol - cloud interactions ● Radiation: circulation coupling; interaction between radiation and clouds ● Land - atmosphere interactions: Role of land processes (snow, soil moisture, soil temperature, and vegetation) in sub-seasonal to seasonal (S2S) prediction ● Physics - dynamics coupling: numerical methods, scale - separation and grey - zone, thermodynamic consistency ● Next generation model development: the challenge of exascale, dynamical core developments, regional refinement, super-parametrization ● High Impact and Extreme Weather: role of convective scale models; ensembles; relevant challenges for model deprocesses (snow, soil moisture, soil temperature, and vegetation) in sub-seasonal to seasonal (S2S) prediction ● Physics - dynamics coupling: numerical methods, scale - separation and grey - zone, thermodynamic consistency ● Next generation model development: the challenge of exascale, dynamical core developments, regional refinement, super-parametrization ● High Impact and Extreme Weather: role of convective scale models; ensembles; relevant challenges for model development

The physics of those processes are really difficult to incorporate in high - resolution models and require the use of high - performance computing.»

Propose numerical experiments aiming to refine numerical techniques and the formulation of atmospheric physics processes, boundary layer processes and land surface processes in models.

In contrast to climate models, which can only approximate the physical processes and may exclude important processes, the empirical result includes all processes that exist in the real world — and the physics is exact.

I believe it (including water vapor clouds) is the the 800 pound gorilla in the room that AGW climate science can't understand because AGW climate science focuses on unvalidated model results and not enough on the actual physics of natural processes involved in the complex climate change process.

Basically, the GCM is attempting to model «all relevant» climate processes in terms of basic physics relationships.

Part of the process involves adjusting model parameters within limits dictated by observations and the principles of physics so as to coax the simulations into good agreement with the real world climate.

The Kalman smoother state - space models are famous for allowing the introduction of arbitrarily complicated explanatory mechanisms for process, even if they only encapsulate superficial physics.

The place where «data fitting,» if you can call it that, comes in to play is when one considers the parameterizations used to help the model compensate for its intrinsic lack of precision due to missing or incomplete physics or processes, or more importantly, the lack of precision due to sub-grid-scale processes like localized weather phenomena.

First, I would like to see a written overview «primer document» as to which climatic processes are included in that GCM, an inventory of the underlying physics that are being modeled, how the climate drivers are assumed to interact with each other, and how instabilities in the model's step - by - step progression are handled and dispositioned.

WCRP - JSC / CAS WGNE promotes co-ordinated numerical experimentation for validating model results, observed atmospheric properties, exploring the natural and forced variability and predictability of the atmosphere, (e.g. the Atmospheric Model Intercomparison Project, AMIP), as well as studies aimed at refining numerical techniques, and the formulation of atmospheric physics procemodel results, observed atmospheric properties, exploring the natural and forced variability and predictability of the atmosphere, (e.g. the Atmospheric Model Intercomparison Project, AMIP), as well as studies aimed at refining numerical techniques, and the formulation of atmospheric physics proceModel Intercomparison Project, AMIP), as well as studies aimed at refining numerical techniques, and the formulation of atmospheric physics processes.

Let's invert this process and actually apply statistical analysis to the distribution of model results Re: the claim that they all correctly implement well - known physics.

Not only is climate computationally intractable, but some CMIP5 models got physics of such basic processes as water vaporization and condensation wrong: https://judithcurry.com/2013/06/28/open-thread-weekend-23/#comment-338257

The models do include empirical bulk paramaterisations for sub-grid or small - scale processes such as cloud formation, as part of their underlying physics.

The first order objective is to acquire a practical capability (coupled atmosphere - ocean general circulations climate modes) to model the seasonal and geographic variability of the climate system in terms of physics / mathematics - based processes.

And if this process of water changing state, which is pretty much just a process of physics and a bit of chemistry, is so very easy to get wrong — specifically, is so easy to model too conservatively so the models predict wrongly that it will be a very slow process when in fact it seems to be a much faster process — how confident can we be that other models and estimates of processes that involve multiple feedbacks that include chemical and biological interactions as well as physical ones aren't even more wildly inaccurate on the «conservative» side?

At one point, there did seem to be a move within IPCC to have an economic modelling assessment parallel to the physics of climate assessment in WG I, but this fell apart because the people involved in the process seem to have become persuaded that the state of the art of economic modelling — or even the question of whether costs and benefits can be measured as money at all — were not up to the task.

Due to physics and error growth problems... these models have not improved appreciably in 15 years and by some measures... have actually become more unstable and in some scenarios... show less skill due to the powerful non-linear processes (mainly H20... the most powerful compound in the universe except for ammonia).

Climate models are amalgams of fundamental physics, approximations to well - known equations, and empirical estimates (known as parameterizations) of processes that either can't be resolved (because they happen on too small a physical scale) or that are only poorly constrained from data.

Features of the model described here include the following: (1) tripolar grid to resolve the Arctic Ocean without polar filtering, (2) partial bottom step representation of topography to better represent topographically influenced advective and wave processes, (3) more accurate equation of state, (4) three - dimensional flux limited tracer advection to reduce overshoots and undershoots, (5) incorporation of regional climatological variability in shortwave penetration, (6) neutral physics parameterization for representation of the pathways of tracer transport, (7) staggered time stepping for tracer conservation and numerical efficiency, (8) anisotropic horizontal viscosities for representation of equatorial currents, (9) parameterization of exchange with marginal seas, (10) incorporation of a free surface that accommodates a dynamic ice model and wave propagation, (11) transport of water across the ocean free surface to eliminate unphysical «virtual tracer flux» methods, (12) parameterization of tidal mixing on continental shelves.