Sentences with phrase «numerical prediction models»

Not exact matches

Stumm said the study would include «putting a network of outpost wells, filling in gaps in information, and using the information in a numerical model to make predictions for management.

Although meteorologists now rely heavily on computer models (numerical weather prediction), it is still relatively common to use techniques and conceptual models that were developed before computers were powerful enough to make predictions accurately or efficiently.

Deike and his co-authors used experimental results — based on water and glycerin mixed with water — and numerical predictions to create their model.

They represent a unique type of atmospheric motion whose forcing mechanism is known with great precision, allowing us to test our numerical models and theoretical predictions.

Apart from ground stations, weather forecasts are heavily dependent on weather satellites for information to start or «initialize» the numerical weather prediction models that are the foundation of modern weather prediction.

«Given the promise shown by the research and the ever increasing computing power, numerical prediction of hailstorms and warnings issued based on the model forecasts, with a couple of hours of lead time, may indeed be realized operationally in a not - too - distant future, and the forecasts will also be accompanied by information on how certain the forecasts are.»

I was working for the Omani Meteorological Department when the implementation of a numerical weather forecasting model prompted the need for some local knowledge in numerical weather predictions.

By incorporating the complexities of channel geometry, fluid flow rates, diffusion coefficients and possible chemical interactions into a numerical model, the behavior of a particular system can be accurately predicted when an intuitive prediction may be extremely difficult.

The astronomers» long - term goal is to find about ten similar examples of these cold flows, which would allow for a much more detailed comparison of their observations with the predictions of numerical models.

In addition, atmospheric scientist Dr. Hendrik Tennekes, a scientific pioneer in the development of numerical weather prediction and former director of research at The Netherlands» Royal National Meteorological Institute, recently compared scientists who promote computer models predicting future climate doom to unlicensed «software engineers.»

Even with the best numerical model of ice flow available, if the data going into it is not accurate, then the predictions will not be reliable.

By its very nature, a model is a simplification of reality, so the final step when we consider predictions made by numerical models is to assess the uncertainty in our predictions.

Potential topics include: (1) Advanced numerical modelling of magnetic flux tubes / loops in the low solar atmosphere (2) Forward modelling of spectroscopic and narrowband EUV observations of the low solar atmosphere, (3) Solar Rotational Tomography of EUV and / or coronagraph coronal observations, (4) Automated detection and prediction of coronal mass ejections, (5) Analysis of solar wind turbulence observations by in situ spacecraft, (6) Eclipse instrumentation, observations and data analysis.

Specializing in the parameterization of land - atmosphere exchange for use in Global Climate, Regional Mesoscale, and Local Cloud - Resolving numerical weather prediction models.

I create parameterizations of land - atmosphere interactions which are incorporated into climate models and numerical weather prediction models.

They can be simulated to some degree in high resolution cloud resolving models; not sure about [numerical weather prediction] models, probably not in climate models.

Success at prediction is enhanced by maximizing the number of different hypotheses (models) you can generate and test against numerical data and other available information.

This claim is complemented with a broad literature synthesis of past work in numerical weather prediction, observations, dynamical theory, and modeling in the central U.S. Importantly, the discussion also distills some notoriously confusing aspects of the super-parameterization approach into clear language and diagrams, which are a constructive contribution to the literature.

-- Pete Wetzel, Ph. D., Research Meteorologist at NASA Goddard Space Flight Center, specializing in parameterizing the interactions between the land surface and the atmosphere for Global Climate, Regional Mesoscale, and local Cloud - resolving numerical weather prediction models.

However, 95 % of the time, each model is performing at about the same skill level as quiescent weather is not particularly challenging for today's numerical prediction systems.

Sea surface temperature (SST) measured from Earth Observation Satellites in considerable spatial detail and at high frequency, is increasingly required for use in the context of operational monitoring and forecasting of the ocean, for assimilation into coupled ocean - atmosphere model systems and for applications in short - term numerical weather prediction and longer term climate change detection.

Many numerical weather prediction centers now use coupled ocean - atmosphere models to produce ensemble forecasts on the subseasonal time scale.

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 development

Mikhail Tolstykh is an expert for global numerical weather prediction models to develop medium - range and seasonal forecasts.

Type 2 dynamic downscaling refers to regional weather (or climate) simulations in which the regional model's initial atmospheric conditions are forgotten (i.e., the predictions do not depend on the specific initial conditions), but results still depend on the lateral boundary conditions from a global numerical weather prediction where initial observed atmospheric conditions are not yet forgotten, or are from a global reanalysis.

«Radiation calculations in global numerical weather prediction (NWP) and climate models are usually performed in 3 - hourly time intervals in order to reduce the computational cost.

Cohen received his Ph.D. in Atmospheric Sciences from Columbia University in 1994 and has since focused on conducting numerical experiments with global climate models and advanced statistical techniques to better understand climate variability and to improve climate prediction.

Dr. Nehrkorn's 30 year research tenure at AER has included work on numerical weather prediction models, data assimilation systems, humidity to cloud relationships, three dimensional analysis of atmospheric quantities and studies of the angular momentum budget of the atmosphere.

Their prediction is based on the quantity of incoming solar radiation and uses 16 - day forecasts from a numerical weather prediction model (WRF).

As Sorokhtin et al. (2007) mention, until recently a sound theory using laws of physics for the greenhouse effect was lacking and all numerical calculations and predictions were based on intuitive models using numerous poorly defined parameters.

Zhang (Applied Physics Lab, University of Washington); 4.1 ± 0.6; Model This is based on numerical ensemble predictions starting on 6/1/2011 using the Pan-arctic Ice - Ocean Modeling and Assimilation System (PIOMAS).

Traditionally numerical weather prediction has advanced progressively by improving single, «deterministic» forecasts with an increasing model accuracy and decreasing initial condition errors.

All our forecasts and reanalyses use a numerical model to make a prediction.

We can perhaps learn from numerical weather prediction where the benefits of developing global prediction models with high vertical and horizontal resolution are clear cut (confirmed most recently by predictions of Sandy).

This is what makes paleoreconstructions possible, and what makes it possible to initialize global numerical weather prediction models with so few observations.)

ERA - Interim combines information from meteorological observations with background information from a forecast model, using the data assimilation approach developed for numerical weather prediction.

Meteorological observations from radiosondes are also applied to benchmark the numerical weather prediction models used to forecast day - to - day weather.

That requires considerable sensitivity research with state - of - the art numerical weather prediction (and climate) models... This hand - waving theory may not hold up when a rigorous scientific hypothesis is tested, yet McKibben does not provide a citation or reference aside from Masters» quotations, which are not peer - reviewed in the slightest.»

«We have groups doing numerical weather prediction, hurricanes, climate, oceans, but in the international arena, countries have whole institutions doing the functions of these individual groups,» said Dr. Ronald J. Stouffer, who designs and runs climate models at the Geophysical Fluid Dynamics Laboratory in Princeton, N.J., a top Commerce Department center for weather and climate work.

When we talk about regional modeling or regional numerical weather prediction we are really doing the same thing except that we are focusing on more and more detail for the region where you are located.

JIGSAW (GEO) is a set of algorithms designed to generate complex, variable resolution unstructured meshes for geophysical modelling applications, including: global ocean and atmospheric simulation, numerical weather prediction, coastal ocean modelling and ice - sheet dynamics.

I think the only way to approach the Arctic - wide temperature changes is through reanalyses (data assimilation by numerical weather prediction models)[link]; see this figure from the ECMWF reanalyses [link]:

In addition, atmospheric scientist Dr. Hendrik Tennekes, a scientific pioneer in the development of numerical weather prediction and former director of research at The Netherlands» Royal National Meteorological Institute, recently compared scientists who promote computer models predicting future climate doom to unlicensed «software engineers.»

The Sea Ice Outlook, an activity of the Sea Ice Prediction Network and a contribution to SEARCH, produces reports in June, July, and August containing a variety of perspectives on Arctic sea ice — from observations of current conditions, to advanced numerical models, to qualitative perspectives from citizen scientists.

The ECMWF provides its supercomputer - run Integrated Forecasting System, a world - renowned numerical weather prediction model, as a basis for some Copernicus services, such as atmospheric forecasts and reanalysis data.

This information is critical for numerical weather prediction models; both in data assimilation, and in creating re-analyses.

There are many drivers for increased resolution (spatial and temporal) surface observations, not least being new high resolution numerical weather prediction (NWP) models.

Much of this progress is due to advances in numerical weather prediction, that is, the use of computer models which approximate the fluid motions of the atmosphere to create forecasts of the weather at some time in the future.

This would be a sterile activity indeed without the input of experimental observation to guide the development of theoretical prediction methods and to keep the relevant numerical models «honest».

The predictions may match the observations for a while, but very soon random fluctuations smaller than the distance between the measurements (they are called «sub-grid-scale eddies» in the vernacular of numerical modellers) grow in size and — as far as the model is concerned — appear out of nowhere and swamp the eddies we thought we knew something about.