Sentences with phrase «numerical models and observations»

Not exact matches

The team compiled data from many studies and for the first time synthesized observations and numerical model output to develop a cohesive view of the carbon cycle in a large coastal region.

«We gradually increased the physical complexity of numerical models based on high - resolution observations, and it is really a success story for the approach we've taken with IRIS.»

«Our job is to collect as many observations as we can to send to the National Hurricane Center in Miami, and to NOAA's numerical models center in Washington.»

Combining satellite observations with ocean numerical modeling, Khazendar and his colleagues developed a hypothesis that reductions in the volume of brine would increase Totten's thinning and melting.

The successful applicant will work with world leading experts in advanced analytical / numerical modelling, analysis of high - resolution observations obtained by cutting - edge ground and space based instruments.

Xie, P., and P.A. Arkin, 1997: Global precipitation: A 17 - year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs.

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.

Space research at our institute is conducted within two Research Programmes: planetary research and numerical modelling in Earth Observation, and ground - based observations and space weather applications in Arctic Research.

It is very clear that changes to certain aspects of the mathematical models, numerical solution methods, and application procedures are in fact based on «the match to observations».

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.

Dong, S., M. O. Baringer, G. J. Goni, C. S. Meinen, and S. L. Garzoli, 2014: Seasonal variations in the South Atlantic Meridional Overturning Circulation from observations and numerical models.

Most notably, in a global study Wahl et al (2017) considered both extreme value analysis and numerical models that were used to simulate storm surges at coastline stretches where no observations exist to quantify ESL and their uncertainties.

The same observations and numerical tools that enable new scientific discoveries have the potential to transform modeling of the climate system.

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

Descriptive physical oceanography seeks to research the ocean through observations and complex numerical models, which describe the fluid motions as precisely as possible.

The SIO 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.

In our work we use observations as well as a hierarchy of numerical models to study dynamical processes in the atmosphere, and climate variability.

Individual responses were based on a range of methods: statistical, numerical models, comparison with previous observations and rates of ice loss, and composites of several approaches.

It costs little to field the observations — the satellites and the radars, the surface in situinstruments, etc. to monitor conditions and their changes; to assimilate the data into variety of numerical models, to run these and form ensemble averages; to disseminate the findings.

The July 2010 Sea Ice Outlook Report is based on a synthesis of 17 individual pan-Arctic estimates using a wide range of methods: statistical, numerical models, comparison with observations and rates of ice loss, composites of several approaches.

• Calibrate the retrospective simulations of ice thickness from our numerical model against the aggregate of all the observation systems by removing the mean difference between the model and the observations to create a Calibrated Model Ice Thickness Remodel against the aggregate of all the observation systems by removing the mean difference between the model and the observations to create a Calibrated Model Ice Thickness Remodel and the observations to create a Calibrated Model Ice Thickness ReModel Ice Thickness Record.

This activity also includes new interactions with NOAA NESDIS scientists regarding satellite observations of the Antarctic ozone layer, their comparison to ground - based and balloon - borne observations, and their interpretation with numerical modeling and analysis tools.

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

The individual responses were based on a range of methods: statistical, numerical models, comparison with previous observations and rates of ice loss, or composites of several approaches; details can be found in the individual outlooks available at the bottom of this page.

The individual responses were based on a range of methods: statistical, numerical models, comparison with previous observations and rates of ice loss, or composites of several approaches; details can be found in the individual outlooks available at the end of this report.

And different models may project different outcomes even under the same assumptions, due to the variety of «equally plausible numerical representations, solutions and approximations for modelling the climate system, given the limitations in computing and observations» [AR5, FAQ 12.1, p. 103And different models may project different outcomes even under the same assumptions, due to the variety of «equally plausible numerical representations, solutions and approximations for modelling the climate system, given the limitations in computing and observations» [AR5, FAQ 12.1, p. 103and approximations for modelling the climate system, given the limitations in computing and observations» [AR5, FAQ 12.1, p. 103and observations» [AR5, FAQ 12.1, p. 1036].

The studies are accomplished through the use of observations and numerical models.

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.

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

Convective and mesoscale processes, intensity change, rainfall, airborne Doppler radar observations, dropsondes, numerical modeling

In it, observations and a numerical model that simulates one or more aspects of the Earth system are combined objectively to generate a synthesized estimate of the state of the system.

Boundary layer structure and dynamics, turbulence observations and theory, air - sea interaction, numerical modeling and physical parameterizations

20th Century Reanalysis: Methods and Applications To better understand and model the observed variability of the earth system, one can rapidly expand the available record by objectively combining disparate observations with numerical model - generated guesses.

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.

Observations and numerical modeling reveal large fluctuations in the ocean heat available in the adjacent bay and enhanced sensitivity of ice - shelf melting to water temperatures at intermediate depth, as a seabed ridge blocks the deepest and warmest waters from reaching the thickest ice.

A combination of numerical models, palaeoclimate data and modern observations indicated that ocean saltiness was key to understanding it.

Her work combines mathematical and numerical modelling in comparison with observations.

The IPCC's methodology relies unduly — indeed, almost exclusively — upon numerical analysis, even where the outputs of the models upon which it so heavily relies are manifestly and significantly at variance with theory or observation or both.

Your process will highlight your strengths in key competencies: numerical and verbal reasoning, communication and presentation, listening and observation, and understanding of business models and concepts.