Sentences with phrase «model forecast wind»

Model forecast wind gusts are consistent w / Category 4 hurricane up entire Florida peninsula... NWS forecasts have been nearly same.

Not exact matches

Weather forecast models, for example, include dozens of parameters from temperature and precipitation to wind speed and lightning.

We use different computer forecast models that feed initial conditions — including temperatures, humidity, wind speed and wind direction from around the United States and around the world, from the surface all the way up to the jet stream — into different equations.

They can wind the clock back to 1850, plug in the known climate variables at the start, then roll the models forward in time and see whether their forecasts match the historical records.

«Consequently, global «water vapor winds» are estimated from the movement of these features and used in numerical weather models to improve long - range forecasts,» Chesters said.

Forecasting — uses weather models (i.e., Doppler radar) to predict wind speeds and patterns at various altitudes.

The latest 24 - hour forecast of the oil's trajectory, created by feeding currents and wind predictions into computer models, shows oil hitting dozens of places along Louisiana shorelines, from Caillou Bay in the west to Breton Sound, just east of where the Mississippi meets the gulf.

But for a forecast model to work, he says, «We have to resolve the boundary conditions ---- data on tides and winds — very far away, out into the open ocean.

The partners develop mathematical models that produce improved forecasts accurate for each quarter - hour, which show how much electricity Germany's installed photovoltaic and wind - farm facilities will generate over the next few hours and days.

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)

The model is called Forecasting a CME's Altered Trajectory (ForeCAT), and it predicts how a CME can wind up being deflected.

With national and local computer models forecasting a major winter storm — including heavy snow, strong winds and coastal flooding — for Long Island this coming weekend, Central Veterinary Associates (CVA) is reminding pet owners that freezing temperatures and blustery weather can have an adverse effect on the well - being of their animal.

Forecasts, wind models, satellite and radar images, tide and current conditions and even data streamed from weather buoys are all available through links to various web services.

The wave model does not forecast surf and wind right at the coast so we have chosen the optimum grid node based on what we know about Playa Grande.

It is based on 2640 NWW3 forecasts of wind since since 2007, at 3 hr intervals, for the closest NWW3 model node to Playa Grande, located 25 km away (16 miles).

Elsewhere, the background forecast model plays a stronger role, helping values of surface air temperature to be derived from other types of observation, such as sea - surface temperatures and winds.

Canadian Ice Service, 4.7, Multiple Methods As with CIS contributions in June 2009, 2010, and 2011, the 2012 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Canadian Ice Service, 4.7 (+ / - 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

The project team and CanWEA also acknowledge and thank Environment and Climate Change Canada which performed the mesoscale atmospheric modeling and provided raw wind - related data for the wind profiling and forecasting.

Both of these reports offer forecasts that are wildly optimistic relative to the mainstream modeling community, but it's not because they predict wind and solar are going to have some unprecedented explosion.

IEA's [World Energy Model] does attempt to base its forecasts on a dropping cost of solar and wind.

2DVAR performs an incremental analysis based on the ambiguous scatterometer wind vector solutions and a model forecast, and selects the most likely solution.

This study modelled power grids in the United States and Canada under four scenarios (see figure 1 - 2) with wind penetration levels ranging from five per cent to 35 per cent of forecast annual system load energy in 2025:

Canadian Ice Service; 5.0; Statistical As with Canadian Ice Service (CIS) contributions in June 2009 and June 2010, the 2011 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic Multi-Year Ice (MYI) extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictors.

More Details about this video animation The video animation shows the near surface (10m) winds and rainfall intensity over the Gulf of Mexico and the surrounding land areas during the main development phase of Hurricane Katrina, August 24 - 30, 2005, based on NCEP - GFS forecast model analyses.

Canadian Ice Service, 4.7 (± 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

In Section 4.2, Tsagouri et al. (2009) developed a new ionospheric forecasting algorithm, called the Solar Wind driven autoregression model for Ionospheric short - term Forecast (SWIF).

This is achieved through the cooperation of an autoregression forecasting algorithm, called Time Series AutoRegressive — TSAR (Koutroumbas et al. 2008), with the empirical Storm Time Ionospheric Model — STIM (Tsagouri & Belehaki 2006, 2008) that formulates the ionospheric storm - time response based on solar wind input, exploiting recent advances in ionospheric storm dynamics that correlate the ionospheric storm effects with solar wind parameters (e.g., the magnitude of the IMF and its rate of change as well as the IMF's orientation in the north - south direction).

Empirical models using solar wind parameters and / or IMF as drivers, usually, are used for now - casting (specification of ionospheric state) or forecasting 1 — 3 h ahead.

The predictions of winds and other variables then drive the air quality model that takes into account pollution sources both biogenic and anthropogenic (human - caused), and removal processes, leading to forecasts of air quality days in advance.

The development of a new ionospheric foF2 forecasting algorithm, called the Solar Wind driven autoregression model for Ionospheric short - term Forecast (SWIF), was recently introduced (Tsagouri et al. 2009).

Examples include the forecasting of tropical cyclone tracks, heavy rainfall, strong winds, and flood prediction through coupling hydrological models to ensembles.

From the Saturday night Baltimore, MD / Washington D.C. NWS discussion: «I can not recall ever seeing model forecasts of such an expansive areal wind field with values so high for so long a time.

A highly technical project ending on 31 August 2012, this involves: «Multi-scale data assimilation, advanced wind modelling and forecasting with emphasis on extreme weather situations for a secure large - scale wind power integration.»

Pichugina Y. L., R. M. Banta, J. B. Olson, J. R. Carley, M. C. Marquis, W. A. Brewer, J. M. Wilczak, I. Djalalova, L. Bianco, E. P. James, S. G. Benjamin and J. Cline (October 2017): Assessment of NWP forecast models in simulating offshore winds through the lower boundary layer by measurements from a ship - based scanning Doppler lidar.

The blanket - exemption treatment is based on increasingly questionable assertions that wind turbines reduce atmospheric carbon dioxide levels that supposedly cause global warming, climate change, extreme weather events and an amazing number of dog, people, Italian pasta, prostitution and other exaggerated or imaginary problems, plus others that exist only in computer models whose forecasts and scenarios bear no resemblance to Real World conditions or events.

This is successfully achieved by using sophisticated short term forecasting models that interpret weather information as it affects the wind farm in real time.»

Early 2015 brought similar news, with several new bursts of westerly winds and corresponding model forecasts of a building El Niño.

, put together a lot of observational data, reanalyses (from the weather forecasting models) and regional models, and concluded that there was some evidence for a decrease in wind speeds, particularly in the Eastern US.