Sentences with phrase «atmosphere model forecasts»

We're now long past the much - discussed «Spring Predictability Barrier,» and coupled ocean - atmosphere model forecasts remain nearly unanimous in predicting a top - tier El Niño for the upcoming autumn and winter months.

Further evidence comes from ocean - atmosphere model forecasts, which suggest a profound deepening of the Gulf of Alaska low this winter along with a greatly enhanced and southerly - shifted storm track over the Eastern Pacific.

Much unlike 2014, ocean - atmosphere model forecasts continued to grow more emphatic regarding the potential for a very significant El Niño event by late summer.

Not exact matches

Jason - 3 measurements will also be ingested by Numerical prediction models coupling the atmosphere and the oceans used for seasonal forecasting.

Modern weather forecasting relies on complicated computer models of the atmosphere.

The Mathematics of the Weather is a forum for the discussion of new numerical approaches for use in numerical forecasting, climate modelling and research into numerical modelling of the atmosphere.

«Again, this indicates the real atmosphere is less sensitive to CO2 than what has been forecast by climate models.

This is an especially important region of the atmosphere because climate models have forecast the deep layer of the lower atmosphere is the area where CO2 - influenced warming should occur first and by the greatest amounts.

«Also, if the atmosphere isn't accumulating heat at the rate forecast by the models, then the theoretical positive climate feedbacks which were expected to amplify the CO2 effect won't be as large,» McNider said.

Seasonal forecasts are often made with coupled ocean - atmoaphere models (more like climate models), as opposed to atmosphere - only models for ordinary weather forecasts.

ECMWF, NCEP GFS, UK MetOffice Unified Model, and Canadian GEM are the top global weather models and each use somewhat different methods to forecast one atmosphere.

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.

Since 2013 the ensemble forecasts have coupled the atmosphere - wave - ocean model from the start of the forecast.

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.

The ensemble and seasonal forecast systems use a coupled atmosphere - ocean model, which includes a simulation of the general circulation of the ocean and the associated coupled feedback processes that exist.

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

«The use of a coupled ocean — atmosphere — sea ice model to hindcast (i.e., historical forecast) recent climate variability is described and illustrated for the cases of the 1976/77 and 1998/99 climate shift events in the Pacific.

«Models are very consistent in forecasting a significant difference between climate trends at the surface and in the troposphere, the layer of atmosphere between the surface and the stratosphere,» said Dr. John Christy, director of UAH's Earth System Science Center.

The team remedied this by combining a regional climate model called the Weather Research and Forecasting Model with two land - surface models that can simulate interactions between the atmosphere and north central India's agricultural land, along with Himalayan mountainous topogrmodel called the Weather Research and Forecasting Model with two land - surface models that can simulate interactions between the atmosphere and north central India's agricultural land, along with Himalayan mountainous topogrModel with two land - surface models that can simulate interactions between the atmosphere and north central India's agricultural land, along with Himalayan mountainous topography.

In the first study, the research team from PNNL and Los Alamos National Laboratory used idealized global model simulations of the aquaplanet with Model for Prediction Across Scales - Atmosphere (MPAS - A) and Weather Research and Forecasting Model (WRF) to run at low, high and variable resolutmodel simulations of the aquaplanet with Model for Prediction Across Scales - Atmosphere (MPAS - A) and Weather Research and Forecasting Model (WRF) to run at low, high and variable resolutModel for Prediction Across Scales - Atmosphere (MPAS - A) and Weather Research and Forecasting Model (WRF) to run at low, high and variable resolutModel (WRF) to run at low, high and variable resolutions.

For 30 + years Atmospheric and Environmental Research (AER) has developed state - of - the - art algorithms for measuring, modeling, simulating and forecasting the Earth's atmosphere state.

Data from NASA's Terra satellite shows that when the climate warms, Earth's atmosphere is apparently more efficient at releasing energy to space than models used to forecast climate change have been programmed to «believe.»

Edward Epstein recognized in 1969 that the atmosphere could not be completely described with a single forecast run due to inherent uncertainty, and proposed a stochastic dynamic model that produced means and variances for the state of the atmosphere.

However, current forecast systems have limited ability on these timescales because models for such climate forecasts must take into account complex interactions among the ocean, atmosphere, and land surface, as well as processes that can be difficult to represent realistically.

A unified treatment of weather and climate models (i.e. the same dynamical cores for the atmosphere and ocean are used for models across the range of time scales) transfers confidence from the weather and seasonal climate forecast models to the climate models used in century scale simulations.

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.

To produce a weather forecast we need to model the dynamics of the atmosphere and the physical processes that occur, such as the formation of clouds, and the other processes in the Earth system that influence the weather such as atmospheric composition, the marine environment and land processes.

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.

GFDL NOAA (Msadek et al.), 4.82 (4.33 - 5.23), Modeling Our prediction for the September - averaged Arctic sea ice extent is 4.82 million square kilometers, with an uncertainty range going between 4.33 and 5.23 million km2 Our estimate is based on the GFDL CM2.1 ensemble forecast system in which both the ocean and atmosphere are initialized on August 1 using a coupled data assimilation system.

Much like the models used to forecast weather, climate models simulate the climate system with a 3 - dimensional grid that extends through the land, ocean, and atmosphere.

Why isn't a TCR type of simulation, but instead using actual history and 200 year projected GHG levels in the atmosphere, that would produce results similar to a TCR simulation (at least for the AGW temp increase that would occur when the CO2 level is doubled) and would result in much less uncertainty than ECS (as assessed by climate model dispersions), a more appropriate metric for a 300 year forecast, since it takes the climate more than 1000 years to equilibrate to the hypothesized ECS value, and we have only uncertain methods to check the computed ECS value with actual physical data?

CAS = Commission for Atmospheric Sciences CMDP = Climate Metrics and Diagnostic Panel CMIP = Coupled Model Intercomparison Project DAOS = Working Group on Data Assimilation and Observing Systems GASS = Global Atmospheric System Studies panel GEWEX = Global Energy and Water Cycle Experiment GLASS = Global Land - Atmosphere System Studies panel GOV = Global Ocean Data Assimilation Experiment (GODAE) Ocean View JWGFVR = Joint Working Group on Forecast Verification Research MJO - TF = Madden - Julian Oscillation Task Force PDEF = Working Group on Predictability, Dynamics and Ensemble Forecasting PPP = Polar Prediction Project QPF = Quantitative precipitation forecast S2S = Subseasonal to Seasonal Prediction Project SPARC = Stratospheric Processes and their Role in Climate TC = Tropical cyclone WCRP = World Climate Research Programme WCRP Grand Science Challenges • Climate Extremes • Clouds, Circulation and Climate Sensitivity • Melting Ice and Global Consequences • Regional Sea - Ice Change and Coastal Impacts • Water Availability WCRP JSC = Joint Scientific Committee WGCM = Working Group on Coupled Modelling WGSIP = Working Group on Subseasonal to Interdecadal Prediction WWRP = World Weather Research Programme YOPP = Year of Polar Prediction

This study evaluates the forecast skill of the fourth version of the Canadian coupled ocean — atmosphere general circulation model (CanCM4) and its model output statistics (MOS) to forecast the seasonal rainfall in Malaysia, particularly during early (October — November — December) and late (January — February — March) winter monsoon periods.

The PNNL research team transferred a set of Community Atmosphere Model version 5.1 (CAM5) physical parameters into the regional model Weather Research and Forecasting with Chemistry (WRF - CModel version 5.1 (CAM5) physical parameters into the regional model Weather Research and Forecasting with Chemistry (WRF - Cmodel Weather Research and Forecasting with Chemistry (WRF - Chem).

As with previous CIS contributions, the 2016 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness / extent, as well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Our prediction is based on the GFDL - FLOR ensemble forecast system, which is a fully - coupled atmosphere - land - ocean - sea ice model initialized using a coupled data assimilation system.

Fortunately meteorologist have very many measurements from the real atmosphere at their disposal and can use that to improve their models, both the simplified conceptual models and the large models that they use in forecasting.

However, forecasts of how ENSO might behave in the future are complicated by a host of interactions between the ocean and atmosphere, and better climate models are needed before scientists can arrive at such predictions, he added.

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.

I know it is a diffilcult problem becuase the data is not synoptic, has various quality / discontinuity issues due to instrumentation, and is widely dispersed both vertically and laterally through the atmosphere, but would something along the lines of what is done with GIStemp or HadCrut for temp, or even, heaven forbid, using RegEM or some other multivariate technique, be a better way to reconstruct the humidty history than using a forecast model?

What this means: Its models don't accurately forecast the impact of fundamental aspects of the atmosphere — clouds, smoke and dust.

While modest warming of the tropical East Pacific did occur, the atmosphere never really responded to the oceanic changes in a meaningful way, and model forecasts by early summer quickly fell toward a borderline event, at best.

The El Niño forecast is based on the Climate Forecast System, or CFS, model and recent trends in the ocean - atmosphere system.

I then went to the ECMWF [the European Centre for Medium - Range Weather Forecasts] and drew down some high - resolution operational analysis data — a cocktail of observations and model data that combine to give a really good picture of the atmosphere.»