Sentences with phrase «used simple statistical models»

In a recent study, Mathias Trachsel (Dept. of Biology, University of Bergen) and Atle Nesje (Dept. of Earth Science, University of Bergen and Uni Research Climate) used simple statistical models to assess and quantify the relative importance of summer temperature and winter precipitation for annual mass balances of eight Scandinavian glaciers.

In the figure below, Dr Gavin Schmidt, director of the NASA Goddard Institute for Space Studies, uses a simple statistical model to estimate what the global temperature record (black line) would be like in the absence of El Niño or La Niña influences (red line).

Not exact matches

They have developed a set of tools that can be used to make accurate, rapid assessments of proposed materials, using a series of relatively simple lab tests combined with computer modeling of the physical properties of the material itself, as well as additional modeling based on a statistical method known as Bayesian inference.

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 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 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.

Klazes (Public), 3.6 (95 % confidence interval of + / - 0.9), Statistical September extent is predicted using an estimated minimum value of the PIOMAS arctic sea ice volume and a simple model for volume - extent relationship.

Kapsch et al, 4.66 (± 0.59), Statistical For the prediction of the September sea - ice extent we use a simple linear regression model that is only based on the atmospheric water vapor in spring (April / May).

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 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 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.

Kapsch et al., 4.1 (± 0.5), Statistical (same as June) For the prediction of the September sea - ice extent we use a simple linear regression model that is only based on the atmospheric water vapor in spring (April / May).

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.

Kapsch et al, 4.75 (4.13 - 5.37), Statistical For the prediction of the September sea - ice extent we use a simple linear regression model that is only based on the atmospheric water vapor in spring (April / May).

Reynolds (Public), 4.06 (3.49 - 4.63), Statistical / Heuristic Because the decline in extent is due to increasing ease with which open water can be revealed by declining volume, a simple method is used to predict September sea ice extent based on May sea ice volume for the Arctic Ocean from the PIOMAS model.

There IS a heat source and a physical reality, that requires no forcing to give it super powers as with puny CO2 the palnts gobble up as much as they can get of, in fact.And explains the stable ice age and the Milankovitch linked interglacials, and how that sawtooth between repeated and predicatble limits can be driven using known energy sources, specific heats and masses, plus simple deterministic physics, no statistical models or Piltdown Mann data set approaches.