Sentences with phrase «heuristic statistical»

Wilson (no organization provided); 2.5 Million Square Kilometers; Statistical and Heuristic Statistical relationship between ice loss and relative strength of El Nino is used for the September minimum.

No heuristic statistical methods for parameter estimation should be used AT ALL for that last work and discussion.

Not exact matches

Computational biology operates with a variety of tools on different levels of complexity, starting with very strict and algorithmic approaches like molecular simulations and finishing with statistical and heuristic techniques.

You do need a model though (which can be statistical, heuristic, or physics - based like a GCM)-- observations on their own are not sufficient.

Andersen, 3.9, Statistical / Heuristic (same as June) I continue to use the same method based on the maximum area in spring at the relatively stable reduction fraction we have seen the last 8 years.

Individual responses continue to be based on a range of methods: statistical, numerical models, comparison with previous rates of sea ice loss, composites of several approaches, estimates based on various non sea ice datasets and trends, and subjective information (the heuristic category).

As with the pan-arctic outlooks, the regional responses are based on statistical methods, numerical models, and heuristic estimates.

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 prHeuristic / 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 prheuristic 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.

Based on February / March SMOS sea ice thickness and September SSMI sea ice concentration we provide a heuristic / statistical guesstimate for the 2015 September sea ice extent: 3.6 + / - 0.7.

Individual responses continue to be based on a range of methods: statistical, numerical models, comparison with previous rates of sea ice loss, estimates based on various non-sea ice datasets and trends, and subjective information (the «heuristic» category).

Among modellers employing heuristics, a commonly made logical error is to presume more information than is possessed about the outcomes of statistical events.

As with the pan-arctic outlooks, the regional responses were based on statistical methods, numerical models, and heuristic estimates.

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.

Kay et al. (National Center for Atmospheric Research); 4.89 + 0.5 Million Square Kilometers, range of 4.0 to 5.8 million sq kilometers; Heuristic, Statistical, Modeling Method is an informal inquiry of 19 climate scientists on June 1, 2010.

Pokrovsky (Main Geophysical Observatory, Russia); 4.9 Million Square Kilometers; Heuristic and Statistical September sea ice extent is predicted through analysis of three climate indicators: the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and Arctic Oscillation (AO) for the last 30 years.

For example, in the Beaufort / Chukchi Seas, physical models, statistical models, and heuristic forecasts all agree, whereas in the East Siberian / Laptev Seas there is disagreement between the model (statistical and physical) and the heuristic forecasts.

This is consistent with the June statistical and heuristic forecasts submitted by Tivy and Pokrovsky, which also suggest more ice in the southern Beaufort Sea compared to previous years.

Arbetter et al. (North American Ice Service / National Ice Center); 4.9 Million Square Kilometers; Statistical / Heuristic Despite the reasonably large current extent (14.665 million km2) and compact concentration (12.461 million km2) in late April, the projected extent for mid-September is another near - record low (4.852 million km2).

Regional outlooks were based on numerical models, statistical methods, and heuristic estimates.

In general, it appears that statistical, semi-empirical as well as heuristic approaches fare reasonably well because they are able to build on sparse or qualitative information concerning the initial conditions in a specific sub-region.

Kaleschke and Tian - Kunze, 3.6 (± 0.7), Heuristic / Statistical (same as June) Based on February / March SMOS sea ice thickness and September SSMI sea ice concentration we provide a heuristic / statistical guesstimate for the 2015 September sea ice extent: 3.6 (± 0.7) milHeuristic / Statistical (same as June) Based on February / March SMOS sea ice thickness and September SSMI sea ice concentration we provide a heuristic / statistical guesstimate for the 2015 September sea ice extent: 3.6 (± 0.7) mStatistical (same as June) Based on February / March SMOS sea ice thickness and September SSMI sea ice concentration we provide a heuristic / statistical guesstimate for the 2015 September sea ice extent: 3.6 (± 0.7) milheuristic / statistical guesstimate for the 2015 September sea ice extent: 3.6 (± 0.7) mstatistical guesstimate for the 2015 September sea ice extent: 3.6 (± 0.7) million km2.

The probability allocation is done according to mathematical = probability principles, not according to statistical inference heuristics.

The mixed statistical / heuristic methods also dropped noticeably.

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 prHeuristic / 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 prheuristic 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.

This last group relies on heuristic arguments that seem to be attempts to do statistical mechanics in their head or by constructing a mental picture that suits them, drawing on a high school level understanding of mechanics.

In the Siberian Arctic, heuristic forecasts by Pokrovsky and statistical forecasts by Maslanik et al. predicted opening of the Northern Sea Route, which were on target (Figure 3).

They neglect the fact that thermodynamics is the valid macroscopic heuristic for statistical mechanics, and that stat mech computations can not be done on an heuristic basis at all; they are horrendously difficult, involving taking limits of nearly infinite sums in just the right way to get the relevant part of the answer and discard the parts that don't scale up to relevance as one goes to large systems.

I am not saying statistical inference honing heuristics does not work to some extent.

Individual responses continue to be based on a range of methods: statistical, numerical models, comparison with previous rates of sea ice loss, estimates based on various non-sea ice datasets and trends, and subjective information (i.e., the «heuristic» category).

As with the pan-arctic outlooks, the regional responses are based on heuristic estimates, numerical modeling, and statistical methods.

A heuristic and statistical method involves using a trial and error approach to solve a problem in conjunction with statistical analysis of real data.

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.

Canadian Ice Service, 4.90, Heuristic / Statistical Three methods are combined.

The first three boxes depict contributions based on heuristic, statistical or modeling methods.

Andersen, 4.00 (3.80 - 4.00), Statistical / Heuristic The estimate is based on the average years since 2007, adjusted to account for the extent peaks this past spring (short periods where the extent increased).

Pokrovsky; 4.5 million square kilometers; Heuristic - statistical There is no trend in our previous estimate in this part of the Arctic (Chukchi).

Slater, 4.45, Statistical / Heuristic My 50 - day forecast (http://cires.colorado.edu/~aslater/SEAICE/) issued on June 6th suggests that 2014 will be near the 3rd lowest rank year on record, which is how I came to derive my estimated extent for this long - lead time.

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.

The regional outlook contributions help shed light on the uncertainties associated with the Pan-Arctic estimates by providing more detail at the regional scale, and were based on numerical models, statistical methods, and heuristic estimates.

Randles, 4.0, + / -0.9, Statistical If I were giving a heuristic estimate, I think I would make the estimate lower.

Let me put it another way: If the recent statistical trend combines both AMO and global warming, then the correct heuristic interpretation of the result is an 80 % increase in likelihood, relative to a world in which both global warming and AMO had ceased.

The drop is mostly due to lower statistical and mixed statistical / heuristic contributions (Figure 2), because these methods are generally at least partially based on extrapolation from current / previous conditions whereas modeling contributions are generally not.

In general, the heuristic approaches forecast a mean September extent around 4.1 million km2, whereas the statistical and dynamical modeling approaches both suggest mean September extent near 5.1 million km2, with the dynamical modeling contributions showing a narrower range.

Shibata et al. (Kitami Institute of Technology); 5.4; Heuristic, Statistical Prediction is based on sea ice thickness, summer melt, outflow, and cloudiness.

Pokrovsky (Main Geophysical Observatory, Russia); 4.9 Million Square Kilometers; Heuristic and Statistical Estimate is unchanged from last month.

As with the Pan-Arctic Outlooks, the regional responses were based on statistical methods, numerical models, and heuristic estimates.