Sentences with phrase «existing modeling errors»

Improved budgeting and forecasting processes significantly reducing existing modeling errors and turnaround by one week.

Not exact matches

A new method developed by scientists on the Florida campus of The Scripps Research Institute (TSRI) takes another tack entirely, combining existing formulas in a kind of algorithmic stew to gain a better picture of molecular structural diversity that is then used to eliminate errors and improve the final model.

Errors may exist in data acquired from third - party vendors, the construction of model portfolios and in coding related to the

His error, however, is in suggesting that this discovery (with limited understanding of its magnitude) somehow throws into doubt existing models of AGW (which are based on much more firmly established physical processes with trends in different climate forcings that are directly testable against the historical temperature record).

Only one of the parties involved has (1) had their claims fail scientific peer - review, (2) produced a reconstruction that is completely at odds with all other existing estimates (note that there is no sign of the anomalous 15th century warmth claimed by MM in any of the roughly dozen other model and proxy - based estimates shown here), and (3) been established to have made egregious elementary errors in other published work that render the work thoroughly invalid.

In addition to «modeling errors,» much of the Clack critique is aimed at the assumed ubiquitous deployment of technologies that either don't yet exist or are only lightly tested and can't be scaled up to the huge scales envisioned.

One might (or might not) argue for such a relation if the models were empirically adequate, but given nonlinear models with large systematic errors under current conditions, no connection has been even remotely established for relating the distribution of model states under altered conditions to decision - relevant probability distributions... There may well exist thresholds, or tipping points (Kemp 2005), which lie within this range of uncertainty.

Previously reported discrepancies between the amount of warming near the surface and higher in the atmosphere have been used to challenge the reliability of climate models and the reality of human - induced global warming... This significant discrepancy no longer exists because errors in the satellite and radiosonde data have been identified and corrected.

General Introduction Two Main Goals Identifying Patterns in Time Series Data Systematic pattern and random noise Two general aspects of time series patterns Trend Analysis Analysis of Seasonality ARIMA (Box & Jenkins) and Autocorrelations General Introduction Two Common Processes ARIMA Methodology Identification Phase Parameter Estimation Evaluation of the Model Interrupted Time Series Exponential Smoothing General Introduction Simple Exponential Smoothing Choosing the Best Value for Parameter a (alpha) Indices of Lack of Fit (Error) Seasonal and Non-seasonal Models With or Without Trend Seasonal Decomposition (Census I) General Introduction Computations X-11 Census method II seasonal adjustment Seasonal Adjustment: Basic Ideas and Terms The Census II Method Results Tables Computed by the X-11 Method Specific Description of all Results Tables Computed by the X-11 Method Distributed Lags Analysis General Purpose General Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in Time Series

Because the full suite of physical processes at the grounding line (e.g., Walker et al., 2013) in general is not represented in modern models, the possibility exists that rates produced by extant models under strong simulated forcing may be greatly in error (Nowicki et al., 2013).

One is related to the systematic errors that are known to exist in models.

The potential exists for spurious numerical dispersion, when combined with errors in parametrizations and incompletely modelled processes, to produce erroneous entropy sources.