Sentences with phrase «in observational error»

Not exact matches

His observational skills and persistence were rightly praised, but errors in his instrument negated what would have been a major discovery.

However, satellite observations are notably cooler in the lower troposphere than predicted by climate models, and the research team in their paper acknowledge this, remarking: «One area of concern is that on average... simulations underestimate the observed lower stratospheric cooling and overestimate tropospheric warming... These differences must be due to some combination of errors in model forcings, model response errors, residual observational inhomogeneities, and an unusual manifestation of natural internal variability in the observations.»

If interested, see the Review of Article # 1 — the introduction to the special issue here; see the Review of Article # 2 — on VAMs» measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; see the Review of Article # 3 — on VAMs» potentials here; and see the Review of Article # 4 — on observational systems» potentials here.

If misalignment is noticed, it is not to be the fault of either measure (e.g., in terms of measurement error), it is to be the fault of the principal who is critiqued for inaccuracy, and therefore (inversely) incentivized to skew their observational data (the only data over which the supervisor has control) to artificially match VAM - based output.

If interested, see the Review of Article # 1 — the introduction to the special issue here; see the Review of Article # 2 — on VAMs» measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; see the Review of Article # 3 — on VAMs» potentials here; see the Review of Article # 4 — on observational systems» potentials here; see the Review of Article # 5 — on teachers» perceptions of observations and student growth here; see the Review of Article (Essay) # 6 — on VAMs as tools for «egg - crate» schools here; see the Review of Article (Commentary) # 7 — on VAMs situated in their appropriate ecologies here; and see the Review of Article # 8, Part I — on a more research - based assessment of VAMs» potentials here and Part II on «a modest solution» provided to us by Linda Darling - Hammond here.

If interested, see the Review of Article # 1 — the introduction to the special issue here; see the Review of Article # 2 — on VAMs» measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; see the Review of Article # 3 — on VAMs» potentials here; see the Review of Article # 4 — on observational systems» potentials here; see the Review of Article # 5 — on teachers» perceptions of observations and student growth here; see the Review of Article (Essay) # 6 — on VAMs as tools for «egg - crate» schools here; and see the Review of Article (Commentary) # 7 — on VAMs situated in their appropriate ecologies here; and see the Review of Article # 8, Part I — on a more research - based assessment of VAMs» potentials here.

Even if one were to stipulate all of the ostensible «errors» Lewis claims, the only way he is actually able to justify his claim of disagreement with observations» ICS is by throwing out the observational ICS estimate used in the paper in favor of once he likes and obviously likes simply because of their low values.

We ultimately face a question of what we trust more: our estimate of our cumulative emissions to date combined with our full knowledge of how much warming that might imply, or an estimate of how warm the system was in 2014 which is subject to error due to observational uncertainty and natural variability.

Whether you are gullible enough to accept the figures as accurate depends on how much credibility you put in the multitude of observational measurements taken by different methods over many decades by diverse groups of researchers that form a strong consilience of mutually supporting evidence for the validity of the estimates and the possible errors.

Observational errors on any one annual mean temperature anomaly estimate are around 0.1 deg C, and the errors from the linear fits are given in the text.

I implied that such evidence would consist of copies of all the processed data used in Forest 2006 for the computation of the error r2 statistic produced by each diagnostic; a copy of all computer code used for subsequent computation and interpolation; and the code used to generate both the CSF 2005 and the Forest 2006 processed MIT model, observational and AOGCM control - run data from the raw data, including all ancillary data used.

I agree that having corrected the error in F3 (AGW) to give absolutely correct values there is still a problem with the observational data.

Consider the multiple possibilities of unfair coins, unexpected externalities that could affect outcomes, skilled coin toss experts and their chaotic human whims, social engineering of observers by dishonest actors and their chaotic whims, observational errors and the whims of observers, one would have to call actual coin tosses a spatial - temporal chaos model, in particular if one decides beforehand to do what one can to generate that outcome.

As an extension, systematic observational errors could perhaps be corrected as part of the regression by estimating a constant shift to apply to each thermometer (treating changes in technology as creating a new thermometer on the same site), though this may make the problem too large.

In a Bayesian statistics framework, this is equivalent to assuming Gaussian observational errors and a uniform «prior» in each of the observableIn a Bayesian statistics framework, this is equivalent to assuming Gaussian observational errors and a uniform «prior» in each of the observablein each of the observables.

As I interpret the evidence, the observational data tend to confirm the modeling for these individual feedbacks at least semiquantitatively, and this suggests to me that the climate sensitivity estimates are probably not grossly in error, even if precise quantitation still eludes us.

Statistics as such has a part to play in error estimation, assessing observational reliability, and, e.g., in the mathematical expression of Thermodynamics and Statistical Mechanics.

Possible explanations for these results include the neglect of negative forcings in many of the CMIP - 3 simulations of forced climate change), omission of recent temporal changes in solar and volcanic forcing [Wigley, 2010; Kaufmann et al., 2011; Vernier et al., 2011; Solomon et al., 2011], forcing discontinuities at the «splice points» between CMIP - 3 simulations of 20th and 21st century climate change [Arblaster et al., 2011], model response errors, residual observational errors [Mears et al., 2011b], and an unusual manifestation of natural internal variability in the observations (see Figure 7A).

In context of the way climate sensitivity is defined by the IPCC, uncertainty in climate sensitivity is decreasing as errors in previous observational estimates are identified and eliminated and model estimates seem to be converging morIn context of the way climate sensitivity is defined by the IPCC, uncertainty in climate sensitivity is decreasing as errors in previous observational estimates are identified and eliminated and model estimates seem to be converging morin climate sensitivity is decreasing as errors in previous observational estimates are identified and eliminated and model estimates seem to be converging morin previous observational estimates are identified and eliminated and model estimates seem to be converging more.

Nic writes «Given Forster & Gregory's regression method and observational error assumptions, the error (and hence probability) distribution for the resulting slope coefficient estimate can be derived from frequentist statistical theory, as used in science for many years.»

In physical sciences, where an OLS regression model with normally distributed errors is validly used to estimate a slope parameter between two variables with observational data, errors in the regressor variable contributing a small part of the total uncertainty, it is usual to accept the uniform prior in the slope parameter (here Y) implied by the regression modeIn physical sciences, where an OLS regression model with normally distributed errors is validly used to estimate a slope parameter between two variables with observational data, errors in the regressor variable contributing a small part of the total uncertainty, it is usual to accept the uniform prior in the slope parameter (here Y) implied by the regression modein the regressor variable contributing a small part of the total uncertainty, it is usual to accept the uniform prior in the slope parameter (here Y) implied by the regression modein the slope parameter (here Y) implied by the regression model.

Given Forster & Gregory's regression method and observational error assumptions, the error (and hence probability) distribution for the resulting slope coefficient estimate can be derived from frequentist statistical theory, as used in science for many years.

The very high significance levels of model — observation discrepancies in LT and MT trends that were obtained in some studies (e.g., Douglass et al., 2008; McKitrick et al., 2010) thus arose to a substantial degree from using the standard error of the model ensemble mean as a measure of uncertainty, instead of the ensemble standard deviation or some other appropriate measure for uncertainty arising from internal climate variability... Nevertheless, almost all model ensemble members show a warming trend in both LT and MT larger than observational estimates (McKitrick et al., 2010; Po - Chedley and Fu, 2012; Santer et al., 2013).

Assessments of our relative confidence in climate projections from different models should ideally be based on a comprehensive set of observational tests that would allow us to quantify model errors in simulating a wide variety of climate statistics, including simulations of the mean climate and variability and of particular climate processes.

In other words, there is a 3 - way comparison: old model vs. new model vs. observational data, where it is explicitly acknowledged that there may be errors in any of the threIn other words, there is a 3 - way comparison: old model vs. new model vs. observational data, where it is explicitly acknowledged that there may be errors in any of the threin any of the three.

Specific aims of the meeting will be to maximize the robustness and policy relevance of the projections provided in the presence of model error, projection uncertainty, observational uncertainties and a heterogeneous set of models.»

But the observational estimate uncertainty includes measurement and related errors that are not present in the model estimate uncertainty (although these appear to be relatively unimportant in this case), while only the model estimates sample decadal / multidecadal climate system internal variability, which very possibly affects the TLC reflection — SST relationship.

As in Y12, we used the point-wise difference between each pair of data sets as an indication of observational uncertainty, although this is likely to be somewhat of an underestimate of the true error.

Dozens of peer - reviewed scientific studies show that the other three explanations presented here («model input errors», «observational errors», and «different variability sequences») are the primary reasons for most or all of the warming rate differences in Exhibit A. [j]

Those interested in a Quality Consultant position should be able to demonstrate the following skills in their resumes: quality assurance expertise, strong observational skills, time management, error detection, teamwork, and problem solving orientation.