Sentences with phrase «different observation data»

a few simple comparisons will demonstrate that you get different paleo answers if you use different observation data sets.

Not exact matches

«Radio observations point to likely explanation for neutron - star merger phenomena: Data distinguish between different theoretical models.»

The observational data of the study consisted of nearly a million (990,301) observations of 94 different bird species, and the results have now been published as part of the international Global Change Biology publication series.

«We have used data from biological observations and analysed the relationship between the different species and their place in the food chain.

The data tell a far different story: «The observations in the last 10 years are, whoa, ice sheets change far more dramatically, both in terms of magnitude of change and timescale, than we experts ever thought possible,» Bindschadler said.

This involves a combination of satellite observations (when different satellites captured temperatures in both morning and evening), the use of climate models to estimate how temperatures change in the atmosphere over the course of the day, and using reanalysis data that incorporates readings from surface observations, weather balloons and other instruments.

De Kleer timed her Gemini and IRTF observations to coincide with observations of the plasma torus by the Japanese HISAKI (SPRINT - A) spacecraft, which is in orbit around Earth, so she can correlate the different data sets.

These skew terms cancel out when relating data sets taken at the same epoch and same orientation, but they must be accounted for when dealing with observations taken at different roll angles.

Therefore, all evaluations should use multiple types of instruments — surveys, focus groups, interviews, observations, and questionnaires — in order to capture and analyze data from as many different angles as possible to triangulate the data most effectively.

Using these data, we calculated a score for each teacher on the eight TES «standards» by averaging the ratings assigned during the different observations of that teacher in a given year on each element included under the standard.

The observation sheet contains the following items: — Write original essays; — Use properly artistic expressions; — Easily find the meaning of words and new expressions; — Quick find anonymous and various synonyms for certain words data; — Change the end of a text; — Write lyrics on a given topic; — Communicate with other children in different situations; — Communicate appropriately with adults.

First, the data set consists of ratings of different lessons implemented without observation and do not represent a normal distribution or population.

Today most states combine different measures, including classroom observations and student test data,...

The point is all these schools focused intensely on different priorities, but the one thing they all did extremely well was making the teaching profession actually a profession, investing in PD, teacher growth, extensive observations and evaluations, data - driven instruction.

Data collection for this study include interviews with administrators, philanthropists, mentors, and students, document and media analysis, as well as a full - year of participant observations in three different school sites in which the mentorship program operates.

Today most states combine different measures, including classroom observations and student test data, to produce a rating that describes effectiveness.

While the data is different product to product, we did learn what to measure and how to listen.In the world of software application development, UX designers and researchers physically watch people using an application and determine information about them and their needs through observation.

I'm not sure what to do with that last observation; perhaps it is that my practical experience exists over the last 20 years which have been different than the whole data sample.

SELECTED GROUP EXHIBITIONS 2017 Glut Data, ASC Chaplin Centre, London 2017 ESTELLE THOMPSON & ERNESTO CÁNOVAS: In Colours where we Meet, Ambachar Contemporary, Munich 2017 Pelé: Art Life Football, National Football Museum, Manchester 2017 Clouded Lands, Fundación Caja Burgos (CAB), Burgos, Spain 2016 Summer Exhibition, Halcyon Gallery, London 2016 Colectiva Monopatin 3, Museo de Arte de Puerto Rico (Part of the Puerto Rico Triennale 2016), Puerto Rico, USA 2016 Le Dessous des Recits, Galerie Gourvennec Ogor, Marseille, France 2016 Non-Profit Observations, Kir Royal, Valencia, Spain 2015 Pelé: Art, Life, Football, Halcyon Gallery, London 2015 The Art of Creating, Halcyon Gallery, London 2014 From Cocoanut Grove to Soho Nights, Paul Smith, London 2014 Summer Exhibition, Royal Academy, London 2014 Landshapes, Gallery Kir Royal Valencia, Spain 2014 Open Dialogues — Generation 14, Royal Academy, Edinburgh, Scotland 2013 Threadneedle Prize, Mall Galleries, London 2013 Summer Exhibition, Halcyon Gallery, London 2013 Essence of Things, Ambacher Contemporary, Munich, Germany 2012 Transfigurative, Pariothall Gallery, Edinburgh 2012 Choice White Space, McClure Art, Edinburgh 2012 (De) Constructions, Rollo Gallery, London 2012 The Open West Prize 2012 exhibition, Gloucester, UK 2011 New Sensations Prize 2011, Saatchi Gallery and Channel 4, Victoria House, London 2011 Bloomberg New Contemporaries, shortlisted, London — 2011 Slade Postgraduate Research 2011, Slade Research Centre, London 2011 Plan B, Two Windows Project, Berlin 2010 New Contemporaries, Royal Scottish Academy, Edinburgh 2010 Different Light Here, Le Garage Gallery, London 2010 Slade Interim Show, Slade Research Centre, University College London 2010 Fine Art exhibition, Candid Gallery, London 2010 Please Be There Tomorrow, Le Garage Gallery, London 2010 Boxers & Fighters, Two Windows Project, Berlin 2010 KunstVlaai / Art Pie, Westergasfabriek, Amsterdam

Information is not the same as data, and we know that observations and models often represent different things.

The magnitude it actually had actually risen, how different these temperatures were from the 1940s, the conflict between model prediction / theory and observation, etc, were the issues the satellite data raised.

In the global mean, there isn't much of an issue for the mid-troposphere — the models and data track each other when you expect they would (the long term trends or after volcanoes, and don't where you expect them not to, such as during La Niña / El Niño events which occur at different times in models and observations).

There is a «model» which has a certain sensitivity to 2xCO2 (that is either explicitly set in the formulation or emergent), and observations to which it can be compared (in various experimental setups) and, if the data are relevant, models with different sensitivities can be judged more or less realistic (or explicitly fit to the data).

Some of them are optimal fingerprint detection studies (estimating the magnitude of fingerprints for different external forcing factors in observations, and determining how likely such patterns could have occurred in observations by chance, and how likely they could be confused with climate response to other influences, using a statistically optimal metric), some of them use simpler methods, such as comparisons between data and climate model simulations with and without greenhouse gas increases / anthropogenic forcing, and some are even based only on observations.

As has been noted by others, this is comparing model temperatures after 2020 to an observation - based temperature in 2015, and of course the latter is lower — partly because it is based on HadCRUT4 data as discussed above, but equally so because of comparing different points in time.

A series of sensitivity tests show that our detection results are robust to observational data coverage change, interpolation methods, influence of natural climate variability on observations, and different model sampling (see Supplementary Information).

All different observations of past CO2 levels have their own problems, be it chemical measurements, ice cores, stomata data or coralline sponges.

The reasons for the differences are not completely clear because each data set is based on a slightly different set of observations, which have been quality controlled, and processed in different ways.

But observations suggest otherwise: «We have analyzed data from different satellites measuring soil moisture and precipitation all over the globe, with a resolution of 50 to 100 kilometers.

Structural uncertainty is attenuated when convergent results are obtained from a variety of different models using different methods, and also when results rely more on direct observations (data) rather than on calculations.

If, on the other hand, the differences between station means and station offsets show large variance because different stations have warmed differently between baseline and observation intervals, then the last term will greatly increase the estimated data variance.

-- Brandt et al., 2017 https://www.nature.com/articles/s41559-017-0081 Here we used a passive microwave Earth observation data set to document two different trends in land area with woody cover for 1992 — 2011: 36 % of the land area (6,870,000 km2) had an increase in woody cover largely in drylands, and 11 % had a decrease (2,150,000 km2), mostly in humid zones.

The only observations that would dictate a zero sensitivity would be ones in which temperature was a completely flat line — this would be very different from their Figure 1 data.

See, the first thing to do is do determine what the temperature trend during the recent thermometer period (1850 — 2011) actually is, and what patterns or trends represent «data» in those trends (what the earth's temperature / climate really was during this period), and what represents random «noise» (day - to - day, year - to - random changes in the «weather» that do NOT represent «climate change»), and what represents experimental error in the plots (UHI increases in the temperatures, thermometer loss and loss of USSR data, «metadata» «M» (minus) records getting skipped that inflate winter temperatures, differences in sea records from different measuring techniques, sea records vice land records, extrapolated land records over hundreds of km, surface temperature errors from lousy stations and lousy maintenance of surface records and stations, false and malicious time - of - observation bias changes in the information.)

When we look at the distributions (e.g. data, 1st difference, 2nd difference) of the observations of global temperature, and of modeled temperature, they are very different.

If being.1 % different than observation is grounds for being declared «physically meaningless», than the law of gravity is in trouble, because recent empirical data was 2 % off.

For example, changes in time of observation, adjustment for a move of a station that was previously sited next to a heat source to a better location (that now allows the station to be classed as Class 1 or 2), switch to a different temperature measurement device or system, etcetera, could explain why smaller classes of raw data don't track well with the overall trend calculated from homogenized station trend data.

Not particularly relevant to the rest of the world, where data quality issues are different (e.g. poor equipment or maintenance, loss of longer term records through conflict or natural disasters, unreliability of observation etc).

We can repeat our earlier observation that CET instrumental to 1659 - this time augmented by the reconstruction using historical records to 1538, demonstrates a temperature profile that looks quite different to significant periods of the remainder of the Northern Hemisphere if the official version of extended climate - as epitomised by the «Hockey stick» - is taken as the appropriate set of data which it should be measured against.

In addition to high - quality data, we need a common data structure for creating a platform for collaboration that includes observations and different kinds of products (e.g. empirical orthogonal functions), both in terms of data files on disks (e.g. netCDF and the «CF» convention) and in the computer memory.

Then in response to the particular observation that the balloon data lie especially far away from the modelers» expectations, the defence is: but we all know the balloon data is so uncertain and tunable that it can yield lots of different interpretations, therefore it does not conflict with the hypothesis, so there's no need to doubt the hypothesis of strong CO2 warming.

It is the reason we want multiple repeated observations of an experiment, prefferably using independant data sets under different circumstances.

Together with the CCS observations discussed above, the contrast with the NPSG coral data (while not directly comparable in terms of time scale), suggests that despite the fact that baseline δ15N declines are observed in both data sets, different biogeochemical mechanisms may underlie the changes in these very different oceanographic regions.

9Because of the unbalanced nature of the data being analyzed (i.e., unequal numbers of observations for the different levels of the classification variables), the General Linear Models procedure in the Statistical Analysis System was used to perform the analyses.

Through constructing a number of such analogs under different assumptions as to the structure of inhomogeneities we can begin to ascertain which data products may be closer to the real world when applied to the real world databank observations.

Although you can interpret this data any number of different ways, here are my top observations.