Sentences with phrase «observation data become»

Not exact matches

Feedback has learnt from long observation that odd bits of data sometimes prove useful in unexpected ways, although too often their usefulness becomes evident only after they have been discarded.

Data - quality issues, including the reanalysis and reprocessing of past observations, have become a topic in its own right in climate research, says Adrian Simmons, a senior scientist at the European Centre for Medium - Range Weather Forecasts in Reading, UK, and chairman of the GCOS steering committee.

«This observation, combined with the transcriptomic data, told us that cells transition through an intermediate state before becoming 2 - cell - like cells» said Maria Elena Torres - Padilla.

The observation data is made available only to the researcher group that submitted the proposal for a year and thereafter the data is stored in the ALMA data archive and becomes accessible and downloadable to everyone who wants to use it for his / her research.

In a playful way the children will become familiar with making predictions, observations, identifying, sorting, reasoning, gathering data, comparing and evaluating.

Not every teacher will become a data automaton of course, but what will help teachers is that increasingly they won't have to be the ones actively collecting every piece of data; instead they will be able to spend more time analyzing and figuring out what to do about it, coupled of course with their own «data» that they collect on students from their intuition and observations.

McNamara says her science lessons have also become more rigorous: Teachers at her school are covering the same units, but instead of talking about «how scientists think,» first graders will have to learn — and use — terms like «inquiry,» «observation» and «data.»

Are you using formative data to produce a summative rating or has the observation become the evaluation?

Data gathered during classroom observations and pre - and post-conference discussions with teachers become meaningful through association.

When district benchmark or performance assessment data become available, similarly analyze this data and compare results to your state data observations.

I'm talking about the potential to have an idea or observation built around empirical data become more engaging and inspiring.

This is the same thing that became evident when RealClimate used that broad range of outputs to explain why there are «no» clear model - data inconsistencies regarding the tropical troposphere temperature observations.

And he has an interesting take on the difference between «observational data» and «models» — basically, there's no clean distinction — models are empirical summaries of the observational data, and observations are processed through models before they become usable data.

This data - treatment technique revealed that, as you go back farther in time, the daytime observations become progressively warmer compared to nighttime observations.

Even after satellite observations became available, substantial work was needed to determine the combination of data that best captures flooding and to develop global analysis techniques to extract the maximum information about wetlands.

Bearing in mind the paucity of data in many parts of the world the further back in time you go, the potential to «double check» a single observation with many other observations from the same grid / date / conditions in order to validate it and minimise the uncertainty becomes limited.

In this second article we re-examine related events concerning the 1850/1880 CRU / Giss temperature records, and then pay particular attention to the reliability of those readings that have become the basis of our modern climate industry, examine those who carried out the original observations, and look at the circumstances under which data was collected.

I simply asked questions here and don't see any statements that could easily become «wrong» — the boxplots and commentary on discrepancy between models and observations is simply summarizing data and won't change.

Before the 19th century virtually all weather observations were made by amateurs, providing data that would become essential for tracking long - term changes.

Simultaneously exploiting global observations and local high - resolution simulations with the data assimilation and machine learning tools that have recently become available presents the key opportunity for dramatic progress in Earth system modeling.

Until the data becomes available the best I can do is create a plausible conceptual overview which matches observations without any clear abuse of the laws of physics.

Incorporating climate data and observations as they become available into the model helps refine the results and predictions.

This task has become easier over the last decade with the development of advanced methods of Data Assimilation commonly used in atmospheric sciences to optimally combine a short forecast with the latest meteorological observations in order to create accurate initial conditions for weather forecasts generated several times a day by the National Weather Services (e.g., [194,195,196,197,198]-RRB-.

Quite egalitarian, so in fact contrarians, scientists who hold ideas outside of the mainstream can prosper provided their ideas have some factual basis and use the scientific method (Scientific method: based on existing obervations pose an hypothesis; using new observations or experiments, test the predictions of that hypothesis; on the basis of the new data either reject the hypothesis or modify it to fit the better understanding, or accept that the initial hypothesis was right at which point it becomes a «theory» or explanatory model).