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Related Concept Videos

Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

3.2K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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Accuracy and Precision01:52

Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate...
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What is Weather?01:07

What is Weather?

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Overview
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Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

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Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
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Precipitation Processes01:12

Precipitation Processes

2.4K
The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Related Experiment Videos

Can deep learning beat numerical weather prediction?

M G Schultz1, C Betancourt1, B Gong1

  • 1Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|February 15, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep learning (DL) show promise for improving weather forecasts. However, fully replacing current numerical weather models with DL requires significant scientific breakthroughs.

Keywords:
deep learningmachine learningnumerical weather predictionspatiotemporal pattern recognitionweather AI

Related Experiment Videos

Area of Science:

  • Meteorology
  • Artificial Intelligence
  • Machine Learning

Background:

  • Recent advancements in artificial intelligence (AI) have spurred interest in applying deep learning (DL) methods to meteorology.
  • DL has demonstrated success in various fields like image recognition and strategic games.
  • Preliminary evidence suggests AI and neural networks can enhance weather prediction accuracy through big data mining.

Purpose of the Study:

  • To explore the potential of completely replacing numerical weather models and data assimilation systems with DL approaches.
  • To review state-of-the-art machine learning concepts and their suitability for weather data analysis.
  • To assess the feasibility of DL in revolutionizing weather forecasting.

Main Methods:

  • Review of current machine learning concepts.
  • Analysis of the statistical properties of weather data.
  • Discussion on the applicability of DL to meteorological data.

Main Results:

  • Deep learning methods are being increasingly applied to meteorology.
  • There is evidence that DL can improve weather forecast accuracy.
  • Current numerical weather models are unlikely to be replaced by DL in the near future.

Conclusions:

  • While DL shows potential in meteorology, it cannot yet fully replace numerical weather models.
  • Fundamental breakthroughs in AI and data assimilation are necessary for DL to supersede current systems.
  • The integration of machine learning in weather and climate modeling is an active and evolving research area.