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What is Weather?01:07

What is Weather?

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Precipitation Processes01:12

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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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Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

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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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Precipitation Gravimetry01:03

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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Types of Coprecipitation01:10

Types of Coprecipitation

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Coprecipitation is the contamination of a precipitate by otherwise soluble species and occurs via different processes. In colloidal precipitates, coprecipitation occurs via surface adsorption. For instance, barium sulfate has a primary layer of adsorbed barium ions and a secondary layer of nitrate counterions. This results in contamination of the precipitate by barium nitrate.
Sometimes, ions in a crystal lattice can undergo isomorphous replacement by inclusions of similar charge and size. For...
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Video Experimental Relacionado

Updated: May 23, 2025

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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Pronóstico del tiempo basado en datos de extremo a extremo

Anna Allen1, Stratis Markou2, Will Tebbutt3,4

  • 1Department of Computer Science and Technology, University of Cambridge, Cambridge, UK. av555@cam.ac.uk.

Nature
|March 20, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Aardvark Weather es un nuevo sistema de aprendizaje automático de extremo a extremo que reemplaza los sistemas tradicionales de predicción climática numérica (NWP). Este enfoque basado en datos logra pronósticos meteorológicos globales y locales precisos, superando a los métodos existentes.

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Área de la Ciencia:

  • Inteligencia artificial
  • Ciencias atmosféricas
  • Ciencias computacionales

Sus antecedentes:

  • La predicción meteorológica precisa es vital para numerosos sectores y la seguridad pública.
  • El aprendizaje automático (ML) ha mejorado la predicción numérica del tiempo (NWP), pero a menudo todavía se basa en componentes NWP.
  • Los modelos ML existentes se enfrentan a limitaciones de velocidad y precisión debido a la dependencia de NWP.

Objetivo del estudio:

  • Desarrollar y evaluar un sistema de aprendizaje automático de extremo a extremo basado en datos para la predicción del tiempo.
  • Para demostrar que un solo modelo ML puede reemplazar toda la tubería NWP.
  • Evaluar el rendimiento de este sistema con respecto a las líneas de base establecidas de la PNM.

Principales métodos:

  • Desarrollado Aardvark Weather, un sistema de extremo a extremo impulsado por datos que ingiere datos de observación.
  • Se han generado pronósticos globales en cuadrícula y pronósticos de estaciones locales utilizando el modelo ML.
  • Comparó las previsiones de Aardvark Weather con una línea de base de NWP operativa y un sistema de última generación.

Principales resultados:

  • Los pronósticos globales de Aardvark Weather superaron la línea de base operativa de NWP para múltiples variables y plazos de entrega.
  • Las previsiones de la estación local demostraron habilidad hasta diez días, rivalizando con los sistemas NWP y asistidos por humanos.
  • El ajuste de extremo a extremo mejoró aún más la precisión de los pronósticos locales.

Conclusiones:

  • Se puede lograr un pronóstico meteorológico hábil sin depender del NWP en el despliegue.
  • Los modelos de aprendizaje automático de extremo a extremo basados en datos ofrecen importantes beneficios de velocidad y precisión.
  • Aardvark Weather representa una nueva generación de modelos de predicción del tiempo, reduciendo los costos y permitiendo la personalización.