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What is Weather?01:07

What is Weather?

18.0K
Overview
18.0K
Precipitation Processes01:12

Precipitation Processes

363
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...
363
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.6K
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...
1.6K
Precipitation Gravimetry01:03

Precipitation Gravimetry

4.6K
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...
4.6K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

229
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...
229
Types of Coprecipitation01:10

Types of Coprecipitation

537
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...
537

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Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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エンドツーエンドデータ駆動の天気予報

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
まとめ
この要約は機械生成です。

Aardvark Weatherは,伝統的な数値気象予測 (NWP) システムを置き換える新しいエンドツーエンドの機械学習システムです. このデータに基づいたアプローチは 既存の方法よりも グローバルとローカルで正確な天気予報を実現します

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Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
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Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
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科学分野:

  • 人工知能
  • 大気科学
  • コンピュータ科学

背景:

  • 正確な天気予報は多くの分野と公衆の安全にとって不可欠です.
  • 機械学習 (ML) は数値的な天気予報 (NWP) を強化しましたが,多くの場合まだNWPのコンポーネントに依存しています.
  • 既存のMLモデルは,NWPに依存しているため,速度と精度に制限があります.

研究 の 目的:

  • 天気予報のためのエンドツーエンドのデータ駆動の機械学習システムを開発し評価する.
  • NWPのパイプライン全体を 単一のMLモデルで 置き換えることができるということを示すために
  • 確立されたNWPベースラインに対してこのシステムのパフォーマンスを評価する.

主な方法:

  • エンドツーエンドのデータ駆動システムで 観測データを吸収します
  • グローバル・グリッド・予測とローカル・ステーション・予測をMLモデルで生成した.
  • アードヴァークの天気予報を NWPのベースラインと 最先端のシステムと比較した

主要な成果:

  • Aardvarkの天気予報は NWPのベースラインを 超えました
  • ローカル・ステーションの予測は10日までのスキルを実証し,後処理NWPと人間の支援システムに競争しました.
  • エンドツーエンドのチューニングにより,現地予報の精度がさらに向上しました.

結論:

  • 熟練した天気予報は,配備時にNWPに頼らずに達成できます.
  • データを駆動したエンドツーエンドのMLモデルは,スピードと精度の大きな利点を提供します.
  • Aardvark Weatherは新世代の天気予報モデルであり,コストを削減し,カスタマイズすることができます.