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Related Concept Videos

Precipitation Processes01:12

Precipitation Processes

498
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...
498
Precipitation Gravimetry01:03

Precipitation Gravimetry

6.7K
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...
6.7K
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.9K
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...
1.9K
Precipitation of Ions03:11

Precipitation of Ions

28.0K
Predicting Precipitation
The equation that describes the equilibrium between solid calcium carbonate and its solvated ions is:
28.0K
Types of Coprecipitation01:10

Types of Coprecipitation

687
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...
687
Precipitate Formation and Particle Size Control01:16

Precipitate Formation and Particle Size Control

843
In precipitation gravimetry, the precipitating agent should react specifically or selectively with the analyte. While a specific reagent reacts with the analyte alone, a selective reagent can react with a limited number of chemical species.
The obtained precipitate should be either a pure substance of known composition or easily converted to one by a simple process, such as ignition or drying. In addition, the precipitate should be insoluble and easily filterable. In general, filterability...
843

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Physics-informed deep learning framework to model intense precipitation events at super resolution.

B Teufel1, F Carmo1, L Sushama1

  • 1Department of Civil Engineering, Trottier Institute for Sustainability in Engineering and Design, McGill University, Montreal, Canada.

Geoscience Letters
|April 24, 2023
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Summary

This study introduces an efficient deep learning (DL) framework for high-resolution precipitation modeling. The novel approach couples physical modeling with DL to accurately simulate intense rainfall events for climate resiliency assessments.

Keywords:
Convection permitting modelDeep learningEngineering scaleIntense precipitationRegional climate model

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Area of Science:

  • Climate Science
  • Atmospheric Physics
  • Computational Science

Background:

  • Physical modeling of precipitation at fine spatial scales is computationally intensive.
  • Accurate precipitation data is crucial for various applications, including urban infrastructure design.

Purpose of the Study:

  • To develop a highly efficient framework for fine-scale precipitation modeling.
  • To couple deep learning with physical modeling for improved precipitation simulation.
  • To assess the framework's capability in capturing intense precipitation events.

Main Methods:

  • Coupling a deep learning (DL) framework with physical modeling.
  • Utilizing a recurrent neural network approach considering atmospheric physical processes like advection.
  • Testing the framework with regional climate simulations at 2.5 km and 250 m resolutions for Montreal (summers 2015-2020).

Main Results:

  • The DL framework generates realistic high-resolution precipitation estimates.
  • The model successfully recreates fine details and temporally consistent fields.
  • The framework accurately simulates intense short-duration precipitation events.

Conclusions:

  • The proposed DL framework offers a computationally efficient solution for fine-scale precipitation modeling.
  • The framework is valuable for engineering applications, such as evaluating urban storm drainage system resiliency.
  • The approach is extensible to different resolutions, time periods, and geographical regions.