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Related Concept Videos

Precipitation Processes01:12

Precipitation Processes

2.2K
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...
2.2K
Boundary Layer Characteristics01:18

Boundary Layer Characteristics

327
When a fluid encounters a solid surface, a boundary layer forms due to the interaction between the fluid's motion and the stationary surface. This phenomenon is characterized by a thin region adjacent to the surface where viscous forces dominate, influencing the fluid's velocity profile. The development of the boundary layer begins at the leading edge of the surface and evolves as the fluid moves downstream.As the fluid flows over the surface, friction between the fluid and the wall slows down...
327
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...
3.2K
Precipitation Gravimetry01:03

Precipitation Gravimetry

10.0K
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...
10.0K
Types of Coprecipitation01:10

Types of Coprecipitation

2.7K
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...
2.7K
Precipitate Formation and Particle Size Control01:16

Precipitate Formation and Particle Size Control

2.8K
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...
2.8K

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Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques.

J Christine Chiu1, C Kevin Yang1, Peter Jan van Leeuwen1,2

  • 1Department of Atmospheric Science Colorado State University Fort Collins CO USA.

Geophysical Research Letters
|March 8, 2021
PubMed
Summary
This summary is machine-generated.

New parameterizations for warm rain growth processes were developed using machine learning. Drizzle number concentration is a key factor for autoconversion, improving rain growth models.

Keywords:
accretionautoconversionboundary layer cloudcloud parameterizationmachine learningwarm rain

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Area of Science:

  • Atmospheric Science
  • Cloud Physics
  • Machine Learning Applications

Background:

  • Accurate representation of warm rain microphysical processes is crucial for climate modeling.
  • Existing parameterizations for autoconversion and accretion rates have limitations.

Purpose of the Study:

  • To develop and validate improved parameterizations for autoconversion and accretion rates.
  • To identify key factors influencing warm rain droplet growth.

Main Methods:

  • Utilized machine learning and optimization techniques.
  • Constrained parameterizations with in situ cloud probe measurements from the Atmospheric Radiation Measurement Program (ARM) field campaign.
  • Analyzed relationships between droplet properties and rain rates.

Main Results:

  • New parameterizations show reduced uncertainty (15% for autoconversion, 5% for accretion) compared to existing models.
  • Confirmed cloud and drizzle water content as primary drivers of accretion.
  • Identified drizzle number concentration as a critical, previously overlooked, factor for autoconversion.

Conclusions:

  • The developed parameterizations significantly enhance the representation of warm rain growth.
  • Drizzle number concentration must be incorporated into autoconversion parameterizations for improved accuracy.
  • Findings have implications for weather and climate models.