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Related Concept Videos

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 Processes01:12

Precipitation Processes

486
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...
486
Precipitation Gravimetry01:03

Precipitation Gravimetry

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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...
6.7K
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

1.8K
In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...
1.8K
Types of Coprecipitation01:10

Types of Coprecipitation

664
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...
664
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

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Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
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A Deep Learning-Based Algorithm for Identifying Precipitation Clouds Using Fengyun-4A Satellite Observation Data.

Guangyi Ma1, Jie Huang2, Yonghong Zhang2

  • 1School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary
This summary is machine-generated.

A new Convolutional Neural Network (CNN) algorithm, PCINet, accurately identifies precipitation clouds using Fengyun-4A satellite data. This method enhances quantitative precipitation estimation and nowcasting, especially with visible/near-infrared spectral data during the day.

Keywords:
Fengyun-4Adeep learningnychthemeronprecipitation cloud identification

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Area of Science:

  • Meteorology and Atmospheric Science
  • Remote Sensing Technology
  • Artificial Intelligence in Earth Observation

Background:

  • Accurate precipitation cloud identification from satellite data is crucial for quantitative precipitation estimation and nowcasting.
  • Existing methods require improvement for comprehensive identification across different times of day and night.

Purpose of the Study:

  • To develop and evaluate a novel Convolutional Neural Network (CNN)-based algorithm (PCINet) for precipitation cloud identification.
  • To assess the contribution of visible/near-infrared spectral information to daytime precipitation cloud identification.
  • To compare PCINet's performance against other deep learning models for image segmentation.

Main Methods:

  • Utilized high spatiotemporal and multi-spectral data from the Fengyun-4A (FY-4A) satellite.
  • Developed a CNN architecture (PCINet) featuring a multi-scale structure and skip connection strategy.
  • Explored the impact of visible/near-infrared spectral bands for daytime identification.
  • Conducted comparative analysis with five other deep learning models using long-time series data and case studies of heavy precipitation events.

Main Results:

  • The proposed PCINet algorithm demonstrated superior performance compared to baseline deep learning models.
  • Incorporating visible/near-infrared spectral information significantly improved daytime precipitation cloud identification accuracy.
  • The model provided accurate and near-real-time identification of precipitation clouds.

Conclusions:

  • PCINet offers an effective solution for accurate precipitation cloud identification across day, night, and nychthemeron.
  • The integration of specific spectral bands enhances the model's capability for daytime analysis.
  • The algorithm has significant potential for operational applications in precipitation monitoring and forecasting.