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Related Concept Videos

Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

4.8K
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...
4.8K
Precipitation Processes01:12

Precipitation Processes

5.0K
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...
5.0K
Precipitation Gravimetry01:03

Precipitation Gravimetry

12.8K
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...
12.8K
Types of Coprecipitation01:10

Types of Coprecipitation

5.5K
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...
5.5K
Precipitation of Ions03:11

Precipitation of Ions

25.4K
Predicting Precipitation
The equation that describes the equilibrium between solid calcium carbonate and its solvated ions is:
25.4K
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

5.1K
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...
5.1K

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Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
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Multi-Scale Fourier Temporal Network for Multi-Source Precipitation Nowcasting.

Jing Huang1, Shanmin Yang1, Xiaojie Li1

  • 1School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

A new Multi-Scale Frequency-Temporal Network (MS-FTNet) improves precipitation nowcasting by analyzing frequency domains. This advanced deep learning model enhances accuracy for heavy rainfall events and longer forecast periods.

Keywords:
frequency-domain representationmulti-scale modelingprecipitation nowcastingradar-satellite fusion

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Area of Science:

  • Meteorology
  • Hydrology
  • Computer Science

Background:

  • Accurate precipitation nowcasting is crucial for disaster prevention and hydrometeorological applications.
  • Current deep learning models struggle with complex precipitation dynamics and multi-source data integration.

Purpose of the Study:

  • To develop an advanced deep learning framework for improved precipitation nowcasting.
  • To address limitations in existing models regarding multi-source observations and physical representations.

Main Methods:

  • Proposed a Multi-Scale Frequency-Temporal Network (MS-FTNet) utilizing Fourier transform for frequency-domain modeling.
  • Decomposed precipitation dynamics into low-frequency (stratiform) and high-frequency (convective) components.
  • Introduced Global Feature Collaboration (GFC) and Adaptive Temporal Fusion (ATF) modules for enhanced feature integration and temporal modeling.

Main Results:

  • MS-FTNet demonstrated superior performance over baseline models on the SEVIR dataset.
  • Significant improvements were observed in Mean Squared Error (MSE), Critical Success Index (CSI), and Learned Perceptual Image Patch Similarity (LPIPS).
  • The model excelled particularly in nowcasting heavy precipitation events and for longer forecast lead times.

Conclusions:

  • The MS-FTNet framework offers a novel and effective approach to precipitation nowcasting.
  • Frequency-domain analysis combined with advanced deep learning modules enhances the prediction of complex precipitation patterns.
  • The proposed method shows promise for operational hydrometeorological forecasting and disaster management.