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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

490
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...
490
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
Types of Coprecipitation01:10

Types of Coprecipitation

666
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...
666
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
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

1.9K
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...
1.9K

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Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
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LSTMAtU-Net: A Precipitation Nowcasting Model Based on ECSA Module.

Huantong Geng1,2, Xiaoyan Ge1, Boyang Xie1

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

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces LSTMAtU-Net for precipitation nowcasting, improving radar-based rainfall predictions. The new model enhances accuracy for medium and high-intensity precipitation by better preserving image details.

Keywords:
Convolutional LSTM (ConvLSTM)Efficient Channel and Space Attention (ECSA)U-Net architectureprecipitation nowcasting

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Area of Science:

  • Meteorology
  • Artificial Intelligence
  • Computer Vision

Background:

  • Precipitation nowcasting predicts rainfall in the next 0-2 hours using meteorological data.
  • Current deep learning methods lack physical constraints, leading to loss of detail in predicted rainfall images.

Purpose of the Study:

  • To propose a novel deep learning framework, LSTMAtU-Net, to improve precipitation nowcasting accuracy.
  • To address the loss of image details in current physically unconstrained deep learning models.

Main Methods:

  • Developed LSTMAtU-Net based on the U-Net architecture.
  • Incorporated a Convolutional LSTM (ConvLSTM) unit with vertical flow and depthwise-separable convolution.
  • Introduced an Efficient Channel and Space Attention (ECSA) module to enhance feature learning.

Main Results:

  • The LSTMAtU-Net model demonstrated superior performance compared to existing models on test datasets.
  • The model significantly improved the accuracy of medium- and high-intensity precipitation nowcasting.
  • The ECSA module enhanced attention to precipitation image details.

Conclusions:

  • LSTMAtU-Net effectively solves the detail loss problem in precipitation nowcasting.
  • The proposed model offers a physically informed and accurate approach to short-term rainfall prediction.