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Precipitation and Co-precipitation

Precipitation and Co-precipitation

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...

Precipitation Processes

Precipitation Processes

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...

Precipitation Gravimetry

Precipitation Gravimetry

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...

Types of Coprecipitation

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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.
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Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Precipitation of Ions

Predicting Precipitation
The equation that describes the equilibrium between solid calcium carbonate and its solvated ions is:

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Related Experiment Video

Updated: Sep 17, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

STVMamba: precipitation nowcasting with spatiotemporal prediction model.

Maoyang Zou¹, Longrui Wen¹, Yuanyuan Huang¹

¹School of Artificial Intelligence (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu, 610225, China.

Scientific Reports

|July 2, 2025

Summary

A new Spatial-Temporal Vision Mamba (STVMamba) model offers efficient and accurate rainfall nowcasting by capturing long-range dependencies. It outperforms existing methods on diverse datasets for meteorological bureaus.

Keywords:

Precipitation nowcasting Spatiotemporal prediction Two-tier architecture Vision Mamba

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Area of Science:

Meteorology
Artificial Intelligence
Computer Vision

Background:

Lightweight rainfall nowcasting models are crucial for meteorological bureaus.
Existing deep learning models (recurrent, convolutional, Transformer) have limitations in efficiency and capturing long-range dependencies.

Purpose of the Study:

To introduce the Spatial-Temporal Vision Mamba (STVMamba), a novel model for efficient and accurate precipitation nowcasting.
To overcome the limitations of existing deep learning methods in rainfall prediction.

Main Methods:

Developed STVMamba, a spatiotemporal prediction model with high parallel computational efficiency and linear time complexity.
Utilized Spatial-Temporal Selective Scan (STSS) for global spatiotemporal relationships and Spatial-Temporal Depthwise Separable Convolution (STDSConv) for local relationships.
Employed a two-tier architecture to learn spatiotemporal relationships across multiple spatial scales.

Main Results:

STVMamba achieved state-of-the-art performance on the Sichuan radar echo dataset (MSE, SSIM, CSI-10).
Outperformed existing models on the HKO-7 radar echo dataset (MSE, CSI-10, CSI-20).
Demonstrated superior results on the IMERG satellite dataset (SSIM, CSI-0.5), showing robustness across different conditions.

Conclusions:

STVMamba effectively addresses the limitations of previous deep learning models for rainfall nowcasting.
The model demonstrates high efficiency, strong performance in capturing long-range dependencies, and robustness across diverse datasets.
STVMamba represents a significant advancement in precipitation nowcasting technology for meteorological applications.