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Related Concept Videos

Precipitation Processes01:12

Precipitation Processes

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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...
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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...
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Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

4.0K
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...
4.0K
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

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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...
4.7K
Types of Coprecipitation01:10

Types of Coprecipitation

5.0K
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...
5.0K
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

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A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
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A Two-Step Machine Learning Approach Integrating GNSS-Derived PWV for Improved Precipitation Forecasting.

Laura Profetto1, Andrea Antonini2, Luca Fibbi2,3

  • 1Dipartimento Ingegneria dell'Informazione e Scienze Matematiche (DIISM), Universitá di Siena, 53100 Siena, Italy.

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|October 28, 2025
PubMed
Summary

Global Navigation Satellite System (GNSS) meteorology enhances precipitation nowcasting using Precipitable Water Vapor (PWV). A novel machine learning approach improves short-term weather forecasts, especially during extreme events.

Keywords:
GNSS meteorologyPrecipitable Water Vapor (PWV)machine learningprecipitation nowcastingshort-term weather forecasting

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Area of Science:

  • Atmospheric Science
  • Meteorology
  • Geophysics

Background:

  • Global Navigation Satellite System (GNSS) meteorology offers high-resolution, near-real-time atmospheric data.
  • Precipitable Water Vapor (PWV) derived from GNSS is a crucial indicator of atmospheric moisture.
  • Accurate short-term precipitation forecasting is vital for various applications.

Purpose of the Study:

  • To enhance short-term precipitation forecasting by integrating GNSS-derived PWV with traditional meteorological data.
  • To develop and evaluate a novel two-step machine learning framework for precipitation nowcasting.
  • To improve the accuracy of precipitation forecasts, particularly during extreme weather events.

Main Methods:

  • A hybrid machine learning framework combining Random Forest (RF) and Long Short-Term Memory (LSTM) neural networks was developed.
  • The RF model estimated current precipitation using PWV, surface weather, and auxiliary atmospheric variables.
  • The LSTM network predicted subsequent hour precipitation by analyzing temporal data dependencies.

Main Results:

  • The proposed hybrid RF-LSTM model demonstrated improved precipitation forecasting accuracy.
  • The model showed particular effectiveness in predicting extreme weather events like intense rainfall and thunderstorms.
  • Performance surpassed conventional precipitation nowcasting models.

Conclusions:

  • Integrating GNSS meteorology with advanced machine learning provides a powerful tool for precipitation nowcasting.
  • The developed framework offers a promising solution for meteorological services, early warning systems, and disaster risk management.
  • GNSS-based nowcasting has significant potential for real-time decision-making in weather-sensitive applications.