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Related Concept Videos

Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

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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...
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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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Types of Coprecipitation01:10

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.
Sometimes, ions in a crystal lattice can undergo isomorphous replacement by inclusions of similar charge and size. For...
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Precipitate Formation and Particle Size Control01:16

Precipitate Formation and Particle Size Control

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In precipitation gravimetry, the precipitating agent should react specifically or selectively with the analyte. While a specific reagent reacts with the analyte alone, a selective reagent can react with a limited number of chemical species.
The obtained precipitate should be either a pure substance of known composition or easily converted to one by a simple process, such as ignition or drying. In addition, the precipitate should be insoluble and easily filterable. In general, filterability...
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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...
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Flash NanoPrecipitation for the Encapsulation of Hydrophobic and Hydrophilic Compounds in Polymeric Nanoparticles
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Copula based post-processing for improving the NMME precipitation forecasts.

Farhad Yazdandoost1, Mina Zakipour1, Ardalan Izadi2

  • 1Department of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran.

Heliyon
|September 10, 2021
PubMed
Summary

This study enhances precipitation forecasts using a Bayesian Copula method, improving water resource management for drought-prone regions. The new approach refines data selection and statistical distribution for more accurate rainfall predictions.

Keywords:
CopulaNMMEPost-processingPrecipitation forecastSemi-parametric distribution

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Area of Science:

  • Hydrology and Water Resources
  • Statistical Modeling
  • Climate Science

Background:

  • Reliable precipitation forecasts are crucial for water resource management, especially in drought-affected areas.
  • The North American Multi-Model Ensemble (NMME) is a widely used tool for precipitation forecasting.
  • Existing methods for improving NMME forecasts have limitations in selecting appropriate statistical distributions and data.

Purpose of the Study:

  • To improve monthly or seasonal precipitation forecasts from the North American Multi-Model Ensemble (NMME) using a Bayesian Copula-based method.
  • To introduce innovative approaches for selecting statistical distributions and improved forecast data.
  • To evaluate the proposed methods in the Sistan and Baluchestan province, Iran.

Main Methods:

  • Applied a Bayesian method utilizing Copula functions to enhance NMME precipitation forecasts.
  • Investigated parametric (Exponential, Normal, Gamma, LogNormal, GEV) and non-parametric (Standard Normal Kernel) distributions.
  • Developed a novel mixed distribution combining GEV and Standard Normal Kernel, and a new data selection method based on the center of mass of conditional probability distribution functions (CPDF).

Main Results:

  • Non-parametric distributions, particularly with optimized bandwidth, showed strong performance for time-series precipitation data.
  • A semi-parametric approach using the General Extreme Value (GEV) distribution improved boundary condition estimation and overall forecast performance.
  • The center of mass method for data selection significantly outperformed the maximum likelihood method in improving forecast accuracy.

Conclusions:

  • The proposed Bayesian Copula method with a mixed GEV-Standard Normal Kernel distribution and center of mass data selection effectively improves NMME precipitation forecasts.
  • The approach offers a valuable tool for water resource management, particularly in regions facing drought.
  • The study highlights the importance of appropriate statistical distribution selection and data refinement for accurate climate predictions.