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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 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 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

591
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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Sampling Methods: Overview01:06

Sampling Methods: Overview

302
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Sampling Plans01:23

Sampling Plans

180
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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A Protocol for Conducting Rainfall Simulation to Study Soil Runoff
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Process-Informed Subsampling Improves Subseasonal Rainfall Forecasts in Central America.

Katherine M Kowal1, Louise J Slater1, Sihan Li2

  • 1Department of Geography and the Environment University of Oxford Oxford UK.

Geophysical Research Letters
|July 12, 2024
PubMed
Summary
This summary is machine-generated.

Improving monthly rainfall forecasts in Central America, this study uses a process-informed evaluation. By subsampling climate model members based on wind and sea surface temperature (SST) predictors, forecast skill for rainfall extremes is enhanced.

Keywords:
Central Americaensembleextreme weatherforecastrainfallsubseasonal

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Area of Science:

  • Climate Science
  • Meteorology
  • Hydrology

Background:

  • Subseasonal rainfall forecast skill is vital for managing hydrometeorological extremes.
  • Accurate monthly rainfall predictions are crucial for preparedness in Central America.

Purpose of the Study:

  • To assess if a process-informed evaluation can enhance monthly rainfall forecasts in Central America.
  • To improve the skill of rainfall forecasts by subsampling climate model members based on key predictors.

Main Methods:

  • A constrained ensemble mean was generated by subsampling 130 members from five dynamic forecasting models (C3S multimodel ensemble).
  • Subsampling was based on the models' representation of zonal wind direction and Pacific/Atlantic sea surface temperatures (SSTs) at initialization.
  • The evaluation focused on improving forecasts for Costa Rica and Guatemala.

Main Results:

  • Increased mean squared error skill by 0.4 was observed in multiple months and locations.
  • Improved detection rates for rainfall extremes were achieved.
  • The method demonstrated success in identifying model members that accurately represent rainfall distributions.

Conclusions:

  • Process-informed subsampling significantly enhances monthly rainfall forecast skill in Central America.
  • This method is transferable to other regions influenced by slowly-changing climate processes.
  • The approach effectively refines forecasts by accounting for errors in wind and SST predictors.