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Related Concept Videos

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.
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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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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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The Doppler effect has several practical, real-world applications. For instance, meteorologists use Doppler radars to interpret weather events based on the Doppler effect. Typically, a transmitter emits radio waves at a specific frequency toward the sky from a weather station. The radio waves bounce off the clouds and precipitation and travel back to the weather station. The radio frequency of the waves reflected back to the station appears to decrease if the clouds or precipitation are moving...
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Precipitation Titration: Endpoint Detection Methods01:19

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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.
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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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Related Experiment Video

Updated: Sep 18, 2025

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
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Improving Doppler Radar Precipitation Prediction with Citizen Science Rain Gauges and Deep Learning.

Marshall Rosenhoover1, John Rushing1, John Beck1

  • 1Information Technology and Systems Center, University of Alabama in Huntsville, Huntsville, AL 35899, USA.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
Summary

Citizen science rain gauges improve Doppler radar rainfall estimates using deep learning. This framework corrects biases, enhancing real-time precipitation forecasting accuracy, especially in complex terrains.

Keywords:
citizen sciencedeep learningradar calibrationradar precipitation estimationradar-rain gauge rainfall accumulationrainfall accumulationreal-time weather prediction

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Area of Science:

  • Hydrology
  • Meteorology
  • Data Science

Background:

  • Doppler radar rainfall estimation is challenging in complex terrain due to biases from vertical beam sampling, atmospheric effects, and radar quality.
  • Accurate, real-time precipitation data is crucial for operational forecasting and hydrological applications.

Purpose of the Study:

  • To develop a deep learning framework for correcting biases in radar-derived surface precipitation rates using citizen science rain gauge data.
  • To improve the accuracy of real-time rainfall estimation, particularly in challenging geographical areas.

Main Methods:

  • Citizen science rain gauge data was validated using a two-stage temporal and spatial consistency filter.
  • Piecewise-linear rainfall accumulation functions were created to align gauge and radar data, generating high-quality instantaneous rain rate labels.
  • An adapted ResNet-101 deep learning model was trained to classify rainfall intensity from radar precipitation rate sequences.

Main Results:

  • The deep learning model significantly improved precipitation classification accuracy compared to NOAA's operational radar products, showing gains in precision, recall, and F1 score.
  • The framework demonstrated effectiveness in correcting biases for real-time radar-based rainfall estimation.
  • Generalization to unseen regions was more challenging, especially for high-intensity rainfall, though modest improvements were seen for low-intensity rainfall.

Conclusions:

  • Combining citizen science observations with physically informed deep learning significantly enhances real-time radar rainfall estimation.
  • The developed framework offers a promising approach for improving operational weather forecasting in complex terrain.
  • This study underscores the value of integrating diverse data sources and advanced computational methods in meteorological research.