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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...
413
Variation of Atmospheric Pressure01:18

Variation of Atmospheric Pressure

2.0K
Change in atmospheric pressure with height is particularly interesting. The decrease in atmospheric pressure with increasing altitude is due to the decreasing gravitational force per unit area as we move away from the surface of the earth.
Assuming the air temperature is constant at a given altitude and that the ideal gas law of thermodynamics describes the atmosphere to a good approximation, one can find the variation of atmospheric pressure with height.
Let p(y) be the atmospheric pressure at...
2.0K
Precipitation Gravimetry01:03

Precipitation Gravimetry

5.2K
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...
5.2K
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.7K
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...
1.7K
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

1.6K
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...
1.6K
Vapor Pressure of Fluid01:28

Vapor Pressure of Fluid

957
The vapor pressure of a fluid is a crucial concept in fluid mechanics, influencing phenomena such as boiling and cavitation. Vapor pressure refers to the pressure exerted by a vapor at a state of thermodynamic equilibrium with its corresponding liquid phase at a specific temperature. It represents the tendency of molecules to escape from the fluid surface into the vapor phase.
When a liquid is placed in a closed container with a small air space, and the space is evacuated, vapor molecules will...
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Establishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on

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Summary

A new deep learning model accurately estimates precipitable water vapor (PWV) without needing weighted mean temperature (Tm). This advanced model improves accuracy and shows strong correlation with extreme weather events like typhoons.

Keywords:
accuracy assessmentatmospheric precipitable waterground-based GNSSrandom forest algorithmspatial and temporal characteristics of water vapor

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Area of Science:

  • Atmospheric Science
  • Geophysics
  • Remote Sensing

Background:

  • Precipitable water vapor (PWV) is crucial for understanding atmospheric water content.
  • Ground-based GNSS offers high-resolution PWV but is limited by the Tm parameter's accuracy.
  • Existing models often rely on weighted mean temperature (Tm), impacting PWV inversion precision.

Purpose of the Study:

  • Develop a novel deep learning model for PWV inversion that eliminates the need for the Tm parameter.
  • Enhance the accuracy and reliability of PWV estimations using GNSS data.
  • Investigate the model's performance in capturing extreme weather events.

Main Methods:

  • Utilized data from 17 ground-based GNSS stations and reanalysis products in Hong Kong.
  • Developed a deep learning model for PWV retrieval, excluding the Tm parameter.
  • Validated the model against traditional methods and radiosonde data.
  • Analyzed PWV variations during a typhoon-induced rainstorm event.

Main Results:

  • The new model demonstrated superior accuracy over traditional Tm-dependent models, with average improvements of 38% in BIAS, MAE, and RMSE.
  • Radiosonde validation confirmed the model's high accuracy, showing a BIAS of only 0.8 mm.
  • The model exhibited comparable accuracy to LSTM but with greater universality.
  • PWV retrieved by the new model showed a sharp increase during a typhoon and subsequent decrease, correlating strongly with extreme rainfall.

Conclusions:

  • The developed deep learning model provides a more accurate and reliable method for PWV inversion.
  • The model effectively captures the dynamic changes in atmospheric water vapor during extreme weather events.
  • This approach offers a valuable tool for meteorological monitoring and enhances weather forecasting capabilities.