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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

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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.
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Predicting Precipitation
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Magnetostatic Boundary Conditions01:28

Magnetostatic Boundary Conditions

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An electric field suffers a discontinuity at a surface charge. Similarly, a magnetic field is discontinuous at a surface current. The perpendicular component of a magnetic field is continuous across the interface of two magnetic mediums. In contrast, its parallel component, perpendicular to the current, is discontinuous by the amount equal to the product of the vacuum permeability and the surface current. Like the scalar potential in electrostatics, the vector potential is also continuous...
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When a fluid encounters a solid surface, a boundary layer forms due to the interaction between the fluid's motion and the stationary surface. This phenomenon is characterized by a thin region adjacent to the surface where viscous forces dominate, influencing the fluid's velocity profile. The development of the boundary layer begins at the leading edge of the surface and evolves as the fluid moves downstream.As the fluid flows over the surface, friction between the fluid and the wall slows down...
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Four-hour thunderstorm nowcasting using a deep diffusion model for satellite data.

Kuai Dai1,2, Xutao Li1, Junying Fang3

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China.

Proceedings of the National Academy of Sciences of the United States of America
|December 16, 2025
PubMed
Summary
This summary is machine-generated.

A new AI system uses deep diffusion models and satellite data for advanced thunderstorm nowcasting. This technology provides accurate, high-resolution forecasts up to 4 hours ahead, improving disaster preparedness.

Keywords:
AIconvection nowcastinggeostationary satellite

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Area of Science:

  • Meteorology
  • Artificial Intelligence
  • Satellite Remote Sensing

Background:

  • Convective thunderstorms develop rapidly, causing significant destruction and posing challenges for accurate, timely forecasting (nowcasting).
  • Existing AI methods for nowcasting show promise but often lack sufficient lead time and coverage for effective disaster response.
  • Physics-based numerical weather prediction models struggle to keep pace with the rapid evolution of convective systems.

Purpose of the Study:

  • To develop an AI-based convection nowcasting system using deep diffusion models for enhanced prediction accuracy and lead time.
  • To leverage geostationary satellite data and meteorological expertise for broad-scale, accurate forecasting of convective cloud development and dissipation.
  • To establish a new benchmark in AI-driven nowcasting performance for severe weather events.

Main Methods:

  • Proposed a deep diffusion model for satellite data (DDMS) to simulate complex spatiotemporal patterns of convective clouds.
  • Integrated geostationary satellite brightness temperature data with meteorological expert knowledge.
  • Validated the system using FengYun-4A satellite data over an extended period.

Main Results:

  • Achieved effective convection nowcasting up to 4 hours with broad coverage (20,000,000 km²).
  • Demonstrated high accuracy and resolution (15 min; 4 km) in forecasting convective growth and dissipation.
  • The DDMS system outperformed existing convection nowcasting models in long-term tests and objective validation.

Conclusions:

  • The proposed deep diffusion model significantly advances AI-based convection nowcasting capabilities, offering improved lead time and coverage.
  • The system highlights the potential of diffusion models in predicting convective clouds and the value of AI-enhanced satellite data.
  • The developed system is transferable and can be adapted for global nowcasting by integrating data from multiple satellites.