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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 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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Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments With Global AI-Based Weather Models.

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Geophysical Research Letters
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Summary

New AI weather models like GraphCast and Pangu-Weather show strong skill in predicting severe thunderstorm environments up to 10 days. These advanced artificial intelligence models match or surpass current operational forecasts for key severe weather parameters.

Keywords:
artificial intelligenceconvective environmentsforecastingmedium‐rangesevere convection

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Area of Science:

  • Meteorology
  • Artificial Intelligence
  • Atmospheric Science

Background:

  • Severe convective storms pose significant risks, necessitating accurate forecasting.
  • Current AI weather models offer rapid medium-range forecasts but require evaluation for predicting complex atmospheric conditions crucial for severe thunderstorms.

Purpose of the Study:

  • To assess the forecast skill of leading AI weather models (GraphCast, Pangu-Weather, FourCastNet) for severe thunderstorm environments.
  • To compare AI model performance against established numerical weather prediction systems for convective parameters.

Main Methods:

  • Evaluated GraphCast, Pangu-Weather, and FourCastNet for convective parameters up to 10-day lead times.
  • Compared AI model forecasts against reanalysis data and the ECMWF's IFS operational model.
  • Conducted case studies and seasonal analyses to determine forecast accuracy.

Main Results:

  • GraphCast and Pangu-Weather demonstrated the highest performance among the assessed AI models.
  • These AI models matched or exceeded the performance of the IFS model for atmospheric instability and shear.
  • AI models provide accurate predictions of crucial variables for severe weather environments.

Conclusions:

  • AI weather models show significant potential for improving severe weather forecasting.
  • GraphCast and Pangu-Weather offer a promising avenue for fast, cost-effective severe weather environment predictions.
  • Further process-based evaluations of AI models are foundational for hazard-driven applications.