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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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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Comparing storm resolving models and climates via unsupervised machine learning.

Griffin Mooers1, Mike Pritchard2,3, Tom Beucler4

  • 1Department of Earth System Science, University of California at Irvine, Irvine, CA, 92697, USA. gmooers96@gmail.com.

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New methods compare global storm-resolving models (GSRMs), finding only six of nine similar in atmospheric dynamics. This research aids objective evaluation of complex climate simulation data and the convective response to global warming.

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Area of Science:

  • Climate Science
  • Computational Fluid Dynamics
  • Data Science

Background:

  • Global storm-resolving models (GSRMs) offer unprecedented climate detail but lack objective comparison tools.
  • Quantifying differences in how GSRMs simulate complex atmospheric formations is challenging.
  • This limitation affects various fields relying on complex simulation data.

Purpose of the Study:

  • To develop objective methods for comparing similarities between GSRMs.
  • To enable automated, physically meaningful comparisons of high-resolution climate simulation data.
  • To assess the intercomparison of nine GSRMs and identify similarities in atmospheric dynamics.

Main Methods:

  • Utilized nonlinear dimensionality reduction and vector quantization to estimate distributional distances.
  • Developed an approach to learn similarity from low-dimensional latent data representations.
  • Applied methods to high-dimensional 2D vertical velocity snapshots from nine GSRMs.

Main Results:

  • Successfully intercompared nine GSRMs, revealing that only six exhibit similar atmospheric dynamics.
  • Uncovered signatures of the convective response to global warming in an unsupervised manner.
  • Demonstrated the effectiveness of the developed methods for objective model evaluation.

Conclusions:

  • The developed methods provide a robust framework for objectively evaluating and comparing GSRMs.
  • This approach facilitates a deeper understanding of model behavior and climate change impacts.
  • Paves the way for more reliable assessments of future high-resolution climate simulation data.