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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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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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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.
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
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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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Area of Science:

  • Climate Science
  • Atmospheric Physics
  • Machine Learning Applications

Background:

  • Cloud parameterizations are a major source of uncertainty in climate projections.
  • Existing data-driven machine learning approaches for Earth system models (ESMs) often lack interpretability and physical consistency.

Purpose of the Study:

  • To develop and implement a physically consistent, interpretable machine-learning parameterization for cloud cover in a global atmospheric model.
  • To improve the accuracy and reduce biases in climate projections by enhancing ESMs.

Main Methods:

  • Incorporated a physically consistent cloud cover parameterization derived from storm-resolving simulations using symbolic regression into the ICON global atmospheric model.
  • Applied the Nelder-Mead optimizer for automatic recalibration of the hybrid model against Earth observations in nested stages.

Main Results:

  • The hybrid model significantly reduced biases in cloud cover, with a 75% reduction over the Southern Ocean and 44% in subtropical stratocumulus regions.
  • The improved model demonstrated robustness under +4K surface warming conditions.

Conclusions:

  • Interpretable machine-learned parameterizations, combined with practical tuning methods, can effectively enhance the fidelity of Earth system models.
  • This approach offers a transparent and efficient way to strengthen climate projections.