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Related Concept Videos

Boundary Layer Characteristics01:18

Boundary Layer Characteristics

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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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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 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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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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Uniform Depth Channel Flow: Problem Solving01:18

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Deep Learning Based Cloud Cover Parameterization for ICON.

Arthur Grundner1,2, Tom Beucler3, Pierre Gentine2

  • 1Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) Institut für Physik der Atmosphäre Oberpfaffenhofen Germany.

Journal of Advances in Modeling Earth Systems
|April 10, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning models accurately estimate cloud cover using storm-resolving model data. Neighborhood-based neural networks offer a balance of accuracy and generalizability for climate projections.

Keywords:
SHAPcloud coverexplainable AImachine learningneural networkparameterization

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Area of Science:

  • Climate Science
  • Atmospheric Physics
  • Machine Learning

Background:

  • Improving climate model parameterizations is crucial for accurate climate projections.
  • Storm-resolving models (SRMs) provide high-fidelity data for training.

Purpose of the Study:

  • To develop and evaluate deep learning-based cloud cover parameterizations for climate models.
  • To assess the generalizability of neural networks trained on SRM data.

Main Methods:

  • Utilized the ICOsahedral Non-hydrostatic (ICON) modeling framework.
  • Trained neural networks (NNs) on coarse-grained data from ICON SRMs.
  • Employed SHapley Additive exPlanations for model interpretability.

Main Results:

  • NNs accurately estimated sub-grid scale cloud cover.
  • Globally trained NNs showed good performance on regional data.
  • Identified overemphasis on specific humidity and cloud ice as generalization limitations.

Conclusions:

  • Deep learning can derive accurate and interpretable cloud cover parameterizations.
  • Neighborhood-based NNs present a promising compromise between accuracy and generalizability.
  • Interpretability tools aid in understanding NN behavior and improving climate models.