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Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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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).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

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Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
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Radiation Pressure: Problem Solving01:09

Radiation Pressure: Problem Solving

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The radiation pressure applied by an electromagnetic wave on a perfectly absorbing surface equals the energy density of the wave. The wave's momentum also gets transferred to the surface when an electromagnetic wave is entirely absorbed by it. The rate at which momentum is transmitted to an absorbing surface perpendicular to the propagation direction equals the force on the surface.
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Related Experiment Videos

Predicting atmospheric optical properties for radiative transfer computations using neural networks.

Menno A Veerman1, Robert Pincus2,3, Robin Stoffer1

  • 1Meteorology and Air Quality Group, Wageningen University and Research, Wageningen, The Netherlands.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|February 15, 2021
PubMed
Summary

Machine learning accelerates atmospheric radiative transfer calculations. Neural networks emulate radiation parametrizations, achieving high accuracy and up to four times greater speed than traditional methods.

Keywords:
atmosphereneural networksoptical propertiesradiative transfer

Related Experiment Videos

Area of Science:

  • Atmospheric physics and radiative transfer.
  • Computational modeling and machine learning applications.

Background:

  • Traditional radiation parametrizations in atmospheric models are computationally intensive.
  • Machine learning (ML) offers a promising approach to accelerate these complex calculations.

Purpose of the Study:

  • To develop and evaluate an ML-based parametrization for gaseous optical properties.
  • To emulate the performance of the Radiation Transfer for Gaseous Medium using a Modern-Effect Approximation (RRTMGP) scheme.
  • To assess the trade-off between computational speed and prediction accuracy.

Main Methods:

  • Trained neural networks (NNs) to emulate RRTMGP for gaseous optical properties.
  • Generated training data using randomly perturbed atmospheric profiles calculated with RRTMGP.
  • Optimized computational performance using machine-specific optimized BLAS functions and reduced applicability range for NNs.

Main Results:

  • The ML-based parametrization accurately predicts optical properties.
  • Resulting radiative fluxes show average errors within 0.5 W m⁻² compared to RRTMGP.
  • The NN-based gas optics parametrization is up to four times faster than RRTMGP.

Conclusions:

  • ML-based parametrization significantly speeds up radiative transfer computations.
  • High accuracy is maintained, making it a viable alternative for atmospheric modeling.
  • This approach contributes to advancements in machine learning for weather and climate modeling.