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Variation of Atmospheric Pressure01:18

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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.
Assuming the air temperature is constant at a given altitude and that the ideal gas law of thermodynamics describes the atmosphere to a good approximation, one can find the variation of atmospheric pressure with height.
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When an ideal gas is compressed adiabatically, that is, without adding heat, work is done on it, and its temperature increases. In an adiabatic expansion, the gas does work, and its temperature drops. Adiabatic compressions actually occur in the cylinders of a car, where the compressions of the gas-air mixture take place so quickly that there is no time for the mixture to exchange heat with its environment. Nevertheless, because work is done on the mixture during the compression, its...
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Free expansion of a gas is an adiabatic process. However, there are few differences between free expansion and adiabatic expansion. During free expansion, no work is done, and there is no change in internal energy. But, for an adiabatic expansion, work is done, and there is a change in internal energy. During an adiabatic process, the relation between the pressure and volume is obtained from the condition for the adiabatic process, that is, 
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A thermodynamic process that occurs at constant volume is called an isochoric process. According to the first law of thermodynamics, heat supplied or removed from the system is partially utilized to perform work and change the internal energy of the system. However, in an isochoric process, the volume remains constant. Hence, the work done by the system is zero. Therefore, the exchange of heat changes the internal energy of the system only. 
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Compressing atmospheric data into its real information content.

Milan Klöwer1, Miha Razinger2, Juan J Dominguez2

  • 1Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK. milan.kloewer@physics.ox.ac.uk.

Nature Computational Science
|January 13, 2024
PubMed
Summary
This summary is machine-generated.

Weather and climate data is highly compressible. By identifying and removing redundant information, scientists can achieve significant data compression, preserving crucial details for forecasting.

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Area of Science:

  • Meteorology and Climate Science
  • Information Theory
  • Data Science

Background:

  • Global weather and climate centers generate vast amounts of data annually (hundreds of petabytes).
  • Efficient data compression is crucial for storage and sharing of this climate data.
  • Existing compression methods do not differentiate between real and false information, leaving data precision unassessed.

Purpose of the Study:

  • To define and quantify bitwise real information content for Copernicus Atmospheric Monitoring Service (CAMS) data.
  • To develop optimized compression strategies for weather and climate data.
  • To propose a data compression Turing test for evaluating compressibility and information loss.

Main Methods:

  • Applied information theory to define real information content in CAMS data.
  • Analyzed spatio-temporal correlations to assess data compressibility.
  • Implemented lossless compression algorithms after rounding non-informative bits.
  • Evaluated compression ratios and information preservation.

Main Results:

  • Most CAMS variables contain fewer than 7 bits of real information per value.
  • Data compression factors of 17× were achieved relative to 64-bit floats, preserving 99% of real information.
  • Combined with four-dimensional compression, factors exceeding 60× were obtained.
  • Rounding non-informative bits facilitates lossless compression and encodes data uncertainty.

Conclusions:

  • Weather and climate data exhibit high compressibility due to inherent correlations.
  • A novel approach to data compression effectively reduces storage while retaining essential information.
  • The proposed compression Turing test can guide optimization for end-use applications in weather and climate forecasting.