Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Path Between Thermodynamics States01:21

Path Between Thermodynamics States

Consider the two thermodynamic processes involving an ideal gas that are represented by paths AC and ABC in Figure 1:
Differential Form of Maxwell's Equations01:17

Differential Form of Maxwell's Equations

James Clerk Maxwell (1831–1879) was one of the significant contributors to physics in the nineteenth century. He is probably best known for having combined existing knowledge of the laws of electricity and the laws of magnetism with his insights to form a complete overarching electromagnetic theory, represented by Maxwell's equations. The four basic laws of electricity and magnetism were discovered experimentally through the work of physicists such as Oersted, Coulomb, Gauss, and Faraday.
What is Climate?01:16

What is Climate?

Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.
Pressure and Volume in an Adiabatic Process01:27

Pressure and Volume in an Adiabatic Process

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,
The Clausius–Clapeyron Equation01:29

The Clausius–Clapeyron Equation

The Clausius-Clapeyron equation is a fundamental principle in physical chemistry and thermodynamics that describes the relationship between a substance's vapor pressure and temperature. Named after Rudolf Clausius and Benoît Paul Émile Clapeyron, the equation is integral in predicting a substance's behavior under different temperature conditions.The Clausius-Clapeyron equation allows us to calculate how the pressure at which a liquid boils (its vapor pressure) changes as the temperature changes.
Adiabatic Processes for an Ideal Gas01:18

Adiabatic Processes for an Ideal Gas

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Long-term remission and monocyclic course in Still's disease patients starting canakinumab early: data from the international AIDA network registry.

Seminars in arthritis and rheumatism·2026
Same author

Risk of intestinal involvement in mucocutaneous-onset Behçet's disease: data from the AIDA network registry.

Frontiers in immunology·2026
Same author

Application of machine learning techniques to explore the occurrence of macrophage activation syndrome in Still's disease: results from the GIRRCS AOSD Study Group and the AIDA Network Still's Disease Registry.

Frontiers in immunology·2026
Same author

Risk of major organ involvement in Behçet's patients with mucocutaneous onset: data from the AIDA Network Registry.

Rheumatology (Oxford, England)·2025
Same author

Reproductive Life Stages and Female Sex-Specific Patterns in Uveitis Activity: Data From the AIDA Network Uveitis Registry.

American journal of ophthalmology·2025
Same author

The Risk of Developing Ocular Involvement Among Behçet's Disease Patients Presenting with Mucocutaneous Involvement at Disease Onset: Data from the International AIDA Network Behçet's Disease Registry.

Ophthalmology and therapy·2025

Related Experiment Video

Updated: May 9, 2026

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
13:27

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

The path integral formulation of climate dynamics.

Antonio Navarra1, Joe Tribbia, Giovanni Conti

  • 1Centro Euromediterraneo sui Cambiamenti Climatici, Bologna, Italy. antonio.navarra@cmcc.it

Plos One
|July 11, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel functional integral method to directly model probability evolution in atmospheric dynamics. This approach offers a formal solution to the Fokker-Planck equation, enhancing weather and climate predictions.

More Related Videos

Using Generative Art to Convey Past and Future Climate Transitions
06:10

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Related Experiment Videos

Last Updated: May 9, 2026

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
13:27

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

Using Generative Art to Convey Past and Future Climate Transitions
06:10

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Area of Science:

  • Atmospheric dynamics
  • Statistical physics
  • Quantum mechanics

Background:

  • Atmospheric and oceanic dynamics are chaotic, necessitating statistical approaches.
  • Ensemble systems improve weather prediction by sampling initial conditions and enabling probabilistic forecasts.
  • Current methods focus on probability distributions rather than directly modeling their evolution.

Purpose of the Study:

  • To develop a direct modeling approach for probability evolution in dynamic systems.
  • To apply functional integral methods, inspired by quantum mechanics, to atmospheric science.
  • To provide a generalized framework for modeling probability evolution with various noise types and equations.

Main Methods:

  • Utilizing functional integral formulation, analogous to Feynman's approach in quantum mechanics.
  • Applying methods from statistical physics to derive a formal solution to the Fokker-Planck equation.
  • Developing a generalized framework applicable to Langevin equations with noise, delayed differential equations, and partial differential equations.

Main Results:

  • A formal solution to the Fokker-Planck equation for noisy Langevin-like equations is obtained.
  • The functional integral method is shown to be generalizable to various complex systems, including those with red noise and field equations.
  • The approach provides a robust framework for modeling the evolution of probability distributions.

Conclusions:

  • Direct modeling of probability evolution is feasible using functional integrals.
  • This method offers a powerful new tool for understanding and predicting chaotic systems in meteorology and oceanography.
  • The framework is adaptable to complex models like general circulation models with noise and specific phenomena like ENSO.