Starts 3 Jun 2021 15:00
Ends 3 Jun 2021 16:30
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Stochastic models, an effective tool for simulating atmospheric dispersion and mixing
Zoom Meeting
Lecture 13 of the series:
Environmental Meteorology: From the Fundamentals of Climate to Operational Applications
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Abstract: Turbulence in the atmosphere is typically described as random and unpredictable and requires statistical approaches. In this spirit many processes driven by turbulence can be successfully and efficiently modelled using stochastic approaches. Similarly, but in a wider sense, sub-grid scale dispersion and mixing processes that are not explicitly resolved by atmospheric flow simulations can be often modelled by using stochastic approaches for the considered spatial and temporal scales. I will discuss the formulation and application of simple stochastic models for the simulation of processes related to atmospheric dispersion of gases and particles, with spatial scales ranging from few meters (from an emitting local scalar source) to global.
Environmental Meteorology: From the Fundamentals of Climate to Operational Applications
Register in advance for this meeting:
https://zoom.us/meeting/register/tJ0lcuyurDMrEtbfP9f16GRLzqF1AiT7PHN7
After registering, you will receive a confirmation email containing information about joining the meeting
Abstract: Turbulence in the atmosphere is typically described as random and unpredictable and requires statistical approaches. In this spirit many processes driven by turbulence can be successfully and efficiently modelled using stochastic approaches. Similarly, but in a wider sense, sub-grid scale dispersion and mixing processes that are not explicitly resolved by atmospheric flow simulations can be often modelled by using stochastic approaches for the considered spatial and temporal scales. I will discuss the formulation and application of simple stochastic models for the simulation of processes related to atmospheric dispersion of gases and particles, with spatial scales ranging from few meters (from an emitting local scalar source) to global.