The purpose of this lecture is to demonstrate ensemble DA for multiscale systems. Using a 2D vortex model as an example, we will explore how the EnKF perform under various scenarios and investigate several multiscale algorithms and compare them to the traditional single-scale method.
Recorded video for this lecture (first 10 minutes of the lecture is missing)
Data assimilation seeks to find the best estimate of the state of a dynamical system and its uncertainty by combining information from model forecasts and observations. It has been adopted for state and parameter estimation for a wide range of dynamical systems across many disciplines such as weather, ocean, land, water, air quality, climate, ecosystem and astrophysics. This course covers various data assimilation approaches such as variational, ensemble-based, hybrid and nonlinear methods.
This five-lecture series was designed to help first-year graduate students in the Department of Atmospheric and Oceanic Sciences build their research skills, including programming and scientific data visualization. I've lost all materials after several relocations, now only a raw schedule and descriptions (in Chinese) of the lectures can be found here: PKUAOS Survival Kits.