[PDF][PDF] Hybrid particle-ensemble Kalman filter for Lagrangian data assimilation
A Apte - 2020 - ncmrwf.gov.in
2020•ncmrwf.gov.in
Summary▶ Particle filtering and Kalman filtering are two complementary data assimilation
methods which are▶ ineffective in high dimensional and nonlinear problems, respectively,
but▶ effective in nonlinear problems and high dimensions, respectively.▶ Lagrangian data
assimilation is the problem of using data from Lagrangian/passive instruments (eg drifters
and gliders)▶ Hybrid particle-Kalman filter that I will discuss combines the strengths of both,
for the Lagrangian data assimilation problem.
methods which are▶ ineffective in high dimensional and nonlinear problems, respectively,
but▶ effective in nonlinear problems and high dimensions, respectively.▶ Lagrangian data
assimilation is the problem of using data from Lagrangian/passive instruments (eg drifters
and gliders)▶ Hybrid particle-Kalman filter that I will discuss combines the strengths of both,
for the Lagrangian data assimilation problem.
Summary
▶ Particle filtering and Kalman filtering are two complementary data assimilation methods which are
▶ ineffective in high dimensional and nonlinear problems, respectively, but▶ effective in nonlinear problems and high dimensions, respectively.▶ Lagrangian data assimilation is the problem of using data from Lagrangian/passive instruments (eg drifters and gliders)▶ Hybrid particle-Kalman filter that I will discuss combines the strengths of both, for the Lagrangian data assimilation problem.
ncmrwf.gov.in
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