Interpolating Irregularly Spaced Observations for Filtering Turbulent Complex Systems

DOI10.1137/100800427zbMath1232.93084OpenAlexW2088221162WikidataQ57437206 ScholiaQ57437206MaRDI QIDQ3116451

John Harlim

Publication date: 23 February 2012

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/2c51788de5b63beef5da25dd6b5df386e72a5c4e

zbMATH Keywords

Kalman filterdata assimilationinterpolationsirregularly spaced observation

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Numerical computation using splines (65D07) Filtering in stochastic control theory (93E11) Probabilistic models, generic numerical methods in probability and statistics (65C20) Sequential estimation (62L12)

Related Items (2)

Dynamic Stochastic Superresolution of sparsely observed turbulent systems ⋮ Filtering nonlinear spatio-temporal chaos with autoregressive linear stochastic models

Uses Software

• EnKF

This page was built for publication: Interpolating Irregularly Spaced Observations for Filtering Turbulent Complex Systems