A Multi-Timesegle EnOI-Iike High-Efficiency Approximate Filer for Coupled Model Data Assimilation
Academic Article
Overview
Research
Identity
Additional Document Info
View All
Overview
abstract
AbstractBecause it uses a set of model integrations to simulate the temporally varying background probability distribution function and implement Bayes' theorem, the ensemble Kalman filter (EnKF), which produces an optimal data assimilation solution that coherently combines model dynamics and observational information, has been widely used in weather and climate studies. However, in practice, the EnKF has two limitations: (1) the insufficient representation of error statistics of lowfrequency background flows due to its finite ensemble size and model integration over time and (2) the high demand of computational power for ensemble model integrations in highresolution coupled Earth system models. Given that background error statistics consist of stationary, slowvarying, and fastvarying parts, a multitimescale, highefficiency approximate EnKF (MSHeaEnKF) is designed to increase the representation of lowfrequency background error statistics and enhance its computational efficiency. The MSHeaEnKF is a combination of multitimescale filters implemented by regressions based on data sampled from the time series of a singlemodel solution. Validation shows that with the improved representation of stationary and slowvarying background statistics, the MSHeaEnKF only requires a small fraction of computer resources and shows a comparable performance relative to a finite size EnKF. Our experiments also show that the result can be further improved by using a small set of MSHeaEnKFs through secondstage EnKF filtering if sufficient computer resources are available. This new algorithm makes it practical to assimilate multisource observations into any highresolution coupled Earth system model that is intractable with current computing power for weatherclimate analysis and predictions.
