A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF
A particle filter (PF) is an ensemble data assimilation method that does not assume
Gaussian error distributions. Recent studies proposed local PFs (LPFs), which use …
Gaussian error distributions. Recent studies proposed local PFs (LPFs), which use …
Implementing hybrid background error covariance into the LETKF with attenuation-based localization: Experiments with a simplified AGCM
Recent numerical weather prediction systems have significantly improved medium-range
forecasts by implementing hybrid background error covariance, for which climatological …
forecasts by implementing hybrid background error covariance, for which climatological …
Revisiting online and offline data assimilation comparison for paleoclimate reconstruction: An idealized OSSE study
Data assimilation (DA) has been applied to estimate the time‐mean state, such as annual
mean surface temperature for paleoclimate reconstruction. There are two types of DA for this …
mean surface temperature for paleoclimate reconstruction. There are two types of DA for this …
Estimation of CH emission based on an advanced 4D-LETKF assimilation system
Methane (CH 4) is the second major greenhouse gas after carbon dioxide (CO 2) which has
substantially increased during recent decades in the atmosphere, raising serious …
substantially increased during recent decades in the atmosphere, raising serious …
Empirical determination of the covariance of forecast errors: An empirical justification and reformulation of hybrid covariance models
During the last decade, the replacement of static climatological forecast error covariance
models with hybrid error covariance models that linearly combine localised ensemble …
models with hybrid error covariance models that linearly combine localised ensemble …