PDAF includes a selection of commonly used filter algorithms. All filters are optimized and parallelized. The filter algorithms, which are currently included in PDAF are:
- EnKF / LEnKF (Ensemble Kalman Filter / local EnKF)
- SEEK (Singular "Evolutive" Extended Kalman) filter
- SEIK / LSEIK (Singular "Evolutive" Interpolated Kalman filter / local SEIK
- ETKF / LETKF (Ensemble Transform Kalman filter / local LETKF)
- ESTKF / LESTKF (Error Subspace Transform Kalman filter / local ESTKF)
- NETF / LNETF (Nonlinear Ensemble Transform filter / local NETF)
- PF (Particle filter with importance resmapling)
All filters, except SEEK and PF, are provided with and without localization for optimal compute performance in global and localized applications.
The first three filters (EnKF, SEEK, SEIK) are described and compared in Nerger et al. (2005a), while the local SEIK filter is described in Nerger et al. (2006). The ETKF and SEIK filters have been examined in Nerger et al. (2012b), where also the new ESTKF was introduced. The NETF has been described in Tödter et al. (2016).
Next to the filter algorithms, the following smoothers are available:
- EnKS (Ensemble Kalman Smoother)
- ETKS (Ensemble Transform Kalman Smoother)
- LETKS (Local Ensemble Transform Kalman Smoother)
- ESTKS (Error Subspace Transform Kalman Smoother)
- LESTKS (Local Error Subspace Transform Kalman Smoother)
- LNETS (Local Nonlinear Ensemble Transform Smoother)
The smoother extension was described in Nerger et al. (2014) where also the influence of nonlinearity on the smoothing was studied. The LNETS was studied in Kirchgessner et al. (2017).
A general overview of PDAF is provided in Nerger et al. (2005b) and the implementation strategy used in PDAF as well as its parallel performance have been discussed in Nerger and Hiller (2013). The strategy to implement coupled data assimilation for Earth system models is described in Nerger et al. (2020).