Features and Requirements
- PDAF is implemented in Fortran90 with some features from Fortran 2003. The standard interface also supports models that are written in other languages like C or C++.
- The parallelization uses the MPI (Message Passing Interface) standard. The localized filters use, in addition, OpenMP-parallelization.
- The core routines are fully independent of the model code. They can be compiled separately and can be used as a library.
PDAF provides the following algorithms for data assimilation. All filters are fully implemented, optimized and parallelized.
- LSEIK (Nerger et al., 2006)
- LETKF (Hunt et al., 2007)
- LESTKF (Local Error Subspace Transform Kalman Filter, Nerger et al., 2012, see publications)
- LNETF (localized Nonlinear Ensemble Transform Filter by Toedter and Ahrens (2015), added in version 1.12)
- ESTKF (Error Subspace Transform Kalman Filter, Nerger et al., 2012, see publications)
- ETKF (The implementation follows Hunt et al. (2007) but without localization, which is available in the LETKF implementation)
- EnKF (The classical formulation with perturbed observations by Evensen (1994), Burgers et al. (1998))
- SEEK (The original formulation by Pham et al. (1998))
- SEIK (Pham et al. (1998a, 2001), the implemented variant is described in more detail by Nerger et al. (2005))
- NETF (Nonlinear Ensemble Transform Filter by Toedter and Ahrens (2015), added in version 1.12)
Starting from version 1.9 of PDAF, smoothers algorithms are provided for the following algorithms
- ESTKF & LESTKF
- ETKF & LETKF
- NETF (added in version 1.12)
Simplifying the implementation
PDAF simplifies the implementation of data assimilation systems using existing model code by the following:
- PDAF provides fully implemented, parallelized, and optimized ensemble-based algorithms for data assimilation. Currently, these are ensemble-based Kalman filters like the LSEIK, LETKF, and EnKF methods.
- PDAF is attached to the model source code by minimal changes to the code. These changes only concern the general part of the code, but not the numerics of the model. In addition, a small set of routines is required that are specific to the model or the observations to be assimilated. These routines can be implemented like routines of the model.
- PDAF is called through a well-defined standard interface. This allows, for example, to switch between the LSEIK, LETKF, and LESTKF methods without additional coding.
- PDAF provides parallelization support for the data assimilation system. If your numerical model is already parallelized, PDAF enables the data assimilation system to run several model tasks in parallel within a single executable. However, PDAF can also be used without parallelization, for example to test small systems.
- PDAF does not require that your model can be called as a subroutine. Rather PDAF is added to the model and the formed data assimilation system can be executed pretty much like the model-program would without data assimilation.
- PDAF also offers an offline mode. This is for the case that you don't want to (or even cannot) modify your model source code at all. In the offline mode, PDAF is compiled separately from the model together with the supporting routines to handle the observations. Then the model and the assimilation step are executed separately. While this strategy is possible, we don't recommend it, because it's computationally less efficient.
To compile PDAF a Fortran compiler is required which supports Fortran 2003. PDAF has been tested with a variety of compilers like gfortran, ifort, xlf, pgf90, cce.
- BLAS and LAPACK
The BLAS and LAPACK libraries are used by PDAF. For Linux there are usually packages with these libraries. With commercial compilers the functions are usually provided by optimized libraries (like MKL, ESSL).
- MPI (optional)
If the assimilation program should be executed with parallelization, an MPI library is required (e.g. OpenMPI). The assimilation program can also be compiled and run without parallelization. For this, PDAF provides functions that mimic MPI operations for a single process.
PDAF provides Makefile definitions for different compilers and operating systems.
PDAF has been tested on various machines with different compilers and MPI libraries. Current test machines include:
- Linux Desktop machine, Ubuntu 12.04, ifort compiler
- Linux Desktop machine, Ubuntu 12.04, gfortran, OpenMPI
- Notebook Apple MacBook, Mac OS X 10.12, gfortran 4.8.5 - 6.3, OpenMPI
- Cray XC30 and XC40, Cray compiler CCE 8.3.5 to 8.4.0, MPICH
- Cray CS400, ifort 16.0.2 to 17.0.1, IMPI
- NEC SX-ACE, sxf90 compiler (rev 530), sxmpi
Past test machines also included
- IBM p575 with Power6 processors, AIX6.1, XLF compiler 12.1, ESSL library, POE parallel environment
- IBM BladeCenter with Power6 processors, AIX5.3, XLF compilers 10.1 to 13.1, ESSL library, POE parallel environment
- SGI Altix UltraViolet, SLES 11 operating system, ifort compiler, SGI MPT
The regular tests use a rather small configuration with a simulated model. This model is also included in the test suite of the downloadable PDAF package. In addition, the scalability of PDAF was examined with a real implementation with the finite element ocean model (FEOM, Danilov et al., A finite-element ocean model: Principles and evaluation. Ocean Modeling 6 (2004) 125-150). In these tests up to 4800 processor cores of a supercomputer have been used (see Nerger and Hiller (2013)). To examine PDAF's behavior with large-scale cases, experiments with the simulated model have been performed. By now the biggest case had a state dimension of 1.2.1011. An observation vector of size 2.4.109 was assimilated. For these experiments, the computations used 12000 processor cores. In this case, the distributed ensemble array occupied about 2 GBytes of memory for each core. The overall largest number of processes with which PDAF was validated was 16800 processor cores.