Changes between Version 17 and Version 18 of FirstSteps

Timestamp:

May 31, 2025, 7:43:46 PM (2 days ago)

Author:

lnerger

Comment:

--

FirstSteps

@@ -190 +190 @@
 == Compilation Problems ==
 
-For the compilation, you need `make`. This should be installed on any computer running Linux, Unix or OSX.
+For the compilation, you need `make`. This should be installed on any computer running Linux, Unix or MacOS.
 If it is missing, you cannot compile and should install `make`.
 
@@ -203 +203 @@
 different compiler than gfortran, the directory `make.arch/` provides
 include files for the compilation. To check for a suitable include file
-koos into the directory `make.arch/`. There are files for
-compilation on different computers both with and without parallelization
-(MPI). If you don't find a suitable include file, you might also copy
-an existing one and edit it for your needs. To specify the include file
+look into the directory `make.arch/`. There are files for
+compilation on different computers with differnt variants of the parallelization library MPI. If you don't find a suitable include file, you might also copy
+an existing file and edit it for your needs. To specify the include file
 for the compilation, you just need to set it when running make as
 {{{
@@ -212 +211 @@
 }}}
 
-On MacOS there is usually no gfortran installed. You can install it using the ports of Linux software from Fink or !MacPorts. Likewise, you can install OpenMPI. Then setting PDAF_ARCH=osx_gfortran or PDAF_ARCH=osx_gfortran_openmpi should work to compile the test cases.
+On MacOS there is usually no gfortran installed. You can install it, e.g., using the Homebrew (brew.sh). Likewise, you can install OpenMPI. Then setting PDAF_ARCH=osx_gfortran_openmpi should work to compile the test cases.
 
 
 == Next steps ==
 
-Possible next steps from here can be the following:
+Having done your first experiments with PDAF, possible next steps can be the following:
 
 === Applying data assimilation with PDAF to the Lorenz-96 model ===
 
-The implementation of [wiki:Lorenz_96_model PDAF with the Lorenz-96 model] is a fully-featured example of PDAF coupled to this small model. Next to the Lorenz-96 model and assimilation user routines, the example provides tools for generating synthetic observation, a covariance matrix for ensemble generation and plotting scripts. We used this implementation for different publication where we studied the behavior of different assimilation methods. 
+The implementation of [wiki:Lorenz_96_model PDAF with the Lorenz-96 model] is a fully-featured example of PDAF coupled to this small model. Next to the Lorenz-96 model and assimilation user routines, the example provides tools for generating synthetic observation, a covariance matrix for ensemble generation and plotting scripts. We used this implementation for different publications where we studied the behavior of different assimilation methods. 
 
 === Applying data assimilation with PDAF to the Lorenz-63 model ===
 
-The implementation of [wiki:Lorenz_63_model PDAF with the Lorenz-63 model] is a fully-featured example of PDAF coupled to this small model. Next to the Lorenz-63 model and assimilation user routines, the example provides tools for generating synthetic observation, a covariance matrix for ensemble generation and plotting scripts. The modul only has 3 state variables, so it's too small for localization, but it's a good example for applying the particle filter. 
+The implementation of [wiki:Lorenz_63_model PDAF with the Lorenz-63 model] is a fully-featured example of PDAF coupled to this small model. Next to the Lorenz-63 model and assimilation user routines, the example provides tools for generating synthetic observation, a covariance matrix for ensemble generation and plotting scripts. The modul only has 3 state variables, so it's too small for localization, but it's a good example for applying the particle filter. We used this implementation for our publication on the hybrid nonlinear-Kalman filter, LKNETF.
 
 === Implementing PDAF with your model ===
 
-If you plan to couple PDAF with your model, we recommend to study the [wiki:PdafTutorial PDAF implementation tutorials]. The tutorial provides a step-by-step explanation of the implementation steps. Further, please see the description of the [wiki:GeneralImplementationConcept Implementation Concept of PDAF]. Further, PDAF provides tools to [wiki:EnsembleGeneration generate an ensemble].
-
-=== Model Bindings ===
-
-The coupling routines ('model bindings') to different models, and related user routines for the data assimilation with PDAF are available in different repositories of [https://github.com/PDAF][[BR]]
+If you plan to couple PDAF with your model, we recommend to study the [wiki:PdafTutorial PDAF implementation tutorials]. The tutorial provides a step-by-step explanation of the implementation steps. Further, please see the description of the [wiki:GeneralImplementationConcept Implementation Concept of PDAF]. PDAF also provides tools to [wiki:EnsembleGeneration generate an ensemble].
+
+Beyond the more applied tutorials there are also the Implementation Guids for [wiki:OnlineImplementationGuide_PDAF3 online coupled] and [wiki:OfflineImplementationGuide_PDAF3 offline coupled] data assimilation systems.
+
+=== Model Couplings ===
+
+Coupling routines (also called 'model bindings') to different models, and related user routines for the data assimilation with PDAF are available in different repositories of [https://github.com/PDAF]
+
 Current model bindings include the MITgcm general circulation model, NEMO ocean model, AWI climate model and FESOM ocean model, WRF. See the page: [wiki:ModelsConnectedToPDAF List of models connected to PDAF].
 
 === Full overview of PDAF code Package ===
 
-To get an overview of what is overall contain in the PDAF package, please see the [wiki:SoftwarePackage full description of the PDAF code package].
+To get an overview of what is overall contained in the PDAF package, please see the [wiki:SoftwarePackage full description of the PDAF code package].