Merge pull request #711 from NCAR/doc-fix-qceff

Doc fix qceff-examples & netcdf inflation files
NCAR · Aug 29, 2024 · a85e162 · a85e162
2 parents 6a905aa + a25f3c6
commit a85e162
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diff --git a/CHANGELOG.rst b/CHANGELOG.rst
@@ -22,6 +22,22 @@ individual files.
 
 The changes are now listed with the most recent at the top.
 
+**August 29 2024 :: Bug fixes for shortest_time_between_assimilations and get_close_init. Tag 11.7.1**
+
+Bug fixes:
+
+   - Prevent hang by erroring out if shortest_time_between_assimilations <=0
+   - Catch and error out if max_dist or max_dist_list is <=0 for 3d sphere 
+     3d cartesian get_close_init
+   - Improved defaults for template  model input.nml
+
+Doc fixes:
+
+   - Removed out-of-date info about inflation flavor character strings
+   - Remove out-of-date info on qceff branch checkout
+   - GitHub template for reporting documentation issues
+
+
 **August 26 2024 :: KQCEF. Tag 11.7.0**
 
 - Adds a Quantile-Conserving Ensemble Filter Based on Kernel-Density Estimation to DART.

diff --git a/conf.py b/conf.py
@@ -21,7 +21,7 @@
 author = 'Data Assimilation Research Section'
 
 # The full version, including alpha/beta/rc tags
-release = '11.7.0'
+release = '11.7.1'
 root_doc = 'index'
 
 # -- General configuration ---------------------------------------------------

diff --git a/guide/Manhattan_diffs_from_Lanai.rst b/guide/Manhattan_diffs_from_Lanai.rst
@@ -79,7 +79,7 @@ the model is spun up in perfect_model_obs, but the model itself has variable str
 Additions/Changes to existing namelists for how to use NetCDF IO.
 
 **Note** when using NetCDF restarts, inflation files are NetCDF also. The inflation mean and inflation standard
-deviation are in separate files when you use NetCDF restarts. See :doc:`./netcdf_inflation_files` for details.
+deviation are in separate files when you use NetCDF restarts. 
 
 Calculation of forward operators
 --------------------------------

diff --git a/guide/filters.rst b/guide/filters.rst
diff --git a/guide/important-capabilities-dart.rst b/guide/important-capabilities-dart.rst
@@ -144,10 +144,9 @@ Data assimilation algorithms available in DART
 DART allows users to test the impact of using multiple different types of
 algorithms for filtering, inflation/deflation, and covariance localization.
 
-DART offers numerous **filter algorithms**. These determine how the posterior
-distribution is updated based on the observations and the prior ensemble. The
-following table lists the filters supported in DART along with their type (set
-by *filter_kind* in ``input.nml`` under the “assim_tools_nml” section):
+DART offers numerous **filter algorithms** in the :ref:`QCEFF`. These determine how the prior
+distribution is updated based on the observations. The
+following table lists the filters supported in DART.
 
 +--------------------+----------------------------+--------------------------------------------+
 | Filter #           | Filter Name                | References                                 |

diff --git a/guide/qceff-examples.rst b/guide/qceff-examples.rst
@@ -7,15 +7,9 @@ QCEFF: Examples with the Lorenz 96 Tracer Model
 The Quantile-Conserving Ensemble Filter Framework (QCEFF) tools are available in DART
 as of version v11.
 The DART development team ([email protected]) would be happy to hear about your experiences and is
-anxious to build scientific collaborations using these new capabilities.
+eager to build scientific collaborations using these new capabilities.
 
-To get started, make sure that you are on the quantile_methods branch of DART: 
-
-.. code-block:: text
-
-   git checkout quantile_methods
-
-Build the DART executables for the Lorenz 96 tracer advection model:
+To get started, build the DART executables for the Lorenz 96 tracer advection model:
 
 .. code-block:: text
 

diff --git a/guide/qceff_probit.rst b/guide/qceff_probit.rst
@@ -4,9 +4,9 @@ Quantile-Conserving Ensemble Filter Framework
 ==============================================
 
 The Quantile-Conserving Ensemble Filter Framework (QCEFF) tools are available in DART
-as of version v11.
-The DART development team ([email protected]) would be happy to hear about your experiences
-and is anxious to build scientific collaborations using these new capabilities.
+as of version v11. 
+The DART development team ([email protected]) would be happy to hear about your experiences 
+and is eager to build scientific collaborations using these new capabilities.
 
 The QCEFF options are set using a :ref:`qceff table <qceff table>` given as a namelist option to &algorithm_info_nml.
 

diff --git a/index.rst b/index.rst
@@ -318,7 +318,6 @@ References
    DART build system <guide/quickbuild.rst>
    guide/assimilation-complex-model
    guide/mpi_intro
-   guide/filters
    guide/inflation
    guide/required-model-mod-routines
    guide/suggestions-for-a-simple-model
@@ -508,7 +507,6 @@ References
 
    guide/Manhattan_diffs_from_Lanai
    guide/forward_operator
-   guide/netcdf_inflation_files
    guide/state_structure
    guide/filter_async_modes
    guide/distributed_state

diff --git a/models/clm/tutorial/README.rst b/models/clm/tutorial/README.rst
@@ -887,13 +887,12 @@ related to the observation.  Step 8 is a type of localization in that it restric
 the state update to a subset of CLM variables.  Here, we further restrict the influence
 of the observation to the state space most nearly physically collocated with the observation.
 The spatial localization is set through the the ``assim_tools_nml``, ``cov_cutoff_nml``
-and ``location_nml`` settings witin ``input.nml`` as: 
+and ``location_nml`` settings within ``input.nml`` as: 
 
 ::
 
  # cutoff of 0.03 (radians) is about 200km
  &assim_tools_nml
-    filter_kind                     = 1
     cutoff                          = 0.05
 
 ::

diff --git a/models/cm1/readme.rst b/models/cm1/readme.rst
@@ -467,7 +467,6 @@ An example of the &assim_tools_nml using per-type radii is provided below.
    &assim_tools_nml
      adaptive_localization_threshold = -1
      cutoff                          = 15000.0
-     filter_kind                     = 1
      print_every_nth_obs             = 100
      rectangular_quadrature          = .true.
      sampling_error_correction       = .false.