Merge pull request #612 from NCAR/bkj_quantile_methods_edit

BKJ quantile methods edit

guide/qceff-examples.rst

@@ -24,7 +24,7 @@ Build the DART executables for the Lorenz 96 tracer advection model:
 
 The new quantile options are set using a :ref:`qceff table <QCEFF>` given as a namelist
 option ``qceff_table_filename`` to &algorithm_info_nml. The examples below show how to change the quantile options
-using various qceff tables. You can find the .csv files for these four example in the directory
+using various QCEFF tables. You can find the .csv files for these four examples in the directory
 ``DART/models/lorenz_96_tracer_advection/work``
 
 
@@ -34,9 +34,9 @@ using various qceff tables. You can find the .csv files for these four example i
 
    * - example
      - description
-     - .cvs filename 
+     - .csv filename 
    * - Example A 
-     - boundend normal rank histogram
+     - bounded normal rank histogram
      - all_bnrhf_qceff_table.csv
    * - Example B
      - Ensemble Adjustment Kalman filters

guide/qceff_probit.rst

@@ -71,7 +71,7 @@ There is a complete table with all 25 columns in `Google Sheets <https://docs.go
 
 To add a quantity, add a row to the table.
 For any quantity not listed in the table, the :ref:`Default values` values will be used for all 25 options. 
-You only have to add rows for quantitiies that use non-default values for any of the input options.
+You only have to add rows for quantities that use non-default values for any of the input options.
 Ensure that there are no empty rows in between the quantities listed in the spreadsheet.
 Save your spreadsheet as a .csv file. 
 
@@ -148,5 +148,7 @@ are used:
   * lower_bound    (default -888888)
   * upper_bound    (default -888888)
 
-Note -888888 is a missing value in DART.
+.. note::
+
+   -888888 is a missing value in DART.
 

index.rst

@@ -64,18 +64,24 @@ research labs.
 Nonlinear and Non-Gaussian Data Assimilation Capabilities in DART
 -----------------------------------------------------------------
 
-The default DART algorithms assume a normal distribution to compute ensemble increments 
-for the observed quantity (this is the ensemble adjustment Kalman filter, or EAKF) and 
-then linearly regress the observation increments onto each state variable.
-
-
-DART now implements a Quantile-Conserving Ensemble Filtering Framework :ref:`(QCEFF) <QCEFF>`.
-The QCEFF provides a very general method of computing increments for the prior ensemble of 
-an observed quantity by allowing the use of arbitrary distributions for the prior and the 
-observation error. This is especially useful for bounded quantities like tracer concentrations, 
-depths of things like snow or ice, and estimating model parameters that have a restricted range.
-See this Monthly Weather Review article for details,
-`QCEFF part1 <http://n2t.net/ark:/85065/d7mk6hm4>`_.
+One of the historical drawbacks of ensemble data assimilation techniques is the
+assumption that the quantities being assimilated obey a normal distribution.
+While this is often a safe assumption -- distributions of temperature and
+pressure can be approximated using a normal distribution -- many quantities
+such as precipitation, snow depth and tracer concentration, as well as many
+model parameters aren't normally distributed.
+
+Applying traditional ensemble data assimilation techniques in situations where
+assumptions of gaussianity are invalid can lead to poor forecast skill and
+inconclusive results.
+
+To overcome these problems, DART now implements a novel data assimilation
+technique that no longer requires quantities to be normally distributed. The
+Quantile-Conserving Ensemble Filtering Framework :ref:`(QCEFF) <QCEFF>`
+provides a general method of computing increments for the prior ensemble of an
+observed quantity by allowing the use of arbitrary distributions for the prior
+and the observation error. For a detailed description of the QCEFF, see
+Anderson (2022). [2]_ 
 
 While the QCEFF for computing observation increments can lead to significant improvements in 
 analysis estimates for observed variables, those improvements can be lost when using standard 
@@ -85,8 +91,8 @@ Doing the regression of observation quantile increments in the transformed space
 that the posterior ensembles for state variables also retain the advantages of the observation space
 quantile conserving posteriors. For example, if state variables are bounded, then posterior 
 ensembles will respect the bounds. The posterior ensembles also respect other aspects of the 
-continuous prior distributions. See this Monthly Weather Review article for details, 
-`QCEFF part 2 <http://n2t.net/ark:/85065/d7nv9pbt>`_.
+continuous prior distributions. For a detailed description of this process, see
+Anderson (2023). [3]_
 
 Inflation and localization, methods that improve the quality of ensemble DA, can also negate 
 the advantages of the QCEFF methods. For this reason, both localization and inflation can be 
@@ -258,6 +264,13 @@ References
        Facility. *Bulletin of the American Meteorological Society*, **90**,
        1283-1296, `doi:10.1175/2009BAMS2618.1
        <http://dx.doi.org/10.1175/2009BAMS2618.1>`_
+.. [2] Anderson, J. L., 2022: A Quantile-Conserving Ensemble Filter Framework.
+       Part I: Updating an Observed Variable. *Monthly Weather Review*, **150**,
+       1061–1074, `doi:10.1175/MWR-D-21-0229.1 <http://n2t.net/ark:/85065/d7mk6hm4>`_
+.. [3] Anderson, J. L., 2023: A Quantile-Conserving Ensemble Filter Framework.
+       Part II: Regression of Observation Increments in a Probit and
+       Probability Integral Transformed Space. *Monthly Weather Review*,
+       **151**, 2759–2777, `doi:10.1175/MWR-D-23-0065.1 <http://n2t.net/ark:/85065/d7nv9pbt>`_ 
 
 .. |spaghetti_square| image:: ./guide/images/DARTspaghettiSquare.gif
    :width: 100%