[Dart-dev] DART/branches Revision: 13048

[dart at ucar.edu]

nancy at ucar.edu
2019-03-21 16:32:56 -0600 (Thu, 21 Mar 2019)
205
fix the tool description in both the html and the
doxygen comment at the top of the source file.
no code changes.  a few other html things fixed
and runs thru tidy cleanly.
 
thanks to jeff for the text.




Modified: DART/branches/recam/assimilation_code/programs/obs_impact_tool/obs_impact_tool.f90
===================================================================
--- DART/branches/recam/assimilation_code/programs/obs_impact_tool/obs_impact_tool.f90	2019-03-21 22:08:55 UTC (rev 13047)
+++ DART/branches/recam/assimilation_code/programs/obs_impact_tool/obs_impact_tool.f90	2019-03-21 22:32:56 UTC (rev 13048)
@@ -4,29 +4,35 @@
 !
 ! $Id$
 
-!> This program assists in constructing a table which can be read
-!> by filter at run-time to disable or alter how the assimilation
-!> of different types of observations impact state vector values
-!> based on their quantity.  This tool allows users to group related
-!> collections of observation types and state vector quantities by name
-!> and then express the relationship of the named groups to each
-!> other in a concise way.
+!> The standard DART algorithms compute increments for an observation and then
+!> compute corresponding increments for each model state variable due to that
+!> observation. To do this, DART computes a sample regression coefficient using
+!> the prior ensemble distributions of a state variable and the observation. The
+!> increments for each member of the observation are multiplied by this
+!> regression coefficient and then added to the corresponding prior ensemble
+!> member for the state variable. However, in many cases, it is appropriate to
+!> reduce the impact of an observation on a state variable; this is called
+!> localization. The standard DART algorithms allow users to specify a
+!> localization that is a function of the horizontal (and optionally vertical)
+!> distance between the observation and the state variable. The localization is
+!> a value between 0 and 1 and multiplies the regression coefficient when
+!> updating state ensemble members.
 !>
-!> Normally filter determines the impact of an observation
-!> on the state based on the covariance and localization values.
-!> The impact factor is an additional multiplier in this process.
-!> For example, if the model does not
-!> accurately represent the relationship between different 
-!> parts of the model state (e.g part of the state is
-!> computed offline, or two different models are run
-!> which do not exchange information otherwise), then
-!> this tool can be used to prevent all impact of
-!> an observation on those parts of the state.
+!> Sometimes, it may be desirable to do an additional localization that is a
+!> function of the type of observation and the state vector quantity. This
+!> program allows users to construct a table that is read by filter at run-time
+!> to localize the impact of sets of observation types on sets of state
+!> vectorquantities. Users can create named sets of observation types and sets
+!> of state vector quantities and specify a localization for the impact of the
+!> specified observation types on the state vector quantities.
 !>
-!> We recommend initially only using values of 0.0 or 1.0,
-!> although other values can be used after careful analysis
-!> of the results.
-!>
+!> An example would be to create a subset of observations of tracer
+!> concentration for a variety of tracers, and a subset of dynamic state
+!> variable quantities like temperatures and wind components. It has been common
+!> to set this localization value to 0 so that tracer observations have no
+!> impact on dynamic state quantities, however, the tool allows values between 0
+!> and 1 to be specified.
+!> 
 !> All the listed observation types and state vector quantities
 !> must be known by the system.  If they are not, look at the
 !> &preprocess_nml :: input_items namelist which specifies
@@ -39,8 +45,6 @@
 
 
 
-! program to read an ascii file with directions for which state and observation
-! QTYS should impact which other state and observation QTYS.
 
 ! the format of the ascii input file is:
 !

Modified: DART/branches/recam/assimilation_code/programs/obs_impact_tool/obs_impact_tool.html
===================================================================
--- DART/branches/recam/assimilation_code/programs/obs_impact_tool/obs_impact_tool.html	2019-03-21 22:08:55 UTC (rev 13047)
+++ DART/branches/recam/assimilation_code/programs/obs_impact_tool/obs_impact_tool.html	2019-03-21 22:32:56 UTC (rev 13048)
@@ -38,26 +38,48 @@
 <H2>Overview</H2>
 
 <P>
-Utility program which assists in constructing a table that is read
-by filter at run time to alter how the assimilation
-of different types of observations impact state vector values
-based on their quantity, and other observations based on their 
-observation type or quantity.  This tool allows users to group related
-collections of observation types and state vector quantities by name
-and then express the relationship of the named groups to each
-other in a concise way.
+The standard DART algorithms compute increments for an observation and then
+compute corresponding increments for each model state variable due to that
+observation. To do this, DART computes a sample regression coefficient using
+the prior ensemble distributions of a state variable and the observation. The
+increments for each member of the observation are multiplied by this
+regression coefficient and then added to the corresponding prior ensemble
+member for the state variable. However, in many cases, it is appropriate to
+reduce the impact of an observation on a state variable; this is called
+localization. The standard DART algorithms allow users to specify a
+localization that is a function of the horizontal (and optionally vertical)
+distance between the observation and the state variable. The localization is
+a value between 0 and 1 and multiplies the regression coefficient when
+updating state ensemble members.
 </P>