[Dart-dev] DART/branches Revision: 11856

[dart at ucar.edu]

nancy at ucar.edu
2017-08-01 17:03:11 -0600 (Tue, 01 Aug 2017)
132
this README (README_reg_factor_mod) is in fact describing
the programs in the system_simulation directory.  move the
readme there.




Deleted: DART/branches/rma_trunk/assimilation_code/modules/assimilation/README_reg_factor_mod
===================================================================
--- DART/branches/rma_trunk/assimilation_code/modules/assimilation/README_reg_factor_mod	2017-08-01 22:49:23 UTC (rev 11855)
+++ DART/branches/rma_trunk/assimilation_code/modules/assimilation/README_reg_factor_mod	2017-08-01 23:03:11 UTC (rev 11856)
@@ -1,77 +0,0 @@
-# DART software - Copyright UCAR. This open source software is provided
-# by UCAR, "as is", without charge, subject to all terms of use at
-# http://www.image.ucar.edu/DAReS/DART/DART_download
-#
-# DART $Id$
-
-The module reg_factor_mod is an attempt to deal with sampling errors
-in ensemble filters using a hierarchical Monte Carlo approach in which
-a group of ensembles is used and the group's sample of the regression
-factor between an observation and a state variable is used to compute
-an error estimate. The presence of error generally means that the regression
-factor should be reduced by some factor in order to give the optimal 
-result. 
-
-The regression factor used here is computed as a function of the 
-group size and the ratio of the sample standard deviation to the sample
-mean of the regression coefficient. Files are generated offline for each
-desired group size and contain a list of ratios, Q, and corresponding 
-regression confidence factors. At present, files have been generated for
-groups of 2, 4, 8 and 16. 
-
-The first step in generating these files is to generate a file for an
-infinite group size using the program sys_sim401.f90. In this case, the
-only input parameter is the number of samples that are used to generate
-the regression confidence factors for a series of Q's. In the results used 
-to date, 1000000 samples are used to generate this. Results are generated
-for every 0.01 from Q = 0.0 up to 6.00. The output from this procedure can
-be found in the file undamped_base.
-
-Because the regression confidence factor as a function of Q has a very long
-but small valued tail, for computational efficiency (and error tolerance), 
-the program sys_sim401.f90 has been modified to linearly damp the tail of 
-the distribution for values of Q > 3.0 so that they approach zero for 6.0.
-The output of this procedure can be found in the file damped_base.
-
-Because of sampling error in the Monte Carlo algorithm used to compute the
-regression confidence factors as a function of Q, there is some noise in the
-results from the two steps mentioned so far, especially for values of Q 
-larger than 3.0. A simple smoother is used to reduce this noise by doing 
-7 point centered averages of the result for the damped case. These can be 
-found in the files smoothed_damped_base. 
-
-Next, an attempt is made, rather feebly, to account for additional sampling 
-error that comes from using small groups. The program sys_sim402.f90 does
-this operation. It takes as input the group size, and a sample size that is
-used to construct Monte Carlo statistics as above. Again, results used here
-make use of 1000000 samples. 
-
-sys_sim402.f90 attempts to account for the uncertainty in the sample statistics
-of Q that are obtained from a group of ensembles. It generates group size 
-samples of a distribution with mean 1.0 and standard deviation Q. It then 
-computes Q from each of these samples and finally computes the 1000000 sample
-mean value of Q. In general, for all but the smallest input Q's, the mean
-sample Q is larger. The value of the regression confidence factor for the input
-Q is computed by looking up the value for the 1000000 sample mean Q in the 
-file smoothed_damped_base. 
-
-An additional detail includes sample instability for larger values of Q in
-this algorithm. Occasionally, extremely large sample values of Q can be generated
-that dominate sample statistics. To avoid this, at a cost of some error in a
-very heuristic algorithm, a sample Q that is greater than 10000 is set equal to
-10000 before being used to compute the sample mean of Q. The output of this 
-process is found in the files regconf2, regconf4, regconf8 and regconf16. These
-files are read in by the reg_factor module when a group filter is being 
-executed and the regression confidence factor is computed by table lookup.
-
-Again, there is noise in the output, especially for larger values of input Q. This
-is smoothed using a simple centered 7 point average and the final output is in
-smooth3_regconf2, smooth3_regconf4, smooth3_regconf8 and smooth3_regconf16. The
-program smooth is used to do all the smoothing and the number 3 refers to the input
-half-width of the smoother requested by this program.
-
-
-# <next few lines under version control, do not edit>
-# $URL$
-# $Revision$
-# $Date$

Copied: DART/branches/rma_trunk/assimilation_code/programs/system_simulation/README_reg_factor_mod (from rev 11855, DART/branches/rma_trunk/assimilation_code/modules/assimilation/README_reg_factor_mod)
===================================================================
--- DART/branches/rma_trunk/assimilation_code/programs/system_simulation/README_reg_factor_mod	                        (rev 0)
+++ DART/branches/rma_trunk/assimilation_code/programs/system_simulation/README_reg_factor_mod	2017-08-01 23:03:11 UTC (rev 11856)
@@ -0,0 +1,77 @@
+# DART software - Copyright UCAR. This open source software is provided
+# by UCAR, "as is", without charge, subject to all terms of use at
+# http://www.image.ucar.edu/DAReS/DART/DART_download
+#
+# DART $Id$
+
+The module reg_factor_mod is an attempt to deal with sampling errors
+in ensemble filters using a hierarchical Monte Carlo approach in which
+a group of ensembles is used and the group's sample of the regression
+factor between an observation and a state variable is used to compute
+an error estimate. The presence of error generally means that the regression
+factor should be reduced by some factor in order to give the optimal 
+result.