null_model

Overview

DART interface module for the ‘null_model’. This model provides very simple models for evaluating filtering algorithms. It can provide simple linear growth around a fixed point, a random draw from a Gaussian, or combinations of the two. Namelist controls can set the width of the Gaussian and change both the model advance method and the expected observation interpolation method.

The 18 public interfaces are standardized for all DART compliant models. These interfaces allow DART to advance the model, get the model state and metadata describing this state, find state variables that are close to a given location, and do spatial interpolation for model state variables.

Namelist

The &model_nml namelist is read from the input.nml file. Namelists start with an ampersand & and terminate with a slash /. Character strings that contain a / must be enclosed in quotes to prevent them from prematurely terminating the namelist.

&model_nml
   model_size           = 2,
   delta_t              = 0.05,
   time_step_days       = 0,
   time_step_seconds    = 3600
   noise_amplitude      = 0.0_r8
   advance_method       = 'simple'
   interpolation_method = 'standard'
/

Description of each namelist entry

Item	Type	Description
model_size	integer	Model size.
delta_t	real(r8)	Internal model timestep parameter.
time_step_days	integer	Minimum model advance time in days.
time_step_seconds	integer	Minimum model advance time in seconds.
noise_amplitude	real(r8)	If greater than 0.0 sets the standard deviation of the added Gaussian noise during the model advance.
advance_method	character(64)	Controls the model advance method. The default is ‘simple’ timestepping. A 4-step Runga Kutta method can be selected with the string ‘rk’.
interpolation_method	character(64)	Controls how the expected value of an observation is computed. The default is ‘standard’ which uses a linear interpolation between the two surrounding model points. Other options include ‘square’ which returns the square of the computed value, ‘opposite_side’ which adds on a value from the opposite side of the cyclical domain, and ‘average’ which averages 15 points to get the expected value. Model size should be > 15 to use the last option.

Files

filename	purpose
input.nml	to read the model_mod namelist
preassim.nc	the time-history of the model state before assimilation
analysis.nc	the time-history of the model state after assimilation
dart_log.out [default name]	the run-time diagnostic output
dart_log.nml [default name]	the record of all the namelists actually USED - contains the default values