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ask (turtles.first. innovation = yellow. setColor innovation). }. // Define the model time step routine.
Similar to RSA, DYNIA calculates the probability distribution of parameter values in behavioral parameter sets, but doing so for each individual model time step.
The analysis equation is solved after each model time step, using observations obtained within a time window with a length of one time step.
However, rather than calculating an error criterion which integrates over the entire simulation period (as in Eqs. 5 to 12), DYNIA estimates an error for each individual model time step.
For each individual model time step, the parameter sets are ranked according to the value of the model error, and the top 5% performing parameter sets are taken as the behavioral sets.
Depending on the horizontal grid spacing, the horizontal-to-vertical grid resolution ratio and the flow pattern this limitation may easily become the most restrictive factor in choosing model time step, with the general tendency to become more severe as horizontal resolution becomes finer.
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Differing model time steps substantially altered our estimates of pre-industrial forest conditions.
This numerical solution method provides accuracy and stability when using model time steps that exceed the Courant–Friedrichs Lewy (CFL) restriction.
This is further demonstrated by the second set of sensitivity analyses, where the number of model time steps was chosen by matching the maximum projected value with that observed.
Model simulation time step was set to 5 s.
For a constant diffusivityK and model time-step δt, the random step should be chosen from a distribution with variance 2Kδt.
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