Coding 5

In this section, we’ll practice using some of the most common commands in the ensemble class. We’ll see these commands again later in the workshop as we implement various other tasks.

Goal

Practice using common ensemble commands in preparation for later tasks.

Step 1: Create ensemble object

You can use the ensemble command to return an object that represents a saved state vector ensemble. The command takes the name of an ensemble file as input.

For example (from the NTREND demo):

ens = ensemble('ntrend.ens')

ens =

    static ensemble with properties:

          Label: NTREND Demo
      Variables: T, T_index, T_monthly
         Length: 56161
        Members: 1156

we can see that this object represents a static (time-independent) ensemble with 3 variables and 1156 ensemble members.

Analogously from the LGM demo:

ens = ensemble('lgm.ens')

ens =

    static ensemble with properties:

          Label: LGM Demo
      Variables: SST
         Length: 122880
        Members: 16

we can see the output represents an ensemble with an SST variable over 16 ensemble members.

Step 2: Return ensembleMetadata

You can use the ensemble.metadata command to return an ensembleMetadata object for a saved state vector ensemble. The command does not require any inputs.

For the NTREND demo:

ens = ensemble('ntrend');
metadata = ens.metadata

ensembleMetadata with properties:

      Label: NTREND Demo
  Variables: T, T_index, T_monthly
     Length: 56161
    Members: 1156

  Vector:
              T -  4320 rows   |   lon (144) x lat (30)
        T_index -     1 rows   |   lon (1) x lat (1)
      T_monthly - 51840 rows   |   lon (144) x lat (30) x time sequence (12)

Analogously for the LGM Demo:

metadata = ensemble('lgm').metadata

  ensembleMetadata with properties:

        Label: LGM Demo
    Variables: SST
       Length: 122880
      Members: 16

    Vector:
        SST - 122880 rows   |   site (122880) x time (1)