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From "Huikyo Lee (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (CLIMATE-643) Updating some of examples
Date Mon, 27 Jul 2015 20:21:04 GMT

     [ https://issues.apache.org/jira/browse/CLIMATE-643?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Huikyo Lee updated CLIMATE-643:
-------------------------------
       Due Date: 31/Jul/15  (was: 14/Jun/15)
    Description: 
Paul L. suggested some ideas to update examples. For example, "knmi_to_cru_full_bias.py" needs
to be updated with better description. The model to model bias could be replaced by model
to observation data bias. The goal is providing 5 examples all based on an actual published
papers. 

Currently, OCW examples generate wrong results when there is missing data in observational
datasets. It is important to mask those grid points with missing values in model datasets
so that no metrics calculation is done at those grid points. In other words, if any of observation/model
dataset has missing value at a grid point, non-missing values in the other datasets need to
be masked.

  was:Paul L. suggested some ideas to update examples. For example, "knmi_to_cru_full_bias.py"
needs to be updated with better description. The model to model bias could be replaced by
model to observation data bias. The goal is providing 5 examples all based on an actual published
papers. 


> Updating some of examples
> -------------------------
>
>                 Key: CLIMATE-643
>                 URL: https://issues.apache.org/jira/browse/CLIMATE-643
>             Project: Apache Open Climate Workbench
>          Issue Type: Improvement
>          Components: general
>    Affects Versions: 1.0.0
>            Reporter: Huikyo Lee
>            Assignee: Huikyo Lee
>             Fix For: 1.0.0
>
>
> Paul L. suggested some ideas to update examples. For example, "knmi_to_cru_full_bias.py"
needs to be updated with better description. The model to model bias could be replaced by
model to observation data bias. The goal is providing 5 examples all based on an actual published
papers. 
> Currently, OCW examples generate wrong results when there is missing data in observational
datasets. It is important to mask those grid points with missing values in model datasets
so that no metrics calculation is done at those grid points. In other words, if any of observation/model
dataset has missing value at a grid point, non-missing values in the other datasets need to
be masked.



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