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Posted to dev@climate.apache.org by "Huikyo Lee (JIRA)" <ji...@apache.org> on 2015/07/27 22:21:04 UTC
[jira] [Updated] (CLIMATE-643) Updating some of examples

     [ 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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