Open the site data manager by selecting it in the main toolbar at the top of the screen or click in the main screen and select it from the comprehensive list.





The following screen will be displayed.

 

 

To begin you need to define whether the data you wish to add is Daily, Hourly or Monthly etc. Click once on the data folder you wish to add your data to. After highlighting the folder click on Add.

 

A new dialogue box will open that will ask you to name the site and provide its location:

 

 

To complete this function you will need your data to be in the proper format.

 

These are:


  1. Daily data option 1: the first column is date in 8 digits and the second column is climate data :


19790101 33.3

19790102 29.2

19790103 30.6

19790104 30.5

19790105 30.2

19790106 30.9



  • Daily data option 2: the first column and second column are year and month and the rest 31 columns (column 3 to column 34) are climate data


1972 1 50 50 22 11 33 17 22 11 61 72 44 56 122 22 100 89 11 83 94 11 17 94 67 117 100 100 56 61 72 61 56

1972 2 6 22 39 0 89 44 83 83 56 56 44 56 17 22 56 11 6 28 11 100 50 33 56 0 -17 -22 22 28 33 -999 -999

1972 3 94 22 -67 11 11 -56 100 39 -39 -50 50 22 -6 17 22 22 39 11 -33 44 72 56 11 -28 -22 17 0 11 83 56 111

1972 4 56 56 61 39 22 11 39 39 50 83 128 100 144 83 78 67 128 211 161 111 106 89 89 83 78 128 106 156 156 156 -999



  • Monthly data option 1: the first column is date in 6 digits and the second column is climate data :


197901 33.3

197902 33.2

197903 29.6

197904 25.5

197905 21.2

197906 18.9



  • Monthly data option 2: the first column is recording year and the rest 12 columns are climate data:

1972 30 32 29 26 23 17 12 11 16 17 24 29 1973 31 32 29 24 19 14 13 13 16 16 24 26 1974 29 28 26 21 19 16 10 9 15 20 21 22 1975 32 30 28 25 22 11 11 9 15 23 22 30 1976 32 31 28 24 26 13 14 9 19 18 21 30


If your data is not in one of the above formats contact CLIMsystems and we will assist in reformatting the data.


There are also preformatted data that from time to time you may have access and wish to import using the data manager. See the list below of importers that accept data from a range of global, often web-accessed databases.




After naming the site you need to enter the data on the latitude and longitude. This is critical so that the impact models know where to search in the database for running models. This sort of information is often in the header files for the data.

 

 

After defining the name and location you have a number of choices to make with reference to the data type, its location and format.



Variable type:


Choose from the list.


Filename: Click on the button and find the directory and file you wish to import.


Importer:


Click on the format required.


Scale factor:


This moves the decimal point in your data. Commonly 1 is used and is the default. You will need to check your data to determine if this is the correct scaling factor.


This tool can be used in certain cases where known scaling factors are required to standardise data. Often this is left as 1.


Missing data value:


Commonly the default is chosen.


Click on if your data is in a SimCLIM 4.0 for Desktop-compatible format such as NIWA CliFlo but you wish it to be saved as a two column text file at the same time as it is being imported into SimCLIM 4.0 for Desktop. Sometimes you may have use for this very versatile format, such as for file sharing or importation into other data management tools such as Excel. In most cases you will not need to check this box.


After you have defined all the parameters click on .


When you are returned to the main dialogue box click again in the bottom right to force the importation.


If there is a problem with the data format or a dialogue box has been overlooked you will get an error message.


Return to the variable option and choose the next variable type to import, if you have additional variables to import for that particular site.


Browse the new site data in the data browser to see if it makes sense in terms of the values displayed. It is always wise to check your data for anomalies (outliers).