Recommended parameters settings

It is difficult to give a blanket recommendation of default values for the test parameters, because they depend so strongly on the specifics of the data being tested. The type of data being tested, density of the network, prevalence of systematic errors, climatology of the local area, etc. will all greatly affect the ideal arguments. Still, we will do our best to provide some recommendations:

Automatic tuning of parameters (WIP)

If you don't have the required expertise to determine the ideal values yourself, the next best thing is to automatically tune them using titantuner.

Titantuner's autotune functionality can introduce a perturbations into a reference dataset, then perform a gradient descent using the parameters to minimise a cost function. This eventually spits out the set of parameters that produced the best ratio of correctly flagged perturbations to false alarms.

To get good result with automatic tuning, you should feed it a dataset that is representative of the data you would like to QC, but with as few errors as possible (perhaps manually QCed?). It is important to minimise the number of errors in the data, as undetected errors would otherwise be seen as false alarms under well adjusted parameters, skewing the cost function.

Manual tuning of parameters

Titantuner also presents a GUI for manually tuning parameters. Users can manipulate the parameters directly, and see how their changes affect which data points are flagged. The parameters can be manually adjusted until the sets of flagged and unflagged data are sensible.

Reference values

Precipitation

Temperature

MET Norway is using the following sequence of tests for producing gridded analyses using Synop and citizen observations (Netatmo, etc):

	Test	Parameters
1	range_check	min=-50, max=50
2	buddy_check	radius=5000
3	sct	radius=10000, pos=4 (12 for Synop), neg=8 (12 for Synop)
4	isolation	num_min=5, radius=15000

Tests