Threshold in detail¶
In the previous example the threshold function was called with its default arguments, so only temperature was needed. As for the original Marine heatwave code several other parameters can be set:
threshold(temp, tdim='time', climatologyPeriod=[None,None], pctile=90, windowHalfWidth=5,
smoothPercentile=True, smoothPercentileWidth=31, maxPadLength=None,
coldSpells=False, Ly=False, anynans=False, skipna=False):
Where temp is the temperature timeseries, this is the only input needed. Arguments names are the same as the original MarineHeatWave code, where possible:
climatologyPeriod: list(int), optional Period over which climatology is calculated, specified as list of start and end years. Default is to use the full time series.
pctile: int, optional Threshold percentile used to detect events (default=90)
windowHalfWidth: int, optional Half width of window about day-of-year used for the pooling of values and calculation of threshold percentile (default=5)
smoothPercentile: bool, optional If True smooth the threshold percentile timeseries with a moving average (default is True)
smoothPercentileWidth: int, optional Width of moving average window for smoothing threshold in days, has to be odd number (default=31)
maxPadLength: int, optional Specifies the maximum length (days) over which to interpolate NaNs in input temp time series. i.e., any consecutive blocks of NaNs with length greater than maxPadLength will be left as NaN. If None it does not interpolate (default is None).
coldSpells: bool, optional If True the code detects cold events instead of heat events (default is False)
Ly: bool, optional !! Not yet fully implemented If True the length of the year is < 365/366 days (e.g. a 360 day year from a climate model). This affects the calculation of the climatology (default is False)
tdim - optional, to specify the time dimension name, default is “time” . NB you do not need to pass the time array as in the original as the timeseries is an xarray data array the time dimension is included
anynans: bool, optional Defines in land_check which cells will be dropped, if False only ones with all NaNs values, if True all cells with even 1 NaN along time dimension will be dropped (default is False)
skipna: bool, optional If True percentile and mean function will use skipna=True. Using skipna option is much slower (default is False)
More on missing values later.
This is just showing how we can call the function changing some of the default parameters. In this case we are assuming sst time dimension is called ‘time_0’ and we want a base period from 1 Jan 1984 to 31 Dec 1994.
clim = threshold(sst, climatologyPeriod=[1984,1994], tdim=‘time_0’)
NB after passing the timeseries as first argument, the order of the other ones is irrelevant as they are all keywords arguments.
It is important to notice that differently from the original function which takes a numpy 1D array, because we are using xarray we can pass a 3D array (in fact we could pass any n-dim array) and the code will deal with it. We selected a 12X20 lat-lon region and of these 135 grid cells are ocean.
The function return a dataset with the arrays: - thresh - for the threshold timeseries - seas - for the seasonal mean
Differently from the original function, here the climatologies are saved not along the entire timeseries but only along the new doy dimension. Given that xarray keeps the coordinates with the arrays there is no need to repeat the climatologies along the time axis. We also try to follow the CF conventions and define appropriate variables attributes and some global attributes that record the parameters used to calculate the threshold for provenance.
Handling of dimensions and land points¶
As so before we are passing the full grid to the function without worrying about land points, or how many dimensions it has. Before calculating anything, the code calls the function land_check() (from xmhw.identify). This function handles the dimensions and land points of the grid in two steps: - stacks all dimensions but the time dimension in a new ‘cell’ dimension; - removes all the land points, these are assumed to have all NaN values along the time axis
In our example ‘cell’ will be composed by stacked (lat,lon) points. The resulting array will have (time, cell) dimensions, and the cell points which are land will not be part of it. The climatologies then will be calculated for each cell point. Finally the results will be unstacked before returning the final output. NB This approach can occasionally produce a grid of different size from the original if all the cells at a specific latitude or longitude are masked as land. In that case the final grid will be smaller, you can however easily reindex your results as the original grid. > clim = clim.reindex_like(sst)
Handling of NaNs¶
It is important to understand how the threshold() function is dealing with NaNs. If there are NaNs values in the timeseries that is passed to the function, this could produce wrong results. You can take care of NaNs in the timeseries before passing it to threshold or you can take one of the following approaches: 1) We already saw that land_check() will remove all the points that have all NaNs values along the time dimension. You can choose to be more strict and also exclude any ell points that even just one NaN value. To do so you can set the anynans argument to True. This is a bit of an extreme approach as especially with observations data it is not unusual to have a few NaNs. > clim = threshold(sst, anynans=True)
set skipna to True - this tells the code to skip NaNs when calculating averages and/or the percentile. By default the skipna argument is set to False as using this option can double up the execution time. But if you are working on a small grid than it is a safer option. > clim = threshold(sst, skipnans=True)
use maxPadLength this will trigger interpolation for all NaNs points, with the exception of consecutive blocks with length greater than maxPadLength. > clim = threshold(sst, maxpadlength=5, anynans=True)
Used in conjuction with anynans as shown above you can use it to eliminate only the cell points that have bigger gaps.