In the sections below a brief introduction to handling ensemble is given. For more details on this, reader is revered to the Time series manager training module[MAAMH1] .  


Importing and exporting ensembles data

Similar to importing time series data, The DSS can import ensembles that are in ASCII, DSF0 (format used in Mike DHI products), GRIB, NETCDF and excel file formats. Importing can be from a single file (i.e. ensemble data is stored in one file) or multiple files. DSS users can also extract the traces within an ensemble and then export them individually as shown above for a time series, or as an ensemble (i.e. a group of time series).


Generating ensembles

Within the DSS, the ‘Nearest neighbor resampling' tool is used to generate daily time series ensembles of weather variables based on historical time series. The tool analyses historical time series from one or more locations of different variable types and shuffles the historical record to produce ensembles of weather time series with consistent spatiotemporal statistics. The tool follows the implementation outlined in the following scientific article:

Buishand, A. & Brandsma, T., “Multisite simulation of daily precipitation and temperature in the Rhine basin by nearest-neighbour resampling”, Water Resources Research, Vol. 37, No 11, 2001.


Using ensembles in modeling

The ‘Weather shuffler’ tool can be used to generate an ensemble of, for example, rainfall time series that represents the main input to a rainfall-runoff model (e.g. NAM). The output of such model will then be an ensemble of catchment runoffs translating the uncertainty in rainfall to the corresponding uncertainty in runoff. One could do this by preparing these different rainfall traces, running the rainfall-runoff model individually for each, organizing the outputs in a way that will enable calculating the statistics across the ensemble members rather than along the time axis. Using the ensemble features in the DSS will save time and effort to set up and run the model as well as in analyzing the results. More details on this are shown in the 'Scenario' manager training module.


Ensemble visualization and statistics

The DSS is also capable of plotting the ensemble mean, range and standard deviation. Ensemble statistics such as mean, minimum, maximum and quantiles can be calculated using the 'Advanced statistics' category tools.

[MAAMH1]A hyperlink is needed here.