The basic time series processing is substantially the preliminary step that must be followed in order for the hydrological analysis of data to follow. This concerns non hydrological methods of data manipulation, such as the derivation of time series of lower resolution. More specifically basic processings implemented in the context of NDBHMI includes :
Estimation of time series of smaller resolution
This is the process of estimating new time series as products of the primary ones. Thus, from the daily rainfall time series one can calculate the monthly one by adding the values of the original time series and from the daily temperature time series the average daily temperature time series may be estimated for a given month or year by calculating the mean value of the timeseries.
Estimation of extreme values time series
This concerns the process of calculating timeseries of extreme values (minimum or maximum) of smaller discernibility. For example, from the daily temperature timeseries, the monthly timeseries of maxima and minima can be calculated easily taking the higher and the lower temperature respectively for each month.
Combinations / operations between time series
The processes applied for the derivation of time series from more than one primary time series fall in this category. The summation of rain and equivalent water depth of snow, is a representative case of combining time series and so is the estimation of the daily temperature time series from the corresponding maxima and minima time series.
Time series graphs
Graphs are a valuable tool to the user providing him the means for direct reviewing of time series either as a asepararte one or in combination with others.
Time series checks (spatial / temporal consistency)
These checks concern the reliability and correctness of each measurement's recording or its erroneous migration from printed to electronic form. The processes consist of checks regarding extreme values, temporal consistency, overlapping, voids and a complex way of checking the spatial consistency, according to which each time series is examined in combination which timeseries from neighboring stations.