Extreme sub-daily precipitation in the Nordic-Baltic region

DOI

This dataset contains shapefiles with gridded extreme sub-daily rainfall data for the Nordic-Baltic region, and the average day of year when the extreme events occur. More specifically, this dataset contains shapefiles with rainfall depths corresponding to return periods 5, 10 and 20 years and for durations (i.e. accumulation periods) 1, 3, 6 and 12 hours. The unit is accumulated rainfall (mm) for the selected time window and the values have been estimated by GEV analysis of annual maxima. The day of year data ranges from 1 to 365, and is the median day of year for the annual maxima using all available data from 2000 to 2018. All shapefiles are on a 1×1 degree grid, where the grid has been created from inverse distance weighting of station data, using power 2 in the IDW formula.

The shapefiles containing day of year results have files names of the form “doy_median_acc_time_Th”, where T is the accumulation time in hours. Note that since we are working with shapefiles, there are 4 files per data field (shp, shx, dbf and prj files).

In the same way, the files with the return levels have form “ret_lev_lmom_R_acc_Th.prj” where R is the return period in years (5, 10 or 20), and T is once again the accumulation time in hours. The lmom in the filename stands for L-moments, which is the method that the distribution parameters were fitted with.

For further information see:

Olsson, J., Dyrrdal, A.V., Médus, E., Södling, J., Aniskeviča, S., Arnbjerg-Nielsen, K., Førland, E., Mačiulytė, V., Mäkelä, A., Post, P., Thorndahl, S.L. and L. Wern (2021) Sub-daily rainfall extremes in the Nordic-Baltic region, Hydrology Research, submitted.

Identifier
DOI https://doi.org/10.11582/2021.00094
Metadata Access https://search-api.web.sigma2.no/norstore-archive/oai/v1.0?verb=GetRecord&metadataPrefix=oai_dc&identifier=doi:10.11582/2021.00094
Provenance
Creator Olsson, Jonas
Publisher Norstore Archive
Publication Year 2021
OpenAccess true
Contact Norstore Archive
Representation
Language English
Resource Type Dataset
Discipline Atmospheric Sciences; Geosciences; Meteorology; Natural Sciences