Journal cover Journal topic
Earth System Science Data The data publishing journal
Journal topic

Journal metrics

Journal metrics

  • IF value: 8.792 IF 8.792
  • IF 5-year value: 8.414 IF 5-year
    8.414
  • CiteScore value: 8.18 CiteScore
    8.18
  • SNIP value: 2.620 SNIP 2.620
  • IPP value: 7.67 IPP 7.67
  • SJR value: 4.885 SJR 4.885
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 24 Scimago H
    index 24
  • h5-index value: 28 h5-index 28
Discussion papers
https://doi.org/10.5194/essd-2019-43
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-43
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Data description paper 05 Apr 2019

Data description paper | 05 Apr 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

seNorge_2018, daily precipitation and temperature datasets over Norway

Cristian Lussana, Ole Einar Tveito, Andreas Dobler, and Ketil Tunheim Cristian Lussana et al.
  • Norwegian Meteorological Institute, Oslo, Norway

Abstract. seNorge_2018 is a collection of observational gridded datasets over Norway for: daily total precipitation; daily mean, maximum and minimum temperatures. The time period covers 1957 to 2017, and the data are presented over a high-resolution terrain-following grid with 1 km spacing in both meridional and zonal directions. The seNorge family of observational gridded datasets developed at the Norwegian Meteorological Institute (MET Norway) has a twenty-year long history and seNorge_2018 is its newest member, the first providing daily minimum and maximum temperatures. seNorge datasets are used for a wide range of applications in climatology, hydrology and meteorology. The observational dataset is based on MET Norway's climate data, which has been integrated by the European Climate Assessment and Dataset database. Two distinct statistical interpolation methods have been developed, one for temperature and the other for precipitation. They are both based on a spatial scale-separation approach where, at first, the analysis (i.e., predictions) at larger spatial scales are estimated. Subsequently they are used to infer the small-scale details down to a spatial scale comparable to the local observation density. Mean, maximum and minimum temperatures are interpolated separately, then physical consistency among them is enforced. For precipitation, in addition to observational data, the spatial interpolation makes use of information provided by a climate model. The analysis evaluation is based on cross-validation statistics and comparison with a previous seNorge version. The analysis quality is presented as a function of the local station density. We show that the occurrence of large errors in the analyses decays at an exponential rate with the increase in the station density. Temperature analyses over most of the domain are generally not affected by significant biases. However, during wintertime in data-sparse regions the analyzed minimum temperatures do have a bias between 2 °C and 3 °C. Minimum temperatures are more challenging to represent and large errors are more frequent than for maximum and mean temperatures. The precipitation analysis quality depends crucially on station density: the frequency of occurrence of large errors for intense precipitation is less than 5 % in data-dense regions, while it is approximately 30 % in data-sparse regions. he open-access datasets are available20for public download at: daily total precipitation (DOI: https://doi.org/10.5281/zenodo.2082320, Lussana, 2018b); daily mean (DOI: https://doi.org/10.5281/zenodo.2023997, Lussana, 2018c) , maximum (DOI: https://doi.org/10.5281/zenodo.2559372, Lussana, 2018e) and minimum (DOI: https://doi.org/10.5281/zenodo.2559354, Lussana, 2018d) temperatures.

Cristian Lussana et al.
Interactive discussion
Status: open (extended)
Status: open (extended)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Cristian Lussana et al.
Data sets

seNorge_2018 daily minimum temperature 1957-2017 C. Lussana https://doi.org/10.5281/zenodo.2559353

seNorge_2018 daily maximum temperature 1957-2017 C. Lussana https://doi.org/10.5281/zenodo.2559372

seNorge_2018 daily total precipitation amount 1957-2017 C. Lussana https://doi.org/10.5281/zenodo.2082320

seNorge_2018 daily mean temperature 1957-2017 C. Lussana https://doi.org/10.5281/zenodo.2023997

Model code and software

metno/bliss: v1.0.0 C. Lussana https://doi.org/10.5281/zenodo.2022479

Cristian Lussana et al.
Viewed  
Total article views: 315 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
238 76 1 315 4 5
  • HTML: 238
  • PDF: 76
  • XML: 1
  • Total: 315
  • BibTeX: 4
  • EndNote: 5
Views and downloads (calculated since 05 Apr 2019)
Cumulative views and downloads (calculated since 05 Apr 2019)
Viewed (geographical distribution)  
Total article views: 266 (including HTML, PDF, and XML) Thereof 264 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 19 Jun 2019
Download
Short summary
seNorge_2018 is a collection of observational gridded datasets for: daily total precipitation; daily mean, minimum and maximum temperature for the Norwegian mainland covering the time period from 1957 to the present day. The fields have 1 km of grid spacing. The data are used for applications in climatology, hydrology and meteorology. seNorge_2018 is based on in-situ observations and in data-dense regions it provides a gridded truth, the uncertainty increases with the decrease in data density.
seNorge_2018 is a collection of observational gridded datasets for: daily total precipitation;...
Citation