Uncertainty in gridded precipitation products: Influence of station density, interpolation method and grid resolution. (21st October 2018)

• Record Type: Journal Article
• Authors: Herrera, Sixto; Kotlarski, Sven; Soares, Pedro M. M.; Cardoso, Rita M.; Jaczewski, Adam; Gutiérrez, José M.; Maraun, Douglas
• Other Names: Jack Chris guestEditor.; Katragkou Eleni guestEditor.
• Is Part Of: International journal of climatology. Volume 39:Number 9(2019)
• Page Start: 3717
• Page End: 3729
• Publication Date: 2018-10-21
• Subjects: gridded observations -- interpolation method -- observational uncertainty -- stations density -- scale -- uncertainty; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.5878
• Languages: English
• ISSNs: 0899-8418

Abstract

Abstract : This work analyses three uncertainty sources affecting the observation‐based gridded data sets: station density, interpolation methodology and spatial resolution. For this purpose, we consider precipitation in two countries, Poland and Spain, three resolutions (0.11, 0.22 and 0.44°), three interpolation methods, both areal‐ and point‐representative implementations, and three different densities of the underlying station network (high/medium/low density). As a result, for each resolution and interpolation approach, nine different grids have been obtained for each country and inter‐compared using a variance decomposition methodology. Results indicate larger differences among the data sets for Spain than for Poland, mainly due to the larger spatial variability and complex orography of the former region. The variance decomposition points out to station density as the most influential factor, independent of the season, the areal‐ or point‐representative implementation and the country considered, and slightly increasing with the spatial resolution. In contrast, the decomposition is stable when extreme precipitation indices are considered, in particular for the 50‐year return value. Finally, the uncertainty due to station sub‐sampling inside a particular grid box decreases with the number of stations used in the averaging/interpolation. In the case of spatially homogeneous grid boxes, the interpolation approach obtains similar results for all the parameters, excepting the wet day frequency, independently of the number of stations. When there is a more significant internal variability in the grid box, the interpolation is more sensitive to the number of stations, pointing out to a minimum stations' density for the target resolution (six to seven stations). Abstract : The uncertainty due to the stations density, interpolation method and resolution has been analysed for the precipitation in two countries, Poland and Spain, by means a variance decomposition analysis. The results reflect that the main factor in the development of gridded data sets is the underlying station's density for both mean and extreme precipitation. In addition, a minimum density of six to seven stations per grid box has been identified to reach an effective resolution of 0.44.