Comparison of six methods for the interpolation of daily, European climate data

By N. Hofstra, Malcolm R. Haylock. 2008

401 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/e2d1249a40a4171c488653f320d451db254e84c2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Michael D. Smith
2. Michael J. Wolff
3. Ehouarn Millour
4. Robert J. Wilson
5. Jean-Baptiste Aubin
6. Mathieu Lepot
7. François Clemens
8. P. Pirinen

In Global and Planetary Change

1. Michael D. Smith
2. Robert J. Wilson
3. Ehouarn Millour
4. Francois Forget
5. B. Cantor
6. D. Kass
7. Mark T. Lemmon
8. Jean-Baptiste Aubin

Citations in the corpus

• Interpolation in Time Series : An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment (2017, methodology): “In the review by [20], Taylor’s adjustments of these methods for specific applications (e.g., in [21]) have been proposed for detrended inverse distance weighting, [22] for angular distance weighting, etc.).”
• Global gridded precipitation over land: a description of the new GPCC First Guess Daily product (2013, result): “These results have also been found for other datasets (e.g. Hofstra et al., 2008).15 The results of the comparison of these methods show that the performance does not differ significantly if they are compared in the same climate zone (see Table 2).”
• Spatial variability in correlation decay distance and influence on angular‐distance weighting interpolation of daily precipitation over Europe (2009, background): “However, compared to other interpolation methods (Hofstra et al., 2008), ADW4 performs better for these specific stations and in a gridded dataset based on this station network (Haylock et al., 2008) uncertainty of gridpoint estimates for these stations will be correspondingly higher.”
• Spatial interpolation of monthly climate data for Finland: comparing the performance of kriging and generalized additive models (2013, methodology): “Pearson’s correlation coefficient (COR) (Hofstra et al. 2008).”
• Eight-year climatology of dust optical depth on Mars (2014, methodology): “case of temperature ground stations on the Earth, and Hofstra et al. (2008) evaluate kriging among other methods of spatial interpolation.”