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An Ensemble Version of the E‐OBS Temperature and Precipitation Data Sets

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TLDR
In this article, a new version of the Europe-wide OBS temperature (daily minimum, mean and maximum values) and precipitation dataset is presented, which provides an improved estimation of interpolation uncertainty through the calculation of a 100-member ensemble of realizations of each daily field.
Abstract
We describe the construction of a new version of the Europe‐wide E‐OBS temperature (daily minimum, mean and maximum values) and precipitation dataset. This version provides an improved estimation of interpolation uncertainty through the calculation of a 100‐member ensemble of realizations of each daily field. The dataset covers the period back to 1950, and provides gridded fields at a spacing of 0.25° x 0.25° in regular latitude/longitude coordinates. As with the original E‐OBS dataset, the ensemble version is based on the station series collated as part of the ECA&D initiative. Station density varies significantly over the domain, and over time, and a reliable estimation of interpolation uncertainty in the gridded fields is therefore important for users of the dataset. The uncertainty quantified by the ensemble dataset is more realistic than the uncertainty estimates in the original version, although uncertainty is still underestimated in data‐sparse regions. The new dataset is compared against the earlier version of E‐OBS and against regional gridded datasets produced by a selection of National Meteorological Services (NMSs). In terms of both climatological averages and extreme values, the new version of E‐OBS is broadly comparable to the earlier version. Nonetheless, users will notice differences between the two E‐OBS versions, especially for precipitation, which arises from the different gridding method used.

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Citations
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References
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Book

Generalized Additive Models: An Introduction with R, Second Edition

Simon N Wood
TL;DR: In this article, a simple linear model is proposed to describe the geometry of linear models, and a general linear model specification in R is presented. But the theory of linear model theory is not discussed.
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Generalized Additive Models: An Introduction With R

TL;DR: Robinson, R. (2007). Generalized Additive Models: An Introduction With R.(2007).
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Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States

TL;DR: In this paper, the authors used the PRISM (Parameter-elevation relationships on independent slopes model) interpolation method to develop data sets that reflected, as closely as possible, the current state of knowledge of spatial climate patterns in the United States.
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A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006

TL;DR: In this paper, the authors presented a European land-only daily high-resolution gridded data set for precipitation and minimum, maximum, and mean surface temperature for the period 1950-2006.
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Thin plate regression splines

TL;DR: The production of low rank smoothers for d’≥ 1 dimensional data, which can be fitted by regression or penalized regression methods, are discussed, which allow the use of approximate thin plate spline models with large data sets, and provide a sensible way of modelling interaction terms in generalized additive models.
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