New high-resolution gridded dataset of daily mean, minimum, and maximum temperature and relative humidity for Central Europe (HYRAS)

Christène Razafimaharo,Stefan Krähenmann,Simona Höpp,Thomas Deutschländer,Monika Rauthe

doi:10.1007/s00704-020-03388-w

Christène Razafimaharo, Stefan Krähenmann + Show 3 more

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https://doi.org/10.1007/s00704-020-03388-w

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Abstract

This study presents daily high-resolution (5 km × 5 km) grids of mean, minimum, and maximum temperature and relative humidity for Germany and its catchment areas, from 1951 to 2015. These observational datasets (HYRAS) are based upon measurements gathered for Germany and its neighbouring countries, in total more than 1300 stations, gridded in two steps: first, the generation of a background field, using non-linear vertical temperature profiles, and then an inverse distance weighting scheme to interpolate the residuals, subsequently added onto the background field. The modified Euclidian distances used integrate elevation, distance to the coast, and urban heat island (UHI) effect. A direct station-grid comparison and cross-validation yield low errors for the temperature grids over most of the domain and greater deviations in more complex terrain. The interpolation of relative humidity is more uncertain due to its inherent spatial inhomogeneity and indirect derivation using dew point temperature. Compared with other gridded observational datasets, HYRAS benefits from its high resolution and captures complex topographic effects. HYRAS improves upon its predecessor by providing datasets for additional variables (minimum and maximum temperature), integrating temperature inversions, maritime influence and UHI effect, and representing a larger area. With a long-term observational dataset of multiple meteorological variables also including precipitation, various climatological analyses are possible. We present long-term historical climate trends and relevant indices of climate extremes, pointing towards a significantly warming climate over Germany, with no significant change in total precipitation. We also evaluate extreme events, specifically the summer heat waves of 2003 and 2015.

Highlights

High-resolution observation grids of climate variables have become important components of climatological studies
High-resolution observation grids allow for spatial analyses based on continuous surfaces rather than on irregularly spaced point measurements, and long-term daily grids allow for analyses on different spatial scales and for the analysis of trends
The objectives of this paper are (a) to present the new methodology used for the updated HYRAS dataset (HYRAS-2015) for temperature and relative humidity; (b) to assess the quality of the dataset as it compares with station observations, other observational datasets, and its predecessor (HYRAS-2006); and (c) to present applications of the HYRAS dataset in analysing longterm historical climate in Germany

Summary

Introduction

High-resolution observation grids of climate variables have become important components of climatological studies. These grids consist of station measurements interpolated at a desired resolution using one of the various interpolation methods available. They are used as inputs for various models (Heininger and Cullmann (2015), Gusyev et al (2016), Stahl et al (2017), Höllering et al (2018)), and they can be used for the bias correction of climate models High-resolution observation grids allow for spatial analyses based on continuous surfaces rather than on irregularly spaced point measurements, and long-term daily grids allow for analyses on different spatial scales and for the analysis of trends.

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