The EUSTACE global land station daily air temperature dataset

Abstract

We describe a global dataset of quality‐controlled in situ daily air temperature observations covering the period 1850–2015, developed in the framework of the EUSTACE (EU Surface Temperature for All Corners of Earth) project (http://www.eustaceproject.org). The dataset includes a total of 35,364 daily series of maximum and minimum temperature obtained from seven different collections. About 97% of the series are publicly available in a common format, while the remaining 3% can be obtained from the original data providers. Unlike other similar products, duplicates have been removed without blending of series, which simplifies data traceability and improves the temporal homogeneity of the individual series at the cost of a smaller average length. Residual artificial signals (breakpoints) in the series caused by station relocations, changes in instrumentation, etc., have been detected by means of the combination of four breakpoint detection tests, four variables and three temporal aggregations. The combined results give not only the most probable position of the breakpoints, but also a measure of their likelihood. The reliability of the detection was estimated for each year of each target series, based on the number of reference series and on their correlation with the target series. Moreover, its general performance was evaluated through a benchmark of synthetic series. This product will be combined with datasets of marine and ice in situ air temperature observations and with measurements from satellite to produce the first complete global statistical reconstruction of daily near‐surface air temperature.

Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods

“…Furthermore, as the number and location of true break points in our data set remain unknown, statements about the probability of detection and false alarm rates (Brugnara et al, 2019) are not possible.…”

The EUSTACE Project: Delivering Global, Daily Information on Surface Air Temperature

“…1) Detecting and correcting for non-climatic discontinuities in weather station series: to provide an accurate picture of variations in air temperature, measurements at weather stations have been checked for any jumps in the series and then corrected (Squintu et al, 2019a and b). Such discontinuities might have arisen from changes in the surroundings of the weather station, the instruments used, the location of the station, or the measurement procedure (Brugnara et al, 2019).…”

“…In addition to break detection, the EUSTACE global station dataset has undergone other quality checks both on the air temperature measurements themselves and on reported station altitudes (Brugnara et al 2019). Appendix C provides a link to the resulting dataset of daily mean, maximum and minimum temperature.…”

“…DOI S 10.1175/BAMS-D-19-0095.1. annual averages of differences between each station and neighboring reference series (our relative tests; all methods used), or to the averages of the target station alone (our absolute test; Wang (2008) only used), in the absence of neighboring stations or if available reference series are not suitable(Brugnara et al (2019) provides details). Using multiple methods of detecting discontinuities provides an ability to assess the robustness of the results.Figure 2…”

PISCOeo_pm, a reference evapotranspiration gridded database based on FAO Penman-Monteith in Peru

“…Two conditions were required to consider a station as neighbour: i) five years of data in common (365 days repeated at least five times) and a minimum correlation of 0.6 45 , 49 – 51 . Likewise, to make use of those stations that do not share a common period at the beginning, an iterative application of the algorithm 52 of at least three cycles was developed to search for neighbouring stations according to the horizontal-vertical distance (Supplementary Fig.…”