However, its performance with real world data has been less well studied. Previous studies found that the PHA can perform well at correcting synthetic time series when certain artificial biases are introduced. Since 2011, this homogenized dataset has been updated almost daily by applying the "Pairwise Homogenization Algorithm" (PHA) to the non-homogenized datasets. The widely used Global Historical Climatology Network (GHCN) monthly temperature dataset is available in two formats - non-homogenized and homogenized. Of PHA adjustments might potentially help to identify some of these spurious adjustments. Using station metadata to assess the reliability The homogenized GHCN dataset may have been spurious. Therefore, while the PHA remainsĪ useful tool in the community’s homogenization toolbox, many of the PHA adjustments applied to (18% for Version 3) were associated with a documented event within 1 year, and 67% (69%įor Version 3) were not associated with any documented event. The consistency of PHA adjustments improved when the breakpointsĬorresponded to documented station history metadata events. The adjustments applied for GHCN Version 4, 64% (61% for Version 3) were identified on less thanĢ5% of runs, while only 16% of the adjustments (21% for Version 3) were identified consistently for more than 75% of the runs. Inconsistency in the identified breakpoints (and hence adjustments applied) was revealed. Of stations from 24 European countries for which station history metadata were available. The different breakpoints identified were analyzed for a set Therefore, the homogenized GHCNĭatasets (Version 3 and 4) were downloaded almost daily over a 10-year period (2011-2021) yieldingģ689 different updates to the datasets. Performance with real world data has been less well studied. Well at correcting synthetic time series when certain artificial biases are introduced. Previous studies found that the PHA can perform Since 2011, this homogenizedĭataset has been updated almost daily by applying the “Pairwise Homogenization Algorithm” The widely used Global Historical Climatology Network (GHCN) monthly temperatureĭataset is available in two formats-non-homogenized and homogenized. Moreover, the importance of cooperation in regard to data rescue is underlined and examples of possible partnerships are provided. This paper wants to offer insight into different strategies for data rescue as well as into ongoing activities. Therefore, the selected countries will provide a good overview of the problems and solutions discussed during the EUMETNET meetings. Nevertheless, the problems data rescue is confronted with are similar for all activities. While Austria, Croatia and Slovenia are connected by a partly common history which is also displayed in the history of their meteorological network, Catalonia has a different historical background. This paper will provide information on the data rescue activities of 4 EUMETNET countries focusing on different aspects and in different states of the data rescue process. The activity raised the awareness of the need for data rescue and ongoing activities. The EUMETNET Data Rescue activity was started in order to investigate the potential for additional long‐term stations, to monitor the progress of data rescue in European countries and to support participants with know‐how exchange and international connections to facilitate data exchange. Also in Europe, part of the historic data is still only available in paper archives, without any digital access to these sources. All over the world, different data rescue activities try to extend existing time series and to retrieve data in spatial and temporal data sparse regions. Abstract In situ data are an essential need in the analyses of past and current climate change.
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