the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
MOPREDAScentury: a long-term monthly precipitation grid for the Spanish mainland
Dhais Peña-Angulo
Víctor Trullenque-Blanco
Carlos González-Hidalgo
Abstract. This article describes the development of a monthly precipitation dataset for the Spanish mainland (western Mediterranean basin), covering the period between December 1915 and December 2020. The dataset combines ground observational data from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET) and new data rescued from meteorological yearbooks published prior to 1951 that was never incorporated into the NCDB. The yearbooks data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century, the period when the dataset was more scarce. The final dataset contains records from 11,312 stations, although the number of stations with data in a given month varies largely between 674 in 1939 and a maximum of 5,234 in 1975. Spatial interpolation was used on the resulting dataset to create monthly precipitation grids. The process involved a two-stage process: estimation of the probability of zero-precipitation (dry month), and estimation of precipitation magnitude. Interpolation was carried out using universal kriging, using anomalies (ratios with respect to the 1961–2000 monthly climatology) as dependent variable and several geographic variates as independent variables. Cross-validation results showed that the resulting grids are spatially and temporally unbiased, although the mean error and the variance deflation effect are highest during the first decades of the 20th century, when the observational dataset was more scarce. The dataset is available at https://doi.org/10.20350/digitalCSIC/15136 under an open license, and can be cited as Beguería et al. (2023).
Santiago Beguería et al.
Status: open (until 27 Apr 2023)
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CC1: 'Comment on essd-2023-63', Sixto Herrera, 17 Mar 2023
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Dear Editor and Authors,
attached you have the comments and decision of the work sent for publication in ESSD.
Best regards,
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RC1: 'Reply on CC1', Anonymous Referee #1, 23 Mar 2023
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The paper describes the development of a monthly precipitation dataset for the Spanish mainland, covering the period 1915-2020. The dataset combines observational data from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET) with and new data rescued. The rescue of data considerably increases the density of data available for the variable analysed, monthly precipitation, and represents a significant enrichment of the historical climate data in Spain. The authors already have experience in the methodology used and its use is efficient and well documented. The result is a product that significantly improves the gridded datasets available up to now for Spain, increasing the density of spatial information and, especially, increasing the temporal dimension, providing complete coverage of monthly precipitation for more than a century. The authors clearly indicate the strengths and weaknesses of the dataset, specifically noting in which types of analysis the results may be more deficient when using their data.
However, there are some gaps and doubts about the data rescue process and quality control, which are expressed below.
- Although it is indicated that digitisation was carried out by using special flatbed scanners and manual reading and input, what are the percentages of data recovered in one way or another? And has the same quality control been applied to both?
- Digitization has been carried out on monthly data or another scale (daily, sub-daily,...)?
- 22% (43% before 1950) of the stations have 5 or less years of data (419 only 1 year). These short series usually have data quality problems. Has the contribution of these series been tested in any way, is it relevant and is it homogeneous compared to more stable series?
- The quality control of digitized data is very little restrictive. Individual data that exceed certain relative or absolute thresholds are not checked, nor is visual cross-checking of the digitized data described. The type of quality control used may be sufficient to detect digitization errors that have a significant effect on the gridded product, but it does not guarantee that the retrieved series can be used with confidence for other types of climate analysis that involve the use of the individual series. An additional and exhaustive quality control on the recovered data fraction should then be done.
- Regarding the data discarded in the quality control, to which cases of those described in 80-85 do they correspond?
- How many suspicious data detected at quality control have been recovered by consulting the original sources or others?
- In line 89 “example of data rejection is provided in...” it is not indicated
Citation: https://doi.org/10.5194/essd-2023-63-RC1
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RC1: 'Reply on CC1', Anonymous Referee #1, 23 Mar 2023
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Santiago Beguería et al.
Data sets
MOPREDAScentury: a long-term monthly precipitation grid for the Spanish mainland Santiago Beguería, Dhais Peña-Angulo, Víctor Trullenque-Blanco, and Carlos González-Hidalgo http://hdl.handle.net/10261/291131
Santiago Beguería et al.
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