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Contributors: F F. Isotta (Meteoswiss), C. Frei (Meteoswiss), B. Chimani (ZAMG/GeoSphere Austria), J. Hiebl (ZAMG)
Issued by: MeteoSwiss and ZAMG/GeoSphere Austria / F. Isotta, C. Frei, B. Chimani and J. Hiebl
Issued Date: 15/12/2021
Ref: C3S C3S_M311a_Lot4.2.3.6311_lot3_v1
Official refence number service contract: 2017 2021/ C3S_311a311_Lot4lot3_KNMI/SC1
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History of modifications to this User Guide
Version | Date | Description of modification | Chapters / Sections |
1.0 | 2019-05-01 | First version | Whole document |
1.1 | 2023-04-18 | adapted first version | 'Contributors' and 'Issued by' information |
List of versions covered by this document
Version | Release date | Period covered | Comments/modifications |
LAPrec1871.v1.0 | 2019-05-01 | From 1871-01-01 to 2017-12-31 | First release. Provisional period: 2012-2017. |
LAPrec1871.v1.1 | 2021-02-10 | From 1871-01-01 to 2019-12-31 | Update. Provisional period: 2014-2019. |
LAPrec1871.v1.2 LAPrec1901.v1.2 | 2023-03-23 2023-03-23 | From 1871-01-01 to 2020-12-31 | Update. Provisional period: 2015-2020. |
Acronyms
Version |
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APGD | Alpine precipitation grid dataset |
ARSO | Agencija Republike Slovenije Okolje |
EPSG | European Petroleum Survey Group Geodesy |
ETRS89 / ETRS-LAEA | European Terrestrial Reference System 1989-Lambert Azimuthal Equal Area |
DHMZ | Državni hidrometeorološki zavod |
DWD | Deutscher WetterDienst |
FHMZ | Federalni hidrometeorološki zavod |
HISTALP | Historical Instrumental Climatological Surface Time Series Of The Greater Alpine Region |
LAPrec | Long-term Alpine Precipitation Reconstruction |
MAE | Mean Absolute Error |
MSESS | Mean-Squared Error Skill Score |
OMSZ | Országos Meteorológiai Szolgálat |
PCA | Principal Component Analysis |
PRISM | Parameter-elevation Regressions on Independent Slopes Model |
RSOI | Reduced Space Optimal Interpolation |
UERRA | Uncertainties in Ensembles of Regional ReAnalyses |
ZAMG | Zentralanstalt für Meteorologie und Geodynamik, with January 2023 the insititute was renamed to GeoSphere Austria |
Introduction
Spatial climate analyses that extend back over many decades are an important basis for monitoring climate variations and long-term change (e.g. van der Schrier et al., 2013). They also serve as input for modelling environmental systems (e.g. ecosystems and glaciers, Kittel et al., 2004), and for calibrating climate reconstructions with proxy data (tree rings, Frank & Esper, 2005).
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The present report describes the two data sources integrated into LAPrec (section 1), and it outlines the reconstruction methodology (section 2). The technicalities of production and data access are explained in section 3, and a selection of reconstruction results and analyses are presented in section 4. Section 5 discusses the limitations of the dataset and guides users in the professional application. The report closes with final remarks (section 6).
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Parameter | monthly precipitation sum (mm) |
Domain | Alpine region (approx. 43–49°N, 4–17°E, land area only) |
Time period | two versions: starting 1871 and 1901, respectively |
Time resolution | monthly |
Coordinate system | ETRS89 / ETRS-LAEA (EPSG 3035) |
Grid spacing | 5 km |
Input Data | HISTALP (Auer et al. 2007), APGD (Isotta et al. 2014) |
Method | RSOI (Kaplan et al. 1997; Schiemann et al. 2010) |
Data
The construction of LAPrec builds on two data components, namely a dataset of high-quality long-term station series that contributes information on long-term variations, and a dataset of high-resolution grid data that enhances information on spatial variability. The two data components are described in the following.
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The LAPrec dataset is provided in NetCDF format, following the CF-1.6 convention (see http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html
). Besides the monthly precipitation fields, fields of geographical variables (longitude, longitude and elevation above sea level) and details on the coordinate reference system are included. The fields consist of a regular 5 km x km grid that is spatially referenced in ETRS89 / ETRS-LAEA (European Terrestrial Reference System 1989-Lambert Azimuthal Equal Area, see https://spatialreference.org/ref/epsg/3035/).
Two variants of the dataset exist reflecting the two different starting dates (January 1871 and January 1901) and respective different station numbers (currently 85 and 164). Updates of the dataset might change the exact number of ingoing stations but keep three criteria for missing data defined in chapter 1.1. as well as the starting years 1871 and 1901. Homogenised station series data (no deliverable of this contract) are available from the HISTALP database of ZAMG and according files. In principle, the station series are updated annually. Due to national issues, the timing of the update varies within the year.
Versioning of the LAPrec dataset is in the format vX.Y where an increment of X denotes methodical adaptations and an increment in Y denotes updates in input station data. The file names consist of the dataset acronym, the starting year and the version number, e. g. LAPrec1871.v1.0.nc and LAPrec1901.v1.0.nc.
Results
Reconstruction examples
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Table 2: Error measures (in mm per month) from a leave-one-out cross-validation using all reconstruction stations of the respective reconstruction windowAnchor table2 table2
Dataset | LAPrec1871 | LAPrec1901 |
evaluation period | 1871–2017 | 1901–2017 |
bias | 0.8 | 2.1 |
mean absolute error | 18.6 | 17.3 |
The mean absolute error (MAE) is about 18 mm per month. Nominally, the error is only marginally larger in the longer dataset, but this may be due to differences between the station samples from which the statistics has been calculated. Indeed, the mean absolute error varies considerably between test station, as is shown in Figure 7. MAE is typically twice as large in areas of complex topography (Switzerland, Austria), and in areas of coarse station density (Italy and Croatia), compared to densely sampled flatlands (Swiss Plateau, Eastern France).
In terms of seasonal variation, the mean absolute error is largest in summer, when the monthly sums are larger and when convection induces, generally, smaller-scale precipitation anomalies.
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FHMZ (http://www.fhmzbih.gov.ba/latinica/index.php)
ZAMG (www.zamg.ac.at), GeoSphere Austria (https://www.geosphere.at/)
Meteo Swiss (www.meteoswiss.admin.ch)
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