ALBERT

Description: The availability of highly accessible and reliable monthly gridded data sets of the global land-surface precipitation is a need that has already been identified in the mid-80s when there was a complete lack of a globally homogeneous gauge based precipitation analysis. Since 1989 the Global Precipitation Climatology Centre (GPCC) has built up a unique capacity to assemble, quality assure, and analyse rain gauge data gathered from all over the world. The resulting data base has exceeded 200 yr in temporal coverage and has acquired data from more than 85 000 stations world-wide. This paper provides the reference publication for the four globally gridded monthly precipitation products of the GPCC covering a 111-yr analysis period from 1901–present, processed from this data base. As required for a reference publication, the content of the product portfolio, as well as the underlying methodologies to process and interpolate are detailed. Moreover, we provide information on the systematic and statistical errors associated with the data products. Finally, sample applications provide potential users of GPCC data products with suitable advice on capabilities and constraints of the gridded data sets. In doing so, the capabilities to access ENSO and NAO sensitive precipitation regions and to perform trend analysis across the past 110 yr are demonstrated. The four gridded products, i.e. the Climatology V2011 (CLIM), the Full Data Reanalysis (FD) V6, the Monitoring Product (MP) V4, and the First Guess Product (FG) are public available on easy accessible latitude longitude grids encoded in zipped clear text ASCII files for subsequent visualization and download through the GPCC download gate hosted on ftp://ftp.dwd.de/pub/data/gpcc/html/download_gate.html by the Deutscher Wetterdienst (DWD), Offenbach, Germany. Depending on the product four (0.25°, 0.5°, 1.0°, 2.5° for CLIM), three (0.5°, 1.0°, 2.5°, for FD), two (1.0°, 2.5° for MP) or one (1.0° for FG) resolutions are provided, and for each product a DOI reference is provided allowing for public user access to the products. A preliminary description of the scope of a fifth product – the Homogenized Precipitation Analysis (HOMPRA) – is also provided. Its comprehensive description will be handed later in an extra paper upon completion of this data product. DOIs of the gridded datasets examined: doi:10.5676/DWD_GPCC/CLIM_M_V2011_025, doi:10.5676/DWD_GPCC/CLIM_M_V2011_050, doi:10.5676/DWD_GPCC/CLIM_M_V2011_100, doi:10.5676/DWD_GPCC/CLIM_M_V2011_250, doi:10.5676/DWD_GPCC/FD_M_V6_050, doi:10.5676/DWD_GPCC/FD_M_V6_100, doi:10.5676/DWD_GPCC/FD_M_V6_250, doi:10.5676/DWD_GPCC/MP_M_V4_100, doi:10.5676/DWD_GPCC/MP_M_V4_250, doi:10.5676/DWD_GPCC/FG_M_100

Description: This paper describes the new "First Guess Daily" product of the Global Precipitation Climatology Centre (GPCC). The new product gives an estimate of the global daily precipitation gridded at a spatial resolution of 1° latitude by longitude. It is based on rain gauge data reported in near real-time via the Global Telecommunication System (GTS) and available about three to five days after the end of each observation month. In addition to the gridded daily precipitation totals in mm day−1, the standard deviation in mm day−1, the Kriging interpolation error in % and the number of measurements per grid cell are also encoded into the monthly netCDF product file and provided for all months since January 2009. Prior to their interpolation the measured precipitation values undergo a preliminary automatic quality control. For the calculation of the areal mean of the grid, anomalies are interpolated with ordinary block Kriging. This approach allows for a near real-time release. However, the purely GTS-based data processing lacks an intensive quality control as well as a high data density. Therefore the product is denoted as "First Guess", and DOI referenced under doi:10.5676/DWD_GPCC/FG_D_100. Besides the "First Guess Daily" product, two further products are under developement at GPCC ("Full Data Daily" and a merged satellite-gauge product), which will be based on all available daily data that have undergone a strict quality control. All GPCC products are available free of charge and provided via the GPCC webpage: ftp://ftp-anon.dwd.de/pub/data/gpcc/html/download_gate.html.

Description: The Global Precipitation Climatology Centre Drought Index (GPCC-DI) provides estimations of precipitation anomalies with respect to long term statistics. It is a combination of the Standardized Precipitation Index with adaptations from Deutscher Wetterdienst (SPI-DWD) and the Standardized Precipitation Evapotranspiration Index (SPEI). Precipitation data were taken from the Global Precipitation Climatology Centre (GPCC) and temperature data from NOAA's Climate Prediction Center (CPC). The GPCC-DI is available with several averaging periods of 1, 3, 6, 9, 12, 24 and 48 months for different applications. Since spring 2013, the GPCC-DI is calculated operationally and available back to January 2013. Typically it is released at the 10th day of the following month, depending on the availability of the input data. It is calculated on a~regular grid with 1° spatial resolution. All averaging periods are integrated into one netCDF-file for each month. This dataset can be referenced by the DOI:10.5676/DWD_GPCC/DI_M_100 and is available free of charge from the GPCC website ftp://ftp.dwd.de/pub/data/gpcc/html/gpcc_di_doi_download.html.

Description: In 1970, the Institute of Geography of the University of Berne initiated the phenological observation network BernClim. Seasonality information from plants, fog and snow originally served for applications in urban and regional planning, agricultural and touristic suitability and are now a valuable data set for climate change impacts studies. Covering the growing season volunteer observers record the dates of key development stages of hazel (Coryllus avellana), dandelion (Taraxacum officinale), apple tree (Pyrus malus) and beech (Fagus sylvatica). All observations consist of detailed site information including location, altitude, exposition and inclination that make BernClim unique in detail-richness on decadal time-scales. Quality control (QC) by experts and statistical analyses of the data has been performed to flag impossible dates, dates outside the biologically plausible range, repeated dates in the same year, stretches of consecutive identical dates, and statistically inconsistent dates (outliers in time or in space). Here, we report BernClim data of 7414 plant phenological observations from 1970 to 2018 from 1304 sites at 110 stations, the QC procedure and selected applications (Rutishauser et al., 2019, https://doi.org/10.1594/PANGAEA.900103). The QC points to a very good internal consistency (only 0.2 % were flagged as internally inconsistent) and likely a high quality of the data. BernClim data indicate a trend towards an extended growing season. They also well track the regime shift in the late 1980s.

Description: The PermaSense project is an ongoing interdisciplinary effort between geo-science and engineering disciplines started in 2006 with the goals to make observations possible that previously have not been possible. Specifically the aims are to obtain measurements data in unprecedented quantity and quality based on technological advances. This paper describes a unique ten+ year data record obtained from in-situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt Switzerland at 3500 m a.s.l. Through the utilization of state-of-the-art wireless sensor technology it was possible to obtain more data of higher quality, make this data available in near real-time and tightly monitor and control the running experiments. This data set (DOI: https://doi.org/10.1594/PANGAEA.897640, Weber et al., 2019a) constitutes the longest, densest and most diverse data record in the history of mountain permafrost research worldwide with 17 different sensor types used at 29 distinct sensor locations consisting of over 114.5 million data points captured over a period of ten+ years. By documenting and sharing this data in this form we contribute to making our past research reproducible and facilitate future research based on this data e.g. in the area of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models.