Updated gridded datasets version 2020 provided by the Global Precipitation Climatology Centre (GPCC)

2021 ◽  
Author(s):  
Elke Rustemeier ◽  
Udo Schneider ◽  
Markus Ziese ◽  
Peter Finger ◽  
Andreas Becker
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Land Surface ◽  
Quality Data ◽  
Digital Object Identifier ◽  
Full Data ◽  
High Quality ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Global Precipitation

<p><span>Since its founding in 1989, the Global Precipitation Climatology Centre (GPCC) has been producing global precipitation analyses based on land surface in-situ measurements. </span><span>In the now over 30 years the underlying database has been continuously expanded and includes a high station density and large temporal coverage. Due to the semi-automatic quality control routinely performed on the incoming station data, the GPCC database has a very high quality.</span> <span>Today, the GPCC holds data from </span><span>123,000 stations, about three quarters of them having long time series.</span></p><p><span>The core of the analyses is formed by data from the global meteorological and hydrological services, which provided their records to the GPCC, as well as global and regional data collections.  </span><span>In addition, the GPCC receives SYNOP and CLIMAT reports via the WMO-GTS. These form a supplement for the high quality precipitation analyses and the basis for the near real-time evaluations.</span></p><p><span>Quality control activities include cross-referencing stations from different sources, flagging of data errors, and correcting temporally or spatially offset data. This data then forms the basis for the following interpolation and product generation.</span></p><p><span>In near real time, the 'First Guess Monthly', 'First Guess Daily', 'Monitoring Product', ‘Provisional Daily Precipitation Analysis’ and the 'GPCC Drought Index' are generated. These are based on WMO-GTS data and monthly data generated by the CPC (NOAA). </span></p><p><span>With a 2-3 year update cycle, the high quality data products are generated with intensive quality control and built on the entire GPCC data base. These non-real time products consist of the 'Full Data Monthly', 'Full Data Daily', 'Climatology', and 'HOMPRA-Europe' and are now available in the 2020 version. </span></p><p><span>A</span><span>ll gridded datasets presented in this paper are freely available in netcdf format on the GPCC website https://gpcc.dwd.de and referenced by a digital object identifier (DOI). The site also provides an overview of all datasets, as well as a detailed description and further references for each dataset.</span></p>

scholarly journals Updated gridded datasets version 2020 provided by the Global Precipitation Climatology Centre (GPCC)

2021 ◽  
Author(s):  
Elke Rustemeier ◽  
Udo Schneider ◽  
Markus Ziese ◽  
Peter Finger ◽  
Andreas Becker
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Quality Data ◽  
Digital Object Identifier ◽  
Full Data ◽  
High Quality ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Hydrological Services ◽  
Global Precipitation

<p>Since its founding in 1989, the Global Precipitation Climatology Centre (GPCC) has been producing global precipitation analyses based on land surface in-situ measurements. In the now over 30 years the underlying database has been continuously expanded and includes a high station density and large temporal coverage. Due to the semi-automatic quality control routinely performed on the incoming station data, the GPCC database has a very high quality. Today, the GPCC holds data from more than 123,000 stations, about three quarters of them having long time series.</p><p>The core of the analyses is formed by data from the global meteorological and hydrological services, which provided their records to the GPCC, as well as national meteorological and hydrological services from all over the world.  In addition, the GPCC receives SYNOP and CLIMAT reports via the WMO-GTS. These form a supplement for the high quality precipitation analyses and the basis for the near real-time evaluations.</p><p>Quality control activities include cross-referencing stations from different sources, flagging of data errors, and correcting temporally or spatially offset data. This data then forms the basis for the following interpolation and product generation.</p><p>In near real time, the 'First Guess Monthly', 'First Guess Daily', 'Monitoring Product', ‘Provisional Daily Precipitation Analysis’ and the 'GPCC Drought Index' are generated. These are based on WMO-GTS data and monthly data generated by the CPC (NOAA).</p><p>With a 2-3 year update cycle, the high quality data products are generated with intensive quality control and built on the entire GPCC data base. These non-real time products consist of the 'Full Data Monthly', 'Full Data Daily', 'Climatology', and 'HOMPRA-Europe' and are now available in the 2020 version.</p><p>All gridded datasets presented in this paper are freely available in netcdf format on the GPCC website https://gpcc.dwd.de and referenced by a digital object identifier (DOI). The site also provides an overview of all datasets, as well as a detailed description and further references for each dataset.</p>

scholarly journals The new portfolio of global precipitation data products of the Global Precipitation Climatology Centre suitable to assess and quantify the global water cycle and resources

2016 ◽  
Vol 374 ◽  
pp. 29-34 ◽  
Author(s):  
Udo Schneider ◽  
Markus Ziese ◽  
Anja Meyer-Christoffer ◽  
Peter Finger ◽  
Elke Rustemeier ◽  
...  
Keyword(s):  
Land Surface ◽  
Water Cycle ◽  
Spatial Scales ◽  
Research Programme ◽  
Special Importance ◽  
Product Portfolio ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
The World ◽  
Global Precipitation

Abstract. Precipitation plays an important role in the global energy and water cycle. Accurate knowledge of precipitation amounts reaching the land surface is of special importance for fresh water assessment and management related to land use, agriculture and hydrology, incl. risk reduction of flood and drought. High interest in long-term precipitation analyses arises from the needs to assess climate change and its impacts on all spatial scales. In this framework, the Global Precipitation Climatology Centre (GPCC) has been established in 1989 on request of the World Meteorological Organization (WMO). It is operated by Deutscher Wetterdienst (DWD, National Meteorological Service of Germany) as a German contribution to the World Climate Research Programme (WCRP). This paper provides information on the most recent update of GPCC's gridded data product portfolio including example use cases.

Large-Scale Runoff from Landmasses: A Global Assessment of the Closure of the Hydrological and Atmospheric Water Balances*

2014 ◽  
Vol 15 (6) ◽  
pp. 2111-2139 ◽  
Author(s):  
Christof Lorenz ◽  
Harald Kunstmann ◽  
Balaji Devaraju ◽  
Mohammad J. Tourian ◽  
Nico Sneeuw ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Global Precipitation Climatology Project ◽  
Research Unit ◽  
Climatic Conditions ◽  
Climatic Research Unit ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Global Land ◽  
Global Precipitation

Abstract The performance of hydrological and hydrometeorological water-balance-based methods to estimate monthly runoff is analyzed. Such an analysis also allows for the examination of the closure of water budgets at different spatial (continental and catchment) and temporal (monthly, seasonal, and annual) scales. For this analysis, different combinations of gridded observations [Global Precipitation Climatology Centre (GPCC), Global Precipitation Climatology Project (GPCP), Climate Prediction Center (CPC), Climatic Research Unit (CRU), and University of Delaware (DEL)], atmospheric reanalysis models [Interim ECMWF Re-Analysis (ERA-Interim), Climate Forecast System Reanalysis (CFSR), and Modern-Era Retrospective Analysis for Research and Applications (MERRA)], partially model-based datasets [Global Land Surface Evaporation: The Amsterdam Methodology (GLEAM), Moderate Resolution Imaging Spectroradiometer (MODIS) Global Evapotranspiration Project (MOD16), and FLUXNET Multi-Tree Ensemble (FLUXNET MTE)], and Gravity Recovery and Climate Experiment (GRACE) satellite-derived water storage changes are employed. The derived ensemble of hydrological and hydrometeorological budget–based runoff estimates, together with results from different land surface hydrological models [Global Land Data Assimilation System (GLDAS) and the land-only version of MERRA (MERRA-Land)] and a simple predictor based on the precipitation–runoff ratio, is compared with observed monthly in situ runoff for 96 catchments of different sizes and climatic conditions worldwide. Despite significant shortcomings of the budget-based methods over many catchments, the evaluation allows for the demarcation of areas with consistently reasonable runoff estimates. Good agreement was particularly observed when runoff followed a dominant annual cycle like the Amazon. This holds true also for catchments with an area far below the spatial resolution of GRACE, like the Rhine. Over catchments with low or nearly constant runoff, the budget-based approaches do not provide realistic runoff estimates because of significant biases in the input datasets. In general, no specific data combination could be identified that consistently performed over all catchments. Thus, the performance over a specific single catchment cannot be extrapolated to other regions. Only in few cases do specific dataset combinations provide reasonable water budget closure; in most cases, significant imbalances remain for all the applied datasets.

scholarly journals A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present

2013 ◽  
Vol 5 (1) ◽  
pp. 71-99 ◽  
Author(s):  
A. Becker ◽  
P. Finger ◽  
A. Meyer-Christoffer ◽  
B. Rudolf ◽  
K. Schamm ◽  
...  
Keyword(s):  
Land Surface ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Gridded Data ◽  
Surface Precipitation ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Global Land ◽  
Data Products ◽  
Global Precipitation

Abstract. The availability of highly accessible and reliable monthly gridded data sets of global land-surface precipitation is a need that was already identified in the mid-1980s when there was a complete lack of globally homogeneous gauge-based precipitation analyses. Since 1989, the Global Precipitation Climatology Centre (GPCC) has built up its unique capacity to assemble, quality assure, and analyse rain gauge data gathered from all over the world. The resulting database has exceeded 200 yr in temporal coverage and has acquired data from more than 85 000 stations worldwide. Based on this database, 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. 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 El Niño–Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) sensitive precipitation regions and to perform trend analyses across the past 110 yr are demonstrated. The four gridded products, i.e. the Climatology (CLIM) V2011, the Full Data Reanalysis (FD) V6, the Monitoring Product (MP) V4, and the First Guess Product (FG), are publicly available on easily 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) resolution is 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 submitted later in an extra paper upon completion of this data product. DOIs of the gridded data sets examined are as follows: 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.

scholarly journals Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 649
Author(s):  
Yifeng Liu ◽  
Wei Zhang ◽  
Wenhao Du
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Quality Data ◽  
Time Data ◽  
High Quality ◽  
Real Time System ◽  
High Quality Data ◽  
Learning Framework ◽  
Data Accumulation

Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.

scholarly journals GPCC Drought Index – a new, combined, and gridded global drought index

2014 ◽  
Vol 7 (1) ◽  
pp. 243-270
Author(s):  
M. Ziese ◽  
U. Schneider ◽  
A. Meyer-Christoffer ◽  
K. Schamm ◽  
J. Vido ◽  
...  
Keyword(s):  
Drought Index ◽  
Standardized Precipitation Index ◽  
Regular Grid ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Precipitation Anomalies ◽  
The Standardized Precipitation Index ◽  
Global Precipitation ◽  
Climate Prediction Center

Abstract. 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.

scholarly journals Global gridded precipitation over land: a description of the new GPCC First Guess Daily product

2014 ◽  
Vol 6 (1) ◽  
pp. 49-60 ◽  
Author(s):  
K. Schamm ◽  
M. Ziese ◽  
A. Becker ◽  
P. Finger ◽  
A. Meyer-Christoffer ◽  
...  
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Daily Precipitation ◽  
Grid Cell ◽  
Rain Gauge ◽  
Kriging Interpolation ◽  
Telecommunication System ◽  
Full Data ◽  
Data Set ◽  
Global Telecommunication System

Abstract. 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 1° 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. Therefore, the purely GTS-based data processing lacks an intensive quality control as well as a high data density and is denoted as First Guess. The daily data set is referenced under doi:10.5676/DWD_GPCC/FG_D_100. Two further products, the Full Data Daily and a merged satellite-gauge product, are currently under development at Deutscher Wetterdienst (DWD). These additional products will not be available in near-real time, but based on significantly more and strictly quality controlled observations. All GPCC products are provided free of charge via the GPCC webpage: ftp://ftp-anon.dwd.de/pub/data/gpcc/html/download_gate.html.

Contributions to the Global Precipitation Climatology Centre

1992 ◽  
Vol 1 (1) ◽  
pp. 7-10 ◽  
Author(s):  
Hans Pichler ◽  
Hans Richner ◽  
Rainer Roth ◽  
Jens Taubenheim
Keyword(s):  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Global Precipitation

scholarly journals Evaluating the GPCC Full Data Daily Analysis Version 2018 through ETCCDI indices and comparison with station observations over mainland of China

2020 ◽  
Vol 142 (3-4) ◽  
pp. 835-845
Author(s):  
Yu Yu ◽  
Udo Schneider ◽  
Su Yang ◽  
Andreas Becker ◽  
Zhihua Ren
Keyword(s):  
Daily Precipitation ◽  
Correlation Coefficients ◽  
Full Data ◽  
Global Precipitation Climatology Centre ◽  
Daily Precipitation Data ◽  
Distance Weighting ◽  
The Mainland Of China ◽  
The Mean ◽  
Global Precipitation

Abstract The new 1° × 1° resolution global Full Data Daily Analysis Version 2018 published by the Global Precipitation Climatology Centre (GPCC) of Deutscher Wetterdienst was compared with an analysis of the measurements from the national dataset over the mainland of China with regard to four of the 27 ETCCDI indices (http://etccdi.pacificclimate.org/list_27_indices.shtml) commonly used to determine extreme precipitation (Rx5day, R10mm, CDD and SDII). After extreme value check, integrity check, and homogeneity check, the observations from 2327 surface stations covering the years from 1982 to 2016 fulfilled the criteria for the evaluation. The in situ daily precipitation data were interpolated onto a 1° × 1° grid over the mainland of China by employing Shepard’s angular and distance weighting algorithm. The four aforementioned indices were then calculated on the national station–based analysis being referred to as STA. Moreover, the aforementioned gridded GPCC Full Data Daily product was directly utilized to calculate the same indices (FDDA). The China national means of Rx5day, R10mm, CDD and SDII calculated from FDDA and STA had similar variations and trends with high correlation coefficients, and the mean biases between FDDA and STA were 2.5 mm, 1.2 days, 0.0 day and 0.3 mm respectively. The trends of Rx5day, R10mm and SDII are increasing, whereas the trend of CDD is negative. The distributions of the grid mean and the grid trends of indices over China from FDDA and STA show similar patterns too, indicating that the FDDA shows a surprisingly high fidelity in reproducing almost the same patterns in the four ETCCDI indices chosen compared with the STA-based analysis.

The International Soil Moisture Network in assistance of EO soil moisture validation products, services and models

2020 ◽  
Author(s):  
Daniel Aberer ◽  
Irene Himmelbauer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
Wouter Dorigo ◽  
...  
Keyword(s):  
Quality Control ◽  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Measurement Techniques ◽  
European Space Agency ◽  
Web Portal ◽  
Soil Moisture Data ◽  
Spatial Coverage

<p>The International Soil Moisture Network (ISMN, https://ismn.geo.tuwien.ac.at/) is an international cooperation to establish and maintain a unique centralized global data hosting facility, making in situ soil moisture data easily and freely accessible. This database is an essential means for validating and improving global satellite soil moisture products, land surface -, climate- , and hydrological models. </p><p>In situ measurements are crucial to calibrate and validate satellite soil moisture products. For a meaningful comparison with remotely sensed data and reliable validation results, the quality of the reference data is essential. The various independent local and regional in situ networks often do not follow standardized measurement techniques or protocols, collecting their data in different units, at different depths and at various sampling rates. Besides, quality control is rarely applied and accessing the data is often not easy or feasible.</p><p>The ISMN has been created to address the above-mentioned issues and is building a stable base to assist EO products, services and models. Within the ISMN, in situ soil moisture measurements (surface and sub-surface) are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free.</p><p>Founded in 2009, the ISMN has grown to a widely used in situ data source including 61 networks with more than 2600 stations distributed on a global scale and a steadily growing user community > 3200 registered users strong. Time series with hourly timestamps from 1952 – up to near real time are stored in the database and are available through the ISMN web portal, including daily near-real time updates from 6 networks (> 900 stations). With continuous financial support through the European Space Agency (formerly SMOS and IDEAS+ programs, currently QA4EO program), the ISMN evolved into a platform of benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S), the Copernicus Global Land Service (CGLS) and the online validation service Quality Assurance for Soil Moisture (QA4SM). In general, ISMN data is widely used in a variety of scientific fields (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.).</p><p>About 10’000 datasets are available through the web portal. However, the spatial coverage of in situ observations still needs to be improved. For example, in Africa and South America only sparse data are available. Innovative ideas, such as the inclusion of soil moisture data from low cost sensors (eventually) collected by citizen scientists, holds the potential of closing this gap, thus providing new information and knowledge.</p><p>In this session, we give an overview of the ISMN, its unique features and its benefits for validating satellite soil moisture products.</p>

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