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

2012 ◽  
Vol 5 (2) ◽  
pp. 921-998 ◽  
Author(s):  
A. Becker ◽  
P. Finger ◽  
A. Meyer-Christoffer ◽  
B. Rudolf ◽  
K. Schamm ◽  
...  
Keyword(s):  
Data Base ◽  
Land Surface ◽  
Data Sets ◽  
Gridded Data ◽  
Surface Precipitation ◽  
Global Precipitation Climatology Centre ◽  
Global Land ◽  
Data Products ◽  
Precipitation Analysis ◽  
Global Precipitation

Abstract. 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

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.

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.

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 Rainfall Estimates on a Gridded Network (REGEN) – a global land-based gridded dataset of daily precipitation from 1950 to 2016

2020 ◽  
Vol 24 (2) ◽  
pp. 919-943 ◽  
Author(s):  
Steefan Contractor ◽  
Markus G. Donat ◽  
Lisa V. Alexander ◽  
Markus Ziese ◽  
Anja Meyer-Christoffer ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
In Situ Data ◽  
Global Precipitation Climatology Centre ◽  
Historical Climatology ◽  
Global Land ◽  
Global Historical Climatology Network ◽  
Global Precipitation ◽  
Rainfall Estimates

Abstract. We present a new global land-based daily precipitation dataset from 1950 using an interpolated network of in situ data called Rainfall Estimates on a Gridded Network – REGEN. We merged multiple archives of in situ data including two of the largest archives, the Global Historical Climatology Network – Daily (GHCN-Daily) hosted by National Centres of Environmental Information (NCEI), USA, and one hosted by the Global Precipitation Climatology Centre (GPCC) operated by Deutscher Wetterdienst (DWD). This resulted in an unprecedented station density compared to existing datasets. The station time series were quality-controlled using strict criteria and flagged values were removed. Remaining values were interpolated to create area-average estimates of daily precipitation for global land areas on a 1∘ × 1∘ latitude–longitude resolution. Besides the daily precipitation amounts, fields of standard deviation, kriging error and number of stations are also provided. We also provide a quality mask based on these uncertainty measures. For those interested in a dataset with lower station network variability we also provide a related dataset based on a network of long-term stations which interpolates stations with a record length of at least 40 years. The REGEN datasets are expected to contribute to the advancement of hydrological science and practice by facilitating studies aiming to understand changes and variability in several aspects of daily precipitation distributions, extremes and measures of hydrological intensity. Here we document the development of the dataset and guidelines for best practices for users with regards to the two datasets.

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.

scholarly journals Global terrestrial water storage connectivity revealed using complex climate network analyses

2015 ◽  
Vol 22 (4) ◽  
pp. 433-446 ◽  
Author(s):  
A. Y. Sun ◽  
J. Chen ◽  
J. Donges
Keyword(s):  
Spatial Patterns ◽  
Land Surface ◽  
Water Storage ◽  
Data Sets ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Data Set ◽  
Surface Models ◽  
Global Land ◽  
Terrestrial Water

Abstract. Terrestrial water storage (TWS) exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationship exists between precipitation and TWS, the latter quantity also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and the hydrologic cycle, but also provide new insights and model calibration constraints for improving the current land surface models. This work is the first attempt to quantify the spatial connectivity of TWS using the complex network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS data sets, a remote sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a model-generated data set from the global land data assimilation system's NOAH model (GLDAS-NOAH). Both data sets have 1° × 1° grid resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a cutoff threshold derived from the edge-density function to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show the TWS anomaly hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two data sets reveal that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide further measures for constraining the current land surface models, especially in data sparse regions.

scholarly journals Tropical montane forests are a larger than expected global carbon store

2014 ◽  
Vol 11 (10) ◽  
pp. 2741-2754 ◽  
Author(s):  
D. V. Spracklen ◽  
R. Righelato
Keyword(s):  
Land Surface ◽  
Forest Cover ◽  
Slope Angle ◽  
Remotely Sensed ◽  
Data Sets ◽  
Remotely Sensed Data ◽  
Montane Forests ◽  
Tropical Montane Forests ◽  
Global Land ◽  
Steep Slopes

Abstract. Tropical montane forests (TMFs) are recognized for the provision of hydrological services and the protection of biodiversity, but their role in carbon storage is not well understood. We synthesized published observations (n = 94) of above-ground biomass (AGB) from forest inventory plots in TMFs (defined here as forests between 23.5° N and 23.5° S with elevations ≥ 1000 m a.s.l.). We found that mean (median) AGB in TMFs is 271 (254) t per hectare of land surface. We demonstrate that AGB declines moderately with both elevation and slope angle but that TMFs store substantial amounts of biomass, both at high elevations (up to 3500 m) and on steep slopes (slope angles of up to 40°). We combined remotely sensed data sets of forest cover with high resolution data of elevation to show that 75% of the global planimetric (horizontal) area of TMF are on steep slopes (slope angles greater than 27°). We used our remote sensed data sets to demonstrate that this prevalence of steep slopes results in the global land surface area of TMF (1.22 million km2) being 40% greater than the planimetric area that is the usual basis for reporting global land surface areas and remotely sensed data. Our study suggests that TMFs are likely to be a greater store of carbon than previously thought, highlighting the need for conservation of the remaining montane forests.

scholarly journals An Upper Limit to Seasonal Rainfall Predictability?

2010 ◽  
Vol 23 (12) ◽  
pp. 3332-3351 ◽  
Author(s):  
Seth Westra ◽  
Ashish Sharma
Keyword(s):  
Land Surface ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Parameter Estimates ◽  
Total Precipitation ◽  
Noise Component ◽  
Nonlinear Dynamical ◽  
Surface Precipitation ◽  
Predictive Skill ◽  
Global Precipitation

Abstract The asymptotic predictability of global land surface precipitation is estimated empirically at the seasonal time scale with lead times from 0 to 12 months. Predictability is defined as the unbiased estimate of predictive skill using a given model structure assuming that all relevant predictors are included, thus representing an upper bound to the predictive skill for seasonal forecasting applications. To estimate predictability, a simple linear regression model is formulated based on the assumption that land surface precipitation variability can be divided into a component forced by low-frequency variability in the global sea surface temperature anomaly (SSTA) field and that can theoretically be predicted one or more seasons into the future, and a “weather noise” component that originates from nonlinear dynamical instabilities in the atmosphere and is not predictable beyond ~10 days. Asymptotic predictability of global precipitation was found to be 14.7% of total precipitation variance using 1900–2007 data, with only minor increases in predictability using shorter and presumably less error-prone records. This estimate was derived based on concurrent SSTA–precipitation relationships and therefore constitutes the maximum skill achievable assuming perfect forecasts of the evolution of the SSTA field. Imparting lags on the SSTA–precipitation relationship, the 3-, 6-, 9-, and 12-month predictability of global precipitation was estimated to be 7.3%, 5.4%, 4.2%, and 3.7%, respectively, demonstrating the comparative gains that can be achieved by developing improved SSTA forecasts compared to developing improved SSTA–precipitation relationships. Finally, the actual average cross-validated predictive skill was found to be 2.1% of the total precipitation variance using the full 1900–2007 dataset and was dominated by the El Niño–Southern Oscillation (ENSO) phenomenon. This indicates that there is still significant potential for increases in predictive skill through improved parameter estimates, the use of longer and/or more reliable datasets, and the use of larger spatial fields to substitute for limited temporal records.

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