Trendanalysen des globalen Niederschlags auf Basis von Analysen des Weltzentrums für Niederschlagsklimatologie (WZN)

2021 ◽  
Author(s):  
Markus Ziese ◽  
Elke Rustemeier ◽  
Udo Schneider ◽  
Peter Finger
Keyword(s):  
World Meteorological Organization ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Global Precipitation

<p>Das Weltzentrum für Niederschlagsklimatologie (Global Precipitation Climatology Centre) wurde 1989 auf Anfrage der World Meteorological Organization (WMO) beim Deutschen Wetterdienst (DWD) eingerichtet und befindet sich im operationellen Betrieb. Zu den Aufgaben des WZN gehört das Sammeln von in-situ Niederschlagsmessungen weltweit. Diese Daten werden in ihrer Qualität geprüft und in einer Datenbank archiviert. Auf Basis dieser Daten erstellt das WZN verschiedene gerasterte Niederschlagsanalysen, um die vielfältigen Nutzungsanforderungen zu bedienen, die sich im Hinblick auf zeitnahe Verfügbarkeit, hohe Datenbasis, ausführliche Qualitätskontrolle oder Homogenität der Zeitreihen unterscheiden.</p> <p>Anwendungsgebiete für die WZN-Datensätze sind die Überwachung des Niederschlags in der jüngeren Vergangenheit und Dürremonitoring, Kalibrationsdaten für Satellitenmessungen, Untersuchung des globalen Wasserkreislaufs, Analyse von Niederschlagsextremen bzw. deren Änderungen. Die dafür zur Verfügung gestellten Datensätze unterscheiden sich sowohl in der Datenbasis, als auch der Qualitätskontrolle. Während die nahezu Echtzeitdatensätze auf einigen tausend Stationen mit automatischer oder kombinierter automatischer und manueller Datenprüfung beruhen, basieren die Datensätze für historische Zeiträume auf einigen zehntausend Stationen mit einer aufwändigen statistischen und manuellen Datenprüfung. Um die große Menge an verfügbaren Daten homogenisieren zu können, wurde ein dazu passendes Homogenisierungsverfahren entwickelt.</p> <p>In dem Beitrag wird die Datenbasis und Qualitätskontrolle des WZN dargestellt. Anhand dieser Daten werden Trends des Niederschlags für Europa und weltweit bestimmt und ein Vergleich zwischen verschiedenen Methoden gezeigt. Dies umfasst nicht nur die Menge des Niederschlags, sondern auch Analysen und Trends der Intensität.</p>

Das Weltzentrums für Niederschlagsklimatologie (WZN) – globale Niederschlagsanalysen (nicht nur) für die Klimaforschung

2021 ◽  
Author(s):  
Markus Ziese ◽  
Elke Rustemeier ◽  
Udo Schneider ◽  
Peter Finger
Keyword(s):  
World Meteorological Organization ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Relationale Datenbank ◽  
Global Precipitation

<p>Das Weltzentrum für Niederschlagsklimatologie (WZN, engl. Global Precipitation Climatology Centre (GPCC)) wurde 1989 auf Anfrage der World Meteorological Organization (WMO) beim Deutschen Wetterdienst (DWD) eingerichtet und befindet sich im operationellen Betrieb. Die Aufgabe des WZN ist das Sammeln, die Prüfung und Analyse von in-situ Niederschlagsmessungen weltweit.</p> <p>Die von den Lieferanten bereitgestellten Daten kommen in verschiedenen Dateiformaten an. Diese unterscheiden sich nicht nur von Lieferant zu Lieferant, sondern auch von Lieferung zu Lieferung beim selben Lieferanten. Diese Dateien müssen in ein einheitliches Format gebracht werden, damit die Daten für die weitere Verarbeitung in eine relationale Datenbank importiert werden können. Sowohl beim Umformatieren als auch beim Einbringen in die Datenbank werden die Niederschlagsdaten und Stationsmetainformationen sorgfältig kontrolliert und, wo notwendig und möglich, korrigiert. Das Datenbankmodell erlaubt die parallele Speicherung der originalen und korrigierten Daten je nach Datenlieferant, was einen Vergleich der auf verschiedenen Wegen für eine Station gelieferten Daten ermöglicht. Auf Basis dieser qualitätsgeprüften Daten erzeugt das WZN verschiedene gerasterte Niederschlagsanalysen. Bei einigen dieser Analysen wird ein weiterer Schritt der Qualitätskontrolle bei der Extraktion der Daten aus der Datenbank eingefügt.</p> <p>Um die vielen verschiedenen Nutzungsanforderungen an gerasterte Datensätze erfüllen zu können, erzeugt das WZN verschiede Analyseprodukte. Diese unterscheiden sich in der Aktualität der verfügbaren Daten, und damit einhergehend in der Stationsbasis, der durchgeführten Qualitätskontrolle und räumlichen und zeitlichen Auflösung.</p> <p>Da das WZN nicht Eigentümer, sondern Nutzer der Daten, ist, stellt es nicht die Stationsdaten und Stationsmetadaten öffentlich zur Verfügung. Hingegen können die gerasterten Datensätze frei und ohne Registrierung genutzt werden. Es besteht die Möglichkeit, im Rahmen eines Gastaufenthalts beim WZN auch mit den Stationsdaten zu arbeiten.</p> <p>In dem Beitrag wird auf den Aufbau, die Datenbasis und –prozessierung des WZN eingegangen und die verschiedenen verfügbaren Analyseprodukte werden mit Anwendungsbereichen vorgestellt. Einige der vorgestellten Analyseprodukte werden im Winter 2021/2022 in einer aktualisierten Version veröffentlicht.</p>

scholarly journals The verification of seasonal precipitation forecasts for early warning in Zambia and Malawi

2015 ◽  
Vol 12 (1) ◽  
pp. 31-36 ◽  
Author(s):  
O. Hyvärinen ◽  
L. Mtilatila ◽  
K. Pilli-Sihvola ◽  
A. Venäläinen ◽  
H. Gregow
Keyword(s):  
Regional Climate ◽  
Global Precipitation Climatology Project ◽  
Brier Score ◽  
Seasonal Precipitation ◽  
Seasonal Forecasts ◽  
African Countries ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Global Precipitation

Abstract. We assess the probabilistic seasonal precipitation forecasts issued by Regional Climate Outlook Forum (RCOF) for the area of two southern African countries, Malawi and Zambia from 2002 to 2013. The forecasts, issued in August, are of rainy season rainfall accumulations in three categories (above normal, normal, and below normal), for early season (October–December) and late season (January–March). As observations we used in-situ observations and interpolated precipitation products from Global Precipitation Climatology Project (GPCP), Global Precipitation Climatology Centre (GPCC), and Climate Prediction Centre (CPC) Merged Analysis of Precipitation (CMAP). Differences between results from different data products are smaller than confidence intervals calculated by bootstrap. We focus on below normal forecasts as they were deemed to be the most important for society. The well-known decomposition of Brier score into three terms (Reliability, Resolution, and Uncertainty) shows that the forecasts are rather reliable or well-calibrated, but have a very low resolution; that is, they are not able to discriminate different events. The forecasts also lack sharpness as forecasts for one category are rarely higher than 40 % or less than 25 %. However, these results might be unnecessarily pessimistic, because seasonal forecasts have gone through much development during the period when the forecasts verified in this paper were issued, and forecasts using current methodology might have performed better.

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 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 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.

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.

scholarly journals Using GRACE to Estitmate Snowfall Accumulation and Assess Gauge Undercatch Corrections in High Latitudes

2018 ◽  
Vol 31 (21) ◽  
pp. 8689-8704 ◽  
Author(s):  
Ali Behrangi ◽  
Alex Gardner ◽  
John T. Reager ◽  
Joshua B. Fisher ◽  
Daqing Yang ◽  
...  
Keyword(s):  
High Latitude ◽  
Surface Air Temperature ◽  
Global Precipitation Climatology Project ◽  
Dynamic Correction ◽  
Northern Asia ◽  
Near Surface ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Terrestrial Water ◽  
Global Precipitation

Ten years of terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) were used to estimate high-latitude snowfall accumulation using a mass balance approach. The estimates were used to assess two common gauge-undercatch correction factors (CFs): the Legates climatology (CF-L) utilized in the Global Precipitation Climatology Project (GPCP) and the Fuchs dynamic correction model (CF-F) used in the Global Precipitation Climatology Centre (GPCC) monitoring product. The two CFs can be different by more than 50%. CF-L tended to exceed CF-F over northern Asia and Eurasia, while the opposite was observed over North America. Estimates of snowfall from GPCP, GPCC-L (GPCC corrected by CF-L), and GPCC-F (GPCC corrected by CF-F) were 62%, 64%, and 46% more than GPCC over northern Asia and Eurasia. The GRACE-based estimate (49% more than GPCC) was the closest to GPCC-F. We found that as near-surface air temperature decreased, the products increasingly underestimated the GRACE-based snowfall accumulation. Overall, GRACE showed that CFs are effective in improving GPCC estimates. Furthermore, our case studies and overall statistics suggest that CF-F is likely more effective than CF-L in most of the high-latitude regions studied here. GPCP showed generally better skill than GPCC-L, which might be related to the use of satellite data or additional quality controls on gauge inputs to GPCP. This study suggests that GPCP can be improved if it employs CF-L instead of CF-F to correct for gauge undercatch. However, this implementation requires further studies, region-specific analysis, and operational considerations.

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