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

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.

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

Journal of Climate ◽

10.1175/jcli-d-18-0163.1 ◽

2018 ◽

Vol 31 (21) ◽

pp. 8689-8704 ◽

Cited By ~ 13

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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The Global Precipitation Climatology Project Version 3 Products

10.5194/egusphere-egu21-8468 ◽

2021 ◽

Author(s):

George J. Huffman ◽

Ali Behrangi ◽

Robert F. Adler ◽

David T. Bolvin ◽

Eric J. Nelkin ◽

...

Keyword(s):

Global Precipitation Climatology Project ◽

Climate Data ◽

Precipitation Climatology ◽

Global Precipitation Climatology Centre ◽

Early Results ◽

Precipitation Measurement ◽

Key Points ◽

Precipitation Estimation ◽

Global Precipitation Measurement ◽

Global Precipitation

The Global Precipitation Climatology Project (GPCP) is currently providing a next-generation Version 3.1 Monthly product, which covers the period 1983-2019.&#160; This modernized product includes higher spatial resolution (0.5&#176;x0.5&#176;); a wider coverage (60&#176;N-S) by geosynchronous IR estimates, now based on the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) algorithm, with monthly recalibration using Goddard Profiling (GPROF) algorithm retrievals from selected passive microwave sensors; and improved calibrations of Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) and Advanced Infrared Sounder (AIRS) precipitation, used outside 60&#186;N-S.&#160; The merged satellite estimate is adjusted to the Tropical Combined Climatology (TCC) at lower latitudes, and the Merged CloudSat, TRMM, and GPM (MCTG) climatology at higher latitudes.&#160; Finally, V3.1 provides a merger of the satellite-only estimates with the Global Precipitation Climatology Centre (GPCC) monthly 1&#176;x1&#176; gauge analyses.&#160;As well, the GPCP team is advancing a companion global Version 3 Daily product, in which the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) Final Run V06 estimates are used where available (initially restricted to 60&#176;N-S), and rescaled TOVS/AIRS data in high-latitude areas, all calibrated to the GPCP V3.1 Monthly estimate.&#160; Since IMERG currently extends back to June 2000, daily PERSIANN-CDR data will be used for the period January 1983&#8211;May 2000 to complete the record.This presentation will provide early results for, and the latest status of, the Monthly and Daily GPCP products as a function of time and region.&#160; Key points include examining homogeneity over time and across time and space boundaries between input datasets.&#160; One key activity is to refine the V3 products while we continue to produce the Version 2 GPCP products for on-going use.

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

10.5194/dach2022-106 ◽

2021 ◽

Author(s):

Markus Ziese ◽

Elke Rustemeier ◽

Udo Schneider ◽

Peter Finger

Keyword(s):

World Meteorological Organization ◽

Precipitation Climatology ◽

Global Precipitation Climatology Centre ◽

Global Precipitation

Das Weltzentrum f&#252;r 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 geh&#246;rt das Sammeln von in-situ Niederschlagsmessungen weltweit. Diese Daten werden in ihrer Qualit&#228;t gepr&#252;ft und in einer Datenbank archiviert. Auf Basis dieser Daten erstellt das WZN verschiedene gerasterte Niederschlagsanalysen, um die vielf&#228;ltigen Nutzungsanforderungen zu bedienen, die sich im Hinblick auf zeitnahe Verf&#252;gbarkeit, hohe Datenbasis, ausf&#252;hrliche Qualit&#228;tskontrolle oder Homogenit&#228;t der Zeitreihen unterscheiden. Anwendungsgebiete f&#252;r die WZN-Datens&#228;tze sind die &#220;berwachung des Niederschlags in der j&#252;ngeren Vergangenheit und D&#252;rremonitoring, Kalibrationsdaten f&#252;r Satellitenmessungen, Untersuchung des globalen Wasserkreislaufs, Analyse von Niederschlagsextremen bzw. deren &#196;nderungen. Die daf&#252;r zur Verf&#252;gung gestellten Datens&#228;tze unterscheiden sich sowohl in der Datenbasis, als auch der Qualit&#228;tskontrolle. W&#228;hrend die nahezu Echtzeitdatens&#228;tze auf einigen tausend Stationen mit automatischer oder kombinierter automatischer und manueller Datenpr&#252;fung beruhen, basieren die Datens&#228;tze f&#252;r historische Zeitr&#228;ume auf einigen zehntausend Stationen mit einer aufw&#228;ndigen statistischen und manuellen Datenpr&#252;fung. Um die gro&#223;e Menge an verf&#252;gbaren Daten homogenisieren zu k&#246;nnen, wurde ein dazu passendes Homogenisierungsverfahren entwickelt. In dem Beitrag wird die Datenbasis und Qualit&#228;tskontrolle des WZN dargestellt. Anhand dieser Daten werden Trends des Niederschlags f&#252;r 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 Intensit&#228;t.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0157.1 ◽

2014 ◽

Vol 15 (6) ◽

pp. 2111-2139 ◽

Cited By ~ 37

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.

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Comparison of Gridded Multisatellite Rainfall Estimates with Gridded Gauge Rainfall over West Africa

Journal of Applied Meteorology and Climatology ◽

10.1175/2007jamc1586.1 ◽

2008 ◽

Vol 47 (1) ◽

pp. 185-205 ◽

Cited By ~ 25

Author(s):

Benjamin L. Lamptey

Keyword(s):

Global Precipitation Climatology Project ◽

Rain Gauge ◽

Guinea Coast ◽

Rain Gauge Data ◽

Precipitation Climatology ◽

Global Precipitation Climatology Centre ◽

Surface Climate ◽

Gauge Data ◽

Global Precipitation ◽

Rainfall Climatology

Abstract Two monthly gridded precipitation datasets of the Global Precipitation Climatology Project (GPCP; the multisatellite product) and the Global Precipitation Climatology Centre (GPCC) Variability Analysis of Surface Climate Observations (VASClimO; rain gauge data) are compared for a 22-yr period, from January 1979 to December 2000, over land areas (i.e., latitudes 4°–20°N and longitudes 18°W–15°E). The two datasets are consistent with respect to the spatial distribution of the annual and seasonal rainfall climatology over the domain and along latitudinal bands. However, the satellite generally overestimates rainfall. The inability of the GPCC data to capture the bimodal rainfall pattern along the Guinea coast (i.e., south of latitude 8°N) is an artifact of the interpolation of the rain gauge data. For interannual variability, the gridded multisatellite and gridded gauge datasets agree on the sign of the anomaly 15 out of the 22 yr (68% of the time) for region 1 (between longitude 5° and 18°W and north of latitude 8°N) and 18 out of the 22 yr (82% of the time) for region 2 (between longitude 5°W and 15°E and north of latitude 8°N). The datasets agreed on the sign of the anomaly 14 out of the 22 yr (64% of the time) over the Guinea Coast. The magnitudes of the anomaly are very different in all years. Most of the years during which the two datasets did not agree on the sign of the anomaly were years with El Niño events. The ratio of the seasonal root-mean-square differences to the seasonal mean rainfall range between 0.24 and 2.60. The Kendall’s tau statistic indicated statistically significant trends in both datasets, separately.

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

10.5194/dach2022-104 ◽

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

Das Weltzentrum f&#252;r 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 Pr&#252;fung und Analyse von in-situ Niederschlagsmessungen weltweit. 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 m&#252;ssen in ein einheitliches Format gebracht werden, damit die Daten f&#252;r die weitere Verarbeitung in eine relationale Datenbank importiert werden k&#246;nnen. Sowohl beim Umformatieren als auch beim Einbringen in die Datenbank werden die Niederschlagsdaten und Stationsmetainformationen sorgf&#228;ltig kontrolliert und, wo notwendig und m&#246;glich, korrigiert. Das Datenbankmodell erlaubt die parallele Speicherung der originalen und korrigierten Daten je nach Datenlieferant, was einen Vergleich der auf verschiedenen Wegen f&#252;r eine Station gelieferten Daten erm&#246;glicht. Auf Basis dieser qualit&#228;tsgepr&#252;ften Daten erzeugt das WZN verschiedene gerasterte Niederschlagsanalysen. Bei einigen dieser Analysen wird ein weiterer Schritt der Qualit&#228;tskontrolle bei der Extraktion der Daten aus der Datenbank eingef&#252;gt. Um die vielen verschiedenen Nutzungsanforderungen an gerasterte Datens&#228;tze erf&#252;llen zu k&#246;nnen, erzeugt das WZN verschiede Analyseprodukte. Diese unterscheiden sich in der Aktualit&#228;t der verf&#252;gbaren Daten, und damit einhergehend in der Stationsbasis, der durchgef&#252;hrten Qualit&#228;tskontrolle und r&#228;umlichen und zeitlichen Aufl&#246;sung. Da das WZN nicht Eigent&#252;mer, sondern Nutzer der Daten, ist, stellt es nicht die Stationsdaten und Stationsmetadaten &#246;ffentlich zur Verf&#252;gung. Hingegen k&#246;nnen die gerasterten Datens&#228;tze frei und ohne Registrierung genutzt werden. Es besteht die M&#246;glichkeit, im Rahmen eines Gastaufenthalts beim WZN auch mit den Stationsdaten zu arbeiten. In dem Beitrag wird auf den Aufbau, die Datenbasis und &#8211;prozessierung des WZN eingegangen und die verschiedenen verf&#252;gbaren Analyseprodukte werden mit Anwendungsbereichen vorgestellt. Einige der vorgestellten Analyseprodukte werden im Winter 2021/2022 in einer aktualisierten Version ver&#246;ffentlicht.

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The Global Precipitation Climatology Project

Measuring Precipitation From Space ◽

10.1007/978-1-4020-5835-6_3 ◽

2007 ◽

pp. 25-35 ◽

Cited By ~ 1

Author(s):

Arnold Gruber ◽

Bruno Rudolf ◽

Mark M. Morrissey ◽

Toshiyuki Kurino ◽

John E. Janowiak ◽

...

Keyword(s):

Global Precipitation Climatology Project ◽

Precipitation Climatology ◽

Global Precipitation

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

10.5194/egusphere-egu21-14611 ◽

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

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 123,000 stations, about three quarters of them having long time series.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.&#160; 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.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.In near real time, the 'First Guess Monthly', 'First Guess Daily', 'Monitoring Product', &#8216;Provisional Daily Precipitation Analysis&#8217; and the 'GPCC Drought Index' are generated. These are based on WMO-GTS data and monthly data generated by the CPC (NOAA). 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. 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.

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

Hydrology and Earth System Sciences ◽

10.5194/hess-24-919-2020 ◽

2020 ◽

Vol 24 (2) ◽

pp. 919-943 ◽

Cited By ~ 9

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.

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