Extreme precipitating events in satellite and rain-gauge products over the Sahel

Over the recent decades, Extreme Precipitation Events (EPE) have become more frequent over the Sahel. Their properties, however, have so far received little attention. In this study the spatial distribution, intensity, seasonality and interannual variability of EPEs are examined, using both a reference dataset, based on a high-density rain-gauge network over Burkina Faso and 24 precipitation gridded datasets. The gridded datasets are evaluated in depth over Burkina Faso while their commonalities are used to document the EPE properties over the Sahel. EPEs are defined as the occurrence of daily-accumulated precipitation exceeding the all-day 99th percentile over a 1°x1° pixel. Over Burkina Faso, this percentile ranges between 21 and 33 mm day-1. The reference dataset show that EPEs occur in phase with the West African monsoon annual cycle, more frequently during the monsoon core season and during wet years. These results are consistent among the gridded datasets over Burkina Faso but also over the wider Sahel. The gridded datasets exhibit a wide diversity of skills when compared to the Burkinabe reference. The Global Precipitation Climatology Centre Full Data Daily version 1 (GPCC-FDDv1) and the Global Satellite Mapping of Precipitation gauge Reanalysis version 6.0 (GSMaP-gauge-RNL v6.0) are the only products that properly reproduce all of the EPE features examined in this work. The datasets using a combination of microwave and infrared measurements are prone to overestimate the EPE intensity, while infrared-only products generally underestimate it. Their calibrated versions perform than their uncalibrated (near-real-time) versions. This study finally emphasizes that the lack of rain-gauge data availability over the whole Sahel strongly impedes our ability to gain insights in EPE properties.

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

<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

<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>

The inherent uncertainty of precipitation variability, trends, and extremes due to internal variability, with implications for Western US water resources

The approximately century-long instrumental record of precipitation over land reflects a single sampling of internal variability. Thus, the spatiotemporal evolution of the observations is only one realization of `what could have occurred' given the same climate system and boundary conditions, but different initial conditions. While climate models can be used to produce initial-condition large ensembles that explicitly sample different sequences of internal variability, an analogous approach is not possible for the real world. Here, we explore the use of a statistical model for monthly precipitation to generate synthetic ensembles based on a single record. When tested within the context of the NCAR Community Earth System Model version 1 Large Ensemble (CESM1-LE), we find that the synthetic ensemble can closely reproduce the spatiotemporal statistics of variability and trends in winter precipitation over the extended contiguous United States, and that it is difficult to infer the climate change signal in a single record given the magnitude of the variability. We additionally create a synthetic ensemble based on the Global Precipitation Climatology Centre (GPCC) dataset, termed the GPCC-synth-LE; comparison of the GPCC-synth-LE with the CESM1-based ensembles reveals differences in the spatial structures and magnitudes of variability, highlighting the advantages of an observationally-based ensemble. We finally use the GPCC-synth-LE to analyze three water resource metrics in the Upper Colorado River Basin: frequency of dry, wet, and whiplash years. Thirty-one year ‘climatologies’ in the GPCC-synth-LE can differ by over 20% in these key water resource metrics due to sampling of internal variability, and individual ensemble members in the GPCC-synth-LE can exhibit large near-monotonic trends over the course of the last century due to sampling of variability alone.

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

<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>

Comparison of Precipitation Projections of CMIP5 and CMIP6 Global Climate Models over Yulin, China

This study compared precipitation projections of Coupled Model Intercomparison Project 5 (CMIP5) and 6 (CMIP6) GCMs over Yulin City, China. The performance of CMIP5 and CMIP6 GCMs in replicating Global Precipitation Climatology Centre (GPCC) precipitation climatology of the city was evaluated using different statistical metrics. The best performing GCMs common to both CMIP5 and CMIP6 were selected and subsequently downscaled to GPCC resolution using linear scaling method to spatiotemporal changes in precipitation. The study revealed BCC.CSM1.1(m), IPSL.CM5A.LR, MRI.CGCM3 and MIROC5 of CMIP5 and their equivalents BCC-CSM2-MR, IPSL-CM6A-LR, MRI.ESM2.0 and MIRCO6 of CMIP6 as the most suitable GCMs for the projection of rainfall in Yulin. Changes in precipitation were in the range of -14.0 − 0.0% and − 22.0 − 0.2% during 2021−2060 for RCP4.5 and SSP2-4.5 respectively. The highest decrease of -29.7 ̶ -22.0% was projected by MRI-ESM-2-0 for SSP2-4.5, while − 28.0 − -20.0% by MIROC5 for RCP4.5. For RCP8.5 and SSP5-8.5, precipitation was projected to decrease in the range of -17.0 ̶ -2.0% and − 32.0 ̶ 0.0%, respectively during 2021 ̶ 2060 by most of the GCMs. An increase in precipitation up to 12.3% was projected only by IPSL-CM5A-LR for RCP8.5 for this period. The highest decrease was projected by MIROC5 (-40.2 − -29.0%) for RCP8.5 and IPSL-CM6A-LR (-40.2 − -26.0%) for SSP5-8.5. Overall, the results revealed a higher decrease in precipitation in Yulin city by CMIP6 GCMs compared to those projected by their corresponding GCMs of CMIP5 for both scenarios.

Evaluating the Performance of Cumulus Convection Parameterization Schemes in Regional Climate Modeling System

In this study, we explored the performance of the cumulus convection parameterization schemes of Regional Climate Modeling System (RegCM) towards the Indian summer monsoon (ISM) of a catastrophic year through various numerical experiments conducted with different convection schemes (Kuo, Grell amd MIT) in RegCM. The model is integrated at 60KM horizontal resolution over Indian region and forced with NCEP/NCAR reanalysis. The simulated temperature at 2m and the wind at 10m are validated against the forced data and the total precipitation is compared with the Global Precipitation Climatology Centre (GPCC) observations. We find that the simulation with MIT convection scheme is close to the GPCC data and NCEP/NCAR reanalysis. Our results with three convection schemes suggest that the RegCM with MIT convection scheme successfully simulated some characteristics of ISM of a catastrophic year and may be further examined with more number of convection schemes to customize which convection scheme is much better.

Aridity Indices to Assess Desertification Susceptibility: A Methodological Approach Using Gridded Climate Data

Desertification is a land degradation phenomenon with dire and irreversible consequences, affecting different regions of the world. Assessment of spatial susceptibility to desertification requires long-term series of precipitation (P) and evapotranspiration (PET). An approach to desertification analysis is the use of spatially gridded time series of air temperature and precipitation, derived from spatial interpolation of in situ measurements and available globally. The aim of this article was to estimate the susceptibility to desertification over Southeast Brazil using monthly gridded data from the Global Precipitation Climatology Centre (GPCC), and from the Global Historical Climatology Network (GHCN). Two indices were used to estimate desertification susceptibility: the aridity index Ia (P/PET) and D (PET/P). Validation of these datasets was performed using in situ observations (1961—2010) from the National Institute of Meteorology (INMET) – (68 weather stations). Determination coefficient (r²) and the Willmott’s coefficient of agreement (d) between gridded and observed data revealed satisfactory accuracy and precision for grids of precipitation (r2 > 0.93, d > 0.90), air temperature (r2 > 0.94, d > 0.53) and PET (r2 > 0.93, d > 0.63). Areas susceptible to desertification were identified by the index Ia over the Northern regions of Minas Gerais and Rio de Janeiro states. No areas susceptible to desertification were identified using the index D. However, both indices indicated large areas of dry sub-humid climate, which can be strongly affected by drought conditions. Overall, climate gridded variables presented good precision and accuracy when used to identify areas susceptible to desertification.

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

<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>

Evaluation of the Performance of Remotely Sensed Rainfall Datasets for Flood Monitoring in the Transboundary Mono River Catchment, Togo and Benin

<p>The variability and changes noted in the climate over the past decades emphasizes the importance of climate information such as precipitation datasets in the management of flood risks in Benin and Togo. The lack of extensive and functional ground observation networks, introduces satellite-based rainfall datasets as a better alternative which needs however to be evaluated beforehand. This study investigated the performance of four satellite and gauge-based rainfall products –Climate Hazards Group Infrared Precipitation with Station data version v2.0 (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN), Tropical Applications of Meteorology using Satellite data and ground-based observations (TAMSAT) and the Global Precipitation Climatology Centre full daily data (GPCC) – at gauge point level over the Mono River basin which is stretched over Benin and Togo territories. Three synoptic stations located in Tabligbo, Atakpamé and Sokodé were considered because of the completeness of their time series during the study period 1983-2012. The assessments were conducted at daily, dekadal (10-day period), seasonal and annual scale using both continuous and categorical statistics. Results show poor performances at daily and annual temporal scales while the seasonal cycles were well reproduced with Nash-Sutcliffe efficiency equal or higher than 0.94, and correlation coefficient above 0.9. At Tabligbo, CHIRPS and GPCC showed the best statistical results whereas the performance of PERSIANN and TAMSAT varies with the temporal scale and the station. The probability of rainfall detection (POD) and the capability of reproducing extreme daily maxima indicate GPCC as the best product for flood monitoring purposes at daily scale. However, all assessed products exhibited high POD and low false alarm ratio (FAR) at dekadal scale.</p>