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

Journal of Climate ◽

10.1175/jcli-d-21-0390.1 ◽

2022 ◽

pp. 1-60

Keyword(s):

Burkina Faso ◽

West African Monsoon ◽

Rain Gauge ◽

Data Availability ◽

Reference Dataset ◽

Full Data ◽

Global Precipitation Climatology Centre ◽

Extreme Precipitation Events ◽

Infrared Measurements ◽

Precipitation Gauge

Abstract 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.

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Heavy precipitating events in satellites and rain-gauge products over the Sahel

10.5194/egusphere-egu21-8261 ◽

2021 ◽

Author(s):

Sidiki Sanogo ◽

Philippe Peyrillé ◽

Romain Roehrig ◽

Françoise Guichard ◽

Ousmane Ouedraogo

Keyword(s):

Burkina Faso ◽

West African Monsoon ◽

Extreme Rainfall ◽

Rain Gauge ◽

Reference Dataset ◽

Extreme Rainfall Events ◽

Microwave Sensor ◽

Sahel Region ◽

Distribution Intensity ◽

Rain Gauge Network

The Sahel has experienced an increase in the frequency and intensity of extreme rainfall events over the recent decades. These trends are expected to continue in the future. However the properties of these events have so far received little attention. In the present study, we define a heavy precipitating event (HPE) as the occurrence of daily-mean precipitation exceeding a given percentile (e.g., 99th and higher) over a 1&#176;x1&#176; pixel and examine their spatial distribution, intensity, seasonality and interannual variability. We take advantage of an original reference dataset based on a rather high-density rain-gauge network over Burkina Faso (142 stations) to evaluate 22 precipitation gridded datasets often used in the literature, based on rain-gauge-only measurements, satellite measurements, or both. Our reference dataset documents the HPEs over Burkina Faso. The 99th percentile identifies events greater than 26 mm d-1 with a ~2.5 mm confidence interval depending on the number of stations within a 1&#176;x1&#176; pixel. The HPEs occur in phase with the West African monsoon annual cycle, more frequently during the monsoon core season and during wet years. The evaluation of the gridded rainfall products reveals that only two of the datasets, namely the rain-gauge-only based products GPCC-DDv1 and REGENv1, are able to properly reproduce all of the HPE features examined in the present work. A subset of the remaining rainfall products also provide satisfying skills over Burkina Faso, but generally only for a few HPE features examined here. In particular, we notice a general better performance for rainfall products that include rain-gauge data in the calibration process, while estimates using microwave sensor measurements are prone to overestimate the HPE intensity. The agreement among the 22 datasets is also assessed over the entire Sahel region. While the meridional gradient in HPE properties is well captured by the good performance subset, the zonal direction exhibit larger inter-products spread. This advocates for the need to continue similar evaluation with the available rain-gauge network available in West Africa, both to enhance the HPE documentation and understanding at the scale of the region and to help improve the rainfall dataset quality.

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The effect of climate change on urban drainage: an evaluation based on regional climate model simulations

Water Science & Technology ◽

10.2166/wst.2006.592 ◽

2006 ◽

Vol 54 (6-7) ◽

pp. 9-15 ◽

Cited By ~ 41

Author(s):

M. Grum ◽

A.T. Jørgensen ◽

R.M. Johansen ◽

J.J. Linde

Keyword(s):

Climate Change ◽

Extreme Precipitation ◽

Climate Model ◽

Regional Climate ◽

Rain Gauge ◽

Urban Drainage ◽

Extreme Precipitation Events ◽

Rainfall Extremes ◽

The Future ◽

Precipitation Events

That we are in a period of extraordinary rates of climate change is today evident. These climate changes are likely to impact local weather conditions with direct impacts on precipitation patterns and urban drainage. In recent years several studies have focused on revealing the nature, extent and consequences of climate change on urban drainage and urban runoff pollution issues. This study uses predictions from a regional climate model to look at the effects of climate change on extreme precipitation events. Results are presented in terms of point rainfall extremes. The analysis involves three steps: Firstly, hourly rainfall intensities from 16 point rain gauges are averaged to create a rain gauge equivalent intensity for a 25 × 25 km square corresponding to one grid cell in the climate model. Secondly, the differences between present and future in the climate model is used to project the hourly extreme statistics of the rain gauge surface into the future. Thirdly, the future extremes of the square surface area are downscaled to give point rainfall extremes of the future. The results and conclusions rely heavily on the regional model's suitability in describing extremes at time-scales relevant to urban drainage. However, in spite of these uncertainties, and others raised in the discussion, the tendency is clear: extreme precipitation events effecting urban drainage and causing flooding will become more frequent as a result of climate change.

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A New Land Surface Hydrology within the Noah-WRF Land-Atmosphere Mesoscale Model Applied to Semiarid Environment: Evaluation over the Dantiandou Kori (Niger)

Advances in Meteorology ◽

10.1155/2009/731874 ◽

2009 ◽

Vol 2009 ◽

pp. 1-13 ◽

Cited By ~ 8

Author(s):

B. Decharme ◽

C. Ottlé ◽

S. Saux-Picart ◽

N. Boulain ◽

B. Cappelaere ◽

...

Keyword(s):

Land Surface ◽

Mesoscale Model ◽

West African Monsoon ◽

Radar Data ◽

Rain Gauge ◽

Surface Energy Budget ◽

Surface Hydrology ◽

Land Surface Hydrology ◽

Rain Gauge Network

Land-atmosphere feedbacks, which are particularly important over the Sahel during the West African Monsoon (WAM), partly depend on a large range of processes linked to the land surface hydrology and the vegetation heterogeneities. This study focuses on the evaluation of a new land surface hydrology within the Noah-WRF land-atmosphere-coupled mesoscale model over the Sahel. This new hydrology explicitly takes account for the Dunne runoff using topographic information, the Horton runoff using a Green-Ampt approximation, and land surface heterogeneities. The previous and new versions of Noah-WRF are compared against a unique observation dataset located over the Dantiandou Kori (Niger). This dataset includes dense rain gauge network, surfaces temperatures estimated from MSG/SEVIRI data, surface soil moisture mapping based on ASAR/ENVISAT C-band radar data and in situ observations of surface atmospheric and land surface energy budget variables. Generally, the WAM is reasonably reproduced by Noah-WRF even if some limitations appear throughout the comparison between simulations and observations. An appreciable improvement of the model results is also found when the new hydrology is used. This fact seems to emphasize the relative importance of the representation of the land surface hydrological processes on the WAM simulated by Noah-WRF over the Sahel.

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Reliability of global gridded precipitation products in assessing extremes

10.5194/egusphere-egu21-3246 ◽

2021 ◽

Author(s):

Chandra Rupa Rajulapati ◽

Simon Michael Papalexiou ◽

Martyn P Clark ◽

Saman Razavi ◽

Guoqiang Tang ◽

...

Keyword(s):

Spatial Variability ◽

Extreme Precipitation ◽

Probability Distributions ◽

Extreme Precipitation Events ◽

Return Levels ◽

Time Period ◽

Precipitation Gauge ◽

Global Precipitation ◽

Surface Observations ◽

Precipitation Events

Assessing extreme precipitation events is of high importance to hydrological risk assessment, decision making, and adaptation strategies. Global gridded precipitation products, constructed by combining various data sources such as precipitation gauge observations, atmospheric reanalyses and satellite estimates, can be used to estimate extreme precipitation events. Although these global precipitation products are widely used, there has been limited work to examine how well these products represent the magnitude and frequency of extreme precipitation. In this work, the five most widely used global precipitation datasets (MSWEP, CFSR, CPC, PERSIANN-CDR and WFDEI) are compared to each other and to GHCN-daily surface observations. The spatial variability of extreme precipitation events and the discrepancy amongst datasets in predicting precipitation return levels (such as 100- and 1000-year) were evaluated for the time period 1979-2017. &#160;The behaviour of extremes, that is the frequency and magnitude of extreme precipitation, was quantified using indices of the heaviness of the upper tail of the probability distribution. Two parameterizations of the upper tail, the power and stretched-exponential, were used to reveal the probabilistic behaviour of extremes. The analysis shows strong spatial variability in the frequency and magnitude of precipitation extremes as estimated from the upper tails of the probability distributions. This spatial variability is similar to the K&#246;ppen-Geiger climate classification. The predicted 100- and 1000-year return levels differ substantially amongst the gridded products, and the level of discrepancy varies regionally, with large differences in Africa and South America and small differences in North America and Europe. The results from this work reveal the shortcomings of global precipitation products in representing extremes. The work shows that there is no single global product that performs best for all regions and climates.

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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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An Extended Model in Estimating Consistent Quantiles for Intensity-Duration-Frequency Curves

10.5194/egusphere-egu21-7562 ◽

2021 ◽

Author(s):

Felix Fauer ◽

Jana Ulrich ◽

Oscar E. Jurado ◽

Uwe Ulbrich ◽

Henning W. Rust

Keyword(s):

Extreme Value ◽

Data Availability ◽

Extreme Value Statistics ◽

Statistical Characteristics ◽

Exceedance Probability ◽

Extended Model ◽

Adequate Model ◽

Extreme Precipitation Events ◽

Idf Curves ◽

Intensity Duration Frequency

Intensity-Duration-Frequency (IDF) curves describe the main statistical characteristics of extreme precipitation events. Providing information on the exceedance probability or return period of certain precipitation intensities for a range of durations, IDF curves are an important tool for the design of hydrological structures.Although the Generalized-Extreme-Value (GEV) distribution is an adequate model for annual precipitation maxima of a certain duration, the core problem of extreme value statistics remains: the limited data availability. Hence, it is reasonable to use a model that can describe all durations simultaneously. This reduces the total number of parameters and a more efficient usage of data is achieved. The idea of implementing a duration dependence directly into the parameters of the extreme value distribution and therefore obtaining a single distribution for a range of durations was proposed by Koutsoyiannis et al. (1998). However, while the use of the GEV is justified by a strong theoretical basis, only empirical models exist for the dependence of the parameters on duration.In this study, we compare different models regarding the dependence of the GEV parameters on duration with the aim of finding a model for a wide duration range (1 min - 5 days). We use a combination of existing model features, especially curvature for small durations and multi-scaling for all durations, and extend them by a new feature that allows flattening of the IDF curves for long durations. Using the quantile score in a cross-validation setting, we provide detailed information on the duration and probability ranges for which specific features or a systematic combination of features lead to improved modeling skill.Our results show that allowing curvature or multi-scaling improves the model only for very short or long durations, respectively, but leads to disadvantages in modeling the other duration ranges. In contrast, allowing flattening of the IDF curves leads to an improvement for medium durations between 1 hour and 1 day without affecting other duration regimes.

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Evaluation of Satellite Precipitation Estimates over Australia

Remote Sensing ◽

10.3390/rs12040678 ◽

2020 ◽

Vol 12 (4) ◽

pp. 678 ◽

Cited By ~ 4

Author(s):

Zhi-Weng Chua ◽

Yuriy Kuleshov ◽

Andrew Watkins

Keyword(s):

Rainfall Intensity ◽

Austral Summer ◽

Rain Gauge ◽

Reference Dataset ◽

Rain Gauge Data ◽

Satellite Precipitation ◽

Gauge Data ◽

Precipitation Estimates ◽

Notable Increase ◽

Climate Prediction Center

This study evaluates the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yielded a notable increase in performance. Dependencies of performance on geography, season, and rainfall intensity were also investigated. The skill of satellite precipitation detection was reduced in areas of elevated topography and where cold frontal rainfall was the main precipitation source. Areas where rain gauge coverage was sparse also exhibited reduced skill. In terms of seasons, the performance was relatively similar across the year, with austral summer (DJF) exhibiting slightly better performance. The skill of the satellite precipitation estimates was highly dependent on rainfall intensity. The highest skill was obtained for moderate rainfall amounts (2–4 mm/day). There was an overestimation of low-end rainfall amounts and an underestimation in both the frequency and amount for high-end rainfall. Overall, CMORPH and GSMaP datasets were evaluated as useful sources of satellite precipitation estimates over Australia.

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PPHA-Popularity Prediction Based High Data Availability for Multimedia Data Center

International Journal of Interdisciplinary Telecommunications and Networking ◽

10.4018/ijitn.2019010102 ◽

2019 ◽

Vol 11 (1) ◽

pp. 17-29

Author(s):

Kuo-Chi Fang ◽

Husnu S. Narman ◽

Ibrahim Hussein Mwinyi ◽

Wook-Sung Yoo

Keyword(s):

Data Center ◽

Service Providers ◽

Data Access ◽

Current Data ◽

Multimedia Data ◽

Data Availability ◽

Full Data ◽

Data Center Management ◽

Popularity Prediction ◽

Data Objects

Due to the growth of internet-connected devices and extensive data analysis applications in recent years, cloud computing systems are largely utilized. Because of high utilization of cloud storage systems, the demand for data center management has been increased. There are several crucial requirements of data center management, such as increase data availability, enhance durability, and decrease latency. In previous works, a replication technique is mostly used to answer those needs according to consistency requirements. However, most of the works consider full data, popular data, and geo-distance-based replications by considering storage and replication cost. Moreover, the previous data popularity based-techniques rely on the historical and current data access frequencies for replication. In this article, the authors approach this problem from a distinct aspect while developing replication techniques for a multimedia data center management system which can dynamically adapt servers of a data center by considering popularity prediction in each data access location. Therefore, they first label data objects from one to ten to track access frequencies of data objects. Then, they use those data access frequencies from each location to predict the future access frequencies of data objects to determine the replication levels and locations to replicate the data objects, and store the related data objects to close storage servers. To show the efficiency of the proposed methods, the authors conduct an extensive simulation by using real data. The results show that the proposed method has an advantage over the previous works in terms of data availability and increases the data availability up to 50%. The proposed method and related analysis can assist multimedia service providers to enhance their service qualities.

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Evaluating the skill of satellite data on the individuation of extreme precipitation events in Calabria (southern Italy)

10.5194/egusphere-egu2020-3071 ◽

2020 ◽

Author(s):

Tommaso Caloiero ◽

Roberto Coscarelli ◽

Giulio Nils Caroletti

Keyword(s):

Satellite Data ◽

Extreme Precipitation ◽

Southern Italy ◽

Hot Spot ◽

Rain Gauge ◽

Precipitation Event ◽

The European Union ◽

Extreme Precipitation Events ◽

Daily Data ◽

Precipitation Events

In this study, the skill of TRMM Multi-Satellite Precipitation Analysis (TMPA) data to locate spatially and temporally extreme precipitation has been tested over Calabria, a region in southern Italy.Calabria is a very challenging region for hydrometeorology studies, as i) it is a mainly mountainous region with complex orography; ii) it is surrounded by sea, providing&#160; an abundance of available moisture; iii) it belongs to the Mediterranean region, a hot-spot for climate change.TMPA, which provides daily data at a 0.25&#176; resolution (i.e., about 25 km at southern Italy latitudes), was tested with regards to three extreme precipitation events that occurred between 1998 and 2019, i.e., the years of TMPA&#8217;s operational time frame. The first event, taking place on 07-12/09/2000, lasted for several days and involved most of Calabria. The second (01-04/07/2006) was a very localized midsummer event, which hit a very small area with destructive consequences. Finally, the 18-27/11/2013 event was a ten-day long heavy precipitation event that hit the region in spots.TMPA daily data were compared against validated and homogenized rain gauge data from 79 stations managed by the Multi-Risk Functional Centre of the Regional Agency for Environmental Protection. TMPA was evaluated both in relative and absolute terms: i) the relative skill was tested by checking if TMPA evaluated correctly the presence of extreme precipitation, defined as daily precipitation passing the 99th percentile threshold; ii) the absolute skill was tested by checking if TMPA reproduced correctly the cumulated precipitation values during the events.TMPA proved sufficiently able to locate areas subject to heavy cumulated precipitation during large spatially distributed events over the region. However, it showed difficulties in reproducing very localized events, as the 2006 case study was not detected at all, showing that 25-km spatial resolution and daily time resolution proved inadequate to resolve this type of rainfall event.Results might give insights into the possibility of using satellite data for real-time monitoring of extreme precipitation, especially since the transition from the old TMPA to the new Integrated Multi-satellitE Retrievals for GPM (IMERG) set was completed in January 2020.&#160;Acknowledgments:The Project INDECIS is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).

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Satellite soil moisture improves rainfall just where needed

10.5194/egusphere-egu2020-5303 ◽

2020 ◽

Author(s):

Luca Brocca ◽

Stefania Camici ◽

Christian Massari ◽

Luca Ciabatta ◽

Paolo Filippucci ◽

...

Keyword(s):

Soil Moisture ◽

River Discharge ◽

Large Scale ◽

River Flow ◽

Global Scale ◽

Rain Gauge ◽

Rainfall Runoff ◽

Global Precipitation Climatology Centre ◽

Satellite Rainfall ◽

Global Precipitation

Soil moisture is a fundamental variable in the water and energy cycle and its knowledge in many applications is crucial. In the last decade, some authors have proposed the use of satellite soil moisture for estimating and improving rainfall, doing hydrology backward. From this research idea, several studies have been published and currently preoperational satellite rainfall products exploiting satellite soil moisture products have been made available.The assessment of such products on a global scale has revealed an important result, i.e., the soil moisture based products perform better than state of the art products exactly over regions in which the data are needed: Africa and South America. However, over these areas the assessment against rain gauge observations is problematic and independent approaches are needed to assess the quality of such products and their potential benefit in hydrological applications. On this basis, the use of the satellite rainfall products as input into rainfall-runoff models, and their indirect assessment through river discharge observations is an alternative and valuable approach for evaluating their quality.For this study, a newly developed large scale dataset of river discharge observations over 500+ basins throughout Africa has been exploited. Based on such unique dataset, a large scale assessment of multiple near real time satellite rainfall products has been performed: (1) the Early Run version of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement), IMERG Early Run, (2) SM2RAIN-ASCAT (https://doi.org/10.5281/zenodo.3405563), and (3) GPM+SM2RAIN (http://doi.org/10.5281/zenodo.3345323). Additionally, gauge-based and reanalysis rainfall products have been considered, i.e., (4) the Global Precipitation Climatology Centre (GPCC), and (5) the latest European Centre for Medium-Range Weather Forecasts reanalysis, ERA5. As rainfall-runoff model, the semi-distributed MISDc (Modello Idrologico Semi-Distribuito in continuo) model has been employed in the period 2007-2018 at daily temporal scale.First results over a part of the dataset reveal the great value of satellite soil moisture products in improving satellite rainfall estimates for river flow prediction in Africa. Such results highlight the need to exploit such products for operational systems in Africa addressed to the mitigation of the flood risk and water resources management.

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