Inter-comparison of remotely sensed precipitation datasets over Kenya during 1998–2016

Ayugi, Brian; Tan, Guirong; Ullah, Waheed; Boiyo, Richard; Ongoma, Victor

doi:10.1016/j.atmosres.2019.03.032

Cited by 67 publications

(67 citation statements)

References 72 publications

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“…The mean, the standard deviation (Std), the bias, the standard error, the root‐mean‐square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (CC) are the main statistical metrics used to evaluate the performance and efficacy of PERSIANN‐CDR, CHIRPS, and CRU for the studied period ranging from 1983 to 2016. These statistical metrics are chosen because they have been widely used to evaluate the performance of satellite‐based rainfall products using ground observation as reference data and are found to provide a consistent and trustful products' evaluation (Ayugi et al, ; Deng et al, ; Dinku et al, ; Sun et al, ; Ullah et al, ; Xu et al, ).…”

Section: Study Area Data and Methodologymentioning

confidence: 99%

“…As a result, recent studies focusing on monitoring extreme weather events such as floods and droughts across many regions have resorted to using of satellite‐based rainfall estimate products (Nicholson et al, ; Serrat‐Capdevila et al, ; Tan & Santo, ; Xu et al, ) These products are preferred due their capability in monitoring precipitation over spatial and temporal scales unlike in situ rain gauge data (Maggioni et al, ; Ongoma et al, ; Peña‐Arancibia et al, ). Numerous studies have been conducted to examine the role of satellite‐based rainfall estimate products, regional climate models, and reanalysis data to quantify rainfall and establish reliable forecast systems (Ayehu et al, ; Ayugi et al, ; Dinku et al, ; Ongoma et al, ). Moreover, different data sets have been analyzed to check their performance and their response when applied on hydrological models, environmental assessment, or extreme events analysis (Caracciolo et al, ; Deng et al, ; Gebrechorkos et al, ; Kim et al, ; Pérez‐Zanón et al, ; Tang et al, ; Wang et al, ; Zhang et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Many previous studies have proven that the satellite‐based rainfall estimates products can give reliable measurement of the rainfall variability on both spatial and temporal scales (Ayugi et al, ; Bayissa et al, ; Dembélé & Zwart, ; Dinku et al, ; Ullah et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, they can predict the evaluation of extreme weather events, despite few existing uncertainties associated with complex topography. The uncertainties are observed in varying timescales associated with overestimate or underestimate of rainfall amount (Ayugi et al, ; Fenta et al, ). Consequently, a number of satellite‐based rainfall estimate products algorithms have been enhanced with the purpose of producing accurate rainfall data over spatial and temporal caption.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparative Analysis of the Performance of Satellite‐Based Rainfall Products Over Various Topographical Unities in Central East Africa: Case of Burundi

Nkunzimana

Alriah

Zhi

et al. 2020

Earth and Space Science

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This study aims to evaluate the performance of the Climate Hazards Group Infrared Precipitation with Station observation Version 2 (CHIRPS v2.0), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record (PERSIANN-CDR) and the Climate Research Unit Time Series Version 4 (CRU) products to identify which product delivers reliable rainfall caption over Burundi. The station data with long-term records have been used as a reference to evaluate the performance of the three products for 34 years ranging from 1983 to 2016. Statistical metrics and precipitation detection capability have been used to measure the accuracy of each product over spatial and time scales. The result analysis carried out that the CHIRPS product has good performance compared to CRU and PERSIANN-CDR products. It performs well over annual, monthly, and seasonal scales, and it strongly agrees with ground observation over the study domain with the lowest correlation coefficient (CC) = 0.78. The PERSIANN-CDR product performs poorly over the study domain, though it seems to correlate with in situ rain gauge data in some regions where the CC > 0.7. It highly underestimates the rainfall amount for both short rains and long rains. The CRU shows a good performance in most of the regions with CC ≥ 0.74; however, it slightly overestimates rainfall amount over the less wet area including the north and the east regions and highly underestimates the heavy rain over mountainous region. The evaluation at daily scale shows that both CHIRPS and PERSIANN-CDR moderately perform in most of the regions except the southern and western parts where CC < 0.3. The two products exhibit a good detection ability of light rainfall (<1 mm/day) but poorly detect heavy rainfall (>20 mm/day). The CHIRPS product can deliver reliable and useful information for monitoring meteorological hazards over Burundi.

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Section: Study Area Data and Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Analysis of the Performance of Satellite‐Based Rainfall Products Over Various Topographical Unities in Central East Africa: Case of Burundi

Nkunzimana

Alriah

Zhi

et al. 2020

Earth and Space Science

View full text Add to dashboard Cite

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“…The relationship between large-scale weather systems and local climate varies from region to region, making necessary to evaluate and correct them at local scale [23,24], but the scarcity of land surface observation is one of the greatest difficulties in assessing dataset performances [25]. Previous studies have tried to assess the performance of satellite-derived and model-derived datasets in East Africa [26][27][28][29][30][31][32], in particular in Kenya [33][34][35], in order to address the lack of data from land-based meteorological stations. However, these studies have a more regional perspective rather than a local focus, and further investigation on their use at local scale is needed.…”

Section: Introductionmentioning

confidence: 99%

Comparison and Bias-Correction of Satellite-Derived Precipitation Datasets at Local Level in Northern Kenya

et al. 2020

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Understanding ongoing trends at local level is fundamental in research on climate change. However, in the Global South it is hampered by a lack of data. The scarcity of land-based observed data can be overcome through satellite-derived datasets, although performance varies according to the region. The purpose of this study is to compute the normal monthly values of precipitation for the eight main inhabited areas of North Horr Sub-County, in northern Kenya. The official decadal precipitation dataset from the Kenyan Meteorological Department (KMD), the Global Precipitation Climatology Centre (GPCC) monthly dataset and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) monthly dataset are compared with the historical observed data by means of the most common statistical indices. The GPCC showed the best fit for the study area. The Quantile Mapping correction is applied to combine the high resolution of the KMD dataset with the high performance of the GPCC set. A new and more reliable bias-corrected monthly precipitation time series for 1983–2014 results for each location. This dataset allows a detailed description of the precipitation distribution through the year, which can be applied in the climate change adaptation and tailored territorial planning.

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Evaluation of Climate Hazards Group InfraRed Precipitation Station (CHIRPS) satellite‐based rainfall estimates over Finchaa and Neshe Watersheds, Ethiopia

Geleta

Deressa

2020

Engineering Reports

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Recent advancements in the field of remote sensing have led to the development of high resolution satellite‐based rainfall products to improve the quality of observed rainfall data through proper evaluation and validation process of the products. This study therefore, intended to evaluate the performance of CHIRPS (Climate Hazards Group InfraRed Precipitation Station) satellite‐based rainfall products in Finchaa and Neshe watersheds of Blue Nile Basin. Daily ground‐ and satellite‐based rainfall data are collected from Ethiopian National Meteorological Agency and CHIRPS dataset of CHG (Climate Hazards Group) respectively, for the time slice of 25 years (1991–2015). The performance of CHIRPS product is evaluated using quantitative statistical performance indicators and graphical comparison methods. CHIRPS satellite product tends to slightly overestimate the mean rainfall depth at the study area. A positive strong linear correlation (R = 0.93 and R2 = 0.86) and a smaller amount of noise, bias and error (NSE = 0.84, PBIAS = 0.98%, and RMSE = 46.99 mm) have been found between CHIRPS satellite‐based and the ground‐based rainfall products when compared. The overall results in this study also indicated the good performance of CHIRPS satellite‐based rainfall estimates in maintaining patterns of observed measurements at monthly, seasonal and annual time steps across the watersheds.

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Inter-comparison of remotely sensed precipitation datasets over Kenya during 1998–2016

Cited by 67 publications

References 72 publications

Comparative Analysis of the Performance of Satellite‐Based Rainfall Products Over Various Topographical Unities in Central East Africa: Case of Burundi

Comparative Analysis of the Performance of Satellite‐Based Rainfall Products Over Various Topographical Unities in Central East Africa: Case of Burundi

Comparison and Bias-Correction of Satellite-Derived Precipitation Datasets at Local Level in Northern Kenya

Evaluation of Climate Hazards Group InfraRed Precipitation Station (CHIRPS) satellite‐based rainfall estimates over Finchaa and Neshe Watersheds, Ethiopia

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