Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China

Bai, Lei; Shi, Chunxiang; Li, Lanhai; Yang, Yi‐Chun; Wu, Jun

doi:10.3390/rs10030362

Cited by 150 publications

(93 citation statements)

References 65 publications

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“…To overcome uncertainties that may result from scarceness of rain gauge observation, blending station data have been added to CHIRPS to enhance its performance (Funk et al, ). CHIRPS data are chosen by many scientists as one of the best data used in hydrological studies and monitoring extremes weather events especially over African continent (Ayehu et al, ; Bai et al, ; Cattani et al, ; Gebrechorkos et al, ; Ullah et al, ). CHIRPS is believed to give a good caption in mountainous areas (Funk et al, ; Dinku et al, ).…”

Section: Study Area Data and Methodologymentioning

confidence: 99%

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%

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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“…Spanning 50 • S-50 • N (and all longitudes) [28], CHIRPS is a fusion of satellite images and data from rain-gauge stations, which is widely used for hydrology modeling, where there are sparse in situ rainfall stations. Bai et al (2018) compared CHIRPS data with 2480 rainfall stations in China. Although the accuracy of CHIRPS data is not ideal when compared with a single rainfall station, it performs well at the watershed scale [29].…”

Section: Datamentioning

confidence: 99%

Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model

et al. 2020

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Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation–water level prediction has attracted wide attention in flood forecasting and other fields; however, a clear method to model the relationship of precipitation and water level using grid precipitation products with a neural network model is lacking. The issues of the method include how to select a neural network model, as well as how to influence the modeling results with different types and resolutions of remote sensing data. The purpose of this paper is to provide some findings for the issues. We used the back-propagation (BP) neural network and a nonlinear autoregressive exogenous model (NARX) time series network to model the relationship between precipitation and water level, respectively. The water level of Pingshan hydrographic station at a catchment area in the Jinsha River Basin was simulated by the two network models using three different grid precipitation products. The results showed that when the ground station data are missing, the grid precipitation product is a good alternative to construct the precipitation–water level relationship. In addition, using the NARX network as a model fitting network using extra inputs was better than using the BP neural network; the Nash efficiency coefficients of the former were all higher than 97%, while the latter were all lower than 94%. Furthermore, the input of grid products with different spatial resolutions has little significant effect on the modeling results of the model.

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“…is dataset merges three types of information: global climatology, satellite estimates, and in situ observations [10], generating several precipitation products with time steps from 6-hourly to 3-monthly aggregates.…”

Section: Introductionmentioning

confidence: 99%

Using CHIRPS Dataset to Assess Wet and Dry Conditions along the Semiarid Central-Western Argentina

Rivera

Hinrichs

Marianetti

2019

Advances in Meteorology

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e Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset was conceived as a tool for monitoring drought and environmental change over land. Recent validation efforts along South America have assessed its suitability for reproducing the main spatial and temporal features of precipitation. Nevertheless, little has been done regarding the ability of CHIRPS for the assessment of wet and dry conditions, particularly in areas where in situ precipitation records are scarce. In this paper, we investigated the performance of CHIRPS for monitoring wet and dry events along the semiarid Central-Western Argentina. Using the Standardized Precipitation Index (SPI), we compared the CHIRPS database with records from 49 meteorological stations along the study area for the period 1987-2016. Results indicate that the CHIRPS dataset adequately reproduced the temporal variability of SPI on multiple timescales (1 month, 3 months, and 6 months), particularly in the region dominated by warm season precipitation. e large overestimation of the seasonal precipitation in the region dominated by cold season precipitation can introduce errors that are reflected in the performance of CHIRPS over the western portion of the domain. e frequency of wet and dry classes was accurately reproduced by CHIRPS on timescales larger than 1 month (SPI1), given the existence of a wet bias that produces an underestimation of the frequency of zero values. is bias is further translated to the evaluation of the SPI1 during the spatial and temporal assessment of historical dry (1998) and wet (2016) events, especially for the classification of extreme dry/wet months. e results from the evaluation indicate that CHIRPS is a suitable tool for assessing dry and wet conditions for timescales longer than 1 month and can support decision-making process within the hydrometeorological agencies over the region.

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Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China

Cited by 150 publications

References 65 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

Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model

Using CHIRPS Dataset to Assess Wet and Dry Conditions along the Semiarid Central-Western Argentina

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