Assessment of extreme rainfall estimates from satellite-based: Regional analysis

Palharini, Rayana Santos Araujo; Vila, Daniel; Rodrigues, Daniele Tôrres; Palharini, Rodrigo Cassinelli; Mattos, Enrique Vieira; Pedra, George Ulguim

doi:10.1016/j.rsase.2021.100603

Cited by 8 publications

(11 citation statements)

References 51 publications

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“…The correlation is below 0.4 in the Lower São Francisco (Figure 3c) and it is in this region where the lowest POD (Figure 3d) and highest FAR (Figure 3e) are found. Similar values were found by [18] who observed in inland and coast northeastern regions, regiões que abrangem o SFB, that 3B42 product of TRMM demonstrated a better performance, as demonstrated in the metrics for inland and coast northeastern regions, bias = 2.82 mm/day and − 2.94 mm/day; r = 0.18 and 0.30; std = 8.53 mm/day and 6.97 mm/day; rmse = 14.75 mm/day and 7.03 mm/day, respectively.…”

Section: Resultssupporting

confidence: 90%

“…Warm clouds have little vertical development and low ice content, which is essential for the electrification process of clouds. These conditions contribute to a deficience on estimating precipitation by satellites [5,8,18,50,54]. In addition, it is reasonable assume that IMERG's resolution can be not sufficient to represen(mainly) the local convection in the region, thus the rainfall is underestimated.…”

Section: Resultsmentioning

confidence: 99%

“…The data series observed by each surface gauge does not exceed 10% of missing data (Table 1). Condition imposed by several researchers as [18,42,43]. To assess the IMERG estimates, the dates with missing data were excluded from the two databases, Gauge and IMERG.…”

Section: Precipitation Datamentioning

confidence: 99%

“…In particular, the rainfall estimated from the tropical rainfall measuring mission (TRMM) algorithms [9] and more recently the global precipitation measurement (GPM) has been widely used in regional and global meteorological and climatological studies [8,[10][11][12][13][14][15]. In this context, several studies have been performed to evaluate the precipitation estimates from the TRMM and GPM satellites in different regions of Brazil [5,[16][17][18][19]. Overall, the results of these studies suggest that the accuracy of satellite precipitation estimates depends on factors such as terrain, type of precipitation and local climate.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the Integrated Multi-SatellitE Retrievals for the Global Precipitation Measurement (IMERG) Product in the São Francisco Basin (Brazil)

Rodrigues

Silva

Reis

et al. 2021

Water

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The São Francisco River basin is one of the largest in the Brazilian territory. This basin has enormous economic, social and cultural importance for the country. Its water is used for human and animal supply, irrigation and energy production. This basin is located in an area with different climatic characteristics (humid and semiarid) and studies related to precipitation are very important in this region. In this scenario, the objective of this investigation is to present an assessment of rainfall estimated through the Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (IMERG) product compared with rain gauges over the São Francisco river basin in Brazil. For that, a period from of 20 years and 18 surface weather stations were used to evaluate the product. Based on different evaluation techniques, the study found that the IMERG is appropriate to represent precipitation over the basin. According to the results, the performance of the IMERG product depends on the location where the rain occurs. The bias ranged from −1.67 to 0.34 mm, the RMSE ranged from 5.36 to 10.36 mm and the values of the correlation coefficients between the daily data from the IMERG and rain gauge ranged from 0.28 to 0.61. The results obtained by Student t-test, density curves and regression analysis, in general, show that the IMERG is able to satisfactorily represent rain gauge data. The exception is the eastern portion of the basin, where the product, on average, underestimates the precipitation (p-value < 0.05) and presents the worst statistical metrics.

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Section: Resultssupporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Section: Precipitation Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of the Integrated Multi-SatellitE Retrievals for the Global Precipitation Measurement (IMERG) Product in the São Francisco Basin (Brazil)

Rodrigues

Silva

Reis

et al. 2021

Water

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“…This is consistent with the findings of Tang et al, (2019) for the Mekong River Basin. CHIRPS and PERSIANN-CDR are the least skilful in capturing extremes with a very low CC and large positive and negative biases (Araujo Palharini et al, 2021). For instance, numerous precipitation products have been observed to both underestimate and overestimate low and high precipitation values in Brazil (Palharini et al, 2020), consequently resulting in corresponding underestimations and overestimations of low and high streamflows.…”

Section: Discussionmentioning

confidence: 99%

Global scale evaluation of precipitation datasets for hydrological modelling

Gebrechorkos,

Leyland,

Dadson

et al. 2023

Preprint

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Abstract. Precipitation is the most important driver of the hydrological cycle but is challenging to estimate over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (ERA5 global reanalysis (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERCCDR)) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against more than 1800 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly and daily time scales, MSWEP followed by ERA5 demonstrated a higher CC and KGE than other datasets for more than 50 % of the stations. Whilst, ERA5 was the second-highest performing dataset, it showed the highest error and bias in about 20 % of the stations. The PERCCDR is the least well performing dataset with large bias (percentage of bias up to 99 %) and errors (normalised root mean square error up to 247 %) with a higher KGE and CC than the other products in less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.

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Comparison of the performance of estimated precipitation data via remote sensing in the Midwest Region of Brazil

Moraes

Gonçalves

2023

Theor Appl Climatol

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Assessment of extreme rainfall estimates from satellite-based: Regional analysis

Cited by 8 publications

References 51 publications

Evaluation of the Integrated Multi-SatellitE Retrievals for the Global Precipitation Measurement (IMERG) Product in the São Francisco Basin (Brazil)

Evaluation of the Integrated Multi-SatellitE Retrievals for the Global Precipitation Measurement (IMERG) Product in the São Francisco Basin (Brazil)

Global scale evaluation of precipitation datasets for hydrological modelling

Comparison of the performance of estimated precipitation data via remote sensing in the Midwest Region of Brazil

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