The performance of the IMERG satellite-based product in identifying sub-daily rainfall events and their properties

Freitas, Emerson da Silva; Coelho, Victor; Xuan, Yunqing; Melo, Djair Alves de; Gadelha, André N.; Santos, Elias A.; Galvão, Carlos de Oliveira; Filho, Geraldo M. Ramos; Barbosa, Luís Romero; Huffman, George J.; Petersen, Walt; Almeida, Cristiano das Neves

doi:10.1016/j.jhydrol.2020.125128

Cited by 67 publications

(43 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A multitude of techniques are developed and available for the estimation and retrieval of rainfall for satellite sensors (Kidd and McGregor 2007). The algorithm of satellitebased rainfall is mainly based on microwave and infrared sensors together with ground-based data (radar or rain gauge) or multiple sensors (Freitas et al 2020;Kidd and Levizzani 2011;Tapiador et al 2012). The nature of the Fig.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Performance of daily satellite-based rainfall in groundwater basin of Merapi Aquifer System, Yogyakarta

Rahmawati

Rahayu²,

Yuliasari

2021

Theor Appl Climatol

View full text Add to dashboard Cite

Evaluation of the performance of daily satellite-based rainfall (CMORPH, CHIRPS, GPM IMERG, and TRMM) was done to obtain applicable satellite rainfall estimates in the groundwater basin of the Merapi Aquifer System (MAS). Performance of satellite data was assessed by applying descriptive statistics, categorical statistics, and bias decomposition on the basis of daily rainfall intensity classification. This classification is possible to measure the performance of daily satellite-based rainfall in much detail. CM (CMORPH) has larger underestimation compared to other satellite-based rainfall assessments. This satellite-based rainfall also mostly has the largest RMSE, while CHR (CHIRPS) has the lowest. CM has a good performance to detect no rain, while IMR (GPM IMERG) has the worst performance. IMR and CHR have a good performance to detect light and moderate rain. Both of them have larger H frequencies and lower MB values compared to other satellite products. CHR mostly has a good performance compared to TR (TRMM), especially on wet periods. CM, IMR, and TR mostly have a good performance on dry periods, while CHR on wet periods. CM mostly has the largest MB and lowest AHB values. CM and CHR have better accuracy to estimate rain amount compared to IMR and TR. All in all, all 4 satellite-based rainfall assessments have large discrepancy compared with rain gauge data along mountain range where orographic rainfall usually occurs in wet periods. Hence, it is recommended to evaluate satellite-based rainfall with time series of streamflow simulation in hydrological modeling framework by merging rain gauge data with more than one satellite-based rainfall than to merge both IMR and TR together.

show abstract

Section: Discussionmentioning

confidence: 99%

“…In this research, we use the final run and current version of GPM IMERG that is version 06. It is because this version has (i) the inclusion of additional sensors specifically of TRMM, (ii) parent GPM product improvement, and (iii) the refinement of morphing components (Freitas et al 2020).…”

Section: Datamentioning

confidence: 99%

Performance of daily satellite-based rainfall in groundwater basin of Merapi Aquifer System, Yogyakarta

Rahmawati

Rahayu²,

Yuliasari

2021

Theor Appl Climatol

View full text Add to dashboard Cite

show abstract

“…A multitude of technique is developed and available for the estimation and retrieval of rainfall for satellite sensors (Kidd and McGregor 2007). The algorithm of satellite-based rainfall is mainly based on microwave and infrared sensors together with ground-based data (radar or rain gauge) or multiple sensors (Freitas et al 2020;Kidd and Levizzani 2011;Tapiador et al 2012). The nature of error can change with the update of retrieval algorithm and the change of data source leading to different performance of satellite-based rainfall for different region, season and precipitation type, i.e., rainfall intensity (Ebert et al 2007;Guo et al 2015).…”

Section: Discussionmentioning

confidence: 99%

“…In this research, we use the nal run and current version of GPM IMERG that is the version 06. It is because this version has (i) inclusion of additional sensors speci cally of TRMM, (ii) parent GPM product improvement, and (iii) the re nement of morphing components (Freitas et al 2020).…”

Section: Methodsmentioning

confidence: 99%

Performance of Daily Satellite-Based Rainfall in Groundwater Basin of Merapi Aquifer System, Yogyakarta

Rahmawati

Rahayu²,

Yuliasari³

2021

Preprint

View full text Add to dashboard Cite

Evaluation of the performance of daily satellite-based rainfall (CMORPH, CHIRPS, GPM IMERG, and TRMM) was done to obtain applicable satellite rainfall estimates in groundwater basin of Merapi Aquifer System (MAS). Performance of satellite data was assessed by applying descriptive statistics, categorical statistics, and bias decomposition on the basis of daily rainfall intensity classification. This classification is possible to measure the performance of daily satellite-based rainfall in much detail.CM (CMORPH) has larger underestimation compared to other satellite-based rainfall. This satellite-based rainfall also mostly has the largest RMSE, while CHR (CHIRPS) is the lowest. CM has a good performance to detect no rain, while IMR (GPM-IMERG) has the worst performance. IMR and CHR have a good performance to detect light and moderate rain. Both of them have larger H frequencies and lower MB values compared to other satellite products. CHR mostly has a good performance compared to TR (TRMM) especially on wet periods. CM, IMR, and TR mostly have a good performance on dry periods, while CHR on wet periods. CM mostly has the largest MB and lowest AHB values. CM and CHR have better accuracy to estimate rain amount compared to IMR and TR. All in all, all 4 satellite-based rainfall has large discrepancy compared with rain gauge data along mountain range where orographic rainfall usually occurs in wet periods. Hence, it is recommended to evaluate satellite-based rainfall with time series of streamflow simulation in hydrological modeling framework by merging rain gauge data with more than one satellite-based rainfall except to merge both IMR and TR together.

show abstract

“…The algorithm of GPM-IMERG aims to produce rainfall estimates every 30 min with a spatial resolution of 0.1 • between latitudes 60 • (version 5) and global (version 6), using all estimates of passive microwave precipitation (PMW), together with the infrared (IR) estimates available in the TRMM and GPM era (Huffman et al, 2019a,b;Huffman et al, 2020). The GPM-IMERG has been successfully used in the Amazon Basin (Oliveira et al, 2018;Althoff et al, 2020;da S. Freitas et al, 2020).…”

Section: Precipitation Datasetsmentioning

confidence: 99%

On the Hydroclimate-Vegetation Relationship in the Southwestern Amazon During the 2000–2019 Period

Gutierrez-Cori

Espinoza

et al. 2021

Front. Water

View full text Add to dashboard Cite

The southern Amazonia is undergoing a major biophysical transition, involving changes in land use and regional climate. This study provides new insights on the relationship between hydroclimatic variables and vegetation conditions in the upper Madeira Basin (~1 × 106 km2). Vegetative dynamics are characterised using the normalized difference vegetation index (NDVI) while hydroclimatic variability is analysed using satellite-based precipitation, observed river discharge, satellite measurements of terrestrial water storage (TWS) and downward shortwave radiation (DSR). We show that the vegetation in this region varies from energy-limited to water-limited throughout the year. During the peak of the wet season (January-February), rainfall, discharge and TWS are negatively correlated with NDVI in February-April (r = −0.48 to −0.65; p < 0.05). In addition, DSR is positively correlated with NDVI (r = 0.47–0.54; p < 0.05), suggesting that the vegetation is mainly energy-limited during this period. Outside this period, these correlations are positive for rainfall, discharge and TWS (r = 0.55–0.88; p < 0.05), and negative for DSR (r = −0.47 to −0.54; p < 0.05), suggesting that vegetation depends mainly on water availability, particularly during the vegetation dry season (VDS; late June to late October). Accordantly, the total rainfall during the dry season explains around 80% of the VDS NDVI interannual variance. Considering the predominant land cover types, differences in the hydroclimate-NDVI relationship are observed. Evergreen forests (531,350 km2) remain energy-limited during the beginning of the dry season, but they become water-limited at the end of the VDS. In savannas and flooded savannas (162,850 km2), water dependence occurs months before the onset of the VDS. These differences are more evident during extreme drought years (2007, 2010, and 2011), where regional impacts on NDVI were stronger in savannas and flooded savannas (55% of the entire surface of savannas) than in evergreen forests (40%). A spatial analysis reveals that two specific areas do not show significant hydroclimatic-NDVI correlations during the dry season: (i) the eastern flank of the Andes, characterised by very wet conditions, therefore the vegetation is not water-limited, and (ii) recent deforested areas (~42,500 km2) that break the natural response in the hydroclimate-vegetation system. These findings are particularly relevant given the increasing rates of deforestation in this region.

show abstract

The performance of the IMERG satellite-based product in identifying sub-daily rainfall events and their properties

Cited by 67 publications

References 85 publications

Performance of daily satellite-based rainfall in groundwater basin of Merapi Aquifer System, Yogyakarta

Performance of daily satellite-based rainfall in groundwater basin of Merapi Aquifer System, Yogyakarta

Performance of Daily Satellite-Based Rainfall in Groundwater Basin of Merapi Aquifer System, Yogyakarta

On the Hydroclimate-Vegetation Relationship in the Southwestern Amazon During the 2000–2019 Period

Contact Info

Product

Resources

About