From TRMM to GPM, how do improvements of post/near-real-time satellite precipitation estimates manifest?

Shen, Zhehui; Yong, Bin; Long, Yi; Wu, Hao; Xu, Hui

doi:10.1016/j.atmosres.2022.106029

Cited by 21 publications

(14 citation statements)

References 67 publications

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“…Because sub-daily data were not available in the study area, the lowest temporal scale used in this study to evaluate the performances of four SPPs was daily resolution. At the sub-daily resolution, Shen et al [54] found a significant difference in the performance of SPPs. Therefore, the findings of comparing four SPPs at the sub-daily scale, as well as the conclusions drawn from those comparisons, may be somewhat different from those drawn at the daily scale.…”

Section: Discussionmentioning

confidence: 99%

“…All PERSIANN family products showed better detection skills in the autumn season. However, the uncertainty of remote sensing data (Huang et al [23], Shen et al [54]) may also affect the research results.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the findings of comparing four SPPs at the sub-daily scale, as well as the conclusions drawn from those comparisons, may be somewhat different from those drawn at the daily scale. Moreover, the uncertainty of remote sensing data [23,54,55] may also affect the research results.…”

Section: Discussionmentioning

confidence: 99%

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Assessment of Multi-Satellite Precipitation Products over the Himalayan Mountains of Pakistan, South Asia

et al. 2022

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Performance assessment of satellite-based precipitation products (SPPs) is critical for their application and development. This study assessed the accuracies of four satellite-based precipitation products (PERSIANN-CDR, PERSIANN-CCS, PERSIANN-DIR, and PERSIANN) using data of in situ weather stations installed over the Himalayan Mountains of Pakistan. All SPPs were evaluated on annual, seasonal, monthly, and daily bases from 2010 to 2017, over the whole spatial domain and at point-to-pixel scale. The assessment was conducted using widely used evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (rBias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI)). Results showed: (1) PERSIANN and PERSIANN-DIR products efficiently traced the spatio-temporal distribution of precipitation over the Himalayan Mountains. (2) On monthly scale, the estimates of all SPPs were more consistent with the reference data than on the daily scale. (3) On seasonal scale, PERSIANN and PERSIANN-DIR showed better performances than the PERSIANN-CDR and PERSIANN-CCS products. (4) All SPPs were less accurate in sensing daily light to medium intensity precipitation events. Subsequently, for future hydro-meteorological investigations in the Himalayan range, we advocate the use of monthly PERSIANN and PERSIANN-DIR products.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Assessment of Multi-Satellite Precipitation Products over the Himalayan Mountains of Pakistan, South Asia

et al. 2022

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“…The evaluation work can be generally classified into two categories: (1) direct comparison of the gridded precipitation to the corresponding rainfall gauge's precipitation data; (2) evaluation of gridded precipitation products based on their predictive ability of streamflow rate in a hydrological modeling framework. Evaluation works involved in different gridded precipitation datasets have been made over a range of scales from basin to global extent [17][18][19][20][21][22][23][24][25]. Meanwhile, recently some efforts have been put into evaluating and validating gridded precipitation over the TP and the basins within it [6,[26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Hydrological Application of Four Gridded Precipitation Datasets over a Large Southeastern Tibetan Plateau Basin

Zhang

et al. 2022

Remote Sensing

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Reliable precipitation is crucial for hydrological studies over Tibetan Plateau (TP) basins with sparsely distributed rainfall gauges. In this study, four widely used precipitation products, including the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of the water resources (APHRODITE), the High Asia Reanalysis (HAR), and the satellite-based precipitation estimates from Global Precipitation Measurement (GPM) and Tropical Rainfall Measurement Mission (TRMM), were comprehensively evaluated by combining statistical analysis and hydrological simulation over the Upper Brahmaputra (UB) River Basin of TP during 2001–2013. In respect to the statistical assessment, the overall performances of GPM and HAR are comparable to each other, and both are superior to the other two datasets. For hydrological assessment, both daily and monthly GPM-based streamflow simulations perform the best not only at the UB outlet with very good results, but they also illustrate satisfactory results at Yangcun and Lhasa hydrological stations within the UB. Runoff simulation using HAR only performs well at the UB outlet, whereas it shows poor results at both Yangcun and Lhasa stations. The simulated results based on APHRODITE and TRMM show poor performances at UB. Generally, the GPM shows an encouraging potential for hydro-meteorological investigation over UB, although with some bias in flood simulation.

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“…La precipitación es un componente principal del ciclo hidrológico. Medidos atreves de instrumentos basados en pluviómetros terrestres (Shen et al 2022). Para su análisis en los recursos hídricos se debe contar con datos temporales fiables con buena calidad, por la falta de cobertura de estaciones hidrometeorológicas se recurre a otras fuentes para completar la información (Lakew et al 2017).…”

Section: Introductionunclassified

Modelacion Hidrológica Con Precipitaciones Obtenidas Por Satélite en La Cuenca Del Río Ramis Perú

Chayña

2022

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En los últimos años, muchos investigadores han orientado sus estudios a aprovechar los beneficios que se pueden obtener a través de las Estimaciones de Precipitación Basadas en Satélite (EPB), en escalas de tiempo diarias, mensuales y anuales. El uso de EPBs es una de las alternativas para solucionar el problema de las cuencas con poco o ningún instrumento. El objetivo de la investigación es evaluar estimaciones de precipitación basadas en satélites, utilizando modelación hidrológica en la cuenca del río Ramis, Perú. La evaluación se llevó a cabo durante 16 años (2003 - 2019), utilizando EPBs, Tropical Rainfall Measurement Mission (TRMM-3B42) y Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Climate Data Record (PERSIANN-CDR). Los resultados de la comparación con estaciones meteorológicas indican que PERSIANN-CDR es mejor que TRMM-3B42 con r = 0,50 y 0,38 respectivamente. Sin embargo, el uso de las EPBS como datos de entrada en el modelo hidrológico, Herramienta de Evaluación de Suelos y Aguas (SWAT), los resultados obtenidos calibrados para TRMM-3B42 son insatisfactorios, con r = 0,77, NSE = -0,24 y el porcentaje de sesgo PBIAS = 56,50 %, para PERSIANN-CDR con r = 0,63, NSE = -0,01 y PBIAS = 62,30 %. Obteniendo un buen resultado r = 0,86 y NSE = 0,73 al utilizar las medidas de las estaciones meteorológicas. La evaluación de los datos de entrada en el modelo hidrológico muestra la magnitud del error de los EPBs, cuyos datos de entrada deben ser corregidos antes de ser utilizados

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From TRMM to GPM, how do improvements of post/near-real-time satellite precipitation estimates manifest?

Cited by 21 publications

References 67 publications

Assessment of Multi-Satellite Precipitation Products over the Himalayan Mountains of Pakistan, South Asia

Assessment of Multi-Satellite Precipitation Products over the Himalayan Mountains of Pakistan, South Asia

Evaluation and Hydrological Application of Four Gridded Precipitation Datasets over a Large Southeastern Tibetan Plateau Basin

Modelacion Hidrológica Con Precipitaciones Obtenidas Por Satélite en La Cuenca Del Río Ramis Perú

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