Using Satellite Error Modeling to Improve GPM-Level 3 Rainfall Estimates over the Central Amazon Region

Oliveira, Rômulo; Maggioni, Viviana; Vila, Daniel; Porcacchia, Leonardo

doi:10.3390/rs10020336

Cited by 22 publications

(15 citation statements)

References 26 publications

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“…We used the Roebber performance diagram (known as a performance diagram) to represent the geometric relationship between these four quality measures. Many researchers have utilized this diagram to elaborate the performance of remotely sensed precipitation estimates in different regions, such as Pakistan [11], Egypt [2], and Brazil [58,59]. In a performance diagram, if the values of POD, SR, CSI, and BIAS for an SPP are closer to unity, then the performance of that SPP is considered as reliable.…”

Section: Performance Of Precipitation Products At Daily Scalementioning

confidence: 99%

Assessment of IMERG-V06 Precipitation Product over Different Hydro-Climatic Regimes in the Tianshan Mountains, North-Western China

et al. 2019

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This study presents an assessment of the version-6 (V06) of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product from June 2014 to December 2017 over different hydro-climatic regimes in the Tianshan Mountains. The performance of IMERG-V06 was compared with IMERG-V05 and the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 precipitation products. The precipitation products were assessed against gauge-based daily and monthly precipitation observations over the entire spatial domain and five hydro-climatologically distinct sub-regions. Results showed that: (1) The spatiotemporal variability of average daily precipitation over the study domain was well represented by all products. (2) All products showed better correlations with the monthly gauge-based observations than the daily data. Compared to 3B42V7, both IMERG products presented a better agreement with gauge-based observations. (3) The estimation skills of all precipitation products showed significant spatial variations. Overall performance of all precipitation products was better in the Eastern region compared to the Middle and Western regions. (4) Satellite products were able to detect tiny precipitation events, but they were uncertain in capturing light and moderate precipitation events. (5) No significant improvements in the precipitation estimation skill of IMERG-V06 were found as compared to IMERG-V05. We deduce that the IMERG-V06 precipitation detection capability could not outperform the efficiency of IMERG-V05. This comparative evaluation of the research products of Global Precipitation Measurement (GPM) and TRMM products in the Tianshan Mountains is useful for data users and algorithm developers.

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Section: Performance Of Precipitation Products At Daily Scalementioning

confidence: 99%

Assessment of IMERG-V06 Precipitation Product over Different Hydro-Climatic Regimes in the Tianshan Mountains, North-Western China

et al. 2019

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“…Numerical models can provide spatial precipitation estimates with physical bases but require large computational resources and have large uncertainties associated with parameters [12,13]. Satellite sensors can detect precipitation information globally and can be applied to high mountains and deserts, but satellite data poorly represent variability in local areas and complex topographical regions due to their low resolutions and deficiencies in retrieval algorithms [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed

Zhao

2021

Remote Sensing

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Satellites are capable of observing precipitation over large areas and are particularly suitable for estimating precipitation in high mountains and poorly gauged regions. However, the coarse resolution and relatively low accuracy of satellites limit their applications. In this study, a downscaling scheme was developed to obtain precipitation estimates with high resolution and high accuracy in the Heihe watershed. Shannon’s entropy, together with a semi-variogram, was applied to establish the optimal precipitation station network. A combination of the random forest (RF) method and the residual correction approach with the established rain gauge network was applied to downscale monthly precipitation products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results indicated that the RF model showed little improvement in the accuracy of IMERG-based precipitation downscaling. Including residual modification could improve the results of the RF model. The mean absolute error (MAE) and root mean square error (RMSE) values decreased by 19% and 21%, respectively, after residual corrections were added to the RF approach. Moreover, we found that enough rain gauge records are necessary for and remain an important component of tuning model performance. The application of more rain gauges improves the performance of the combined RF and residual modification methods, with the MAE and RMSE values reduced by 8% and 9%, respectively. Residual correction, together with enough precipitation stations, can effectively enhance the quality of the precipitation patterns and magnitudes obtained in the RF downscaling process. The proposed downscaling scheme is an effective tool for increasing the accuracy and spatial resolution of precipitation fields in the Heihe watershed.

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“…Therefore, error modeling is vital for improving the use of satellite-based precipitation product (GPM IMERG Precipitation estimates) in precipitation-sensitive applications such as hydrological modeling [40]. The first step to error modeling is to recognize the physical error factors and then evaluate the related error magnitudes.…”

Section: Introductionmentioning

confidence: 99%

“…The first step to error modeling is to recognize the physical error factors and then evaluate the related error magnitudes. Research on error analysis of the satellite precipitation product has been reported in several past studies, which considered the dependence on precipitation rates and types, as well as surface conditions like soil moisture and land cover [40,41]. A multidimensional satellite rainfall error model (SREM2D) developed by Hossain and Anagnostou [42] has been used in several error modeling studies of satellite rainfall products [43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin

et al. 2020

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The Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Level 3 estimates rainfall from passive microwave sensors onboard satellites that are associated with several uncertainty sources such as sensor calibration, retrieval errors, and orographic effects. This study aims to provide a comprehensive investigation of multiple machine learning (ML) techniques (Random Forest, and Neural Networks), to stochastically generate an error-corrected improved IMERG precipitation product at a daily time scale and 0.1°-degree spatial resolution over the Brahmaputra river basin. In this study, we used the operational IMERG-Late Run version 06 product along with several meteorological and land surface parameters (elevation, soil type, land type, soil moisture, and daily maximum and minimum temperature) to produce an improved precipitation product in the Brahmaputra basin. We trained, tested, and optimized ML algorithms using 4 years (from 2015 through 2019) of reference rainfall data derived from the rain gauge. The ML generated precipitation product exhibited improved systematic and random error statistics for the study area, which is a strong indication for using the proposed algorithms in retrieving precipitation across the globe. We conclude that the proposed ML-based ensemble framework has the potential to quantify and correct the error sources for improving and promoting the use of satellite-based precipitation estimates for water resources applications.

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Using Satellite Error Modeling to Improve GPM-Level 3 Rainfall Estimates over the Central Amazon Region

Cited by 22 publications

References 26 publications

Assessment of IMERG-V06 Precipitation Product over Different Hydro-Climatic Regimes in the Tianshan Mountains, North-Western China

Assessment of IMERG-V06 Precipitation Product over Different Hydro-Climatic Regimes in the Tianshan Mountains, North-Western China

An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed

Machine Learning-Based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin

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