Comparison analysis of six purely satellite-derived global precipitation estimates

Chen, Hanqing; Yong, Bin; Shen, Yan; Liu, Jiufu; Hong, Yang; Zhang, Jianyun

doi:10.1016/j.jhydrol.2019.124376

Cited by 75 publications

(68 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…c. Several studies have confirmed that the errors of SPPs are related to the rainfall intensity, such as Kirstetter et al, 2013, Chen et al, 2013, Chen et al, 2020. Chen et al (2020 even found that a power function is observed between the retrieval accuracy and the precipitation intensity, indicating that the errors of SPPs increase with precipitation intensity.…”

Section: Interactive Commentmentioning

confidence: 91%

Interactive comment on “Performance of GPM-IMERG precipitation products under diverse topographical features and multiple-intensity rainfall in an arid region”

Chen¹

2020

Preprint

Self Cite

View full text Add to dashboard Cite

This work focused on the impact of several crucial factors including elevation, season, hydrological region and precipitation intensity on the precipitation retrievals of IMERG suite (i.e., Early, Late and Final runs) over Saudi Arabia. This is a very lengthy study, covering several important factors that influence the performance of satellite precipitation retrievals. However, I noted some issues that need clarification. a. The pertinent studies investigating the impact of these four crucial factors on the C1

show abstract

Section: Interactive Commentmentioning

confidence: 91%

Interactive comment on “Performance of GPM-IMERG precipitation products under diverse topographical features and multiple-intensity rainfall in an arid region”

Chen¹

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Regarding the component analysis of errors for five SPPs in different topographies, high-accuracy and high spatiotemporal resolution (hourly temporal and 0.1° spatial resolution) ground observations from 26326 rain gauges were used as the benchmark. The spatial distribution of the rain gauge can be found in our published 160 paper (i.e., Chen et al, 2019b;Chen et al, 2020). However, this product has large uncertainties in cold seasons due to freezing weather.…”

Section: Reference Productsmentioning

confidence: 99%

“…The satellite-based instruments have the ability to overcome the limitations of rain gauges and ground-based radars to provide the precipitation estimates with high spatiotemporal resolution and even covering the globe (Kidd et al, 2011a). However, satellite precipitation products contain a large number of random errors, systematic 60 errors and large uncertainties, especially over complex mountains (Tian et al, 2010a;Maggioni et al, 2016a;Chen et al, 2020). Therefore, it is necessary to comprehensively analyze the errors of satellite precipitation products, especially for their satellite-only versions.…”

mentioning

confidence: 99%

“…To date there are several evaluation studies investigating major components of the total bias for satellite precipitation products at several regions, such as mainland China (Yong et al, 2016;Xu et al, 2016;Su et al, 2018;Chen et al, 2020), the contiguous United…”

mentioning

confidence: 99%

“…Precipitation intensity is also an important factor associated with the errors of satellite precipitation estimates (Chen et al, 2020). Previous efforts found that satellite 110 https://doi.org/10.5194/hess-2020-294 Preprint.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Global component analysis of errors in five satellite-only global precipitation estimates

Chen¹,

Yong²,

Wang³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Revealing the error components for satellite-only precipitation products (SPPs) can help algorithm developers and end-users substantially understand their error features and meanwhile is fundamental to customize retrieval algorithms and error adjustment models. Two error decomposition schemes were employed to explore the error components for five SPPs (i.e., MERG-Late, IMERG-Early, GSMaP-MVK, GSMaP-NRT, and PERSIANN-CCS) over different seasons, rainfall intensities, and topography classes. Firstly, this study depicted global maps of the total bias (total mean squared error) and its three (two) independent components for these five SPPs over four seasons for the first time. We found that the evaluation results between similar regions could not be extended to one another. Hit and/or false biases are major components of the total bias in most regions of the global land areas. In addition, the proportions of the systematic error are less than 20 % of total errors in most areas. One should note that each SPP has larger systematic errors in several regions (i.e., Russia, China, and Conterminous United States) for all four seasons, these larger systematic errors from retrieval algorithms are primarily due to the missed precipitation. Furthermore, IMERG suite and GSMaP-NRT display less systematic error in the rain rates with intensity less than 40 mm/day, while the systematic errors of GSMaP-MVK and PERSIANN-CCS increase with increasing rainfall intensity. Given that mean elevation cannot reflect the complex degree of terrain, we introduced the standard deviation of elevation (SDE) to replace mean elevation to better describe topographic complexity. Compared with other SPPs, GSMaP suite shows a stronger topographic dependency in the four bias scores. A novel metric namely normalized error component (NEC) was proposed to fairly evaluate the impact of the solely topographic factor on systematic (random) error. It is found that these products show different topographic dependency patterns in systematic (random) error. Meanwhile, the pattern of the impact of the solely topographic factor on systematic (random) error is similar to the relationship between systematic (random) error and topography because the average precipitations of all topography categories are very close. Finally, the potential directions of the improvement in satellite precipitation retrieval algorithms and error adjustment models were identified in this study.

show abstract

Application of the WRF model rainfall product for the localized flood hazard modeling in a data-scarce environment

et al. 2022

View full text Add to dashboard Cite

Urban flood hazard model needs rainfall with high spatial and temporal resolutions for flood hazard analysis to better simulate flood dynamics in complex urban environments. However, in many developing countries, such high-quality data are scarce. Data that exist are also spatially biased toward airports and urban areas in general, where these locations may not represent flood-prone areas. One way to gain insight into the rainfall data and its spatial patterns is through numerical weather prediction models. As their performance improves, these might serve as alternative rainfall data sources for producing optimal design storms required for flood hazard modeling in data-scarce areas. To gain such insight, we developed Weather Research and Forecasting (WRF) design storms based on the spatial distribution of high-intensity rainfall events simulated at high spatial and temporal resolutions. Firstly, three known storm events (i.e., 25 June 2012, 13 April 2016, and 16 April 2016) that caused the flood hazard in the study area are simulated using the WRF model. Secondly, the potential gridcell events that are able to trigger the localized flood hazard in the catchment are selected and translated to the WRF design storm form using a quantile expression. Finally, three different WRF design storms per event are constructed: Lower, median, and upper quantiles. The results are compared with the design storms of 2- and 10-year return periods constructed based on the alternating-block method to evaluate differences from a flood hazard assessment point of view. The method is tested in the case of Kampala city, Uganda. The comparison of the design storms indicates that the WRF model design storms properties are in good agreement with the alternating-block design storms. Mainly, the differences between the produced flood characteristics (e.g., hydrographs and the number of flood gird cells) when using WRF lower quantiles (WRFLs) versus 2-year and WRF upper quantiles (WRFUs) versus 10-year alternating-block storms are very minimal. The calculated aggregated performance statistics (F scores) for the simulated flood extent of WRF design storms benchmarked with the alternating-block storms also produced a higher score of 0.9 for both WRF lower quantiles versus 2-year and WRF upper quantile versus 10-year alternating-block storm. The result suggested that the WRF design storms can be considered an added value for flood hazard assessment as they are closer to real systems causing rainfall. However, more research is needed on which area can be considered as a representative area in the catchment. The result has practical application for flood risk assessment, which is the core of integrated flood management.

show abstract

Comparison analysis of six purely satellite-derived global precipitation estimates

Cited by 75 publications

References 56 publications

Interactive comment on “Performance of GPM-IMERG precipitation products under diverse topographical features and multiple-intensity rainfall in an arid region”

Interactive comment on “Performance of GPM-IMERG precipitation products under diverse topographical features and multiple-intensity rainfall in an arid region”

Global component analysis of errors in five satellite-only global precipitation estimates

Application of the WRF model rainfall product for the localized flood hazard modeling in a data-scarce environment

Contact Info

Product

Resources

About