Monthly adjustment of Global Satellite Mapping of Precipitation (GSMaP) data over the VuGia^|^ndash;ThuBon River Basin in Central Vietnam using an artificial neural network

Ngo‐Duc, Thanh; Matsumoto, Jun; Kamimera, Hideyuki; Bui, Hai

doi:10.3178/hrl.7.85

Cited by 29 publications

(16 citation statements)

References 25 publications

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“…Dealing with these potential errors requires individual calibration methods being fitted into the specific situations responsible for the satellite precipitation data errors. Poor performance over mountainous and coastal regions is a typical issue (McCollum and Ferraro (2005); Kubota et al (2007); Ngo-Duc et al (2013); Shige et al (2013); Mega and Shige (2016)). In such regions, one of the reasons for the poor performance is orographic convection, with relatively low cloud tops caused by the interaction between low-level winds and topographic barriers.…”

Section: Introductionmentioning

confidence: 99%

“…Orographic convection sometimes brings heavy rainfall, even though its convective top is low, and researchers have tried to improve representation of this kind of convection (e.g., Kwon et al (2008)). Ngo-Duc et al (2013) improved monthly precipitation data from the Global Satellite Mapping of Precipitation (GSMaP; Aonashi et al (2009) ;Ushio et al (2009)) with a corrective method applying an artificial neural network over central Vietnam. Yamamoto and Shige (2015) (hereafter YS15) introduced an algorithm to distinguish orographic rainfall in the GSMaP.…”

Section: Introductionmentioning

confidence: 99%

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Precipitation estimation performance by Global Satellite Mapping and its dependence on wind over northern Vietnam

Nodzu

Matsumoto

Trinh-Tuan

et al. 2019

Prog Earth Planet Sci

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The performance of the Global Satellite Mapping of Precipitation data Microwave-Infrared Combined Reanalysis Product (GSMaP RNL), version 6, was evaluated, using northern Vietnam as the test area. The Vietnam Gridded Precipitation (VnGP) Dataset was used for comparison purposes. Particular emphasis was placed on the investigation of heavy-rain days (precipitation over 50 mm day −1 ). Wind data from operational radiosonde observations at Hanoi were also used to examine the effect of interaction between wind and topography, on the GSMaP performance, and the basic relationship between the VnGP precipitation and lower tropospheric wind. Results showed that heavy-rain days generally occurred during May-August. Regions with higher and lower precipitation in the GSMaP, compared to the VnGP, were found to be distributed in a complex manner, at a scale similar to that of tributary basins. The GSMaP and the VnGP showed similar precipitation values when associated with westerly wind in the lower troposphere at Hanoi. Positive influences of westerly wind on estimated precipitation were observed along the northeastern foot of the mountain ranges, whereas negative influences were found along the southeastern foot. The results implied that the GSMaP rainfall estimation algorithm, taking account of orographic effects, may improve the accuracy of rainfall reproducibility, by using a more appropriate wind dataset to evaluate orographic convection.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Precipitation estimation performance by Global Satellite Mapping and its dependence on wind over northern Vietnam

Nodzu

Matsumoto

Trinh-Tuan

et al. 2019

Prog Earth Planet Sci

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“…Many previous studies have validated the performance of the GSMaP in various regions and over different periods by comparing its results with ground-based precipitation data from gauges and radars (e.g., Kubota et al 2009Ngo-duc et al 2013;Prakash et al 2016;Beck et al 2017;Nodzu et al 2019;Trinh-Tuan et al 2019;Palharini et al 2020). The GSMaP algorithm mainly consists of an algorithm for retrieving precipitation from PMW sensors (the PMW algorithm) and an algorithm for estimating precipitation from both PMW sensors and IR radiometer data (the PMW-IR combined algorithm).…”

Section: Introductionmentioning

confidence: 99%

An Approach to Reliability Characterization of GSMaP Near-Real-Time Precipitation Product

Yamaji

Kubota

Yamamoto

2021

Journal of the Meteorological Society of Japan

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Reliability information of satellite precipitation products is required for various applications. This study describes and evaluates a reliability flag of the Global Satellite Mapping of Precipitation Near-Real-Time precipitation product (GSMaP_NRT). This flag was developed to characterize the reliability of GSMaP_NRT data simply and qualitatively by considering its algorithm characteristics. The reliability at each pixel is represented by any one of ten levels (10 being the best and 1 the worst) by considering three major factors: 1) "surface type reliability"-which takes into account that estimation of rainfall using passive microwave sensors is better over the oceans than over land and coastal areas; 2) "low-temperature reliability"-which takes into account the lower reliability due to surface snow cover in low-temperature conditions; and 3) "Moving Vector with Kalman Filter (MVK) propagation reliability"-which means that the reliability gets worse with the increase in time since the last overpass of the passive microwave sensor. To evaluate the utility of the reliability flag, statistical indices are calculated for each reliability level using gauge-calibrated ground radar data around Japan. It is found that the reliability flag represents the differences in GSMaP accuracy: the accuracy worsens as the reliability decreases. The GSMaP errors exhibit seasonal changes that are well represented by the ten levels of the reliability flag, indicating that the reliability flag can be used to catch seasonal variations in GSMaP accuracy due to changes in environmental factors. This study also raises the possibility of improving the reliability flag by using information related to heavy orographic rainfall. It is shown how the error features of heavy orographic rainfall differ from those of the total rainfall, and it is suggested that heavy orographic rainfall information can be utilized to further improve the reliability flag.

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“…In addition, the reliability of gauge-based daily precipitation datasets also depend on many factors such as distribution of gauge stations, accessibility of gauge observations, and quality control of gauge observations (Xie et al, 2007;Yatagai et al, 2009). However, many methods of rainfall bias correction have been applied to these gridded precipitation data to improve the accuracy of rainfall estimates in different parts of the ICP (e.g., Ngo-Duc et al, 2013;Ono et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Rainfall pattern over the middle of Indochina Peninsula during 2009–2010 summer monsoon

Mahavik

Satomura

Shige

et al. 2014

Hydrological Research Letters

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Abstract:Rainfall patterns during summer monsoon in 2009 and 2010 over the middle of the Indochina Peninsula (ICP) are investigated using calibrated daily accumulated radar rainfall (CDARR). Empirical orthogonal function (EOF) analysis applied to CDARR shows that the first three modes explain 40% of the total rainfall variance. The pattern of the first EOF mode is only positive over the radar observation area with a large value near the foot of the Annam range in the eastern region of the radar site. The second EOF mode is a dipole pattern that has positive and negative regions in the eastern and western regions of the radar observation area, respectively. The third EOF mode also shows a dipole pattern with positive and negative areas in the southern and northern regions of the observation area, respectively. Composite analysis results suggest that the first EOF mode is possibly produced by a difference in positive vorticity, in which the difference in the southerly wind component likely causes orographic rainfall in the eastern region of the radar site. In addition, the second and third EOF modes are possibly produced by differences in westerly and southwesterly wind components, respectively.

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Monthly adjustment of Global Satellite Mapping of Precipitation (GSMaP) data over the VuGia^|^ndash;ThuBon River Basin in Central Vietnam using an artificial neural network

Cited by 29 publications

References 25 publications

Precipitation estimation performance by Global Satellite Mapping and its dependence on wind over northern Vietnam

Precipitation estimation performance by Global Satellite Mapping and its dependence on wind over northern Vietnam

An Approach to Reliability Characterization of GSMaP Near-Real-Time Precipitation Product

Rainfall pattern over the middle of Indochina Peninsula during 2009–2010 summer monsoon

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Monthly adjustment of Global Satellite Mapping of Precipitation (GSMaP) data over the VuGia^|^ndash;ThuBon River Basin in Central Vietnam using an artificial neural network

Cited by 29 publications

References 25 publications

Precipitation estimation performance by Global Satellite Mapping and its dependence on wind over northern Vietnam

Precipitation estimation performance by Global Satellite Mapping and its dependence on wind over northern Vietnam

An Approach to Reliability Characterization of GSMaP Near-Real-Time Precipitation Product

Rainfall pattern over the middle of Indochina Peninsula during 2009&ndash;2010 summer monsoon

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Rainfall pattern over the middle of Indochina Peninsula during 2009–2010 summer monsoon