Improvement of a combination of TMPA (or IMERG) and ground-based precipitation and application to a typical region of the East China Plain

Wu, Zhiyong; Zhang, Yuliang; Sun, Zhenli; Lin, Qiaoyan; He, Honglin

doi:10.1016/j.scitotenv.2018.05.272

Cited by 18 publications

(18 citation statements)

References 24 publications

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“…Other researchers have also proposed an improved method for analyzing the hydrological utility of the 3B42V7 data. For example, Wu et al [45] have developed a multi-source precipitation-merging method to improve the applicability of the product's data in a typical region of the East China Plain. In the field of drought monitoring, some studies have found that 3B42V7 data can reflect the temporal and spatial evolution of droughts in mainland China, and 3B42V7 data is suitable for the monitoring and evaluation of meteorological drought at a large scale [22,23].…”

Section: Discussionmentioning

confidence: 99%

“…The accuracy and the applicability of the TMPA-3B42 product has been examined not only at the national scale [22][23][24][25][26][27][28][29], but also at the regional scale. For example, in the southeast of China [30][31][32], southwest China [33][34][35], northeast China [36,37], northwest China [38][39][40][41], and other regions [42][43][44][45]. In the above studies, the comparisons of rain gauge station data and precipitation product data were widely used as the key methods for evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Error Distribution and Error Cause Analysis of TMPA-3B42V7 Satellite-Based Precipitation Products over Mainland China

Deng

Wang

Lai

2019

Water

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With a high spatial resolution and wide coverage, satellite-based precipitation products have compensated for the shortcomings of traditional measuring methods based on rain gauge stations, such as the sparse and uneven distribution of rain gauge stations. However, the accuracy of satellite precipitation products is not high enough in some areas, and the causes of their errors are complicated. In order to better calibrate and apply the product’s data, relevant research on this kind of product is required. Accordingly, this study investigated the spatial error distribution and spatial influence factors of the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) post-process 3B42V7 (hereafter abbreviated as 3B42V7) data over mainland China. This study calculated accuracy indicators based on the 3B42V7 data and daily precipitation data from 797 rain gauge stations across mainland China over the time range of 1998–2012. Then, a clustering analysis was conducted based on the accuracy indicators. Moreover, the geographical detector (GD) was used to perform the error cause analysis of the 3B42V7. The main findings of this study are the following. (1) Within mainland China, the 3B42V7 data accuracy decreased gradually from the southeast coast to the northwest inland, and shows a similar distribution for precipitation. High values of systematic error (>1.0) is mainly concentrated in the southwest Tibetan Plateau, while high values of random error (>1.0) are mainly concentrated around the Tarim Basin. (2) Mainland China can be divided into three areas by the spectral clustering method. It is recommended that the 3B42V7 can be effectively used in Area I, while in Area III the product should be calibrated before use, and the product in Area II can be used after an applicability study. (3) The GD result shows that precipitation is the most important spatial factor among the seven factors influencing the spatial error distribution of the 3B42V7 data. The relationships between spatial factors are synergistic rather than individual when influencing the product’s accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Error Distribution and Error Cause Analysis of TMPA-3B42V7 Satellite-Based Precipitation Products over Mainland China

Deng

Wang

Lai

2019

Water

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“…To illustrate the rationality of data interpolation, the accuracy of IDW with different observation densities was analyzed (shown in Figure 3) [38]. In this study, the average area controlled by a single station (CA) was 7.5 × 10 3 km 2 per gauge and the correlation coefficients (CC) is 0.81 > 0.8 (indicating a high correlation) according to Figure 3.…”

Section: Data Interpolationmentioning

confidence: 92%

Quantitative Assessment Model for the Effects of Drought Mitigation on Regional Agriculture Based on an Expectation Index of Drought Mitigation Effects

Zhang

Jin

Jiang

et al. 2019

Water

Self Cite

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The extent of water deficit and drought loss mitigation, through human activities such as defense, mitigation, and resistance to drought risk, is revealed by its effects on agriculture. To analyze the distribution regularities of the effects of drought mitigation on agriculture and to provide better insight on drought mitigation actions, an expectation index of drought mitigation effects (EDRE) was formulated based on the definitions of the drought system. The crop yield when no drought mitigation measures were implemented was calculated via tests and simulations, and expectations of drought-related yield loss with and without drought mitigation measures (EDRL and EDRM, respectively) were calculated in the drought loss risk assessment model. Then, a quantitative assessment model for the effects of drought mitigation on regional agriculture was built. Using a case study from the Huaibei Plain in the Anhui Province of China, it was found that drought mitigation effects decrease gradually from North to South. Moreover, small values of drought mitigation effects correspond to large EDRM and small EDRL values. It is necessary to urgently improve drought mitigation measures in locations where EDRE is small and EDRL is large, or where EDRE is small and EDRM is large. The main drought mitigation measures were identified through correlation analysis. Additionally, the adaptation of drought mitigation measures to local conditions leads to a spatial distribution regularity.

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“…(2001) consisting in a procedure that weights the individual input components by the inverse of the random error to produce a final merged product. Other merging method was reported by Wu et al (2018). In this method, the P from rain gauges is added to the first-guess field when combining the P estimates of TRMM Multi-Satellite P Analysis (TMPA) 3B42 with rain gauges.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, better integration procedures can be developed. The integration can be based on the fact that the information obtained by satellite allows capturing the spatial variability of parameters that can be determined based on the interaction of electromagnetic energy with rain-laden clouds and that FM capture their accuracy (Nerini et al 2015, Wu et al 2018). Hengl et al (2007) reports the possibility to use RK to interpolate FM using estimations from satellite images as auxiliary variable.…”

Section: Introductionmentioning

confidence: 99%

Geostatistical integration to improve representativeness of satellite precipitation estimates and field measurements

Tapia-Silva¹

Kalpa Publications in Computing

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Since the network of rainfall gauges and ground radars is generally not dense enough, satellite data have been used to estimate Precipitation (P). These data have the ability to capture the spatial variability pattern of the parameter, but are often inaccurate in relation to the value of the field measured parameter. Therefore, geostatistical methods were evaluated to improve the spatial representativeness of field measurements (FM) and satellite estimates. The work has been made for a hydrological sub region in the Mexican tropic. The geostatistical methods used to interpolate P-FM were ordinary kriging (KO), universal kriging (KU) and regression kriging (RK) as well as the Inverse Distance Weighted (IDW) mechanical interpolator for comparison purposes. Furthermore, the values at the pixel centers of the Tropical Rainfall Monitoring Mission (TRMM) images were interpolated using OK and evaluated using leave-one-out cross validation (LOO-CV). The best LOO-CV evaluated method consisted of the RK interpolation of the point FM taking as auxiliary variable the OK interpolation of the TRMM cell centers. It is concluded that the geostatistical integration between rainfall estimates from satellite data and FM data is promising because satellite information has the ability to capture spatial variability and the point FM add accuracy to the results. These characteristics combined can produce a P product useful for modeling activities and environmental management.

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Improvement of a combination of TMPA (or IMERG) and ground-based precipitation and application to a typical region of the East China Plain

Cited by 18 publications

References 24 publications

Spatial Error Distribution and Error Cause Analysis of TMPA-3B42V7 Satellite-Based Precipitation Products over Mainland China

Spatial Error Distribution and Error Cause Analysis of TMPA-3B42V7 Satellite-Based Precipitation Products over Mainland China

Quantitative Assessment Model for the Effects of Drought Mitigation on Regional Agriculture Based on an Expectation Index of Drought Mitigation Effects

Geostatistical integration to improve representativeness of satellite precipitation estimates and field measurements

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