Ground validation and error decomposition for six state-of-the-art satellite precipitation products over mainland China

2022 ◽  
pp. 106017
Author(s):  
Huajin Lei ◽  
Hongyu Zhao ◽  
Tianqi Ao
Keyword(s):  
State Of The Art ◽  
Mainland China ◽  
Satellite Precipitation ◽  
Ground Validation ◽  
Error Decomposition

scholarly journals Tracking the error sources of spatiotemporal differences in TRMM accuracy using error decomposition method

2018 ◽  
Vol 49 (6) ◽  
pp. 1960-1976 ◽  
Author(s):  
Jialing Wang ◽  
Hua Chen ◽  
Chong-Yu Xu ◽  
Qiang Zeng ◽  
Qingjing Wang ◽  
...  
Keyword(s):  
High Intensity ◽  
Decomposition Method ◽  
High Elevation ◽  
Precipitation Intensity ◽  
Xiangjiang River ◽  
Satellite Precipitation ◽  
Low Intensity ◽  
Error Components ◽  
Error Decomposition ◽  
Precipitation Events

Abstract Tropical Rainfall Measuring Mission (TRMM) products are widely utilized, but the causes of the differences in their spatiotemporal accuracy require further investigation to improve satellite precipitation estimation. In this study, the spatiotemporal accuracy of TRMM 3B42 V7 data was systematically evaluated using the rain gauge data of the densely gauged Xiangjiang River basin, a humid region in South China. The effects of the precipitation intensity and elevation on different error components derived from the error decomposition method were analysed to reveal the causes of spatiotemporal differences of the data errors. The results showed the following. (1) TRMM performs better in the wet season than in the dry season, and it underestimates precipitation in winter and in high-elevation areas. (2) Precipitation intensity directly influences the occurrence and magnitude of error components. Most of the missed precipitation (precipitation detected only by rain-gauged data) and false precipitation (precipitation detected only by TRMM data) occur in low-intensity precipitation events. Hit events (precipitation detected by both TRMM and rain-gauged data) tend to overestimate low-intensity precipitation and underestimate high-intensity precipitation. Elevation has no direct relation with daily bias, but affects the distribution of occurrence and intensity of precipitation events. (3) Missed precipitation is the main contributing source of error in winter. The negative error increases in high-elevation areas, which is contributed by the larger proportion of high intensity hit precipitation and the missed events. This study is not only beneficial for understanding the effect of topography and climate factors on the accuracy of TRMM precipitation data but also provides a reference for the application and error improvement of satellite precipitation products.

Evaluation and integration of the top-down and bottom-up satellite precipitation products over mainland China

2020 ◽  
Vol 581 ◽  
pp. 124456 ◽  
Author(s):  
Ling Zhang ◽  
Xin Li ◽  
Yanping Cao ◽  
Zhuotong Nan ◽  
Weizhen Wang ◽  
...  
Keyword(s):  
Mainland China ◽  
Top Down ◽  
Bottom Up ◽  
Satellite Precipitation

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

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1435
Author(s):  
Zifeng Deng ◽  
Zhaoli Wang ◽  
Chengguang Lai
Keyword(s):  
Influence Factors ◽  
Mainland China ◽  
Rain Gauge ◽  
Error Distribution ◽  
Time Range ◽  
Similar Distribution ◽  
Spatial Error ◽  
Cause Analysis ◽  
Satellite Precipitation ◽  
Daily Precipitation Data

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.

scholarly journals Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China

2016 ◽  
Vol 8 (5) ◽  
pp. 440 ◽  
Author(s):  
Bin Yong ◽  
Bo Chen ◽  
Yudong Tian ◽  
Zhongbo Yu ◽  
Yang Hong
Keyword(s):  
Mainland China ◽  
Component Analysis ◽  
Error Component ◽  
Satellite Precipitation ◽  
Precipitation Estimates

scholarly journals How Does the Evaluation of the GPM IMERG Rainfall Product Depend on Gauge Density and Rainfall Intensity?

2018 ◽  
Vol 19 (2) ◽  
pp. 339-349 ◽  
Author(s):  
Fuqiang Tian ◽  
Shiyu Hou ◽  
Long Yang ◽  
Hongchang Hu ◽  
Aizhong Hou
Keyword(s):  
Rainfall Intensity ◽  
Mainland China ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Evaluation Metrics ◽  
Retrieval Algorithm ◽  
Rainfall Events ◽  
Ground Validation ◽  
Global Precipitation ◽  
Rain Gauge Network

Abstract This study investigates the dependency of the evaluation of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) rainfall product on the gauge density of a ground-based rain gauge network as well as rainfall intensity over five subregions in mainland China. High-density rain gauges (1.5 gauges per 100 km2) provide exceptional resources for ground validation of satellite rainfall estimates over this region. Eight different gauge networks were derived with contrasting gauge densities ranging from 0.04 to 4 gauges per 100 km2. The evaluation focuses on two warm seasons (April–October) during 2014 and 2015. The results show a strong dependency of the evaluation metrics for the IMERG rainfall product on gauge density and rainfall intensity. A dense rain gauge network tends to provide better evaluation metrics, which implies that previous evaluations of the IMERG rainfall product based on a relatively low-density gauge network might have underestimated its performance. The decreasing trends of probability of detection with gauge density indicate a limited ability to capture light rainfall events in the IMERG rainfall product. However, IMERG tends to overestimate (underestimate) light (heavy) rainfall events, which is a consistent feature that does not show strong dependency on gauge densities. The results provide valuable insights for the improvement of a rainfall retrieval algorithm adopted in the IMERG rainfall product.

scholarly journals Prospects of Machine Translation in the Chinese Context

2009 ◽  
Vol 50 (4) ◽  
Author(s):  
Duoxiu Qian
Keyword(s):  
Hong Kong ◽  
Machine Translation ◽  
State Of The Art ◽  
Mainland China ◽  
The State ◽  
Foreign Languages ◽  
Commercial Software ◽  
Chinese Context ◽  
The World ◽  
Historical Sketch

Abstract China has been among the several leading countries in the research and applications of Machine Translation (MT) and Machine-aided Translation (MAT) ever since the 1950s. The first part of this paper is a historical sketch of MT and MAT in the Chinese context, highlighting some important stages in its development which have laid the foundation for later achievements. It is shown that the research of MT in this region is similar to that in other parts of the world, with the attention gradually turning to MAT. The second part deals with the state of the art of MT and MAT research and applications in Mainland China, Taiwan and Hong Kong, respectively. Then popular commercial software dedicated to the translation from Chinese into other foreign languages, and vice versa, are introduced, with an appraisal of both their merits and demerits. Finally, prospects of MT and MAT in the Chinese context is discussed. It is suggested that, for mutual benefits, MT and MAT research in the Chinese context should cooperate with the outside world more closely.

scholarly journals Ground Validation of GPM IMERG Precipitation Products over Iran

2019 ◽  
Vol 12 (1) ◽  
pp. 48 ◽  
Author(s):  
Fatemeh Fadia Maghsood ◽  
Hossein Hashemi ◽  
Seyyed Hasan Hosseini ◽  
Ronny Berndtsson
Keyword(s):  
Extreme Rainfall ◽  
Rain Gauge ◽  
Accurate Estimation ◽  
Precipitation Data ◽  
Satellite Precipitation ◽  
Country Level ◽  
Rainfall Index ◽  
Precipitation Measurement ◽  
Ground Validation ◽  
Global Precipitation

Accurate estimation of precipitation is crucial for fundamental input to various hydrometeorological applications. Ground-based precipitation data suffer limitations associated with spatial resolution and coverage; hence, satellite precipitation products can be used to complement traditional rain gauge systems. However, the satellite precipitation data need to be validated before extensive use in the applications. Hence, we conducted a thorough validation of the Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) product for all of Iran. The study focused on investigating the performance of daily and monthly GPM IMERG (early, late, final, and monthly) products by comparing them with ground-based precipitation data at synoptic stations throughout the country (2014–2017). The spatial and temporal performance of the GPM IMERG was evaluated using eight statistical criteria considering the rainfall index at the country level. The rainfall detection ability index (POD) showed that the best IMERG product’s performance is for the spring season while the false alarm ratio (FAR) index indicated the inferior performance of the IMERG products for the summer season. The performance of the products generally increased from IMERG-Early to –Final according to the relative bias (rBIAS) results while, based on the quantile-quantile (Q-Q) plots, the IMERG-Final could not be suggested for the applications relying on extreme rainfall estimates compared to IMERG-Early and -Late. The results in this paper improve the understanding of IMERG product’s performance and open a door to future studies regarding hydrometeorological applications of these products in Iran.

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