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

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.

Accuracy assessment and error cause analysis of GPM (V06) in Xiangjiang river catchment

“…Xiangjiang River is one of the main tributaries of the Yangtze River. It originates in Lingui, Guangxi Autonomous Region, flows through Hunan Province, and drains into Dongting Lake (Wang et al 2018). The Xiangjiang river catchment, the area of which is 94,660 km², is located in southern China, and lies in between 24°31 0 N-29°00 0 N and 110°31 0 E-114°00 0 E (Figure 1).…”

“…Table 5 indicates evaluation results of three sets of GPM products during the period from 2006 to 2018, compared to TRMM results calculated by Wang et al (2018).…”

“…Most of these studies have used ground-based observations to verify data accuracy. Some of them have pointed out that the TMPA research products (3B42 V6 and V7) perform better than their real-time counterparts (RTV6 and RTV7) because they use monthly gauged dataset to amend data (Wang et al 2018). The later version, V7, performs better than the previous V6 because of an improved algorithm and updated data inputs (Chen et al 2013a(Chen et al , 2013bZulkafli et al 2014).…”

Evaluation of Multi-Satellite Precipitation Datasets and Their Error Propagation in Hydrological Modeling in a Monsoon-Prone Region

“…Detection, systematic, and random errors are three main error sources of satellite-based datasets [35,88]. False alarms (when gauges do not observe the satellite-detected precipitation) and missed rain (when the gauge-observed precipitation are not actually detected by satellites) constitute the detection errors [89].…”

Precipitation Changes in the Three Gorges Reservoir Area and the Relationship with Water Level Change

“…However, satellite-based precipitation data are measured indirectly, which is easily limited by cloud characteristics and retrieval algorithms. The accuracy of precipitation monitoring still needs improvement [ 18 , 19 , 20 ]. Second, most of the previous studies only analyzed a relatively short time period, which did not cover the entire impounding period of TGP from 2003 to 2010 and the stable operation period after 2011, and the long time-series precipitation changes before 2003 were also not considered [ 11 , 12 , 14 , 15 ].…”