Effects of temporal scales and space mismatches on the TRMM 3B42 v7 precipitation product in a remote mountainous area

Abstract

The accurate measurement of precipitation is essential to understanding regional hydrological processes and hydrological cycling. Quantification of precipitation over remote regions such as the Tibetan Plateau is highly unreliable because of the scarcity of rain gauges. The objective of this study is to evaluate the performance of the satellite precipitation product of tropical rainfall measuring mission (TRMM) 3B42 v7 at daily, weekly, monthly, and seasonal scales. Comparison between TRMM grid precipitation and point-based rain gauge precipitation was conducted using nearest neighbour and bilinear weighted interpolation methods. The results showed that the TRMM product could not capture daily precipitation well due to some rainfall events being missed at short time scales but provided reasonably good precipitation data at weekly, monthly, and seasonal scales. TRMM tended to underestimate the precipitation of small rainfall events (less than 1 mm/day), while it overestimated the precipitation of large rainfall events (greater than 20 mm/day). Consequently, TRMM showed better performance in the summer monsoon season than in the winter season. Through comparison, it was also found that the bilinear weighted interpolation method performs better than the nearest neighbour method in TRMM precipitation extraction.

Evaluation of the GPM IMERG v5 and TRMM 3B42 v7 Precipitation Products in the Yangtze River Basin, China

“…Previous studies also reported TRMM's unsatisfactory results when monitoring extreme rainfall [24,50]. One possible reason is that the PR and TMI sensors embedded in TRMM were not sensitive to detect light or heavy rainfall, while the GPM DPR and GMI were better at detecting light and solid rainfall events [16,51].…”

“…A grid-to-point approach (NN method) was also used to compare between the point-based rain gauges and pixel-based SPPs. However, attention should be given to the errors that may be induced after resampling and undertaking grid-to-point techniques due to scale mismatch issues, and the errors that have already existed due to fundamentally different measurements between rain gauges and SPPs [15,33,51]. Moreover, various interpolation methods and grid-to-point techniques can also produce different evaluation results.…”

Multiscale Comparative Evaluation of the GPM IMERG v5 and TRMM 3B42 v7 Precipitation Products from 2015 to 2017 over a Climate Transition Area of China

“…The precipitation distribution in Figure 8b showed an underestimation of satellite estimates when the rainfall rates were either less than 0.5 mm/day or greater than 25 mm/day. Ebrahimi et al [41] pointed out that satellite sensors are not sensitive to light rainfall and heavy rainfall, which might lead to unsatisfactory monitoring of extreme rainfall. The IMERG v5 product, with relatively high POD and CSI, showed better performance in precipitation detection of the Huaihe River basin.…”

“…It was demonstrated that IMERG v5 was more suitable for characterizing the spatial variation of precipitation. However, it is worth pointing out that the accuracy of satellite precipitation at daily scale is still poor, which limits the application of satellite precipitation products to some extent [41]. Xu et al [40] found that the performance of GPM and TRMM strongly depends on topography and rainfall intensity over the southern Tibetan Plateau.…”

“…However, there is a typical scale mismatch issue between point-based rain gauge data and the gridded precipitation products [22]. Many interpolation methods (inverse distance weighting, kriging, spline, and Thiessen polygon for example) [22,33,53] and grid-to-point techniques (such as nearest neighbor, bilinear weighted interpolation) [41] have been proposed to compare the point-based rain gauge data and pixel-based satellite precipitation data. Each method has its advantages and disadvantages, and its performance depends on various factors and varies from region to region [22].…”