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

Abstract

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.

Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

“…This finding emphasizes the need to assess the impact of temperature on the performance of SRPs. According to Nodzu et al [42], a similar behavior of the SRPs was found in Vietnam.…”

Are the Latest GSMaP Satellite Precipitation Products Feasible for Daily and Hourly Discharge Simulations in the Yellow River Source Region?

“…Post-real-time SPPs generally show better performance than their near-time-time versions because of the gauge-based adjustment in several regions of the world [16,[60][61][62][63]. However, the post-real-time product GSMaP-MVK significantly overestimates precipitation with higher overestimation magnitudes than the near-real-time product GSMaP-NRT-Gauge (Figures 3 and 6), and GSMaP-MVK presents the lower overall score than GSMaP-NRT-Gauge (Figure 6).…”

Impact of climate change on flood inundation in a tropical river basin in Indonesia

“…Furthermore, for the GSMaP Reanalysis data, the wind dataset from JRA-55 is used to detect orographic rainfall. Nevertheless, according to Nodzu et al (2019), who assessed rainfall in areas with very complex topography (including several mountainous ranges) in northern Vietnam, some bias remains in GSMaP Reanalysis ver. 6; specifically, they observed higher (lower) rainfall on the leeward (windward) side of mountains in their case study.…”