Abstract
This paper described the development of a spatial downscaling algorithm to produce finer grid resolution for satellite precipitation data (0.05°) in humid tropics. The grid resolution provided by satellite precipitation data (>0.25°) was unsuitable for practical hydrology and meteorology applications in the high hydrometeorological dynamics of Southeast Asia. Many downscaling algorithms have been developed based on significant seasonal relationships, without vegetation and climate conditions, which were inapplicable in humid, equatorial, and tropical regions. Therefore, we exploited the potential of the low variability of rainfall and monsoon characteristics (period, location, and intensity) on a local scale, as a proxy to downscale the satellite precipitation grid and its corresponding rainfall estimates. This study hypothesized that the ratio between the satellite precipitation and ground rainfall in the low-variance spatial rainfall pattern and seasonality region of humid tropics can be used as a coefficient (constant value) to spatially downscale future satellite precipitation datasets. The spatial downscaling process has two major phases: the first is the derivation of the high-resolution coefficient (0.05°), and the second is applying the coefficient to produce the high-resolution precipitation map. The first phase utilized the long-term bias records (1998–2008) between the high-resolution areal precipitation (0.05°) that was derived from dense network of ground precipitation data and re-gridded satellite precipitation data (0.05°) from the Tropical Rainfall Measuring Mission (TRMM) to produce the site-specific coefficient (SSC) for each individual pixel. The outcome of the spatial downscaling process managed to produce a higher resolution of the TRMM data from 0.25° to 0.05° with a lower bias (average: 18%). The trade-off for the process was a small decline in the correlation between TRMM and ground rainfall. Our results indicate that the SSC downscaled method can be used to spatially downscale satellite precipitation data in humid, tropical regions, where the seasonal rainfall is consistent.
Highlights
Precise information on spatiotemporal rainfall is critical for accurate hydrology predictions and simulations in humid tropical regions
Water 2018, 10, 409 in-situ measurements, because they provide wide coverage, are publicly available, and are grid-based. Their suitability for small basins is hindered by their coarse grid size [1,2]. This is conspicuous for most humid tropical catchments in Southeast Asia, where the region comprises of small land–sea ration area—especially islands and peninsula
The rainfall distribution pattern over Peninsular Malaysia is strongly influenced by seasonal monsoons, and the area is classified into five local climate regions: northwest, east, west, southwest, and highland (>400 m above sea level) (Figure 2) [27]
Summary
Precise information on spatiotemporal rainfall is critical for accurate hydrology predictions and simulations in humid tropical regions. Water 2018, 10, 409 in-situ measurements, because they provide wide coverage, are publicly available, and are grid-based. Their suitability for small basins is hindered by their coarse grid size [1,2]. This is conspicuous for most humid tropical catchments in Southeast Asia, where the region comprises of small land–sea ration area—especially islands and peninsula. The new satellite precipitation data product from Global Precipitation Mission and GsMAP has higher resolution (0.1◦ ) than its predecessor, the Tropical Rainfall Measuring Mission (TRMM), it is only available from 2015 onwards. Numerous efforts have been made to improve the coarse grids by spatial downscaling
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