Abstract
Precipitation is a crucial component of the water cycle and plays a key role in hydrological processes. Recently, satellite-based precipitation products (SPPs) have provided grid-based precipitation with spatiotemporal variability. However, SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution of these products is still relatively coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation based on a combination of rainfall observation data with multiple SPPs for the period of 2003–2017 across South Korea. A Random Forest (RF) machine-learning algorithm model was applied for producing a new merged precipitation product. In addition, several statistical linear merging methods have been adopted to compare with the results achieved from the RF model. To investigate the efficiency of RF, rainfall data from 64 observed Automated Synoptic Observation System (ASOS) installations were collected to analyze the accuracy of products through several continuous as well as categorical indicators. The new precipitation values produced by the merging procedure generally not only report higher accuracy than a single satellite rainfall product but also indicate that RF is more effective than the statistical merging method. Thus, the achievements from this study point out that the RF model might be applied for merging multiple satellite precipitation products, especially in sparse region areas.
Highlights
Precipitation has a significant role in supporting human life on earth
The performance of primary precipitation products, as well as the results obtained from the Random Forest (RF) merging approach, were compared with observation data from the Automated Synoptic Observation System (ASOS)
CHIRPSv2 generally has shown the worst performance with the highest median Mean Absolute Error (MAE) (4.65 mm/d), followed by Tropical Rainfall Measuring Mission (TRMM)
Summary
Precipitation has a significant role in supporting human life on earth. It directly affects our daily life and production activities. Information about the variability of precipitation, such as intensity, duration, and frequency, is extremely important [1,2,3]. Precipitation information is collected by three main methods: ground-based observation systems, weather radar systems, and satellite monitoring systems [4]. The rain gauge station is the primary method to obtain rainfall information with high reliability. To monitor the spatial distribution of rain in a given area, the number of stations needs to satisfy certain requirements. Mountainous areas where there is a significant change in topography often face a significant challenge; that is, the density of measuring stations is sparse, discrete, and unevenly distributed. Developing and maintaining a dense network of measurements is a major financial obstacle for developing countries [5,6,7]
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