Abstract
Attaining accurate precipitation data is critical to understanding land surface processes and global climate change. The development of satellite sensors and remote sensing technology has resulted in multi-source precipitation datasets that provide reliable estimates of precipitation over un-gauged areas. However, gaps exist over high latitude areas due to the limited spatial extent of several satellite-based precipitation products. In this study, we propose an approach for the reconstruction of the Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation data over Northeast China based on the interaction between precipitation and surface environment. Two machine learning algorithms, support vector machine (SVM) and random forests (RF), are implemented to detect possible relationships between precipitation and normalized difference vegetation index (NDVI), land surface temperature (LST), and digital elevation model (DEM). The relationships between precipitation and geographical location variations based on longitude and latitude are also considered in the reconstruction model. The reconstruction of monthly precipitation in the study area is conducted in two spatial resolutions (25 km and 1 km). The validation is performed using in-situ observations from eight meteorological stations within the study area. The results show that the RF algorithm is robust and not sensitive to the choice of parameters, while the training accuracy of the SVM algorithm has relatively large fluctuations depending on the parameter settings and month. The precipitation data reconstructed with RF show strong correlation with in situ observations at each station and are more accurate than that obtained using the SVM algorithm. In general, the accuracy of the estimated precipitation at 1 km resolution is slightly lower than that of data at 25 km resolution. The estimation errors are positively related to the average precipitation.
Highlights
Precipitation is a significant factor affecting surface drought and wetness conditions, ecosystem health, and regional environment change [1,2]
The basic idea of the reconstruction method in this study is to build estimation models using samples extracted from available Tropical Rainfall Measuring Mission (TRMM) 3B43 pixels; the models are established based on the relationship between precipitation and land surface characteristics
A reconstruction algorithm is proposed for monthly TRMM 3B43 precipitation
Summary
Precipitation is a significant factor affecting surface drought and wetness conditions, ecosystem health, and regional environment change [1,2]. Precipitation data are the basic observation items of meteorological stations. Meteorological ground observation stations have mainly been used to observe precipitation. With the improvements in observation technology, ground. 2017, 9, 781 precipitation stations are automated and fundamental for the precipitation observation system [3,4]. The observation site can only reflect the precipitation information of limited discrete points. The individual site can only represent the precipitation within a certain radius around the location, especially in complex terrains, which is influenced by local environmental factors. Acquiring precipitation observations over mountainous and underdeveloped regions is still difficult due to the sparse rain gauge network
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
