Due to limited number of weather stations and interruption of data collection, the temperature field data may be incomplete. In the past, spatial interpolation is usually used for filling the data gap. However, the interpolation method does not work well for the case of the large-scale data loss. Matrix completion has emerged very recently and provides a global optimization for temperature field data reconstruction. A recovery method is proposed for improving the accuracy of temperature field data by using sparse low-rank matrix completion (SLR-MC). The method is tested using continuous gridded data provided by ERA Interim and the station temperature data provided by Jiangxi Meteorological Bureau. Experimental results show that the average signal-to-noise ratio can be increased by 12.5%, and the average reconstruction error is reduced by 29.3% compared with the matrix completion (MC) method.
It is important to eliminate systematic biases in the field of soil moisture data assimilation. One simple method for bias removal is to match cumulative distribution functions (CDFs) of modeled soil moisture data to satellite soil moisture data. Traditional methods approximate numerical CDFs using 12 or 20 uniformly spaced samples. In this paper, we applied the Douglas–Peucker curve approximation algorithm to approximate the CDFs and found that three nonuniformly spaced samples can achieve the same reduction in standard deviation. Meanwhile, the matching results are always closely related to the temporal and spatial availability of soil moisture observed by automatic soil moisture station (ASM). We also applied the new nonuniformly spaced sampling method to a shorter time series. Instead of processing a whole year of data at once, we divided it into 12 datasets and used three nonuniformly spaced samples to approximate the model data’s CDF for each month. The matching results demonstrate that NU-CDF3 reduced the SD, improved R, and reduced the RMSD in over 70% of the stations, when compared with U-CDF12. Additionally, the SD and RMSD have been reduced by over 4% with R improved by more than 9%.
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