Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina
نویسندگان
چکیده
Precipitation plays a crucial role from social and economic perspective in Subtropical Argentina (STAr). Therefore, it renders the need for continuous reliable precipitation records to develop serious climatological researches. However, this region are frequently inhomogeneous scarce, which makes necessary deal with data filling methods. Choosing best method complete series relies on rain gauge network density complexity of orography, among other factors. Most comparative-method studies literature focused dense station networks while, contrastingly, STAr's is remarkably poor (between 10 1000 times lower). The research aims at assessing performance several interpolation methods STAr. In sense, large number was evaluated dry wet seasons, interpolating raw monthly their anomalies applied different time-series subsets. general, most performances improve when seasonal subset. Multiple Linear Regression (MLR) stands out as infilling regions regardless orography or season. Despite bibliography invokes that kriging ones, work similar one Inverse Distance Weighted (IDW) Angular (ADW, used generate CRU dataset).
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ژورنال
عنوان ژورنال: Atmospheric Research
سال: 2021
ISSN: ['1873-2895', '0169-8095']
DOI: https://doi.org/10.1016/j.atmosres.2021.105482