Central Asia precipitation a new method-PTPLS

What is it about?

Accuracy of any gridded climatic datasets is as important as their availability for regional climate and ecological studies. In this study, the accuracy of estimated precipitation in Central Asia from three recently developed reanalysis datasets, MERRA, ERA-Interim and CFSR, is evaluated through comparisons with observations from 399 stations during 1979-2010. An interpolated precipitation dataset from station observations and a satellite remotely-sensed dataset, TRMM 3B42, are included in the evaluation. Major results show that MERRA data have higher accuracy than ERA-Interim and CFSR, although they all overestimate the observed precipitation especially in late spring and early summer months, suggesting errors in their ways of representing convective precipitation in that region. In comparison, the interpolated and satellite sensed data, which provide no upper air information/data, have higher accuracy. While all these datasets have difficulty in describing stations’ precipitation in mountainous areas, the reanalysis datasets have particularly large discrepancies. In examining the discrepancy in the reanalysis data, a Precipitation-Topography Partial Least Square method is proposed to incorporate certain terrain/geographic effects on precipitation in mapping the gridded data to the station locations for comparison with the observation. The outcome suggests the estimated station precipitation by this new method is closer to the observed than the method without considering those factors. The improvement by this method and by possible other methods taking into account different details/aspects of the influences indicates that it is only meaningful to compare the accuracy or relevance of gridded datasets to station observations in a relative sense among various datasets.

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Why is it important?

Firstly, evaluating three different precipitation datasets. And a new interpolation method PTPLS is developed.

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