Abstract
We consider the problem of improving the quality of downscaled daily precipitation data over the Missouri River Basin (MRB) at the resolution of the observed data provided based on surface observations. We use the observed precipitation as the response variable and simulated historical data provided by MIROC5 (Model of Interdisciplinary Research on Climate) as the independent variable to evaluate the use of a standard Tobit model in relation to simple linear regression. Although the Tobit approach is able to incorporate the zeros into the downscaling process and produce zero predictions with more accuracy, it is not as successful in predicting the magnitude of the positive precipitation due to its heavy model dependency. The paper also lays the groundwork for a more extensive spatiotemporal modeling approach to be pursued in the future.
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Acknowledgements
We would like to thank the two anonymous reviewers for their helpful comments. We are grateful to the US Department of Agriculture National Institute of Food and Agriculture for their partial support under Grant 2011-67003-30213 to JCET from the Center for Research on the Changing Earth System (CRCES).
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Popuri, S.K., Neerchal, N.K., Mehta, A. (2015). Comparison of Linear and Tobit Modeling of Downscaled Daily Precipitation over the Missouri River Basin Using MIROC5. In: Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (eds) Machine Learning and Data Mining Approaches to Climate Science. Springer, Cham. https://doi.org/10.1007/978-3-319-17220-0_4
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DOI: https://doi.org/10.1007/978-3-319-17220-0_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17219-4
Online ISBN: 978-3-319-17220-0
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