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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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Spatio-temporal precipitation climatology over complex terrain using a censored additive regression model
Reto Stauffer1,2, Georg J Mayr2, Jakob W Messner1,2
1Department of Statistics, Faculty of Economics and Statistics University of Innsbruck Austria.
International Journal of Climatology : a Journal of the Royal Meteorological Society
|July 18, 2017
Summary
This study introduces a new spatio-temporal model for daily precipitation climatology. The model accurately estimates precipitation distribution, even with many zero values, outperforming existing methods.
Area of Science:
- Environmental science and atmospheric research.
- Statistical modeling and data analysis.
Background:
- Traditional precipitation climatology models often use monthly or annual means, assuming normal distribution.
- This assumption is inadequate for daily precipitation data due to frequent zero values and non-negative constraints.
Purpose of the Study:
- To develop a novel spatio-temporal model for daily precipitation climatology.
- To accurately estimate the full precipitation distribution on a daily time scale, especially over complex terrain.
Main Methods:
- Development of a new spatio-temporal model utilizing a left-censored normal distribution.
- Application of the model to daily precipitation data, accounting for zero-inflated and non-negative characteristics.
Main Results:
- The novel method effectively handles the non-normal distribution and high frequency of zero precipitation observations.
- The model provides a comprehensive climatological distribution at high spatial and temporal resolutions.
Conclusions:
- The developed model offers a superior approach to daily precipitation climatology compared to existing methods.
- This new method is accurate and competitive for estimating precipitation, even in challenging geographical areas.
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