Trend assessment for daily snow depths with changepoint considerations
| Autor: | Jaechoul Lee, Jonathan Woody, Robert Lund, Y. Xu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
010504 meteorology & atmospheric sciences Ecological Modeling Model selection Instrumentation Model parameters Seasonality medicine.disease Snow 01 natural sciences Storage model 010104 statistics & probability Climatology medicine Environmental science 0101 mathematics 0105 earth and related environmental sciences |
| Zdroj: | Environmetrics. 31 |
| ISSN: | 1099-095X 1180-4009 |
| Popis: | This paper develops methods to estimate a long‐term trend in a daily snow depth record. The methods use a storage equation model for the daily snow depths that allows for seasonality, support set features (snow depths cannot be negative), correlation, and mean level shift changepoint features. Changepoints can occur in snow processes whenever observing stations move or station instrumentation is changed; they are critical features to consider when estimating a long‐term trend. A likelihood objective function is developed for the storage model and is used to estimate model parameters. Genetic algorithms are used to optimize a minimum descriptive length model selection criterion that estimates the changepoint numbers and locations. The methods are applied in the analysis of a daily series recorded near Warm Lake, Idaho, from 1948 to 2009. |
| Databáze: | OpenAIRE |
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