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- Title
Diagnosis of seasonally varying regression slope coefficients and application to the MJO.
- Authors
Roundy, Paul E.
- Abstract
Simple linear regression is frequently applied to diagnose relationships between time series in weather and climate. Yet, when similar calculations are applied based on data from different times of the year, the coefficients vary. This article introduces a new algorithm to estimate seasonally varying regression slope coefficients. The algorithm generates a seasonal time series of the variance of the predictor and of covariance between the predictor and the predictand. It does so by fitting the seasonal cycle and select harmonics thereof to the time series of products between the values of the predictor and the predictand and to the time series of the squares of the values of the predictor. On a given day of the year, the resulting seasonal covariance divided by the seasonal variance constitutes the best fit to the regression slope coefficient on that day. The algorithm is tested on artificial data, compared with a previous algorithm with similar intent, and then applied to the relationship between a Madden-Julian Oscillation index and global atmospheric patterns. Statistical significance is assessed through a Monte Carlo technique that randomly selects from the sample of years included in a training set.
- Subjects
REGRESSION analysis; COEFFICIENTS (Statistics); WEATHER; CLIMATOLOGY; ELECTRICAL harmonics; TIME series analysis
- Publication
Quarterly Journal of the Royal Meteorological Society, 2017, Vol 143, Issue 705, p1946
- ISSN
0035-9009
- Publication type
Article
- DOI
10.1002/qj.3054