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Time series filtering (e.g., smoothing) can be done in the spectral domain without loss of endpoints. However, filtering is commonly performed in the time domain using convolutions, resulting in lost points near the series endpoints. Multiple incarnations of a least squares minimization approach are developed that retain the endpoint intervals that are normally discarded due to filtering with convolutions in the time domain. The techniques minimize the errors between the predetermined frequency response function (FRF)—a fundamental property of all filters—of interior points with FRFs that are to be determined for each position in the endpoint zone. The least squares techniques are differentiated by their constraints: 1) unconstrained, 2) equal-mean constraint, and 3) an equal-variance constraint. The equal-mean constraint forces the new weights to sum up to the same value as the predetermined weights. The equal-variance constraint forces the new weights to be such that, after convolved with the input values, the expected time series variance is preserved. The three least squares methods are each tested under three separate filtering scenarios [involving Arctic Oscillation (AO), Madden–Julian oscillation (MJO), and El Niño–Southern Oscillation (ENSO) time series] and compared to each other as well as to the spectral filtering method—the standard of comparison. The results indicate that all four methods (including the spectral method) possess skill at determining suitable endpoints estimates. However, both the unconstrained and equal-mean schemes exhibit bias toward zero near the terminal ends due to problems with appropriating variance. The equal-variance method does not show evidence of this attribute and was never the worst performer. The equal-variance method showed great promise in the ENSO project involving a 5-month running mean filter, and performed at least on par with the other realistic methods for almost all time series positions in all three filtering scenarios. [ABSTRACT FROM AUTHOR]

Changes in the frequency of occurrence of snowfall during El Niño–Southern Oscillation (ENSO) events are presented for the continental United States. This study is motivated by the need to improve winter climate forecasts for government agencies (i.e., U.S. Department of Transportation and Department of Energy) and winter entertainment facilities and the need for climatological studies. Daily snowfall data from 442 stations in the U.S. Historical Climatology Network are utilized. Selected stations each have more than 20 yr with 15 or more snowfall events per year during a 97-yr (1900–97) period of study. Three categories are created for each ENSO phase, based on the magnitude of daily snowfall amounts (in millimeters)—light: (0–50.8], moderate: (50.8–152.4], and heavy: (152.4–304.8]. Differences between neutral and cold or warm ENSO winters are created to show regions with increased or decreased occurrences in each snowfall category. Statistical tests are applied at each station to provide confidence levels for the identified changes in snowfall frequency. Simple field significance tests are completed for regions that show coherent ENSO signals. Results reveal several regions with significant changes in the frequency of occurrence of snowfall between neutral and cold or warm ENSO phases. For example, the Pacific Northwest has increased (decreased) occurrences of light, moderate, and heavy snowfalls during the cold- (warm-) phase ENSO winter, with the exception of light snows during the warm phase. Other regions with significant changes include the northern and eastern Great Lakes, the Northeast Corridor, and New England. The results may allow government agencies and private companies to mitigate adverse impacts of winter storms based on predictions of upcoming ENSO phases. Winter entertainment facilities, such as ski resorts, may actually benefit from these results. Combined with other winter-precipitation studies and the ever-improving ability to forecast each ENSO phase, this analysis of snow-event frequencies should aid in preparation for winter storms. [ABSTRACT FROM AUTHOR]

Describes a study of the economic value of El Nio-Southern Oscillation (ENSO) prediction to United States (US) agriculture. Analysis of data and models from meteorology, plant science and economics under a framework based on Bayesian decision analysis; Estimated annual value of perfect ENSO prediction to US agriculture.