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- Author or Editor: GLENN W. BRIER x
- Journal of Applied Meteorology and Climatology x
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Abstract
Plans are underway to attempt to reduce the destructive force of hurricanes by artificially modifying their structure by means of cloud seeding. Since the natural variability of meteorological elements observed in hurricanes is high, the success of the project depends upon establishing a cause and effect relationship between the seeding and the hurricane's response. The small sample of mature hurricanes coupled with rigorous selection criteria make a randomized experiment impractical. Therefore, an evaluation technique based on the concept of randomization in time is developed.
Abstract
Plans are underway to attempt to reduce the destructive force of hurricanes by artificially modifying their structure by means of cloud seeding. Since the natural variability of meteorological elements observed in hurricanes is high, the success of the project depends upon establishing a cause and effect relationship between the seeding and the hurricane's response. The small sample of mature hurricanes coupled with rigorous selection criteria make a randomized experiment impractical. Therefore, an evaluation technique based on the concept of randomization in time is developed.
Abstract
The theorem of singular value decomposition is used to represent a data matrix X as the product of a system with a response R to a forcing function F. Algebraically, R is the matrix of principal components and F the transpose of the matrix of eigenvectors of X′X. If the data are such that the eigenvectors are orthogonal functions of time and they have some recognizable non-random structure permitting predictability in time, then the observed response at time t can be used with the extrapolated forcing function to predict some physical quantity (e.g., temperature, pressure). This method is called the time extrapolated eigenvector prediction (TEEP). An example is given to illustrate the method with a known forcing function, the annual solar heating cycle. We have access to efficient computer routines which will facilitate an extension to much larger data sets.
Abstract
The theorem of singular value decomposition is used to represent a data matrix X as the product of a system with a response R to a forcing function F. Algebraically, R is the matrix of principal components and F the transpose of the matrix of eigenvectors of X′X. If the data are such that the eigenvectors are orthogonal functions of time and they have some recognizable non-random structure permitting predictability in time, then the observed response at time t can be used with the extrapolated forcing function to predict some physical quantity (e.g., temperature, pressure). This method is called the time extrapolated eigenvector prediction (TEEP). An example is given to illustrate the method with a known forcing function, the annual solar heating cycle. We have access to efficient computer routines which will facilitate an extension to much larger data sets.
Abstract
Abstract
Abstract
A number of different analyses are used to examine 63 years of daily United States rainfall data to determine whether a suggested periodicity of 18 cycles per year exists. Although a slight tendency was found for such a period to persist in phase during the 63 years, the amplitude is extremely small. On the basis of the several tests performed we conclude that there is no reality to a periodicity at or near 18 cycles per year.
Abstract
A number of different analyses are used to examine 63 years of daily United States rainfall data to determine whether a suggested periodicity of 18 cycles per year exists. Although a slight tendency was found for such a period to persist in phase during the 63 years, the amplitude is extremely small. On the basis of the several tests performed we conclude that there is no reality to a periodicity at or near 18 cycles per year.
Abstract
A reanalysis of the Climax I and II experiments is described. The concern prompting this reanalysis is a suggestion arising from Colorado State University analyses of extended area effects. Those analyses suggested a regionwide pattern of precipitation that, by chance, may have favored the randomly selected seeding days for some of the important meteorological partitions used in earlier analyses. In order to address this concern, this reanalysis employs excellent covariate relationships developed before the initiation of Climax II and which account for over half of the target variability for most meteorological partitions of major interest (e.g., warm 500 mb temperatures and southwest 700 mb wind directions). The statistical evidence of seeding-induced increases for this reanalysis is generally much stronger than in the previous analysis, which did not utilize covariates. For example, the joint one-sided Wilcoxon test statistic P-value for testing the null hypothesis that seeding did not induce a precipitation increase during warm 500 mb temperatures of the Climax I and II experiments is now 0.0013, compared to 0.0550 in the previous analysis. However, the reanalysis also indicates that previous estimates of increases attributed to seeding based strictly on ratios of seeded to non-seeded precipitation amount means are apparently too large. For example, the estimated precipitation increase of the combined Climax I and II data for the warm 500 mb temperature partition is reduced from 41 to 25% when the full set of data is employed in this reanalysis.
Abstract
A reanalysis of the Climax I and II experiments is described. The concern prompting this reanalysis is a suggestion arising from Colorado State University analyses of extended area effects. Those analyses suggested a regionwide pattern of precipitation that, by chance, may have favored the randomly selected seeding days for some of the important meteorological partitions used in earlier analyses. In order to address this concern, this reanalysis employs excellent covariate relationships developed before the initiation of Climax II and which account for over half of the target variability for most meteorological partitions of major interest (e.g., warm 500 mb temperatures and southwest 700 mb wind directions). The statistical evidence of seeding-induced increases for this reanalysis is generally much stronger than in the previous analysis, which did not utilize covariates. For example, the joint one-sided Wilcoxon test statistic P-value for testing the null hypothesis that seeding did not induce a precipitation increase during warm 500 mb temperatures of the Climax I and II experiments is now 0.0013, compared to 0.0550 in the previous analysis. However, the reanalysis also indicates that previous estimates of increases attributed to seeding based strictly on ratios of seeded to non-seeded precipitation amount means are apparently too large. For example, the estimated precipitation increase of the combined Climax I and II data for the warm 500 mb temperature partition is reduced from 41 to 25% when the full set of data is employed in this reanalysis.
