Abstract
Remote sensing tools represent an attractive proposition for measuring wind speeds over the oceans because, in principle, they also offer a mechanism for determining the spatial variability of flow. Presented here is the continuation of research focused on the uncertainties and biases currently present in these data and quantification of the number of independent observations (scenes) required to characterize various parameters of the probability distribution of wind speeds. Theoretical and empirical estimates are derived of the critical number of independent observations (wind speeds derived from analysis of remotely sensed scenes) required to obtain probability distribution parameters with an uncertainty of ±10% and a confidence level of 90% under the assumption of independent samples, and it is found that approximately 250 independent observations are required to fit the Weibull distribution parameters. Also presented is an evaluation of Weibull fitting methods and determination of the fitting method based on the first and third moments to exhibit the “best” performance for pure Weibull distributions. Further examined is the ability to generalize parameter uncertainty bounds presented previously by Barthelmie and Pryor for distribution parameter estimates from sparse datasets; these were found to be robust and hence generally applicable to remotely sensed wind speed data series.
Corresponding author address: Prof. S. C. Pryor, Atmospheric Science Program, Department of Geography, Indiana University, 701 E. Kirkwood Ave., Bloomington, IN 47405. spryor@indiana.edu
