A wave averaged energy equation: comment on "Global Estimates of Wind Energy Input to Subinertial Motions in the Ekman-Stokes Layer" by Bin Liu, Kejian Wu and Changlong Guan

In a recent paper, Liu et al. (2007) formulate an expression for how surface gravity waves modify the Ekman layer energy budget. They then diagnose the effect in the world oceans using available data. This comment addresses the formulation of the energy equation that is fundamental to their stud

Dctnr Wrds Wtht Vwls

Despite the promise of the title, this article is not concerned with the intelligibility of dictionary words without vowels (that is, with the letters AEIOUY removed), but instead with that tiny group of words which contain no vowels at all. For the sake of completeness, we include a considerably larger group of words which appear vowelless, but in reality are not -- words which use the letters V or W to represent vowel sounds. On the other hand, we do not include two burgeoning classes of vowelless words quite familiar to the average reader -- abbreviations such as MR., MRS., or DR. which are pronounced as if the full word were present, and acronymic words such as TV, DDT, MPH or TNT, usually printed in capitals and invariably pronounced by spelling out the individual letters

New insights on the impact of coefficient instability on ratio-correlation population estimates

In this study we examine the regression-based ratio-correlation method and suggest some new tools for assessing the magnitude and impact of coefficient instability on population estimation errors. We use a robust sample of 904 counties from 11 states and find that: (1) coefficient instability is not a universal source of error in regression models for population estimation and its impact is less than commonly assumed; (2) coefficient instability is not related to bias, but it does decrease precision and increase the allocation error of population estimates; and (3) unstable coefficients have the greatest impact on counties under 20,000 in population size. Our findings suggest that information about the conditions that affect coefficient instability and its impact on estimation error might lead to more targeted and efficient approaches for improving population estimates developed from regression models