A method for merging flow-dependent forecast error statistics from an ensemble with static statistics for use in high resolution variational data assimilation

The background error covariance matrix, B, is often used in variational data assimilation for numerical weather prediction as a static and hence poor approximation to the fully dynamic forecast error covariance matrix, Pf. In this paper the concept of an Ensemble Reduced Rank Kalman Filter (EnRRKF) is outlined. In the EnRRKF the forecast error statistics in a subspace defined by an ensemble of states forecast by the dynamic model are found. These statistics are merged in a formal way with the static statistics, which apply in the remainder of the space. The combined statistics may then be used in a variational data assimilation setting. It is hoped that the nonlinear error growth of small-scale weather systems will be accurately captured by the EnRRKF, to produce accurate analyses and ultimately improved forecasts of extreme events.

Revisiting the emergence of the modern business enterprise: entrepreneurship and the Singer global distribution system

This paper approaches the question of why entrepreneurial firms exist from a broad business historical perspective. It observes that the original development of the modern business enterprise was very strongly associated with entrepreneurial innovation rather than an extension of managerial routine. The widely-used theory of the entrepreneur as a specialist in judgmental decision making is applied to the particular point in time when entrepreneurs had to develop novel organizational designs in what Chandler described as the prelude to the ‘managerial revolution’. The paper illustrates how the theory of entrepreneurship then best explains the rise of the modern corporation by focusing on the case study of vertical integration par excellence, Singer.