Blueprint of a Kingdom Business is a wonderful collection of stories and thoughts from a man who truly lives out the principles he outlines in the book. E.F. Parrish eloquently puts into words some easy to follow steps on how to be a kingdom business owner. Follow the "Blueprint" and you will undoubtedly have a powerful kingdom impact!

Kevin did not have an easy life. All he ever wanted was to have a perfect family like the one he had read about in the Bible. A family which sits around at the dinner table laughing, talking and caring for each other. Falling in love with someone who loves him as much as he loves her. A wife and kids that he can come home to after work. But fate had something else in store for him.From an abusive early childhood, to traumatic life events and later on, an unsuccessful love life. Just like any other person, this 'pure angel' was affected by all the cruelties and suffering that life brings. After being stuck in a loveless relationship with a woman, he tries to convince himself that this is all that life is meant to be and hoping for something more is futile.Until one day, a trip to an Asian massage parlor changes his life. A trip which leads him to Emily. That is when Kevin regains his hope. Emily is like a breath of fresh air for him. Seeing her situation makes him want to rescue her from her life. Countless sessions and dates to the pharmacy bring them closer together. But she is an Asian massage girl after all. Will this angel finally find his happy ending with her? Will he be able to convince her to leave her life of danger and risks behind? Or will he be unsuccessful in his pursuit and continue living a dejected and sad life, trying to convince himself to be content and not hope for more?What does God have in store for these two lost souls?

The Kalman filter is a data assimilation scheme which, unlike currently operational methods such as optimal interpolation (01), makes systematic use of forecast model dynamics in order to accurately determine the evolution of the forecast error covariance matrix. Previous studies with a simple one-dimensional model indicated that the Kalman filter, if applied operationally, would yield analyzed and subsequent forecast fields superior, to those resulting from 01. These studies did not address the enormous computational burden that the Kalman filter would appear to pose if applied in an operational setting, to an actual numerical weather prediction (NWP) model. In this report we introduce a number of techniques which, taken together, reduce dramatically the computational complexity of the Kalman filter. The new filter algorithm gains its efficiency, in part, by taking explicit advantage of the fact that forecast errors are significantly correlated only over rather small distances. It also utilizes fully the vector-processing capabilities of the CYBER 205 computer. Part of the overall method is an analysis algorithm which processes observations one at a time, i.e., it loops on observations rather than analysis grid points, thereby eliminating both the necessity of matrix inversions and the necessity of restarting the entire analysis to accommodate late-arriving observations. This analysis algorithm would be useful in OI schemes as well as in the Kalman filter. We apply the Kalman filter to a two-dimensional shallow-water channel model. Numerical experiments demonstrate, first of all, that the filter is indeed computationally feasible in two dimensions. The results show also that actual forecast error correlations, which are computed exactly by the Kalman filter differ markedly from the rather simple, homogeneous, correlations prescribed currently in the NMC OI analysis system. The experiments suggest a number of improvements to our computational approach, which should render the Kalman filter practical for operational data assimilation into fully three-dimensional NWP models.