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Abstract This article describes a teaching strategy that synergizes computing and management, aimed at the running of complex projects in industry and academia, in the areas of civil engineering, physics, geosciences, and a number of other related fields. The course derived from this strategy includes four parts: (a) Computing with a selected set of modern paradigms—the stress is on Control Flow and Data Flow computing paradigms, but paradigms conditionally referred to as Energy Flow and Diffusion Flow are also covered; (b) Project management that is holistic—the stress is on the wide plethora of issues spanning from the preparation of project proposals, all the way to incorporation activities to follow after the completion of a successful project; (c) Examples from past research and development experiences—the stress is on experiences of leading experts from academia and industry; (d) Student projects that stimulate creativity—the stress is on methods that educators could use to induce and accelerate the creativity of students in general. Finally, the article ends with selected pearls of wisdom that could be treated as suggestions for further elaboration.

A considerable number of authors presented experiments in which degradation of large scale circulation occurred in regional climate integrations when large-scale nudging was not used (e.g., von Storch et al., 2000; Biner et al., 2000; Rockel et al., 2008; Sanchez-Gomez et al., 2008; Alexandru et al., 2009; among others). We here show an earlier 9-member ensemble result of the June-August precipitation difference over the contiguous United States between the "flood year" of 1993 and the "drought year" of 1988, in which the Eta model nested in the COLA AGCM gave a rather accurate depiction of the analyzed difference, even though the driver AGCM failed in doing so to the extent of having a minimum in the area where the maximum ought to be. It is suggested that this could hardly have been possible without an RCM's improvement in the large scales of the driver AGCM. We further revisit the issue by comparing the large scale skill of the Eta RCM against that of a global ECMWF 32-day ensemble forecast used as its driver. Another issue we are looking into is that of the lateral boundary condition (LBC) scheme. The question we ask is whether the almost universally used but somewhat costly relaxation scheme is necessary for a desirable RCM performance? We address this by running the Eta in two versions differing in the lateral boundary scheme used. One of these is the traditional relaxation scheme and the other is the Eta model scheme in which information is used at the outermost boundary only and not all variables are prescribed at the outflow boundary. The skills of these two sets of RCM forecasts are compared against each other and also against that of their driver. A novelty in our experiments is the verification used. In order to test the large scale skill we are looking at the forecast position accuracy of the strongest winds at the jet stream level, which we have taken as 250 hPa. We do this by calculating bias adjusted equitable threat scores (Mesinger 2008) and frequency bias scores for wind speeds greater than a chosen wind speed threshold, with the ECMWF analyses used as truth. We also calculate a traditional RMS difference between the forecast and analyzed winds at this same level. Our results show the Eta RCM skill in forecasting large scales with no interior nudging to be just about the same as and usually even slightly higher than that of the driver model. As to the LBC impact, no disadvantage compared to relaxation was seen from using the Eta scheme, in spite of its requiring information from the outermost RCM boundary only.