Design patterns are recognized, named solutions to common design problems. The use of the most commonly referenced design patterns should promote adaptable and reusable program code. When a system evolves, changes to code involving a design pattern should, in theory, consist of creating new concrete classes that are extensions or subclasses of previously existing classes. Changes should not, in theory, involve direct modifications to the classes in prior versions that play roles in a design patterns. We studied five systems, three proprietary systems and two open source systems, to identify the observable effects of the use of design patterns in early versions on changes that occur as the systems evolve. In four of the five systems, pattern classes are more rather than less change prone. Pattern classes in one of the systems were less change prone. These results held up after normalizing for the effect of class size -larger classes are more change prone in two of the five systems. These results provide insight into how design patterns are actually used, and should help us to learn to develop software designs that are more easily adapted.

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Abstract. Due to limitation of the domain size and limited observations used in regional data assimilation and forecasting systems, regional forecasts suffer a general deficiency in effectively representing large-scale features such as those in global analyses and forecasts. In this paper, a scaledependent blending scheme using a low-pass Raymond tangent implicit filter was implemented in the Data Assimilation system of the Weather Research and Forecasting model (WRFDA) to reintroduce large-scale weather features from global model analysis into the WRFDA analysis. The impact of the blending method on regional forecasts was assessed by conducting full cycle data assimilation and forecasting experiments for a 2-week-long period in September 2012.It is found that there are obvious large-scale forecast errors in the regional WRFDA system running in full cycle mode without the blending scheme. The scale-dependent blending scheme can efficiently reintroduce the large-scale information from National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analyses, and keep small-scale information from WRF analyses. The blending scheme is shown to reduce analysis and forecasting error of wind, temperature and humidity up to 24 h compared to the full cycle experiments without blending. It is also shown to increase precipitation prediction skills in the first 6 h forecasts.