The various implementations of the ensemble Kalman filter (EnKF) differ from each other in several ways. The effects of these differences are not yet well and completely explored and they include the use of sequential or simultaneous assimilation of observations and the application of localization to the observation error covariance matrix (R-localization) or the background error covariance matrix (B-localization). This study seeks to examine and better understand the effects of these differences, both individually and in combination. To that end, a B-localized sequential scheme, a B-localized simultaneous scheme, an R-localized sequential scheme and an Rlocalized simultaneous scheme are compared using a primitive equation two-layer model with simulated observations and an imperfect model assumption. The comparisons in initial assimilation experiments show that the use of sequential or simultaneous assimilation and R-/B-localization impacts the accuracy of the EnKF analyses and forecasts. Diagnostic experiments show that the schemes generate different amounts of imbalance in the analysis as a result of systematic differences among the schemes in height gradient and wind increments. These disparities in analysis balance translate into accuracy differences during the subsequent forecast and analysis steps. Additional simplified experiments suggest that the differences caused by the sequential or simultaneous assimilation and the R-/B-localization are a function of the characteristic shape of the background error covariances in the model system. Several elements of the forecast-assimilation system including the use of digital filter initialization, the number and type of observations, the ensemble size and the ratio of forecast error to observation error are identified that can significantly increase or decrease the magnitude of the observed differences caused by sequential or simultaneous assimilation and the R-/B-localization.

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The U.S. Congress passed the Workforce Innovation and Opportunity Act in 2014. Some constituencies may view the new law as a radical departure from earlier approaches to human capital development at a national level. Yet the workforce investment system under Workforce Innovation and Opportunity Act is premised upon a dual customer approach to serve labor demand (employers) and labor supply (job seekers) concurrently. With the rhetoric about the jobs and skills debate, it is easy to take for granted what is at stake behind supply-and demanddriven approaches within a workforce system. This paper's review is intended to revisit the characteristics and clarify the roles of the two customers in workforce policy in the United States, identify potential concerns of one customer's dominance, and suggest some ways to complement this balancing act. As a result of this analysis, one can potentially see both sides of the vision behind an integrated workforce development system.

Workforce development is a set of processes that govern the identification, recruitment, assessment and training of job seekers into employment as well as the maintenance and advancement of these persons in their careers. Given the complexity of what workforce development entails, a systems approach is illustrated to ensure that the broader goal of human capital investment is met. The paper identifies the foundational assumptions of systems thinking, classifies the key stakeholders in workforce development and provides brief case studies on where systems thinking has been deployed. The paper also enunciates an outcomes framework to give the metrics by which progress towards performance goals can be measured.