This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

In this article, we suggest that graduate programs in predominantly white institutions can and should be sites of self-education and tribal nation building. In arguing this, we examine how a particular graduate program and the participants of that program engaged tribal nation building, and then we suggest that graduate education writ large must also adopt an institutional orientation of nation building. We connect Guinier’s notion of democratic merit to our discussion of nation building as a way to suggest a rethinking of “success” and “merit” in graduate education. We argue that higher education should be centrally concerned with capacity building and graduates who aim to serve their communities.