Operational, high-resolution analyses are a vital part of the National Weather Service (NWS) forecasting process. Currently, the Real-Time Mesoscale Analysis (RTMA) system fills this need by providing hourly analyses using a two-dimensional variational assimilation scheme. While success has been shown with the RTMA, an ensemble Kalman filter (EnKF) approach should outperform the RTMA, since the EnKF utilizes purely flow-dependent covariances during assimilation. The purpose of this study is to compare surface wind and temperature analyses from an EnKF to those of the RTMA to determine the relative skill of each approach. To reveal the influence of complex terrain, comparisons are performed for both the U.S. Pacific Northwest and Midwest. As expected, EnKF analysis increments reveal structures that align with the instantaneous flow, particularly regarding the wind field, for which the EnKF produces superior analyses. The EnKF is no better than the RTMA in strongly varying terrain, which may be a result of enhanced representativeness error in such regions. In contrast, temperature analysis increments are far less sensitive to flow dependence and are similar for both the EnKF and RTMA. RTMA temperature analyses possess slightly better skill than those produced by the EnKF, likely due to sampling error within the EnKF. Similar results for wind and temperature are found when assimilating significantly more and less observations in both systems. The implications of these results for operational production of finescale analyses are discussed.