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Texas Tech University: "Comparison of Surface Analysis Techniques Toward Operational Use at the National Weather Service."


This project extends a recently completed COMET Cooperative Project conducted by Texas Tech University, University of Washington, and NWS Seattle researchers. That project focused on comparing two operational NWS surface analysis techniques, the match-observations-all (MOA) and the real time mesoscale analysis (RTMA) systems, to an ensemble Kalman filter (EnKF), a relatively new technique that likely provides analysis improvements through its use of flow dependence during data assimilation and incorporation of analysis uncertainty. Both an objective verification against observations and a subjective evaluation of NWS forecasters at four different offices commenced during the Cooperative Project. These tasks were not completed, however, due to difficulties in 1) configuring and running the Environmental Modeling Center (EMC) RTMA system on university computing resources, and 2) configuring the MOA system on the NWS Seattle computing system. These difficulties were resolved toward the end of the Cooperative Project 2-year timeframe, and the Partners Project proposed here will focus on completing the planned thorough evaluation of the MOA, RTMA, and EnKF surface analysis techniques.
The main goals of this project are to 1) conduct a thorough 3-month subjective evaluation of MOA, RTMA, and EnKF surface analyses by NWS forecasters at the Seattle, Portland, Spokane, and Pendleton forecast offices, and 2) extend the objective, domain-wide verification of the RTMA and the EnKF already completed to terrain- and flow-specific features such as rapidly varying terrain height, coastlines, fronts, and regions of high winds. By gaining a better understanding of which surface analysis system performs best under different terrain and weather conditions, the researchers hope to implement the best surface analysis technique at the participating forecast offices and to develop a plan for a similar nationwide system.