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Please note that this group is not just for upperclassmen. One of the purposes is to involve both upper- and lowerclassmen. This way students can be exposed to these issues early on and can enhance their abilities as their education progresses. This group is being formed in order to provide opportunities for students to enhance their understanding of data assimilation and modeling issues, which are prominent issues in the atmospheric sciences and will continue to be very important in the foreseeable future. Moreover, this group will support research efforts in RWIC and the atmospheric sciences department, especially the research being conducted in the AHPCRC (Army High Performance Computing Research Center) project. DAAMG will provide an infrastructure in which students learn about contemporary data assimilation and modeling issues, perform data assimilation and modeling tasks, work with state of the art data assimilation and modeling systems, develop their computer, programming, and scripting skills, and learn from or mentor other students (depending on qualifications and experience). Tasks:
For 'senior' group members, unix, programming, scripting, and visualization are helpful. Note that these skills will be developed through your classes at UND and will be further developed through participation in the AHPCRC summer institute. Participation in the AHPCRC summer institute will be reserved for students who are at least of junior standing at the begnning of the summer (after a student's sophomore year). Preference will be given to students who have completed their junior year as they are expected to have superior computer skills relative to younger students and are thus expected to gain more from participating in the summer institute. For 'junior' group members, the required skills are a desire to learn and an ability to work with others.
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The Data Assimilation and Modeling Group will focus on data assimilation,
which in general terms is the combination of diverse data into a
coherent picture of the state of the atmosphere, oftentimes with the
purpose of using the results in numerical weather prediction. It is a
critically important step in producing numerical weather prediction
output and thus in forecasting. Anyone who has in interest in
forecasting or, more generally, in analyzing atmospheric conditions,
inherently has an interest in data assimilation. If you need to know
what the atmosphere has, is, or will do, this is important to you.
Because of it's importance, data assimilation is a prominent issue in
atmospheric sciences that will continue to be so for the forseeable
future.