This NSF fund is solely for support of student participants at the Workshop on Meteorological Sensitivity Analysis and Data Assimilation. The 75 expected participants will present, review, discuss, and evaluate works pertaining to atmospheric sensitivity analysis and data assimilation, but will include representation from oceanography and other fields that employ similar techniques. The fund will provide partial support for at least 8 students or postdocs who will attend. The workshop will be held at a fairly remote, self-contained facility at which all participants will share meals and evenings, providing many opportunities for dialogs with and between experts to make maximum use of our time. Presentations will include a mix of tutorials on fundamentals, overview talks on recent paths of developments, posters or short seminars on new works, and some informal debates. The students will also have an opportunity to present their own works.
Intellectual Merit: The intellectual merit of the work is in extending the notion of model tuning to align it with the basic tenets of spatial verification. The framework will improve forecasts by allowing a user to tune the model parameters to have desirable spatial features. The PIs have contributed to the development of the spatial techniques as well as to the sensitivity analysis methods suitable for this problem.
Broader Impacts: The broader impacts of the research are multi-pronged. The student involved in this project will become an expert in some of the most sophisticated topics in numerical prediction models: spatial verification, sensitivity analysis, and model tuning. This highly interdisciplinary framework itself has general applicability in that it can be employed to improve predictions from any numerical model involving inexact parameterizations of physical processes or inexact knowledge of physical constants, including weather, climate, ocean, etc. Given that spatial verification is emerging as the verification method of choice for future forecasts, the method will have wide-spread use. The method will be disseminated in the form of journal articles, and computer code to be included in a verification R package produced by National Center for Atmospheric Research (NCAR).
