Potential of 4d-VAR for exigent forecasting of severe weather 1

Severe storms, tropical cyclones, and associated tornadoes, floods, lightning, and microbursts threaten life and property. Reliable, precise, and accurate alerts of these phenomena can trigger defensive actions and preparations. However, these crucial weather phenomena are difficult to forecast. The objective of this paper is to demonstrate the potential of 4d-VAR (four-dimensional variational data assimilation) for exigent forecasting (XF) of severe storm precursors and to thereby characterize the probability of a worst-case scenario. 4d-VAR is designed to adjust the initial conditions (IC) of a numerical weather prediction model consistent with the uncertainty of the prior estimate of the IC while at the same time minimizing the misfit to available observations. For XF, the same approach is taken but instead of fitting observations, a measure of damage or loss or an equivalent proxy is maximized or minimized. For example, XF of maximized significant tornado parameter (STP) would delineate relative probabilities of the threat of tornadogenesis as a function of time and place. To accomplish this will require development of a specialized cost function for 4d-VAR. When 4d-VAR solves the XF problem a by-product will be the value of the background cost function that provides a measure of the likelihood of occurrence of the forecast exigent conditions, and the value of the STP cost function that provides an estimate of the likelihood of tornadogenesis. 4d-VAR has been previously applied to a special case of XF in hurricane modification research. A summary of a case study of Hurricane Andrew (1992) is presented as a prototype of XF. The study of XF is expected to advance forecasting high impact weather events, refine methodologies for communicating warning and potential impacts of exigent weather events to a threatened population, be extensible to commercially viable products, such as forecasting freezes for the citrus industry, and be a useful pedagogical tool. Further, by including parameter sensitivity in the adjoint model, XF could be extended to include parametric uncertainty.

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