This paper shows that the prediction capability of wildfire progression can be improved by estimation of a single prevailing wind vector parametrized by a wind speed and a wind direction to drive a wildfire simulation created by FARSITE. Estimations of these wind vectors are achieved in this work by a gradient-free optimization via a grid search that compares wildfire model simulations with measured wildfire perimeters, where noisy observations are modeled as uncertainties on the locations of the vertices of the measured wildfire perimeters. Two optimizations are established to acquire the optimal wind speed and wind direction. To formulate a perimeter optimization, an uncertainty-weighted least-squares error is computed between the vertices of the simulated and measured wildfire perimeter. The challenge in this approach is to match the number of vertices on the simulated and measured wildfire perimeter via interpolation of perimeter points and their uncertainties. For a surface area optimization, an uncertainty-weighted surface area error is introduced to capture the surface of the union minus the intersection of the simulated and measured wildfire perimeter. The challenge in this approach is to formulate a surface area error, weighted by the uncertainties on the locations of the vertices of the measured wildfire perimeter. The optimization in this paper is based on an iterative refinement of a grid of the wind vector and provides robustness to intermittent erroneous results produced by FARSITE, while allowing parallel execution of wildfire model calculations. This paper is an extension of the work in Tan et al., (2021). Results on wind vector estimation are illustrated on two historical wildfire events: the 2019 Maria Fire that burned south of the community of Santa Paula in the area of Somis, CA, and the 2019 Cave Fire that started in the Santa Ynez Mountains of Santa Barbara County.