We explore the feasibility of using commercial aircraft as sensors for observing weather phenomena at a continental scale. We focus specifically on the problem of wind forecasting and explore the use of machine learning and inference methods to harness air and ground speeds reported by aircraft at different locations and altitudes. We validate the learned predictive model with a field study where we release an instrumented high-altitude balloon and compare the predicted trajectory with the sensed winds. The experiments show the promise of using airplane in flight as a large-scale sensor network. Beyond making predictions, we explore the guidance of sensing with value-of-information analyses, where we consider uncertainties and needs of sets of routes and maximize information value in light of the costs of acquiring data from airplanes. The methods can be used to select ideal subsets of planes to serve as sensors and also to evaluate the value of requesting shifts in trajectories of flights for sensing.