Accurate weather forecasts enhance sustainability by facilitating decision making across a broad range of endeavors including public safety, transportation, energy generation and management, retail logistics, emergency preparedness, and many others. This paper presents a method for combining multiple scalar forecasts to obtain deterministic predictions that are generally more accurate than any of the constituents. Exponentially-weighted forecast bias estimates and error covariance matrices are formed at observation sites, aggregated spatially and temporally, and used to formulate a constrained, regularized least squares regression problem that may be solved using quadratic programming. The model is re-trained when new observations arrive, updating the forecast bias estimates and consensus combination weights to adapt to weather regime and input forecast model changes. The algorithm is illustrated for 0-72 hour temperature forecasts at over 1200 sites in the contiguous U.S. based on a 22-member forecast ensemble, and its performance over multiple seasons is compared to a state-of-the-art ensemble-based forecasting system. In addition to weather forecasts, this approach to consensus may be useful for ensemble predictions of climate, wind energy, solar power, energy demand, and numerous other quantities.