1. A method of multi-model blending, comprising: obtaining, with a processing device, one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models; obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T; assembling a training data set including designating the one or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables; training a machine learning algorithm based on the training data set; and obtaining a blended model based on the machine learning algorithm.