Solar forecasters have hitherto been restricting the application of numerical weather prediction (NWP) to day-ahead forecasting scenarios. With the hourly updated NWP models, such as the National Oceanic and Atmospheric Administration’s Rapid Refresh (RAP) or High-Resolution Rapid Refresh (HRRR), it is theoretically possible to utilize NWP for hour-ahead solar forecasting. Nonetheless, for NWP-based hourly forecasts to be useful, they ought to be post-processed, because such forecasts are almost always affected by the inherent bias of dynamical weather models. In this regard, Kalman filtering is herein used as a post-processing tool. Using one year of RAP and HRRR forecasts and ground-based observations made at seven geographically diverse locations in the contiguous United States, it is demonstrated that the post-processed versions of hourly updated NWP forecasts are sometimes able to attain a higher accuracy than those from classic time-series families of models, not only at long-range, but also at short-range forecast horizons. Although these improvements are marginal in terms of squared loss (about 1%–2%), since NWP models have a very different forecast-generating mechanism (solving the governing equations of motion) from that of time series methods (extrapolating data), NWP-based forecasts can be expected to be less correlated with those forecasts from time series models. Consequently, one should find this diversity profoundly rewarding during forecast combination, for the combined forecasts are able to consistently result in smaller error than the best component forecasts.