The ability to assess the parameterizations of moist processes by performing weather forecasts with climate models is discussed. The advantages include an assessment of the moist processes as a function of the observed atmospheric state, an increased ease of using field experiment data, and identification of errors before longer time scale biases develop. Examples are provided using simulations with the NCAR and GFDL climate models. While the results do not always unambiguously identify parameterization errors, the results are strong enough to recommend that a weather forecasting approach should be a technique regularly available to evaluate new parameterizations of atmospheric moist processes.