Explicit Cloud-Scale Models for Operational Forecasts: A Note of Caution

Abstract

As computational capacity has increased, cloud-scale numerical models are slowly being modified from pure research tools to forecast tools. Previous studies that used cloud-scale models as explicit forecast tools, in much the same way as a mesoscale model might be used, have met with limited success. Results presented in this paper suggest that this is due, at least in part, to the nature of cloud-scale models themselves. Results from over 700 cloud-scale model runs indicate that, in some cases, differences in the initial soundings that are smaller than can be measured by the current observing system result in unexpected differences in storm longevity. In other cases, easily measurable differences in the initial soundings do not result in significant differences in storm longevity. There unfortunately appears to be no set of parameters that can be used to determine whether the initial sounding is near some part of the cloud-model parameter space that displays this sensitivity. Because different cloud models share similar philosophies, if not similar design, this sensitivity to initial soundings places a fundamental limit on how well the current slate of cloud-scale models can be expected to perform as explicit forecast tools. Given these results, it is not clear that using state-of-the-art cloud-scale models as explicit forecasting tools is appropriate. However, cloud-model ensembles may help to address some inescapable problems with explicit forecasts from cloud models. The most useful application of cloud-scale models in operational forecasts may be a probabilistic one in which the models are used as members of ensembles, a process that has been demonstrated for models of larger-scale processes.