Parameterizating Convective Vertical Motions for Aircraft Icing Forecasts

Cloud liquid water (CLW) needed for significant aircraft icing develops in upward vertical motions in a saturated atmosphere. A substantial percentage, if not a majority, of the upward motions in the atmosphere are convective. Unfortunately, numerical forecast models do not resolve convective motions unless their resolutions are very high. Therefore, the CLW from operational numerical models has never been successfully used in icing forecasts. However, the environmental conditions leading to convective motions are often well resolved in coarser resolution models. These conditions are embodied in a three-ingredient method for forecasting convection long-used by human forecasters, a potentially unstable atmosphere, a parcel with a level of free convection (LFC), and a process that lifts the parcel to its LFC. While this method is most often used for thunderstorm forecasting, it also applies to weaker convection and even to some non-convective vertical motions such as a stratocumulus-topped boundary layer. VVICE is an algorithm that examines numerical forecast model information for the three ingredients. At every model grid point, it first determines the most unstable parcel by finding the level with the highest equivalent potential temperature. Then it examines the model information for potential lifting mechanisms at that level. These include two-dimensional frontogenesis, Eckman-layer lifting, and the model's own forecast vertical motion. The diagnosed upward motion is inflated by a function of the model resolution and its height above ground. VVICE follows the lifted parcel upward, layer-by-layer, to see if it reaches its lifting condensation level (LCL) and its LFC. In layers above the parcel's LCL, it computes the condensed CLW. Upon reaching the LFC, the parcel accelerates due to positive buoyancy and continues to condense CLW. VVICE tracks the parcel's upward velocity until reduced to zero again above the equilibrium level. VVICE diminishes the CLW by precipitation- and ice-generating processes. When CLW is diagnosed below 0C, VVICE computes a quantitative aircraft performance loss metric from the temperature and CLW based on an aerodynamic analysis of the expected ice accumulation on a standard airfoil. Verification of VVICE with pilot reports shows skill at light and moderate icing. While its skill is not as good as some other icing algorithms, it does reduce the overforecast bias which is characteristic of these other algorithms.