26 August 2014

The Role of Moist Processes in the Intrinsic Predictability of Indian Ocean Cyclones


Tropical cyclones are major components of tropical weather systems that are also the costliest and deadliest natural hazards in the tropics. Although numerical weather prediction models forecast midlatitude weather systems relatively well, tropical weather systems remain challenging. A team of scientists led by U.S. Department of Energy researchers at Pacific Northwest National Laboratory investigated the mechanisms behind  error cascades (i.e., growth of model errors across spatial scales) in simulations of Indian Ocean tropical cyclones within different scales. The team examined possible differences in estimating inherent intrinsic error growth from mesoscale simulations that rely on convective parameterizations versus convection-permitting simulations. Comparing results from simulations of four Indian Ocean tropical cyclones at 10 km resolution with parameterized convection, they found that moist convection plays a major role in intrinsic error growth that ultimately limits the intrinsic predictability of tropical cyclones. Model intrinsic errors start to build up from convection regions and ultimately affect larger scale simulations. Also, errors at small scales grow faster than at larger scales. The gradual increase in prediction error in the large scale is a manifestation of an upscale cascade of error energy from convective to large scale. The convection-permitting simulations generally reproduce the observed tropical cyclone tracks and intensities better than simulations that rely on convective parameterizations, particularly when the model resolution is relatively low. A better understanding of error growth in tropical cyclones forecasts can potentially lead to improved methods and modeling for tropical cyclones forecast.