Assessing the impact of total precipitable water and lightning on SHIPS intensity forecasts

Tropical cyclones are complex phenomenon whose intensity and structural evolution is influenced by changes in the large-scale synoptic environment, air-sea interactions, microphysics, convective organization, etc. The scales of these influences range from the largest synoptic scales to those scales that affect individual convective clouds. Despite the concerted efforts to improve intensity forecasts, the sheer complexity of the problem has resulted in relatively slow progress, relative to track forecasting. This study represents an effort to further improve our understanding and forecasts of tropical cyclone intensity change through improved use of existing observational systems.

Recent studies have shown or implied that tropical cyclone intensity forecasts can be better inferred with the use of passive microwave–based Total Precipitable Water (TPW) and information concerning lightning strike density. With these findings in mind, this study tests the relative utility of new predictors created from TPW and lightning datasets and tries to understand how these factors are related to future intensity changes. The TPW data consists of two datasets, namely thirteen-years (1995-2007) of 6-hourly TPW constructed from passive microwave sensors on the Defense Meteorological Satellites Program (DMSP) suite of satellites and a NESDIS operational dataset that blends DMSP satellites information with TPW estimates from the passive microwave sensors on NOAA polar orbiting satellites. The lightning density comes from the World Wide Lightning Location Network (WWLLN) datasets, which has been bias-corrected to better represent space-based climatological estimates and is available 2005-2008. TPW information is currently available for operational forecasts and lightning density will soon be available via the GOES Lightning Mapper, which will be launched on GOES-R in approximately 2016. The Statistical Hurricane Intensity Prediction Scheme (SHIPS) will be used to assess the impact of these data on intensity forecasts. SHIPS is a statistical-dynamical model that utilizes static (from remotely sensed data and analyses, model analyses and storm statistics) and forecast variables (from numerical weather prediction models) to make forecasts of future intensity changes. The model not only is a useful/skillful forecast tool, but can be used to diagnose complicated relationships between predictors. In this study, the SHIPS model framework will be used to diagnose the potential utility and impact of these TPW and lightning information initially in the Atlantic Basin. Both TPW and lighting datasets, which are relatively independent to the existing predictors used by SHIPS, will be used to create storm-relative predictors for the SHIPS model. The relative impact of the TPW, and lightning based predictors (individually and in combination) and a physical interpretation of how these factors may be related to future intensity changes will be presented.

The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or decision.