One of the many challenges in hurricane forecasting is incorporating observational data into forecast models. Data assimilation, as scientists refer to it, is the process of combining observational data – information obtained by satellites, radars or from instruments deployed into storms by aircraft – into a numerical weather prediction model.
Incorporating real-world temperature, wind, moisture or atmospheric pressure from multiple sources is a core component of hurricane science and vital to provide improved forecasts of both the track and intensity of storms. How to incorporate new types of observational information into a model is at the very heart of hurricane forecasting.
At the American Meteorology Society’s 31st annual Hurricane and Tropical Meteorology meeting in San Diego this week, Rosenstiel School Professor Sharan Majumdar discussed new approaches to improve data assimilation.
Citing the Ph.D. research of Rosenstiel School graduate student Ting-Chi Wu, Majumdar discussed the assimilation of temperature and moisture data obtained from satellite-based advanced Infrared (IR) soundings measured by polar-orbiting satellites of Typhoon Sinlaku during the 2008 Pacific typhoon season. Wu’s studied the period of storm intensification as Sinlaku intensified into a category-2 typhoon. Her conclusion was that the assimilation of temperature and moisture show promise in improving forecasts of hurricane and typhoon intensity, though more work needs to be done to improve their use.
Majumdar also provided an overview of strategies to assimilate observations to improve numerical weather forecasts of the track and structure of storms. He showed that targeted aircraft observations in select areas and satellite observations from both within and outside a tropical cyclone are beneficial.