LAUSR

Within the realm of satellite remote sensing, optimal data acquisition to study natural phenomena under time, resource, and cost constraints is a well-known problem. Furthermore, since the sensors themselves are at remote locations with sparse ground connectivity, the optimal method must use a computationally light forecasting algorithm, which assimilates information from the observations at possibly irregular intervals, in near real time. In this article, we propose and demonstrate the ensemble -guided cyclone track forecasting (EGCTF) method for application in remote tropical cyclone tracking. The algorithm uses ensemble data produced by numerical weather prediction models to guide the forecasting process while assimilating measured cyclone center positions. The algorithm was tested and analyzed with the Global Ensemble Forecasting System (GEFS) data and the National Hurricane Center data for the 2018 year hurricanes within the Atlantic basin. Compared with a baseline method that uses the GEFS-issued mean ensemble track (AEMN) for forecasting and no data assimilation, the proposed algorithm exhibited positive forecast skill for more than 290 test cases over forecast periods spanning 6–48 h. The skill is seen to improve with lengthening forecast periods, with five test cases showing greater than 75% skill for a forecast period of 6 h to 247 test cases for the forecast period of 48 h.