  Tropical cyclones are important drivers of coastal flooding which have severe
negative public safety and economic consequences. Due to the rare occurrence of
such events, high spatial and temporal resolution historical storm
precipitation data are limited in availability. This paper introduces a
statistical tropical cyclone space-time precipitation generator given limited
information from storm track datasets. Given a handful of predictor variables
that are common in either historical or simulated storm track ensembles such as
pressure deficit at the storm's center, radius of maximal winds, storm center
and direction, and distance to coast, the proposed stochastic model generates
space-time fields of quantitative precipitation over the study domain.
Statistically novel aspects include that the model is developed in Lagrangian
coordinates with respect to the dynamic storm center that uses ideas from
low-rank representations along with circular process models. The model is
trained on a set of tropical cyclone data from an advanced weather forecasting
model over the Gulf of Mexico and southern United States, and is validated by
cross-validation. Results show the model appropriately captures spatial
asymmetry of cyclone precipitation patterns, total precipitation as well as the
local distribution of precipitation at a set of case study locations along the
coast.
