1502 Estimating Long-Term Tropical Cyclone Rainfall Frequency—A Physics-Based Approach

Wednesday, 15 January 2020
Hall B (Boston Convention and Exhibition Center)
Monika Feldmann, ETH Lausanne, Lausanne, Switzerland; and K. Emanuel, L. Zhu, and U. Lohmann

Tropical cyclones pose a significant flood risk to vast land regions in their path due to extreme precipitation. Thus it is imperative to quantitatively assess this risk.

Here, we introduce a tropical cyclone rainfall algorithm (TCR) based on physical principles that derives the precipitation rate and accumulation of tropical cyclones from vertical wind and moisture content. We combine the precipitation algorithm with the Coupled Hurricane Intensity Prediction System (CHIPS). The computational efficiency allows to simulate tropical cyclones over long time frames, permitting frequency estimates of extremely rare events.

To assess the performance of the TCR algorithm in combination with CHIPS, we compare exceedance frequencies of tropical cyclone precipitation derived from two independent observational data sets to the frequencies derived from the TCR algorithm. The algorithm performs well in the Gulf region and southern Atlantic states, while performance decreases slightly with increasing latitude.
A selected overview can be seen in the enclosed image, where tropical cyclone rainfall frequencies from rain gauge data, radar data and the TCR algorithm are compared to one another at six different locations.

Due to the physical nature of the model setup, it is transferable to different regions and climate scenarios. This allows it to provide information for long-term risk analyses concerning tropical cyclone rainfall and assess the occurrence of extremely severe and rare events at different locations.

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