BaCla is a new stochastic parametric precipitation model to estimate rainfall associated with Tropical cyclones (TCs) in a computationally efficient way. It is validated for a number of calibration cases along with the current benchmark IPET deterministic method. The results of these models are compared with the observed StageIV-based rainfall. Two predictive parameters, the pressure deficit [hPa] (Δ𝑃) and maximum sustained wind speed [m/s] (vmax) are tested for the BaCla model. Frank copula is used by the BaCla model to predict the peak amount of rainfall. It employs the adapted Holland wind profile to create a 1D rain profile. Asymmetry may be added to make a 2D rain profile. The calibration study challenges the assumption that the radius of maximum wind speed (Rvmax) is equal to the radius of maximum precipitation (Rpmax) in the BaCla model. Additionally, it is observed that the radial fit overestimates rainfall in scenarios where the peak amount of rainfall is less than 2.8mm/hr. These observations lead to modifications in the relationship between Rvmax and Rpmax, the radial fit threshold, and the fitting coefficients of the adapted Holland wind profile for rainfall distribution in the improved BaClHa model. The new BaClHa model is verified with new and calibrated sets of TCs. The BaClHa model shows potential in achieving good accuracy along with global applicability at a limited computational expense.

Torrential rain from tropical cyclones can have a devastating impact, causing loss of life and billions in damages. To better understand the risk faced by coastal communities, it is important to estimate how often a tropical cyclone could occur and how much rainfall it will produce. One way to do this is by analyzing past storms and building parametric models of rainfall rates during tropical cyclone events. While many parametric precipitation models –such as the Bader model– exist, their accuracy remains limited and many challenges still need to be overcome. The most important challenges are output overestimation and a poor representation of rainfall over land. Therefore, this thesis aims to reduce
these biases by answering the following research question:

"How can the bias in the radial rainfall distributions of a tropical cyclone in Bader’s
parametrized model be reduced and be used for reliable rainfall estimates both above land and the ocean?"

To answer this question, several new data sources were introduced from the TRMM/GPM satellites and Stage IV to improve the Bader model. While this original model only predicted precipitation based on maximum wind speed (vmax), the updated model also considers pressure deficit ΔP. The results suggest that ΔP can be a useful parameter to reduce bias and improve accuracy. However, it also
leads to larger uncertainty ranges. Next, four precipitation profiles were proposed. A profile where precipitation is constant for low maximum precipitation values based on the predicted total rainfall (area under the graph) was selected for further exploration.

The new models are explored during a case study of Hurricane Florence. Both the ΔP and vmax based models produced satisfactory results, compared to the benchmark IPET model. Moreover, an alternative fit above land has been proposed, where the highest precipitation is simulated at the eye. The proposed land fit improved the median of the predictions based on both vmax and ΔP. The ΔP
based model performed the best in the case study, however, no definitive conclusion could be reached upon which model is most suitable overall as more case studies would be required.

Finally the updated model has been compared to the original Bader model. The new data ensured better representation over land, the overestimation of precipitation was reduced, and the model was applied with more confidence outside of the training data set. Consequently, results showed an improvement
on its prediction capabilities. As a concluding remark, this research project highlights the importance of having insightful data to enhance the decision-making and risk management of natural hazards: a model that accurately quantifies uncertainty and the risks associated with a TC, representing a valuable tool for better understanding flood risk. Nonetheless, there are still several ways to further improve the modeled profiles (e.g., by including more data, introducing asymmetry or adding temporal autocorrelation).