Hurricane Forecasting Project. This is the repository of the paper "Simple yet effective: a comparative study of statistical models for yearly hurricane forecasting" developed jointly with Phillip Otto (https://www.gla.ac.uk/schools/mathematicsstatistics/staff/philippotto/) and Raffaele Mattera (https://web.uniroma1.it/disse/node/7826).
we study the problem of forecasting the next year’s number of Atlantic hurricanes, which is relevant in many fields of applications such as land-use planning, hazard mitigation, reinsurance and long-term weather derivative market. Consider- ing a set of well-known predictors, we compare the forecasting accuracy of both machine learning and simpler models, showing that the latter may be more adequate than the first. Quantile regression models, which are adopted for the first time for forecasting hurricane numbers, provide the best results. Moreover, we construct a new index showing good properties in anticipating the direction of the future num- ber of hurricanes. We consider different evaluation metrics based on both magnitude forecasting errors and directional accuracy.
National Hurrican Center (Ground truth for prediction market)(https://www.nhc.noaa.gov/) PSL El Niño Index Dashboard (https://psl.noaa.gov/enso/dashboard.html) Bacelona Supercompuer Center: Seasonal Hurricane Forecasts (https://seasonalhurricanepredictions.bsc.es/) Historical hurricane monthly data (https://www.nhc.noaa.gov/data/#monthly) Other statistics about hurricanes (https://tropical.atmos.colostate.edu/Realtime/index.php?arch&loc=northatlantic)
To Get start you need an R studio and Python working Installation and the Library listed below. The repository contains a set of models that training using a R libraries, while LSTM is trained using a Python library.
List of libraries R libraries that need to be installed before setting up the project.
# List of libraries
libraries <- c("dplyr", "car", "tscount", "quantreg", "ProbCast", "readr",
"tidyverse", "ggplot2", "plotly", "scatterplot3d")
# Function to check and install libraries
install_if_missing <- function(lib) {
if (!require(lib, character.only = TRUE)) {
install.packages(lib, dependencies = TRUE)
library(lib, character.only = TRUE)
}
}
# Apply the function to each library
sapply(libraries, install_if_missing)Instructions and examples for using the project. Show code snippets or screenshots as necessary.
- Pietro Colombo - [https://www.linkedin.com/in/pietro-colombo-62b295129/]
- pietro.colombo@glasgow.ac.uk
Project Link: "To be put on Archive")
We thanks Jethro Browell (https://github.com/jbrowell) and Gabriel Dantas (https://www.gla.ac.uk/pgrs/gabrieldantasdeoliveirarolim/) for their insightfull contribution for starting the project.