DOI: 10.5061/dryad.t1g1jwtbg
title: Data from: TC-GEN: Data-driven tropical cyclone downscaling using machine learning-based high-resolution weather model

description: Synthetic downscaling of tropical cyclones (TCs) is critically important
 to estimate the long-term hazard of rare high-impact storm events.
 Existing downscaling approaches rely on statistical or
 statistical-deterministic models that are capable of generating large
 samples of synthetic storms with characteristics similar to observed
 storms. However, these models do not capture the complex two-way
 interactions between a storm and its environment. In addition, these
 approaches either necessitate a separate TC size model to simulate storm
 size or involve post-processing to capture the asymmetries in the
 simulated surface wind. In this study, we present an innovative
 data-driven approach for TC synthetic downscaling. Using a machine
 learning-based high-resolution global weather model (ML-GWM), our approach
 can simulate the full life cycle of a storm with asymmetric surface wind
 that accounts for the two-way interactions between the storm and its
 environment. This approach consists of multiple components: a data-driven
 model for generating synthetic TC seeds, a blending method that seamlessly
 integrates storm seeds into the surrounding while maintaining the seed
 structure, and a model based on a recurrent neural network to correct for
 biases in storm intensity. Compared to observations and synthetic storms
 simulated using existing statistical-deterministic and statistical
 downscaling approaches, our method shows the ability to effectively
 capture many aspects of TC statistics, including track density, landfall
 frequency, landfall intensity, and outermost wind extent. Leveraging the
 computational efficiency of ML-GWM, our approach shows substantial
 potential for TC regional hazard and risk assessment.
description: # Data from: TC-GEN: Data-driven tropical cyclone downscaling using
 machine learning-based high-resolution weather model Replication materials
 for Jing et al. (2024). The materials in this repository reproduce the
 figures, tables, and calculations appearing in the main text and
 supplement material of the paper. If you find meaningful errors in the
 code or have questions or suggestions, please contact Renzhi Jing at
 [jingrenzhi.go@gmail.com](mailto:jingrenzhi.go@gmail.com) ### Organization
 of repository * **scripts**: scripts for replication of figures and
 calculations. * **data**: output data that are used to generate figures
 ### Data * **data/fig1_data.mat** * selected case to illustrate the basic
 concept of TC-GEN. The data includes four variables: `predictors` (the
 simulated environment map), `Intensity` (the storm's intensity),
 `location` (the storm's latitude and longitude), and `time` (52
 snapshots representing different time points) * **data/fig2_data.mat** *
 Selected simulated storms to demonstrate the effectiveness of PCA
 analysis. The data includes four variables: `recon_50`, `recon_100`,
 `recon_500`, and `original_data`. These represent environmental fields
 reconstructed using 50, 100, and 500 principal components, along with the
 raw fields. * **data/fig3_data.mat** * Selected synthetic tropical cyclone
 seeds using 500 principal components. The data is structured as [20, 4,
 150, 150], where '20' represents the number of storms,
 '4' represents the variables (in the order of mean sea level
 pressure, u wind, v wind, and temperature), and '150 x 150'
 denotes the spatial field dimensions. * **data/fig4_data.mat** * Selected
 cases to show the idea of Poisson blending, compared to the basic
 copy-and-paste method. The data includes four variables: `src_data`
 (synthetic storm seeds), `tgt_data` (target environmental fields),
 `paste_data` (results from the copy-and-paste method), and `blend_data`
 (results from Poisson blending). * **data/fig7** * *fig7_ibt_tracks.csv*  
 historical tropical cyclone tracks between 1979 and 2014, derived from
 IBTrACS dataset. The columns include: `sid` (storm ID), `year`, `month`,
 `day`, `hour` (storm time), `lat` (storm latitude), `lon` (storm
 longitude), `vmax` (maximum wind intensity in knots), and `isocean` (a
 flag indicating whether the storm is over the ocean). *
 *fig7_ke08_simulation.csv*   simulated storms using KE08 method. The
 columns are same with *fig7_ibt_tracks.csv*. *
 *fig7_pepc_10_simulation.csv*   simulated storms using PepC method. The
 columns are same as those in *fig7_ibt_tracks.csv*. *
 *fig7_tcgen_hourly.csv*   simulated storms using TC-GEN hourly method. The
 columns include: `SID` (storm id), `simulated_time` (time of simulated
 storms), `simulated_lat` (latitude of simulated storms), `simulated_lon`
 (longitude of simulated storms), `simulated_wind` (maximum wind speed of
 simulated storms), `bias_correct_wind` (bias-corrected wind speed), and
 `isocean` (a flag indicating whether the storm is over the ocean). *
 *fig7_tcgen_monthly.csv*  simulated storms using TC-GEN monthly method.
 The columns are same as those in *fig7_tcgen_hourly.csv*. *
 *fig7_tcgen_hourly_resampling.csv*   simulated storms using TC-GEN hourly
 method with genesis locations resampled according to historical
 distribution patterns. The columns are same as those in
 *fig7_tcgen_hourly.csv*. * **data/fig9** * *fig9_ibt_tracks_1900_2022*  
 historical tropical cyclone tracks between 1900 and 2022, derived from
 IBTrACS dataset. The columns are same with those in *fig7_ibt_tracks.csv*.
 * *fig9_ibt_landfall_sampling_errors*   landfall sampling errors of
 historical North Atlantic tropical cyclones at each of the 185 gates. The
 columns include: `gate_id` (gate id), `ibt_freq` (landfall frequency),
 `ibt_se` (standard error of landfall frequency). * *fig9_landfall_stats*  
 landfall statistics of historical storms and simulated storms. The columns
 are: * `gate_id`: gate Iid * `*_freq`: landfall frequency for each
 dataset, where columns starting with `ke06` represent simulated storms
 using the KE08 method, columns starting with `simu` represent simulated
 storms using the TC-GEN hourly method, and `simu_monthly` represents
 simulated storms using the TC-GEN monthly method. * `pepc_*`: landfall
 frequency of simulated storms using the PepC method. We provide results
 from 10 independent simulations. * **data/fig11** * *fig11_snp.mat*  
 snapshot of selected storm to show the concept of TC outer size. *
 *fig11_tc_size_era5.csv*   historical tropical cyclone size extracted from
 ERA5 reanalysis. The columns include: `sid` (storm id), `time` (storm
 time), `r2` (outer size at 2m/s wind level), `r6` (outer size at 6m/s wind
 level), and `r8` (outer size at 8m/s wind level). *
 *fig11_tc_size_simulation.csv*   outer size of simulated historical
 tropical cyclones using TC-GEN hourly method. The columns are same with
 those in *fig11_tc_size_era5.csv*. * **data/world_countries** * Country
 boundaries from IPUMS, the raw data could be downloaded at:
 [https://international.ipums.org/international/gis.shtml](https://international.ipums.org/international/gis.shtml). ### Scripts R files (plot_Figure*.R) generate the figures in the paper and write them to ./figures/. The figures produced by these scripts might look slightly different from the published versions due to post-processing in Adobe Illustrator. ### Code/Software Scripts were written in Python 3.6.1 and R 4.2.3.