Downloads provided by UsageCounts
Jupyter notebooks, Python scripts, and data files used to make figures analyzing tropical cyclones (TCs) in the NASA GISS-E3 global climate model. Much of this code was adapted from Matlab code written by Jeffrey Strong. This code and dataset is for a paper submitted to Journal of Advances in Modeling Earth Systems in 2021 by Rick Russotto, Jeffrey Strong, Suzana Camargo, Adam Sobel, Gregory Elsaesser, Maxwell Kelley, Anthony Del Genio, Yumin Moon, and Daehyun Kim. It is being uploaded to Zenodo in advance of submitting the revised version to JAMES. The code was earlier posted here: https://github.com/rdrussotto/GISS-E3_TC_notebooks Versions of libraries used for paper: Python 3.6.12, NumPy 1.19.2, Matplotlib 3.2.0 (including Basemap), Pandas 1.1.5, XArray 0.16.2, SciPy 1.5.2 Notebooks used to make figures: Figure 1: Plot_Tracks.ipynb Figure 2: Plot_Density.ipynb Figures 3-7: Plot_Statistics.ipynb Figures 8-9: Plot_Storm_Tangential.ipynb Figure 10: Plot_Radial_Profiles.ipynb Figures 11-12: Sensitivity_Test_Plots.ipynb Figure 13-14: Intermediate_Error_Plots_Simplified.ipynb Figures 15-16: Intermediate_Error_Plots_Simplified_2.ipynb Additional dependencies: TC track preprocessing scripts: Read_Zhao_TCs.ipynb (for modeled TCs) Preprocess_IBTrACS.ipynb (for observed TCs) Storm-centered diagnostics: Storm_Centered_Plots.py Storm_Centered_V1_Prec.py Retrieve_Utan_Vtan.py Radial-height profiles: Figures 10 and S1 depend on Matlab scripts (not included here) developed by Yumin Moon for paper in Journal of Climate, Moon et al. (2020), https://doi.org/10.1175/JCLI-D-19-0172.1, and adapted by Jeffrey Strong for this study. Sensitivity test analysis: Figures 11-16 depend on Matlab scripts developed by Jeffrey Strong (not included here) for processing climate variables in the model results and comparing to observations. Data files: Radial profiles: YuminEtAl_Figs.mat Sensitivity test line plots (Figures 11-12): JS_C180_ParamSensTest.mat JS_C180v2_ParamSensTest.mat Values plotted in error matrix plots for Figures 13-16: (use np.load in NumPy) Fig13.npy Fig14.npy Fig15.npy Fig16.npy Modeled TC tracks: zhao_tracks_v1.nc zhao_tracks_v2.nc (Observed TC data from IBTrACS project available at https://www.ncdc.noaa.gov/ibtracs/index.php?name=ib-v4-access) Storm-centered TC quantities: storm_centered_peak_intensity_v1.nc storm_centered_peak_intensity_v2.nc storm_centered_reference_winds_v1.nc storm_centered_reference_winds_v2.nc storm_centered_peak_intensity_prec_v1.nc
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 9 | |
| downloads | 5 |

Views provided by UsageCounts
Downloads provided by UsageCounts