{"id":604,"date":"2023-06-21T17:41:27","date_gmt":"2023-06-21T16:41:27","guid":{"rendered":"https:\/\/research.reading.ac.uk\/huracan\/?page_id=604"},"modified":"2023-12-06T12:22:41","modified_gmt":"2023-12-06T12:22:41","slug":"storm_assess","status":"publish","type":"page","link":"https:\/\/research.reading.ac.uk\/huracan\/science\/tools-analysis\/storm_assess\/","title":{"rendered":"storm_assess"},"content":{"rendered":"

Introduction<\/h3>\n

storm_assess<\/span><\/strong> is a Python module to read ascii-format output from the TRACK<\/em> and TempestExtremes<\/em> cyclone-tracking algorithms, originally developed by Joanne Camp (Met Office & Bureau of Meteorology), and is available on JASMIN at: \/gws\/nopw\/j04\/huracan\/tools\/python\/storm_assess\/<\/span><\/p>\n

A version of storm_assess<\/span>, developed by Malcolm Roberts (Met Office), was developed for reading NetCDF-format track output data (from HighResMIP), which is available here<\/a>.<\/p>\n

A simple example: load a TRACK<\/em> output file and plot a pressure-wind relationship<\/h3>\n

import numpy as np<\/span>
\nimport matplotlib.pyplot as plt<\/span>
\nfrom storm_assess import track<\/span><\/p>\n

# Load a list of storms and get the wind speed and central pressure data
\ntrack_fn = ‘track_filename’<\/span>
\nstorms = list(track.load(track_fn))<\/span>
\nmslp_minima = [storm.mslp_min for storm in storms]<\/span>
\nvmax_maxima = [storm.vmax for storm in storms]<\/span><\/p>\n

# Fit
\ncoeff = np.polyfit(vmax_maxima,mslp_minima,2.)<\/span>
\npoly1d = np.poly1d(coeff)<\/span>
\nfit_x = np.linspace(min(vmax_maxima),max(vmax_maxima),len(vmax_maxima))<\/span>
\nfit_y = poly1d(fit_x)<\/span><\/p>\n

# Plot
\nplt.scatter(vmax_maxima,mslp_minima,color=’r’,marker=’.’,alpha=0.5)<\/span>
\nplt.plot(fit_x,fit_y,color=’r’,label=’dataset name’)<\/span>
\nplt.ylabel(r’p$_{min}$’)<\/span>
\nplt.xlabel(r’v$_{max}$’)<\/span>
\nplt.legend()<\/span>
\nplt.show()<\/span><\/p><\/blockquote>\n

Each storm<\/span> object contains attributes that describe the location and additional fields (i.e., wind speed, central pressure etc) for each track. For example:<\/p>\n

storm = storms[i]<\/span>
\nstorm.obs
\nstorm.vmax<\/span>
\nstorm.mslp_min<\/span>
\nstorm.obs[t].date<\/span>
\nstorm.obs[t].lon<\/span>
\nstorm.obs[t].lat<\/span>
\nstorm.obs_at_vmax()<\/span>
\nstorm.obs_at_min_mslp().date<\/span>
\n<\/span><\/p><\/blockquote>\n

Any suggestions to improve the functionality of storm_assess<\/span> may be sent to Alex Baker<\/a> or Leo Saffin<\/a>.<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

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