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NEXRAD SITES WITH LAT/LON

NEXRAD SITES WITH LAT/LON

Author: Hamid Ali Syed | @hamidrixvi | syed44@purdue.edu

Import packages

import warnings
warnings.filterwarnings("ignore")
import datetime as dt
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
import pandas as pd
import seaborn as sns
import cartopy.crs as ccrs
import cartopy.feature as feat
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.patches as mpatches
import plotly.graph_objects as go
import glob
import os

The add_Map() function is to add coastlines, land, ocean, and state boundaries to the axes.

def add_Map(ax, b = 0, t=0, l = 0, r = 0):
    gl = ax.gridlines(crs=ccrs.PlateCarree(), linewidth=0.3, color='black', alpha=0.3, 
                         linestyle='-', draw_labels=True)
    gl.xlabels_top = t
    gl.xlabels_bottom = b
    gl.ylabels_left = l
    gl.ylabels_right=r
    gl.xlines = True
    gl.ylines = True
    gl.xformatter = LONGITUDE_FORMATTER
    gl.yformatter = LATITUDE_FORMATTER
    ax.add_feature(feat.BORDERS, lw = 0.5)
    ax.add_feature(feat.LAND, lw = 0.3, fc = [0.9,0.9,0.9])
    ax.add_feature(feat.COASTLINE, lw = 0.5)
    ax.add_feature(feat.OCEAN, alpha = 0.5)
    ax.add_feature(feat.STATES.with_scale("10m"), alpha = 0.5, lw = 0.5)

Read NEXRAD data

df = pd.read_csv("nexrad_site_list.csv")

df
ID NAME LAT LON
0 KVNX VANCE AFB 36.740617 -98.127717
1 PACG SITKA 56.852778 -135.529167
2 PAEC NOME 64.511389 -165.295000
3 TJUA SAN JUAN 18.115667 -66.078167
4 TJRV JOSE APONTE DE LA TORR 18.256000 -65.637000
... ... ... ... ...
207 KOHX NASHVILLE 36.247222 -86.562500
208 KYUX YUMA 32.495281 -114.656711
209 RKSG CAMP HUMPHREYS 37.207569 127.285561
210 TFLL FT LAUDERDALE 26.143056 -80.343889
211 KLGX LANGLEY HILL NW WASHIN 47.116944 -124.106667

212 rows × 4 columns

Plotting the data

fig = plt.figure(figsize = [10,6])
ax = plt.axes(projection = ccrs.PlateCarree((-91+-85)/2))
# ax.set_global()
ax.set_extent([-180, -60, 10, 60])
sns.scatterplot(data=df, x = "LON", y = "LAT", ax=ax, transform=ccrs.PlateCarree())


theta = np.linspace(0,359,10000)

for lat,lon in zip(df["LAT"],df['LON']):
    ax.add_patch(mpatches.Circle(xy=[lon, lat],
                                 radius=((200)*(np.pi/180)), # I have used 200 km here as random range
                                 facecolor='none',
                                 ec = "k",
                                 lw = 1,
                                 transform=ccrs.PlateCarree()))
add_Map(ax, b = 1, l = 1)
plt.show()
_images/nexrad_sites_9_0.png

Let’s try plotly

fig = go.Figure(data=go.Scattergeo(
    lon = df['LON'],
    lat = df['LAT'],
    text = df['ID'],
    mode = 'markers',
    hovertext=df[["NAME", "ID"]],
    marker=dict(
        color='rgba(0,0,0,0)',
            size=50,
            opacity=0.5,
            line=dict(
                color='MediumPurple',
                width=2,
            )

)))


fig.update_geos(
    resolution=50,
    showcountries=True, countrycolor="RebeccaPurple",
    showsubunits=True, subunitcolor="Blue"
    )

fig.update_layout(
    title = 'NEXRAD SITES (Hover to see Name & ID)',
    geo_scope='usa',
    mapbox_style="white-bg",
    mapbox_layers=[
        {
            "below": 'traces',
            "sourcetype": "raster",
            "sourceattribution": "United States Geological Survey",
            "source": [
                "https://basemap.nationalmap.gov/arcgis/rest/services/USGSImageryOnly/MapServer/tile/{z}/{y}/{x}"
            ]
        }
      ])
# fig.update_layout(margin={"r":0,"t":40,"l":0,"b":0})
fig.show()

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