Plot a psudo-sounding with MesoWest Data

In the Salt Lake City area there are many weather stations at elevations higher than the valley floor. Farnsworth Peak is such a weather station at almost 700 hPa. I wondered what it would look like to plot station weather data on a skew-t graph, and see how it compares to rawinsonde balloons.

Here I use the MesoWest API and the Wyoming Sounding data to plot these graphs. I use the SkewT 1.1.0 module for Python to create the SkewT graph. The balloon observations are in green and the real observations are plotted in blue with the station id next to the point. A red line is drawn to create the "psudo-sounding" from the temperature observations at the different elevations. It doesn't match up exactly, except, for FWP, which seems to be an accurate representation of the temperatures of the atmosphere at that elevation.

See more of the code on GitHub: https://github.com/blaylockbk/pyBKB and https://github.com/blaylockbk/Ute_other/blob/master/MesoWest_psudo_sounding.py

## Brian Blaylock

## 27 June 2016

# A lot of the stations in Utah are at elevations.

# I want to plot station data on a skew-t graph to get a 

# psudo vertical profile for any time of day.

import matplotlib.pyplot as plt

import numpy as np

from datetime import datetime, timedelta

import sys

sys.path.append('B:\pyBKB')                                        #for running on PC (B:\ is my network drive)

from BB_MesoWest import MesoWest_stations_radius as MW

from skewt_900_700 import SkewT #this version plots between 900-700 hPa

import wyoming_sounding

##############################

request_time = datetime(2016,6,29,12,0)

##############################

MWdatetime = request_time.strftime('%Y%m%d%H%M')

a = MW.get_mesowest_radius_winds(MWdatetime,'10')

p = a['PRESS']>0 #pressure is in these indexes

pres = a['PRESS'][p]/100

temp = a['TEMP'][p]

hght = a['ELEVATION'][p]

dwpt = a['TEMP'][p]*np.nan #don't get the dewpoint now

wd   = a['WIND_DIR'][p]

ws   = a['WIND_SPEED'][p]

stns = a['STNID'][p]

# sort all that data based on pressure

from operator import itemgetter

pres,temp,hght,dwpt,wd,ws,stns = zip(*sorted(zip(pres,temp,hght,dwpt,wd,ws,stns),key=itemgetter(0)))

data = {'drct':wd,

        'dwpt':dwpt,        #don't plot dwpt right now

        'hght':hght,

        'pres':pres,

        'sknt':ws,

        'temp':temp}

S=SkewT.Sounding(soundingdata=data)

S.plot_skewt(title='psudo sounding\n'+MWdatetime,color='r',lw=.8,parcel_type=None,

             pmin=700.,pmax=900.)

for i in range(0,len(stns)):

    if temp[i]>0: #wont accept nan values

        print temp[i],pres[i],stns[i]

        S.plot_point(temp[i],pres[i])

        S.plot_text(temp[i],pres[i],str(stns[i]))

        

# Add real sounding to plot

# If request_time is in the afternoon, then request the evening sounding

if request_time.hour > 18:

    # Get the evening sounding (00 UTC) for the next day

    next_day = request_time + timedelta(days=1)

    request_sounding = datetime(next_day.year,next_day.month,next_day.day,0)    

elif request_time.hour < 6:

    # Get the evening sounding (00 UTC) for the same

    request_sounding = datetime(request_time.year,request_time.month,request_time.day,0)    

else:

    # Get the morning sounding (12 UTC) for the same day

    request_sounding = datetime(request_time.year,request_time.month,request_time.day,12)

print "SLC sounding for", request_sounding

a = wyoming_sounding.get_sounding(request_sounding)

balloon = {'drct':a['wdir'],

           #'dwpt':a['dwpt'],

           'hght':a['height'],

           'pres':a['press'],

           'sknt':a['wspd'],

           'temp':a['temp']}

# Add to plot

T = SkewT.Sounding(soundingdata=balloon)

S.soundingdata=T.soundingdata

S.add_profile(color='g',lw=.9,bloc=1.2)

             

Plotting Sounding Data from University of Wyoming's Website

Plotting sounding data can be difficult, but it is made easy with this python module: https://pypi.python.org/pypi/SkewT

It requires a .txt file with data in the format of the University of Wyoming sounding data archive (example) or you can create your own dictionary.

Here I grab the sounding data from the University of Wyoming's website and process it with the following steps:

1. Use urllib2 to open the url and read the data
2. Parse out the html tags using BeautifulSoup
3. Separate the text by a new line "\n"
4. Write each line of text to a new .txt file reinserting the new line with +"\n" and skipping the first three lines of the file (this puts it in the same format as the University of Wyoming website)
5. Draw the Skew-T Plot (NOTE: requires the SkewT package here)

Here is the download function on github. 
As always, please share other solutions you may come up with :)

# Brian Blaylock

# July 6, 2015

# Download, process, and Plot Sounding Data from University of Wyoming

import urllib2
from bs4 import BeautifulSoup
from skewt import SkewT

stn = '72572' #72572 is ID for SLC
year= '2015'
month = '06'
day = '12'
hour = '12' #either 12 or 00

# 1)
# Wyoming URL to download Sounding from
url = 'http://weather.uwyo.edu/cgi-bin/sounding?region=naconf&TYPE=TEXT%3ALIST&YEAR='+year+'&MONTH='+month+'&FROM='+day+hour+'&TO='+day+hour+'&STNM='+stn
content = urllib2.urlopen(url).read()

# 2)
# Remove the html tags
soup = BeautifulSoup(content)
data_text = soup.get_text()

# 3)
# Split the content by new line.
splitted = data_text.split("\n",data_text.count("\n"))

# 4)
# Write this splitted text to a .txt document
Sounding_filename = str(stn)+'.'+str(year)+str(month)+str(day)+str(hour)+'.txt'
f = open(Sounding_filename,'w')
for line in splitted[4:]:
    f.write(line+'\n')
f.close()

# 5)
sounding = SkewT.Sounding(filename=Sounding_filename)
sounding.plot_skewt()


And if you wish to plot more than one sounding on the same plot do this...
S = SkewT.Sounding(filename=Sounding_filename)
T = SkewT.Sounding(filename="72572.2015061212.txt")
S.make_skewt_axes(tmax=55)
S.add_profile(color='r',linewidth=5,bloc=0)
S.soundingdata=T.soundingdata
S. add_profile(color="b",bloc=1)