Meteogram¶

Here we draw a surface meteogram, from data that we retrieve from our METAR archive.¶

Imports¶

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, AutoDateLocator,YearLocator, HourLocator,DayLocator,MonthLocator

from metpy.units import units
from datetime import datetime, timedelta
import seaborn as sns

Set the desired start and end time (UTC), and create strings for the figure title.

startTime = datetime(2024,4,20,0)
endTime = datetime(2024,4,23,0)
sTimeStr = startTime.strftime(format="%x %H UTC")
eTimeStr = endTime.strftime(format="%x %H UTC")

Create an empty Dateframe to which we will concatenate the hourly CSV files in the desired time range.

df = pd.DataFrame()

Loop over each hour. Concatenate each hour’s METARs into the full Dataframe.

curTime = startTime
while (curTime <= endTime):
    print (curTime)
    timeStr = curTime.strftime("%y%m%d%H")
    metarCSV = f'/ktyle_rit/scripts/sflist2/complete/{timeStr}.csv'
    dfTemp = pd.read_csv(metarCSV, sep='\s+')
    df = pd.concat([df, dfTemp], ignore_index=True)
    curTime = curTime + timedelta(hours=1)

2024-04-20 00:00:00
2024-04-20 01:00:00
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2024-04-20 08:00:00
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2024-04-20 16:00:00
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2024-04-20 23:00:00
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2024-04-21 06:00:00
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2024-04-21 12:00:00
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2024-04-21 18:00:00
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2024-04-21 23:00:00
2024-04-22 00:00:00
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2024-04-22 03:00:00

2024-04-22 04:00:00
2024-04-22 05:00:00
2024-04-22 06:00:00
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2024-04-22 09:00:00
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2024-04-22 11:00:00
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2024-04-22 13:00:00
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2024-04-22 17:00:00
2024-04-22 18:00:00
2024-04-22 19:00:00
2024-04-22 20:00:00

2024-04-22 21:00:00
2024-04-22 22:00:00
2024-04-22 23:00:00
2024-04-23 00:00:00

Examine the Dataframe

df

	STN	YYMMDD/HHMM	SLAT	SLON	SELV	PMSL	ALTI	TMPC	DWPC	SKNT	...	P03C	CTYL	CTYM	CTYH	P06I	T6XC	T6NC	CEIL	P01I	SNEW
0	DYS	240420/0000	32.43	-99.85	545.0	1014.0	30.01	18.0	9.3	10.0	...	-1.2	-9999.0	-9999.0	-9999.0	-9999.0	18.3	14.4	27.0	-9999.0	-9999.0
1	NUW	240420/0000	48.35	-122.65	14.0	1020.5	30.13	15.6	2.8	6.0	...	-1.6	-9999.0	-9999.0	-9999.0	-9999.0	17.2	13.3	-9999.0	-9999.0	-9999.0
2	NYL	240420/0000	32.65	-114.62	65.0	1005.9	29.71	32.8	-0.6	6.0	...	-1.8	-9999.0	-9999.0	-9999.0	-9999.0	32.8	28.3	-9999.0	-9999.0	-9999.0
3	PALU	240420/0000	68.88	-166.13	3.0	1022.8	30.21	-4.8	-8.2	24.0	...	1.5	-9999.0	-9999.0	-9999.0	-9999.0	-4.8	-8.6	-9999.0	-9999.0	-9999.0
4	PATC	240420/0000	65.57	-167.92	83.0	1021.1	30.13	-9.8	-12.3	21.0	...	-0.5	-9999.0	-9999.0	-9999.0	-9999.0	-9.4	-13.7	110.0	-9999.0	-9999.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
314571	K70	240423/0000	-9999.00	-9999.00	-9999.0	-9999.0	29.95	25.0	22.0	10.0	...	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	39.0	-9999.0	-9999.0
314572	EKE	240423/0000	-9999.00	-9999.00	-9999.0	-9999.0	30.08	18.0	7.0	13.0	...	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0
314573	EZP	240423/0000	-9999.00	-9999.00	-9999.0	-9999.0	30.13	19.0	11.0	15.0	...	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0
314574	F45	240423/0000	-9999.00	-9999.00	-9999.0	-9999.0	30.01	20.0	17.0	11.0	...	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	60.0	-9999.0	-9999.0
314575	0R4	240423/0000	-9999.00	-9999.00	-9999.0	-9999.0	30.18	19.3	2.9	3.0	...	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	20.3	17.7	-9999.0	-9999.0	-9999.0

314576 rows × 35 columns

Select the station from which to subset the Dataframe.

site = 'ALB'

Create a new Dataframe that contains only the rows for the desired site.

dfSub = df.query('STN == @site')

Create a new column that represents the date and time as a datetime64 object.

# Next line suppresses a warning message. See https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy. 
# This won't be necessary with Pandas version 3 and beyond.

pd.options.mode.copy_on_write = True 

dattim = pd.to_datetime(dfSub['YYMMDD/HHMM'],format="%y%m%d/%H%M", utc=True)
dfSub['DATETIME'] = dattim

Examine the subsetted Dataframe.

dfSub

	STN	YYMMDD/HHMM	SLAT	SLON	SELV	PMSL	ALTI	TMPC	DWPC	SKNT	...	CTYL	CTYM	CTYH	P06I	T6XC	T6NC	CEIL	P01I	SNEW	DATETIME
1084	ALB	240420/0000	42.75	-73.8	89.0	1014.9	29.97	13.9	5.6	13.0	...	-9999.0	-9999.0	-9999.0	0.0	16.7	13.3	43.0	-9999.0	-9999.0	2024-04-20 00:00:00+00:00
5427	ALB	240420/0100	42.75	-73.8	89.0	1015.4	29.99	13.3	5.6	12.0	...	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	39.0	-9999.0	-9999.0	2024-04-20 01:00:00+00:00
9721	ALB	240420/0200	42.75	-73.8	89.0	1015.4	29.99	12.2	6.1	17.0	...	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	38.0	-9999.0	-9999.0	2024-04-20 02:00:00+00:00
14026	ALB	240420/0300	42.75	-73.8	89.0	1015.5	29.99	11.7	5.6	17.0	...	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	43.0	-9999.0	-9999.0	2024-04-20 03:00:00+00:00
18331	ALB	240420/0400	42.75	-73.8	89.0	1014.9	29.97	11.7	5.6	14.0	...	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	38.0	-9999.0	-9999.0	2024-04-20 04:00:00+00:00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
294077	ALB	240422/2000	42.75	-73.8	89.0	1018.3	30.07	13.3	-8.3	5.0	...	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	2024-04-22 20:00:00+00:00
298371	ALB	240422/2100	42.75	-73.8	89.0	1018.2	30.06	13.9	-7.2	6.0	...	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	2024-04-22 21:00:00+00:00
302705	ALB	240422/2200	42.75	-73.8	89.0	1018.4	30.07	13.3	-8.3	7.0	...	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	2024-04-22 22:00:00+00:00
306996	ALB	240422/2300	42.75	-73.8	89.0	1018.5	30.07	12.8	-7.8	11.0	...	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	-9999.0	2024-04-22 23:00:00+00:00
311313	ALB	240423/0000	42.75	-73.8	89.0	1018.7	30.08	10.0	-6.1	7.0	...	-9999.0	-9999.0	-9999.0	-9999.0	13.9	10.0	-9999.0	-9999.0	-9999.0	2024-04-23 00:00:00+00:00

73 rows × 36 columns

Select a couple of variables we wish to plot on the meteogram. In this case, 2m temperature and dewpoint.

var1 = dfSub['TMPC']
var2 = dfSub['DWPC']

Examine one of the variables … of course, it’s a Pandas Series.

var1

1084      13.9
5427      13.3
9721      12.2
14026     11.7
18331     11.7
          ... 
294077    13.3
298371    13.9
302705    13.3
306996    12.8
311313    10.0
Name: TMPC, Length: 73, dtype: float64

Pandas Series objects don’t support units. We can, however, create Numpy arrays from the Series values attribute and then attach units to them.

tmpc = var1.values * units('degC')
dwpc = var2.values * units('degC')

Examine one of the units-aware arrays.

tmpc

Magnitude

[13.9 13.3 12.2 11.7 11.7 11.7 11.1 11.1 10.6 10.6 10.0 10.0 10.6 11.1
 12.2 12.2 12.8 15.0 16.1 16.7 14.4 10.0 10.0 9.4 7.8 7.2 6.1 5.6 5.0 3.9
 3.3 2.8 2.8 1.7 0.6 1.7 3.9 7.8 6.7 7.8 8.9 8.3 8.9 8.9 8.3 8.9 8.3 7.8
 6.7 5.0 5.0 3.9 3.9 0.6 2.2 2.8 2.8 3.9 3.9 3.9 5.6 7.2 8.3 9.4 10.6 11.7
 12.2 13.3 13.3 13.9 13.3 12.8 10.0]

Units

degree_Celsius

Now convert into degrees F.

tmpf = tmpc.to('degF')
dwpf = dwpc.to('degF')

Create the meteogram and save the figure to disk.¶

sns.set()

fig, ax = plt.subplots(figsize=(15, 10))

# Improve on the default ticking

ax.xaxis.set_major_locator(HourLocator(interval=3))
hoursFmt = DateFormatter('%d/%H')
ax.xaxis.set_major_formatter(hoursFmt)

ax.plot(dfSub['DATETIME'], tmpf, color='tab:red', label='Temp')
ax.plot(dfSub['DATETIME'], dwpf, color='tab:green', label='Dwpt')

ax.set_title(f'Site: {site}      Date Range: {sTimeStr} - {eTimeStr}')

ax.set_xlabel('Hour (UTC)')
ax.set_ylabel('Temperature °F')
ax.set_xlim(startTime, endTime)
ax.legend(loc='best')

fig.savefig (f'{site}_mgram.png')

../../_images/0a23a1d41004c1448a05b732c64fa10a2e8492cae7dacf87d43470816adbf337.png

Things to try:

Basic Select a site and time range relevant for your case
Basic Create a multi-panel figure, each displaying a variable of interest
Advanced Plot two variables that have different units; create corresponding y-axis labels on the left and right sides of the plot

ATM350 Spring 2024

Meteogram

Contents

Meteogram¶

Here we draw a surface meteogram, from data that we retrieve from our METAR archive.¶

Imports¶

Create the meteogram and save the figure to disk.¶