HW4 Q3: NYS Mesonet Observations during the 2024 Solar Eclipse¶

Imports¶

import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.dates import DateFormatter, AutoDateLocator,HourLocator,DayLocator,MonthLocator,MinuteLocator

TASK: Add a code cell that sets the default Seaborn settings for Matplotlib

# Write your code here
sns.set()

Create a Dataframe from the April 8 2024 NYSM data file. Interpret the time column in datetime format.

df = pd.read_csv('/spare11/atm350/common/hw4/20240408_nysm.csv', parse_dates=['time'])

TASK: Examine the dataframe

# Write your code here
df

	station	time	temp_2m [degC]	temp_9m [degC]	relative_humidity [percent]	precip_incremental [mm]	precip_local [mm]	precip_max_intensity [mm/min]	avg_wind_speed_prop [m/s]	max_wind_speed_prop [m/s]	...	soil_temp_05cm [degC]	soil_temp_25cm [degC]	soil_temp_50cm [degC]	soil_moisture_05cm [m^3/m^3]	soil_moisture_25cm [m^3/m^3]	soil_moisture_50cm [m^3/m^3]	lat	lon	elevation	name
0	ADDI	2024-04-07 23:00:00+00:00	10.0	10.2	27.8	0.0	0.0	0.0	3.7	5.9	...	7.3	5.4	5.2	0.56	0.46	0.43	42.04036	-77.23726	507.614	Addison
1	ADDI	2024-04-07 23:05:00+00:00	9.7	10.0	28.5	0.0	0.0	0.0	2.8	4.8	...	7.3	5.4	5.2	0.56	0.46	0.43	42.04036	-77.23726	507.614	Addison
2	ADDI	2024-04-07 23:10:00+00:00	9.3	9.7	30.0	0.0	0.0	0.0	1.8	3.3	...	7.3	5.4	5.2	0.56	0.46	0.43	42.04036	-77.23726	507.614	Addison
3	ADDI	2024-04-07 23:15:00+00:00	9.0	9.5	29.8	0.0	0.0	0.0	2.4	4.7	...	7.3	5.4	5.2	0.56	0.46	0.43	42.04036	-77.23726	507.614	Addison
4	ADDI	2024-04-07 23:20:00+00:00	8.8	9.3	29.7	0.0	0.0	0.0	2.8	4.9	...	7.3	5.4	5.2	0.56	0.46	0.43	42.04036	-77.23726	507.614	Addison
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37975	YORK	2024-04-08 23:35:00+00:00	14.6	14.8	40.7	0.0	0.0	0.0	1.6	2.4	...	7.7	7.1	7.0	0.25	0.29	0.38	42.85504	-77.84776	177.942	York
37976	YORK	2024-04-08 23:40:00+00:00	14.3	14.7	41.4	0.0	0.0	0.0	1.3	1.7	...	7.7	7.1	7.0	0.25	0.30	0.38	42.85504	-77.84776	177.942	York
37977	YORK	2024-04-08 23:45:00+00:00	13.1	14.4	45.6	0.0	0.0	0.0	1.3	1.6	...	7.7	7.1	7.0	0.25	0.30	0.38	42.85504	-77.84776	177.942	York
37978	YORK	2024-04-08 23:50:00+00:00	11.6	14.1	52.1	0.0	0.0	0.0	1.1	1.5	...	7.7	7.0	7.0	0.25	0.29	0.38	42.85504	-77.84776	177.942	York
37979	YORK	2024-04-08 23:55:00+00:00	11.1	14.0	52.7	0.0	0.0	0.0	1.2	1.4	...	7.7	7.1	7.0	0.25	0.30	0.38	42.85504	-77.84776	177.942	York

37980 rows × 34 columns

TASK: Select start and end time that covers a 3-hour period that includes the peak of the 4/8/24 eclipse over NYS. Use Pandas' Timestamp function. An example invocation to set the start time to 2000 UTC March 8 1970 would be:
timeStart = pd.Timestamp(1970, 3, 8, 14, tz='UTC')

# Write your code here
timeStart = pd.Timestamp(2024, 4, 8, 18, tz = 'UTC')
timeEnd = pd.Timestamp(2024, 4, 8, 21, tz = 'UTC')

Set a boolean mask that includes the period of interest, then create a new Dataframe from that mask.

mask = (df['time'] >= timeStart) & (df['time'] <=timeEnd) 
dfSub = df[mask]

TASK: Examine the subsetted Dataframe

dfSub

	station	time	temp_2m [degC]	temp_9m [degC]	relative_humidity [percent]	precip_incremental [mm]	precip_local [mm]	precip_max_intensity [mm/min]	avg_wind_speed_prop [m/s]	max_wind_speed_prop [m/s]	...	soil_temp_05cm [degC]	soil_temp_25cm [degC]	soil_temp_50cm [degC]	soil_moisture_05cm [m^3/m^3]	soil_moisture_25cm [m^3/m^3]	soil_moisture_50cm [m^3/m^3]	lat	lon	elevation	name
228	ADDI	2024-04-08 18:00:00+00:00	12.0	12.0	39.4	0.0	0.0	0.0	3.3	5.3	...	5.6	5.4	5.4	0.56	0.46	0.44	42.04036	-77.23726	507.614	Addison
229	ADDI	2024-04-08 18:05:00+00:00	11.8	11.9	40.1	0.0	0.0	0.0	3.5	5.3	...	5.6	5.4	5.4	0.56	0.46	0.44	42.04036	-77.23726	507.614	Addison
230	ADDI	2024-04-08 18:10:00+00:00	11.6	11.7	42.6	0.0	0.0	0.0	4.0	5.5	...	5.7	5.4	5.4	0.56	0.46	0.44	42.04036	-77.23726	507.614	Addison
231	ADDI	2024-04-08 18:15:00+00:00	11.7	11.8	40.9	0.0	0.0	0.0	3.8	5.7	...	5.7	5.4	5.4	0.56	0.46	0.44	42.04036	-77.23726	507.614	Addison
232	ADDI	2024-04-08 18:20:00+00:00	11.8	11.9	39.3	0.0	0.0	0.0	4.2	6.8	...	5.7	5.4	5.4	0.56	0.46	0.44	42.04036	-77.23726	507.614	Addison
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37940	YORK	2024-04-08 20:40:00+00:00	14.3	13.9	45.5	0.0	0.0	0.0	0.4	0.7	...	7.4	7.0	7.0	0.25	0.30	0.38	42.85504	-77.84776	177.942	York
37941	YORK	2024-04-08 20:45:00+00:00	14.8	14.3	41.6	0.0	0.0	0.0	0.2	0.6	...	7.4	7.0	7.0	0.25	0.30	0.38	42.85504	-77.84776	177.942	York
37942	YORK	2024-04-08 20:50:00+00:00	15.1	14.5	39.9	0.0	0.0	0.0	0.8	1.1	...	7.4	6.9	7.0	0.25	0.29	0.38	42.85504	-77.84776	177.942	York
37943	YORK	2024-04-08 20:55:00+00:00	15.5	14.7	39.7	0.0	0.0	0.0	1.2	1.7	...	7.4	7.0	7.0	0.25	0.30	0.38	42.85504	-77.84776	177.942	York
37944	YORK	2024-04-08 21:00:00+00:00	15.7	14.7	37.7	0.0	0.0	0.0	1.4	2.0	...	7.4	7.0	7.0	0.25	0.30	0.38	42.85504	-77.84776	177.942	York

4752 rows × 34 columns

Task: define five NYSM stations from across the state. Ideally, choose sites that vary in terms of time of totality and/or degree of totality.

# Write your code here
site1 = "BRAN"
site2 = "RUSH"
site3 = "REDF"
site4 = "TUPP"
site5 = "CHAZ"

TASK: Complete the following code cell in order to produce a well-labeled figure containing two subplots, one on top of the other, containing time series of 2m temperature and incoming solar radiation for the five NYSM sites you have defined above.

fig = plt.figure(figsize=(15,10))
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2, sharex=ax1)

for site in [site1, site2, site3, site4, site5]:
    
    siteData = dfSub.query('station == @site')
    dattim = siteData['time']
    t2m = siteData['temp_2m [degC]']
    srad = siteData['solar_insolation [W/m^2]']
 
    ax1.plot (dattim, t2m, label=site)
    ax2.plot (dattim, srad, label=site)

ax1.set_title ("April 8 2024 2 m T (°C)")
ax1.set_xlabel('Time (UTC)')
ax1.set_ylabel('Temperature (°C)')
ax1.xaxis.set_major_locator(MinuteLocator(interval=15))
dateFmt = DateFormatter('%H%M')
ax1.xaxis.set_major_formatter(dateFmt)
ax1.set_xlim(timeStart, timeEnd)
ax1.legend(loc='best')

ax2.set_title ("April 8 2024 Solar Radiation (W/m**2)")
ax2.set_xlabel('Time (UTC)')
ax2.set_ylabel('Solar Radiation (W/m**2)')
ax2.legend(loc='best')

# Add some space between the two subplots
plt.subplots_adjust(wspace=0.5, hspace=0.5)

../../_images/b4691ca02bd4e01847f025cf1edd9b16f833625e14f41e1928dcce867753ba6e.png

ATM350 Spring 2024

HW4 Q3: NYS Mesonet Observations during the 2024 Solar Eclipse

Contents

HW4 Q3: NYS Mesonet Observations during the 2024 Solar Eclipse¶

Imports¶