Environment Analysis Class Usage¶
Welcome to RocketPy’s Environment Analysis!
This notebook intends to present you a brief description of what is possible to be done by using the most recent class of RocketPy library. Analyzing weather historical data is of upmost importance for rocket launch operations, specially with regards to range closure decision which may impact safety and rocket flight performance, therefore the results presented here may be useful to support your launch operation design.
In this case we are going to use the Spaceport America location to perform an historical analysis over the last 20 years (i.e. from 2002 to 2021). However, Environment Analysis allows for both different location and time range to be analyzed as well.
Initializing class and loading files¶
Let’s start by importing the required libraries for our work:
[1]:
%load_ext autoreload
%autoreload 2
[2]:
%matplotlib widget
[3]:
from rocketpy.EnvironmentAnalysis import EnvironmentAnalysis
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
The next cell will capture the dataset file previous donwloaded. For more details about how to get .nc files for another specific time and location, please consult the following thread:
[4]:
env_analysis = EnvironmentAnalysis(
start_date=datetime(2002, 6, 15), # (Year, Month, Day)
end_date=datetime(2021, 6, 30), # (Year, Month, Day)
start_hour=6,
end_hour=18,
latitude=32.990629,
longitude=-106.976097,
surfaceDataFile="../../data/weather/spaceport_america_single_level_reanalysis_2002_2021.nc",
pressureLevelDataFile="../../data/weather/spaceport_america_pressure_levels_reanalysis_2002_2021.nc",
timezone="America/Denver",
unit_system="imperial",
)
Overview of .nc files¶
Running the EnvironmentAnalysis class requires two NETcdf(.nc) files. NETcdf is a data format that storages climate data easily accessible only through libraries such as netCDF4. In RocketPy we will use these datasets to get variables such as temperature and wind speed at specific times and locations. The first one, SurfaceDataFile, must be an .nc file containing environment information about the surface temperature, U and V components of the wind at 10 and 100 meters from the surface etc, that can be found, for example, in ERA5 hourly data on single levels files (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels). The second one, pressureLevelDataFile, must be an .nc file containing the geopotential, U and V components of wind and temperature for each pressure level. These variables can be found, for example, in ERA5 hourly data on pressure levels files (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels).
Surface level Analysis¶
At this first section we are looking for at the surface level. Data from surface analysis usually come with bigger amounts of information and therefore are important to give us a complete understanding of the scenario faced at the place and time that we are analysing.
Average and record values¶
A good start on our analysis is by checking numerical values that are critical for the selected time range.
[5]:
env_analysis.allInfo()
Pressure Information
Average Surface Pressure: 25.16 ± 0.08 inHg
Average Pressure at 1000 ft: 24.30 ± 0.08 inHg
Average Pressure at 10000 ft: 17.55 ± 0.08 inHg
Average Pressure at 30000 ft: 7.73 ± 0.08 inHg
Sustained Surface Wind Speed Information (33 ft above ground)
Historical Maximum Wind Speed: 28.04 mph
Historical Minimum Wind Speed: 0.14 mph
Average Daily Maximum Wind Speed: 11.38 mph
Average Daily Minimum Wind Speed: 2.61 mph
Elevated Wind Speed Information (328 ft above ground)
Historical Maximum Wind Speed: 37.37 mph
Historical Minimum Wind Speed: 0.07 mph
Average Daily Maximum Wind Speed: 14.19 mph
Average Daily Minimum Wind Speed: 3.82 mph
Wind Gust Information
Historical Maximum Wind Gust: 49.24 mph
Average Daily Maximum Wind Gust: 22.38 mph
Temperature Information
Historical Maximum Temperature: 105.04 degF
Historical Minimum Temperature: 59.55 degF
Average Daily Maximum Temperature: 93.89 degF
Average Daily Minimum Temperature: 70.97 degF
Precipitation Information
Percentage of Days with Precipitation: 0.3%
Maximum Precipitation: 0.5 in
Average Precipitation: 0.0 in
Cloud Base Height Information
Average Cloud Base Height: 12966.10 ft
Minimum Cloud Base Height: 1150.19 ft
Percentage of Days Without Clouds: 46.8 %
It’s also important to look at the variation of temperature and wind distribution throught a typical day, which can be easily done by running the next code cells:
[6]:
env_analysis.plot_average_temperature_along_day()
We can also take a look at sustained surface wind and wind gust plots!
[7]:
env_analysis.plot_average_surface10m_wind_speed_along_day(SAcup_wind_constraints=True)
[8]:
env_analysis.plot_average_sustained_surface100m_wind_speed_along_day()
[9]:
env_analysis.animate_wind_gust_distribution_over_average_day()
[9]:
[10]:
env_analysis.plot_wind_gust_distribution_over_average_day()
The next cell will plot wind gust distribution summarized by every date and every hour available in the source file
[11]:
env_analysis.plot_wind_gust_distribution()
Wind Roses¶
Finally, we can use the previous informations to generate a graphic known as Wind Rose. A Wind Rose is commonly used by meteorologists to identify how wind speed and direction are typically distributed at a particular location. Using a polar coordinate system of gridding, the frequency of winds over a time period is plotted by wind direction, with color bands showing wind speed ranges. These color bands follow the Beaufort wind force scale. The direction of the longest spoke shows the wind direction with the greatest frequency.
Note: Wind Roses plot wind direction, which means the direction from which the wind is blowing, not to be confused with wind heading, which is the direction to which the wind blows.
[12]:
env_analysis.animate_average_wind_rose(filename="wind_rose.gif")
[13]:
env_analysis.plot_average_day_wind_rose_all_hours()
[14]:
# In case you need to plot only a singe windrose with regards to a specific hour
# env_analysis.plot_average_day_wind_rose_specific_hour() #TODO: example input format of specific hour
Pressure Level Analysis¶
At this section, we guide our analysis through pressure profile data available on our dataset. It means we are now stopping to see just surface information and start better understaand how the meteorology varies while the altitude increases.
Average wind profiles¶
Moreover, we can see an animation of how the wind speed profile varies during an average day at Spaceport Location:
[15]:
env_analysis.animate_wind_profile_over_average_day(SAcup_altitude_constraints=True)
[15]:
[16]:
env_analysis.plot_wind_profile_over_average_day(SAcup_altitude_constraints=True)
Finally, the average wind speed and pressure profile can be summarized by every date end hour available in our source file:
[17]:
env_analysis.plot_average_wind_speed_profile(SAcup_altitude_constraints=True)
<module 'matplotlib.pyplot' from 'c:\\Users\\ghceo\\Miniconda3\\envs\\rocketpy_dev\\lib\\site-packages\\matplotlib\\pyplot.py'>
[18]:
env_analysis.plot_average_pressure_profile(SAcup_altitude_constraints=True)
<module 'matplotlib.pyplot' from 'c:\\Users\\ghceo\\Miniconda3\\envs\\rocketpy_dev\\lib\\site-packages\\matplotlib\\pyplot.py'>
Going further with your analysis¶
The best part of using Python is the flexibility, therefore it’s important to highlight that other different experimental and data analysis can be done using EnvironmentAnalysis. For instance, you could ask yourself about historical cloud celling height or any other information available in the source file. Here are some different analysis we could mention:
totalPrecipitation
cloudBaseHeight
surfacePressure
Temperature profile
In the next code we exemplify an analysis regarding temperature profile
[19]:
altitude_list = np.linspace(*env_analysis.altitude_AGL_range, 100)
temperature_profiles = [
dayDict[hour]["temperature"](altitude_list)
for dayDict in env_analysis.pressureLevelDataDict.values()
for hour in dayDict.keys()
]
env_analysis.average_temperature_profile = np.mean(temperature_profiles, axis=0)
# Plot
plt.figure()
plt.plot(env_analysis.average_temperature_profile, altitude_list, "r", label="$\\mu$")
plt.plot(
np.percentile(temperature_profiles, 50 - 34.1, axis=0),
altitude_list,
"b--",
alpha=1,
label="$\\mu \\pm \\sigma$",
)
plt.plot(
np.percentile(temperature_profiles, 50 + 34.1, axis=0),
altitude_list,
"b--",
alpha=1,
)
plt.plot(
np.percentile(temperature_profiles, 50 - 47.4, axis=0),
altitude_list,
"b--",
alpha=0.5,
label="$\\mu \\pm 2\\sigma$",
)
plt.plot(
np.percentile(temperature_profiles, 50 + 47.7, axis=0),
altitude_list,
"b--",
alpha=0.5,
)
for temperature_profile in temperature_profiles:
plt.plot(temperature_profile, altitude_list, "gray", alpha=0.01)
plt.ylim(altitude_list[0], altitude_list[-1])
plt.xlabel(f"Temperature ({env_analysis.unit_system['temperature']})")
plt.ylabel(f"Altitude ASL ({env_analysis.unit_system['length']})")
plt.title("Average Temperature Profile")
plt.legend()
plt.show()
Exporting this notebook¶
[20]:
!jupyter nbconvert environment_analysis_class_usage.ipynb --to html --output env_analysis_output.html
[NbConvertApp] Converting notebook environment_analysis_class_usage.ipynb to html
[NbConvertApp] Writing 4313418 bytes to env_analysis_output.html