Introduction to the thundeR package

In thunder: Computation and Visualisation of Atmospheric Convective Parameters

knitr::opts_chunk$set(
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thundeR

thundeR is a freeware R package and collection of functions for rapid computation and visualisation of convective parameters commonly used in the operational forecasting of severe convective storms. Core algorithm is based on C++ code implemented into R language via Rcpp. This solution allows to compute over 200 thermodynamic and kinematic parameters in less than 0.02s per profile and process large datasets such as reanalyses or operational NWP models in a reasonable amount of time. Package has been developed since 2017 by research meteorologists specializing in severe convective storms and is constantly updated with new features.

Online browser:

Online rawinsonde browser of thundeR package is available at http://rawinsonde.com

Main functions:

• sounding_compute() - A core function for calculating convective parameters commonly used in the operational prediction of severe convective storms. Returns a vector of parameters.

• sounding_plot() - Function to plot a composite of Skew-T, hodograph and selected convective parameters on a single layout

• sounding_save()- Auxiliary function tosounding_plot` that plots a composite of Skew-T, hodograph and selected convective parameters on a single layout and exports graphical file.

• get_sounding() - Download rawinsonde measurement from sounding database of the University of Wyoming in a form convenient to use with thundeR package.

Examples

Examples show aplication of selected thundeR package functions

Example 1

Draw Skew-T, hodograph and convective parameters on a single layout and export to png file:

library(thunder)
data("sounding_vienna") # load example dataset (Vienna rawinsonde profile for 23 Aug 2011 12UTC):
pressure = sounding_vienna$pressure # vector of pressure [hPa]
altitude = sounding_vienna$altitude # vector of altitude [meters]
temp = sounding_vienna$temp  # vector of temperature [degree Celsius]
dpt = sounding_vienna$dpt # vector of dew point temperature [degree Celsius]
wd = sounding_vienna$wd # vector of wind direction [azimuth in degrees]
ws = sounding_vienna$ws # vector of wind speed [knots]
sounding_save(filename = "Vienna.png",
              title = "Vienna - 23 August 2011 1200 UTC",
              pressure, altitude, temp, dpt, wd, ws)

Example 2

Download North Platte rawinsonde profile for 03 Jul 1999 00UTC and export to png file:

profile = get_sounding(wmo_id = 72562, yy = 1999, mm = 7, dd = 3, hh = 0)
head(profile) # show first few rows of downloaded dataset
sounding_save(filename = "NorthPlatte.png", title = "North Platte - 03 July 1999 0000 UTC", profile$pressure, profile$altitude, profile$temp, profile$dpt, profile$wd, profile$ws)

Example 3

Compute convective parameters based on a sample vertical profile data:

pressure = c(1000, 855, 700, 500, 300, 100, 10) # pressure [hPa]
altitude = c(0, 1500, 2500, 6000, 8500, 12000, 25000) # altitude [meters]
temp = c(25, 10, 0, -15, -30, -50, -92) # temperature [degree Celsius]
dpt = c(20, 5, -5, -30, -55, -80, -99) # dew point temperature [degree Celsius]
wd = c(0, 90, 135, 180, 270, 350, 0) # wind direction [azimuth in degress]
ws = c(5, 10, 20, 30, 40, 5, 0) # wind speed [knots]
accuracy = 2 # accuracy of computations where 3 = high (slow), 2 = medium (recommended), 1 = low (fast)
sounding_compute(pressure, altitude, temp, dpt, wd, ws, accuracy)

Example 4

Download sounding and draw hodograph:

data("northplatte")
sounding_hodograph(ws = northplatte$ws, wd = northplatte$wd, 
                   altitude = northplatte$altitude,max_speed = 40)
title("North Platte - 03.07.1999, 00:00 UTC")

Example 5 - thunder in Python

It is possible to launch thunder under Python via rpy2 library. Below you can find the minimum reproducible example. Make sure that pandas and rpy2 libraries are available for your Python environment. If not install required python packages with pip install pandas and pip install rpy2

Launch thunder under Python with rpy2 and numpy:

### load required packages
#> from rpy2.robjects.packages import importr
#> from rpy2.robjects import r,pandas2ri
#> import rpy2.robjects as robjects
#> pandas2ri.activate()

### load thunder package (make sure that it was installed in R before)
#> importr('thunder')

### download North Platte sounding 
#> profile = robjects.r['get_sounding'](wmo_id = 72562, yy = 1999, mm = 7, dd = 3,hh = 0)

### compute convective parameters
#> parameters = robjects.r['sounding_compute'](profile['pressure'], profile['altitude'], #> profile['temp'], profile['dpt'], profile['wd'], profile['ws'], accuracy = 2)


### customize output and print all computed variables, e.g. most-unstable CAPE (first element) equals 9413 J/kg

#> print(list(map('{:.2f}'.format, parameters)))
# '9413.29', '233.35', '1713.74', '0.00', '775.00', '775.00', '15500.00', '-16.55', 
# '137.21', '-66.63', '23.98', '23.98', '23.36', '9413.29', '233.35', '1713.74', '0.00', 
# '775.00', '775.00', '15500.00', '-16.55', '137.21', '-66.63', '23.98', '23.98', '23.36',
# '7805.13', '115.22', '1515.81', '-4.35', '950.00', '950.00', '15000.00', '-14.66', 
# '124.94', '-68.41', '22.46', '22.46', '21.17', '-9.57', '-6.68', '-8.80', '-8.68', 
# '-9.06', '-7.70', '4250.00', '3500.00', '0.00', '2866.00', '50.57', '52.93', '1381.81', 
# '308.98', '29.00', '37.59', '87.03', '0.58', '0.40', '0.47', '8.85', '11.21', '13.88', 
# '20.28', '29.33', '6.84', '21.70', '28.32', '28.32', '27.17', '17.06', '12.53', '12.53',
# '11.74', '7.09', '6.08', '7.77', '7.69', '19.89', '62.07', '110.06', '156.48', '6.25', 
# '7.77', '4.26', '-42.78', '284.67', '5.65', '197.60', '14.19', '218.89', '7.77',
# '31.50', '-12.14', '60.40', '677.12', '4.67', '6.10', '29.46', '29.46', '3.86', '12.35',
# '2783.07', '2783.07', '2534.22', '3886.07', '3886.07', '3395.00']

Important notes

• Remember to always input wind speed data in knots.
• Script will always consider first height level as the surface (h = 0), therefore input height data can be as above sea level (ASL) or above ground level (AGL).
• For efficiency purposes it is highly recommended to input data for a maximum of 16-18 km AGL or lower.
• Values of parameters will be different for different accuracy settings.

# cleaning
file.remove("Vienna.png")
file.remove("NorthPlatte.png")
options(old_options)

thunder documentation built on Nov. 11, 2023, 9:06 a.m.