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https://github.com/bczernecki/thundeR

thundeR - Rapid computation and visualisation of convective parameters from rawinsonde and NWP data
https://github.com/bczernecki/thundeR

cape cin convective-parameters download-sounding hodograph rawinsonde severe-weather thunder tornado

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thundeR - Rapid computation and visualisation of convective parameters from rawinsonde and NWP data

Host: GitHub
URL: https://github.com/bczernecki/thundeR
Owner: bczernecki
License: mit
Created: 2021-02-07T10:51:33.000Z (almost 4 years ago)
Default Branch: master
Last Pushed: 2024-04-11T21:56:21.000Z (7 months ago)
Last Synced: 2024-04-14T07:12:37.494Z (7 months ago)
Topics: cape, cin, convective-parameters, download-sounding, hodograph, rawinsonde, severe-weather, thunder, tornado
Language: R
Homepage: https://bczernecki.github.io/thundeR/
Size: 42.3 MB
Stars: 39
Watchers: 5
Forks: 7
Open Issues: 4
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Citation: CITATION.cff

Awesome Lists containing this project

open-sustainable-technology - thundeR - Rapid computation and visualisation of convective parameters from rawinsonde and Numerical weather prediction data. (Atmosphere / Meteorological Observation and Forecast)

README

# thundeR

**Rapid computation and visualisation of convective parameters from
rawinsonde and numerical weather prediction data**

[![R-CMD-check](https://github.com/bczernecki/thunder/workflows/R-CMD-check/badge.svg)](https://github.com/bczernecki/thunder/actions)
[![Codecov test
coverage](https://codecov.io/gh/bczernecki/thunder/branch/master/graph/badge.svg)](https://app.codecov.io/gh/bczernecki/thunder?branch=master)
[![CRAN
status](https://www.r-pkg.org/badges/version/thunder)](https://cran.r-project.org/package=thunder)
[![CRAN RStudio mirror
downloads](http://cranlogs.r-pkg.org/badges/thunder)](https://cran.r-project.org/package=thunder)
[![](http://cranlogs.r-pkg.org/badges/grand-total/thunder?color=brightgreen)](https://cran.r-project.org/package=thunder)

**`thundeR`** is a freeware R package 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

### Installation

The stable version can be installed from the CRAN repository:

``` r
install.packages("thunder")
```

The development version can be installed directly from the github
repository:

``` r
remotes::install_github("bczernecki/thunder")
```

### Usage

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

``` r
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)
```

![](https://raw.githubusercontent.com/bczernecki/thundeR/master/inst/figures/Vienna.png)

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

``` r
profile = get_sounding(wmo_id = 72562, yy = 1999, mm = 7, dd = 3, hh = 0)

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)
```
![](https://raw.githubusercontent.com/bczernecki/thundeR/master/inst/figures/NorthPlatte.png)

#### Compute convective parameters based on a sample vertical profile data:

``` r
library("thunder")

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)

# MU_CAPE
# 2269.9257
# MU_03km_CAPE
# 575.6293
# MU_LFC_HGT
# 730.0000
# MU_WMAX
# 67.3784
# MU_MIXR
# 14.8759
# MU_CIN_500
# 0.0000
# SB_CAPE_M10
# 998.1443
# SB_HGL_CAPE
# 1616.5384
# SB_EL_HGT
# 8300.0000
# SB_EL_TEMP
# -28.8000
# ML_CAPE
# 1646.0639
# ML_03km_CAPE
# 422.4290
# ML_LFC_HGT
# 975.0000
# ML_WMAX
# 57.3771
# ML_MIXR
# 13.0487
# LR_03km
# -9.0476
# LR_26km
# -5.0000
# LR_500700hPa
# -4.2857
# FRZG_wetbulb_HGT
# 2275.0000
# Delta_thetae_min04km
# 28.8346
# Cold_Pool_Strength
# 12.6322
# RH_01km
# 0.7291
# RH_36km
# 0.4436
# BS_02km
# 8.7821
# BS_36km
# 9.3693
# BS_EFF_MU
# 14.2232
# BS_1km_to_M10
# 13.6499
# BS_ML_LFC_to_M10
# 13.6864
# BS_HGL_to_SM
# 4.8934
# MW_01km
# 2.4251
# MW_13km
# 6.8389
# SRH_1km_RM
# 39.6346
# SRH_250m_LM
# 3.5518
# SRH_36km_LM
# -24.3790
# SV_500m_LM
# 0.0010
# MW_SR_01km_RM
# 10.1501
# MW_SR_03km_LM
# 8.4579
# MW_SR_VM_500m_LM
# 13.7647
# SV_FRA_01km_RM
# 0.9871
# SV_FRA_03km_LM
# -0.2862
# Bunkers_LM_M
# 13.1825
# Corfidi_downwind_M
# 14.6982
# Showalter_Index
# 3.7501
# STP_new
# 0.2005
# SCP_new
# 4.9243
# HSI
# 1.7159
# ML_WMAXSHEAR
# 1033.1051
# EHI_500m
# 0.2798
# EHI_01km_LM
# 0.1987
# SHERBS3_v2
# 0.8642
# TIP
# 2.4356
``` MU_CAPE_M10 MU_CAPE_M10_PT MU_02km_CAPE 998.1443 313.0747 247.9794 MU_HGL_CAPE MU_CIN MU_LCL_HGT 1616.5384 0.0000 730.0000 MU_EL_HGT MU_LI MU_LI_M10 8300.0000 -10.1119 -10.8539 MU_EL_TEMP MU_LCL_TEMP MU_LFC_TEMP -28.8000 17.7000 17.7000 MU_CAPE_500 MU_CAPE_500_M10 MU_CAPE_500_M10_PT 1076.0322 389.3651 137.0814 MU_LI_500 MU_LI_500_M10 SB_CAPE -5.0417 -6.2346 2269.9257 SB_CAPE_M10_PT SB_02km_CAPE SB_03km_CAPE 313.0747 247.9794 575.6293 SB_CIN SB_LCL_HGT SB_LFC_HGT 0.0000 730.0000 730.0000 SB_LI SB_LI_M10 SB_WMAX -10.1119 -10.8539 67.3784 SB_LCL_TEMP SB_LFC_TEMP SB_MIXR 17.7000 17.7000 14.8759 ML_CAPE_M10 ML_CAPE_M10_PT ML_02km_CAPE 670.1001 225.2816 164.0798 ML_HGL_CAPE ML_CIN ML_LCL_HGT 1250.0221 0.0000 975.0000 ML_EL_HGT ML_LI ML_LI_M10 7900.0000 -7.6203 -8.5845 ML_EL_TEMP ML_LCL_TEMP ML_LFC_TEMP -26.4000 15.2500 15.2500 LR_0500m LR_01km LR_02km -10.0000 -10.0000 -10.0000 LR_04km LR_06km LR_16km -7.8571 -6.6667 -6.0000 LR_24km LR_36km LR_26km_MAX -5.7672 -4.2857 -5.7143 LR_500800hPa LR_600800hPa FRZG_HGT -5.1807 -5.8333 2500.0000 HGT_max_thetae_03km HGT_min_thetae_04km Delta_thetae 0.0000 3750.0000 28.0698 Thetae_01km Thetae_02km DCAPE 330.5323 323.6191 598.3100 Wind_Index PRCP_WATER Moisture_Flux_02km 33.9064 27.1046 30.4255 RH_02km RH_14km RH_25km 0.7197 0.6452 0.5550 RH_HGL BS_0500m BS_01km 0.4603 1.9172 3.8344 BS_03km BS_06km BS_08km 12.6560 18.0055 17.4077 BS_26km BS_16km BS_18km 13.3304 16.6478 20.2791 BS_EFF_SB BS_EFF_ML BS_SFC_to_M10 14.2232 13.8968 15.5104 BS_2km_to_M10 BS_MU_LFC_to_M10 BS_SB_LFC_to_M10 9.8830 14.0737 14.0737 BS_MW02_to_SM BS_MW02_to_RM BS_MW02_to_LM 7.3040 10.1410 10.7870 BS_HGL_to_RM BS_HGL_to_LM MW_0500m 7.7860 9.9885 2.3086 MW_02km MW_03km MW_06km 3.3476 4.8003 7.8107 SRH_100m_RM SRH_250m_RM SRH_500m_RM 4.2535 10.0537 19.7206 SRH_3km_RM SRH_36km_RM SRH_100m_LM 152.5219 236.5901 1.5027 SRH_500m_LM SRH_1km_LM SRH_3km_LM 6.9670 14.0023 -13.1308 SV_500m_RM SV_01km_RM SV_03km_RM 0.0039 0.0039 0.0048 SV_01km_LM SV_03km_LM MW_SR_500m_RM 0.0011 -0.0014 10.0863 MW_SR_03km_RM MW_SR_500m_LM MW_SR_01km_LM 9.5359 13.7821 12.8585 MW_SR_VM_500m_RM MW_SR_VM_01km_RM MW_SR_VM_03km_RM 10.1078 10.2253 10.5358 MW_SR_VM_01km_LM MW_SR_VM_03km_LM SV_FRA_500m_RM 12.8371 8.8342 0.9982 SV_FRA_03km_RM SV_FRA_500m_LM SV_FRA_01km_LM 0.9560 0.2592 0.2800 Bunkers_RM_A Bunkers_RM_M Bunkers_LM_A 209.4046 7.7933 122.0585 Bunkers_MW_A Bunkers_MW_M Corfidi_downwind_A 151.9494 7.8107 218.6955 Corfidi_upwind_A Corfidi_upwind_M K_Index 231.3283 9.1794 24.3548 TotalTotals_Index SWEAT_Index STP_fix 44.3548 106.4168 0.3600 STP_fix_LM STP_new_LM SCP_fix 0.1272 0.0708 6.2338 SCP_fix_LM SCP_new_LM SHIP -0.5367 -0.4239 0.6287 DCP MU_WMAXSHEAR SB_WMAXSHEAR 1.1507 1213.1848 1213.1848 MU_EFF_WMAXSHEAR SB_EFF_WMAXSHEAR ML_EFF_WMAXSHEAR 958.3359 958.3359 797.3548 EHI_01km EHI_03km EHI_500m_LM 0.5623 2.1638 0.0988 EHI_03km_LM SHERBS3 SHERBE -0.1863 0.6482 0.7015 SHERBE_v2 DEI DEI_eff 0.9353 1.5198 1.1885

#### Hodograph example:

Download sounding and draw hodograph:

``` r
data("northplatte")
sounding_hodograph(ws = northplatte$ws, wd = northplatte$wd, altitude = northplatte$altitude, max_speed = 38)
title("North Platte - 03 July 1999, 00:00 UTC")
```

![](https://raw.githubusercontent.com/bczernecki/thundeR/master/inst/figures/hodograph.png)

#### Perform sounding computations using Python with rpy2:

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:

``` bash
pip install pandas
pip install rpy2
```

Launch `thunder` under Python with `rpy2`:

``` py
# 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', ...]
```

#### Accuracy tables for `sounding_compute()`

The interpolation algorithm used in the `sounding_compute()` function
impacts accuracy of parameters such as CAPE or CIN and the performance
of the script. The valid options for the `accuracy` parameter are 1, 2
or 3:

**accuracy = 1** - High performance but low accuracy. Dedicated for
large dataset when output data needs to be quickly available (e.g.
operational numerical weather models). This option is around 20 times
faster than high accuracy (3) setting. Interpolation is peformed for 60
levels (m AGL):

``` r
c(0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1600, 1800, 2000, 2200, 2400, 2600, 2800, 3000, 3200, 3400, 3600, 3800, 4000, 4200, 4400, 4600, 4800, 5000, 5200, 5400, 5600, 5800, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500, 10000, 10500, 11000, 11500, 12000, 12500, 13000, 13500, 14000, 15000, 16000, 17000, 18000, 19000, 20000)
```

**accuracy = 2** - Compromise between script performance and accuracy.
Recommended for efficient processing of large numerical weather
prediction datasets such as meteorological reanalyses for research
studies. This option is around 10 times faster than high accuracy (3)
setting. Interpolation is peformed for 318 levels (m AGL):

``` r
c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710, 720, 730, 740, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000, 1025, 1050, 1075, 1100, 1125, 1150, 1175, 1200, 1225, 1250, 1275, 1300, 1325, 1350, 1375, 1400, 1425, 1450, 1475, 1500, 1525, 1550, 1575, 1600, 1625, 1650, 1675, 1700, 1725, 1750, 1775, 1800, 1825, 1850, 1875, 1900, 1925, 1950, 1975, 2000, 2025, 2050, 2075, 2100, 2125, 2150, 2175, 2200, 2225, 2250, 2275, 2300, 2325, 2350, 2375, 2400, 2425, 2450, 2475, 2500, 2525, 2550, 2575, 2600, 2625, 2650, 2675, 2700, 2725, 2750, 2775, 2800, 2825, 2850, 2875, 2900, 2925, 2950, 2975, 3000, 3050, 3100, 3150, 3200, 3250, 3300, 3350, 3400, 3450, 3500, 3550, 3600, 3650, 3700, 3750, 3800, 3850, 3900, 3950, 4000, 4050, 4100, 4150, 4200, 4250, 4300, 4350, 4400, 4450, 4500, 4550, 4600, 4650, 4700, 4750, 4800, 4850, 4900, 4950, 5000, 5050, 5100, 5150, 5200, 5250, 5300, 5350, 5400, 5450, 5500, 5550, 5600, 5650, 5700, 5750, 5800, 5850, 5900, 5950, 6000, 6100, 6200, 6300, 6400, 6500, 6600, 6700, 6800, 6900, 7000, 7100, 7200, 7300, 7400, 7500, 7600, 7700, 7800, 7900, 8000, 8100, 8200, 8300, 8400, 8500, 8600, 8700, 8800, 8900, 9000, 9100, 9200, 9300, 9400, 9500, 9600, 9700, 9800, 9900, 10000, 10100, 10200, 10300, 10400, 10500, 10600, 10700, 10800, 10900, 11000, 11100, 11200, 11300, 11400, 11500, 11600, 11700, 11800, 11900, 12000, 12250, 12500, 12750, 13000, 13250, 13500, 13750, 14000, 14250, 14500, 14750, 15000, 15250, 15500, 15750, 16000, 16250, 16500, 16750, 17000, 17250, 17500, 17750, 18000, 18250, 18500, 18750, 19000, 19250, 19500, 19750, 20000)
```

**accuracy = 3**: High accuracy but low performance setting. Recommended
for analysing individual profiles. Interpolation is performed with 5 m
vertical resolution step up to 20 km AGL (i.e.: `0, 5, 10, ... 20000` m
AGL)

### 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 clip input data
for a maximum of 16-18 km AGL or lower.
- Values of parameters will be different for different accuracy
settings.

### Developers

**thundeR** package has been developed by atmospheric scientists, each
having an equal contribution (listed in alphabetical order):

- Bartosz Czernecki (Adam Mickiewicz University in Poznań, Poland)

- Piotr Szuster (Cracow University of Technology, Poland)

- Mateusz Taszarek (CIMMS/NSSL in Norman, Oklahoma, United States)

### Contributions

[Feel free to submit issues and enhancement
requests.](https://github.com/bczernecki/thunder/issues)

### Acknowledgments:
Development of the thundeR package has been supported by the Polish National Science Center (Grant No. 2020/39/D/ST10/00768)