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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- date: "April 20, 2020" -->
## wrftools
This package does 3 things
1. help you to define domains
2. extract data as points of spatial features and bricks
3. returns st_bbox from wrf files
I'm not an expert in WRF but i need to use it. One of the very frustating and
tricky part was to make a correct namelist.wps. Hours and hours trying to
get the perfect domains. This functions solves this part while providing
a dynamic maps for actually viewing where are your domains.
## Install
```{r eval = FALSE}
remotes::install_github("atmoschem/wrftools")
```
## wrf_domains (better for low latitude regions)
wrf_domains returns a list of:
1. the map
2. the sf object
The arguments of wrf_domains are the same arguments of namelist.wps.
It include default values but you can change them accordingly.
```{r, eval = F}
library(wrftools)
a <- wrf_domains()
a[[1]] # leaflet
```
## xtractor
(inspired in NCL from DCA/IAG/USP)
Which returns data.frame ready for ggplot2. The class of Time is POSIXct
*points must have a column named "Stations"*
```{r eval = FALSE}
data(cetesb)
cetesb <- cetesb[!is.na(cetesb$Station), "Station"]
#use your wrfout
wrf <- "wrfoA.nc"
t2 = c("T2", "o3", "no", "no2", "PM10", "PM2_5_DRY")
df <- xtractor(atmos = wrf, vars = t2, points = cetesb)
head(df)
# Simple feature collection with 6 features and 31 fields
# geometry type: POINT
# dimension: XY
# bbox: xmin: -47.05721 ymin: -22.90252 xmax: -47.05721 ymax: -22.90252
# epsg (SRID): 4326
# proj4string: +proj=longlat +datum=WGS84 +no_defs
# Station o3 T2 co no no2
# 1 Campinas-Centro 0.0136891948 291.5070 0.6858493 1.186951e-03 0.017522896
# 2 Campinas-Centro 0.0016979938 291.0912 0.2327648 1.000002e-06 0.010418361
# 3 Campinas-Centro 0.0005303844 290.4462 0.1234838 1.000000e-06 0.006545659
# 4 Campinas-Centro 0.0005190288 290.2115 0.1193006 1.000000e-06 0.006521403
# 5 Campinas-Centro 0.0007871886 289.7922 0.1592883 1.000001e-06 0.008744552
# 6 Campinas-Centro 0.0007235801 289.3625 0.1483000 1.000000e-06 0.008334729
# U10 V10 o3_nei T2_nei co_nei no_nei no2_nei
# 1 -3.828670 1.6248466 0.0134919608 291.6059 0.6849796 7.290264e-04 0.017353176
# 2 -3.457098 1.3129230 0.0020016744 291.1794 0.2680614 1.000003e-06 0.011742138
# 3 -3.317390 1.3360039 0.0006680896 290.6286 0.1547405 1.000001e-06 0.008042991
# 4 -3.180607 0.5178746 0.0006177527 290.2608 0.1540482 1.000000e-06 0.008429846
# 5 -2.915890 0.1141155 0.0008107356 289.7930 0.1822766 1.000001e-06 0.010076069
# 6 -2.709186 -0.2479147 0.0008365429 289.3382 0.1891523 1.000000e-06 0.010676160
# U10_nei V10_nei o3_mean T2_mean co_mean no_mean no2_mean
# 1 -3.785948 1.6207790 0.0134919608 291.6059 0.6849796 7.290264e-04 0.017353176
# 2 -3.376240 1.2453150 0.0020016744 291.1794 0.2680614 1.000003e-06 0.011742138
# 3 -3.203223 1.1476679 0.0006680896 290.6286 0.1547405 1.000001e-06 0.008042991
# 4 -3.003846 0.5310050 0.0006177527 290.2608 0.1540482 1.000000e-06 0.008429846
# 5 -2.734402 0.1705305 0.0008107356 289.7930 0.1822766 1.000001e-06 0.010076069
# 6 -2.506655 -0.1514282 0.0008365429 289.3382 0.1891523 1.000000e-06 0.010676160
# U10_mean V10_mean o3_sd T2_sd co_sd no_sd no2_sd
# 1 -3.785948 1.6207790 8.958741e-04 NA 0.001191089 4.948589e-04 0.0001534891
# 2 -3.376240 1.2453150 2.975019e-04 NA 0.029171536 2.649172e-12 0.0011591850
# 3 -3.203223 1.1476679 1.706330e-04 NA 0.036130629 4.944615e-13 0.0017461727
# 4 -3.003846 0.5310050 1.354053e-04 NA 0.042510566 3.130691e-13 0.0023126512
# 5 -2.734402 0.1705305 5.089485e-05 NA 0.028024180 1.430528e-13 0.0016047246
# 6 -2.506655 -0.1514282 1.193552e-04 NA 0.043639222 2.625485e-13 0.0025344522
# U10_sd V10_sd Time geometry
# 1 NA NA 2018-04-19 00:00:00 POINT (-47.05721 -22.90252)
# 2 NA NA 2018-04-19 01:00:00 POINT (-47.05721 -22.90252)
# 3 NA NA 2018-04-19 02:00:00 POINT (-47.05721 -22.90252)
# 4 NA NA 2018-04-19 03:00:00 POINT (-47.05721 -22.90252)
# 5 NA NA 2018-04-19 04:00:00 POINT (-47.05721 -22.90252)
# 6 NA NA 2018-04-19 05:00:00 POINT (-47.05721 -22.90252)
# LT
# 1 2018-04-18 21:00:00
# 2 2018-04-18 22:00:00
# 3 2018-04-18 23:00:00
# 4 2018-04-19 00:00:00
# 5 2018-04-19 01:00:00
# 6 2018-04-19 02:00:00
```
and raster bricks
```{r eval = FALSE}
library(cptcity)
r <- xtractor(atmos = wrf, vars = t2, points = cetesb[1, ], return_list = T)
# A list with raster and data.frame
sp::spplot(r$raster[[2]], "layer.60",
scales = list(draw = T), col.regions = cpt(1224))
```
## Contributing
If you see any errors and know how to improve the function, or add new functions
write an issue and a pull request, please. I will be ver grateful, for sure,
100%.