to_as4wrf.Rdto_as4wrf returns a dataframes with columns lat, long, id, pollutants,
local time and GMT time. This dataframe has the proper format to be used with WRF
assimilation system: "Another Asimilation System 4 WRF (AAS4WRF)" as published
by Vera-Vala et al (2016)
to_as4wrf(sdf, nr = 1, dmyhm, tz, crs = 4326, islist)
| sdf | Gridded emissions, which can be a SpatialPolygonsDataFrame, or a list of SpatialPolygonsDataFrame, or a sf object of "POLYGON". The user must enter a list with 36 SpatialPolygonsDataFrame with emissions for the mechanism CBMZ. When there are no emissions available, the SpatialPolygonsDataFrame must contain 0. |
|---|---|
| nr | Number of repetitions of the emissions period |
| dmyhm | String indicating Day Month Year Hour and Minute in the format "d-m-Y H:M" e.g.: "01-05-2014 00:00" It represents the time of the first hour of emissions in Local Time |
| tz | Time zone as required in for function |
| crs | Coordinate reference system, e.g: "+init=crs:4326". Used to transform the coordinates of the output |
| islist | logical value to indicate if sdf is a list or not |
data-frame of gridded emissions g/h
The user must produce a text file with the data-frame resulting of this function. Then, use this file with the NCL script AAS4WRF.ncl
The reference of the emissions assimilation system is Vara-Vela, A.,
Andrade, M. F., Kumar, P., Ynoue, R. Y., and Munoz, A. G.: Impact of
vehicular emissions on the formation of fine particles in the Sao Paulo
Metropolitan Area: a numerical study with the WRF-Chem model, Atmos. Chem.
Phys., 16, 777-797, doi:10.5194/acp-16-777-2016, 2016.
A good website with timezones is http://www.timezoneconverter.com/cgi-bin/tzc
The crs is the same as used by sp package
It returns a dataframe with id,, long, lat, pollutants, time_lt, time_utc
and day-UTC-hour (dutch)
The pollutants for the CBMZ are: e_so2, e_no, e_ald, e_hcho, e_ora2, e_nh3
e_hc3, e_hc5, e_hc8, e_eth, e_co, e_ol2, e_olt, e_oli, e_tol, e_xyl, e_ket
e_csl, e_iso, e_no2, e_ch3oh, e_c2h5oh, e_pm25i, e_pm25j, e_so4i, e_so4j
e_no3i, e_no3j, e_orgi, e_orgj, e_eci, e_ecj, e_so4c, e_no3c, e_orgc, e_ecc
{ data(gCO) df <- to_as4wrf(sdf = gCO, dmyhm = "29-04-2018 00:00", tz = "America/Sao_Paulo") head(df) df2 <- to_as4wrf(sdf = list(co = gCO, pm = gCO), dmyhm = "29-04-2018 00:00", tz = "America/Sao_Paulo") head(df2) }#> id long lat co pm time_lt time_utc dutch #> 1 1 -46.80660 -23.62 8391.282 8391.282 2018-04-29 2018-04-29 03:00:00 2900 #> 2 2 -46.80172 -23.62 8340.653 8340.653 2018-04-29 2018-04-29 03:00:00 2900 #> 3 3 -46.79684 -23.62 8549.459 8549.459 2018-04-29 2018-04-29 03:00:00 2900 #> 4 4 -46.79196 -23.62 9055.417 9055.417 2018-04-29 2018-04-29 03:00:00 2900 #> 5 5 -46.78708 -23.62 8378.953 8378.953 2018-04-29 2018-04-29 03:00:00 2900 #> 6 6 -46.78220 -23.62 0.000 0.000 2018-04-29 2018-04-29 03:00:00 2900