As defined in the AMS glossary, a parameterization is a simplification of one or several processes in a model. I add that a parameterization commonly adds an error and increases the uncertainty in the model results. In most cases, a parameterization is not derived from physical laws but a fit to data.
These simplifications are employed for different reasons....
Disclaimer: I have not looked at your particular files.
You can regrid the data in Python using the NetCDF-4 module. If you want to interpolate vertically, you can use the WRF module. And for horizontal interpolation, you can use the Scipy Interpolation module.
Here is how you would go about this regridding.
Let $(\psi_1,\phi_1)$ be the latitude and ...
I am not sure about your R code or your snippet of data. However, you could do an ncdump on the file and direct the output to text to get it "from the horses mouth" so to speak.
In general, the lat/long export of the WRF grid will not have a linear pattern. WRF uses a spatially projected grid, likely an equal area lambert method or something similar. The ...
I could run model, i just had to change a bit the namelist. I added three parameters:io_form_auxinput5 = 2,auxinput5_inname = 'wrfchemi_d$<domain>$_$<date>$' and frames_per_auxinput5 = 168. (168 hours of emissions and hours of simulation)
run_days = 0,
Well, the variable PBLH should tell you the PBL height. If you want the level that the PBL is, I suggest modifying the Registry.EM_COMMON file.
Perhaps I can refer you to WRF-Python to help you with your calculation. For example, wrf.interplevel might be the function to use to interpolate to the PBL height. If you interpolate to fractions of the PBL height, ...
I would suggest using cdo for your purpose. At least for variables, which values are independent of the grid cell size, one can use cdo.
cdo (Climate Data Operators) is a command line program to process netCDF and GRIB files. It is developed an maintained by Uwe Schulzweida and colleagues at the Max-Planck Institute for Meteorology in Hamburg, Germany. ...
You can try the R package eixport, with wrf_put
# Read the array emissions,
CO <- wrf_get(file = "Path_to_WRFCHEMI", name = "E_CO")
# Change the values, here you should use your data
CO = rnorm(length(CO))
# Inyect your emissions into the wrfchemi
wrf_put(file = "Path_to_WRFCHEMI", name = "E_CO", POL = CO)
How to deal with the speciation (...
Spatial reference systems are coordinate systems that can be geographical coordinate systems defined on a model ellipsoid or projection coordinate systems.
A projection coordinate system is defined by four groups of parameters:
EPSG codes refer to complete spatial reference systems ...
As you have data with different resolution, I would suggest you to convert your data to raster and resample to match the grid cell of your wrf_inputs. Take care of mass conservation.
Then convert your raster to a matrix, data.frame, or spatial feature of 'POLYGON' your emissions
Then create wrf_chemi files
raster your emissions