15
To understand why the nesting ratio of 3 is preferred to the nesting ratio of 2, it is important to understand the following two features of WRF:
1) Grids are Arakawa C-staggered: mass points are at cell centers,
u-velocities are at east-west cell edges, v-velocities are at
north-south cell edges. See Mesinger and Arakawa 1976, Chapter 4
for a good ...
15
I've been a WRF user for almost 5 years now, and contributed code to a recent public release. I am not aware that WPS (WRF Preprocessing System) has such a tool that takes in the grid and point coordinates and returns the appropriate index. However, it is very straightforward to do so yourself. Some suggest using an external library, I think that may be an ...
9
This question might be better suited for GIS stack exchange... but I'll take a shot:
dx and dy are the distance per grid cell in the projected grid units, which is part of your grid definition. ref_lat and ref_lon are part of your projection definition. i_parent_start and j_parent_start are the horizontal grid cell numbers of the parent grid, where your ...
9
My experience with the WRF is in tropical cyclone simulation, but here are some things to consider:
Smaller scale information might already be resolved in your higher resolution initial conditions, improving the spin-up time of effects you are trying to model
I believe the ECMWF resolution has been statistically downscaled from a 0.75 degree dataset, I ...
8
I can't answer why there's a 24x increase, according to the textbook - but it may well be the case for a specific model. In general,
If you double the resolution of a 3D model in each dimension, you are multiplying the number of cells (or elements) in the model by $2^3 = 8$.
Typically the maximum length of the time step that a model may be run at and ...
8
I am posting my code that calculates i_parent_start and j_parent_start. It is in Java and as people are aware Java does not have functions such as minloc or Python's unravel_index. So I divided my program into three steps.
Read in the xlat_m and xlong_m arrays using java netcdf.
Subtracting desired lat and desired lon from each value of the
2-dimensional ...
7
Yes, of course finer input data can lead to better model accuracy, but only if the model's conceptualization is close to the mark. Of the many problems of climatic model misinterpretation, I would emphasize the following:
a) The implied precision of most climatic modelling output is absurd, so take a long, hard and skeptical look at the data in relation to ...
6
Try NCL. It has a package for WRF-related functions, including the bidirectional grid-coordinates conversion routine.
To convert coordinate pairs to grid indices, follow the documentation here:
wrf_user_ll_to_ij.
A backward (i.e. gird to coordinate) routine can be found here:
wrf_user_ij_to_ll.
They are quite handy.
For your scenario, you may load your ...
6
Use (1) Precipitation, as it is the sum of convective and non-convective precipitation, integrate it over one day, voilà.
converting [$kg/(m^2 s)$] (precip):
$$ precip [kg/(m^2 s)] \cdot dt [s] \cdot \frac{1}{ \rho_w [kg/(m^3)]} \cdot 1000 [mm/m] = total\_precip [mm]$$
where $\rho_w$ is the density of water.
Practically the last two factors cancel.
6
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....
6
Your model grid is an Arakawa C grid:
Image from Skamarock et al. A description of the Advanced Research WRF Version 3
It appears the geogrid grid generation uses the 25x25 as the furthest extents of the grid (and this is backed up by the grid maximum needing to be a multiple of the grid ratio +1). As a result, only the u and v variables will see 25 grid ...
5
WRF model development is done in such a way that users can run the model independently before you start adding more complex options to ingest observations. There is even an "ideal" mode that new users can take advantage of to learn how the system works (not for simulating real Earth situations). In "real" mode, there are typically two types of simulations ...
5
I am going to explain my answer based on how the authors from this paper calculate this Vertically integrated moisture flux convergence as a predictor of thunderstorms. This is the equation that is defined in their paper
$$ VIMFC = -\frac{1}{g} \int_{700\,hPa}^{1000\,hPa}\ (\frac{\partial u q}{\partial x}+\frac{\partial v q}{\partial y}) * dp $$
For a ...
5
I finally solved this problem with the following steps:
Download the ECMWF data separately for model level variables (Q,T,U,V and geopotential) and surface variables. In this way, you will get 'pure' GRIB1 and 'pure' GRIB2 files rather than the hybrid type.
Run ungrib twice: for the model levels files, use this Vtable. For the surface file, use the 'old' ...
4
For any of the basic forecast runs (10 day, ensembles, monthly/seasonal forecasts), I do not believe ECMWF data access is general controlled to ECMWF.INT. Generally most data, including anywhere near enough to use in a WRF forecast, is only available for purchase at extreme fees (generally we're talking 5+ figures per year). The only option you might have ...
4
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)
&time_control
run_days = 0,
run_hours ...
4
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 ...
3
The WRF Forum here describes some testing using a Mac, and they report success with 2 setups:
Mac 64-bit running OSX, with Intel chips, g95 gcc version 4.0.3 (g95 0.92!)
Mac 64-bit running OSX, with Intel chips, PGI pgf90 10.3
There is also some additional information on installing ARWpost on a Mac found on the WRF Forum.
Separately, there is a blog ...
3
Dew point gives an indication of the moisture content of air; it is the temperature at which air can no longer hold water vapour.
The following graph shows the relations of dew point to air temperature (dry bulb) for various levels of humidity.
For humidity less than 100 percent, the air temperature is always higher than the dew point temperature and for a ...
3
For soil mosture you can initialize the model with a dataset that provides that information. If you are doing historical retrospective you are likely going to initialize from a reanalysis dataset and if you are doing a forecasting case then you are likely initializing from GFS, RAP, HRRR or some other model.
For the case of initialization from GFS, the ...
2
There is an feature of the -d option that makes the call of ncks redundant. However, it is available since NCO version 4.2.1 (Aug. 2012).
ncrcat -d Time,13,,132,120 wrfout* summary.nc
# -d dim,[min][,[max][,[stride][,[subcycle]]]]
This feature is deeply hidden in the online manual. You find it here: nco.sourceforge.net/nco.html#Subcycle .
Explanation:
13:...
2
Here is my present approach.
All wrfout-files are located in the same path.
Using pynco in python loop to split the spin-up dataframe
import glob
nco = Nco()
for fn in glob.iglob('*.nc'):
filename, extname = os.path.splitext(fn)
output_fn = filename[11:22] + str(int(filename[22:24]) + 12) + extname
nco.ncks(input=fn, output=output_fn, ...
2
A Skew-T/Log-p plot is a vertical profile from a point, and it appears in that code that the point you are plotting the skew-T for is 60.2 latitude, 11.08 longitude. The snippet of code:
def getXY(lon,lat):
x_nr = 0
y_nr = 0
while (lon[x_nr] < lon_focuspoint):
x_nr += 1
while (lat[y_nr] < lat_focuspoint):
y_nr += 1
finds the closest grid ...
2
There is a WRF domain Wizard offered by ESRL. You can download it here. Also, don't make the same mistake I made using Lambert Conformal- keep stand_lon the same as your ref_lon.
2
A good rule of thumb for down-scaling with WRF is not to interpolate down more than 3 or 4 times; with 3 being the standard recommendation. So while 18 km is probably overkill for an outer domain (this is a personal opinion, there are some operational models like HWRF that start at 18km), 2km would definitely be too fine for a parent domain. The main reason ...
2
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
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. ...
2
You can try the R package eixport, with wrf_put
# example
# 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 (...
2
You have got your emissions over a square kilometer. To get to square meters you divide by 1000000 since $1km^2=10^6m^2$.
1
Did you use the right Vtable? That error message is saying that the soil temperature variable is missing/couldn't be read.
1
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:
model ellipsoid,
projection type
projection parameters
measurement unit
EPSG codes refer to complete spatial reference systems ...
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