Models
monetio is capable of opening output from several different models including CMAQ,
HYSPLIT and GEFS-Aerosol. monetio opens all N-Dimensional output in the Dataset.
CMAQ
CMAQ is a 3D photochemical grid model developed at the U.S. EPA to simulate air composition. monetio is able to read the output IOAPI output and format it to be compatible with it’s datastream.
As an example, lets open some CMAQ data from the Hawaiian volcanic eruption in 2018. First we will set the path to the data files
import monetio as mio
cmaqfile = monetio.__path__ + '/../data/aqm.t12z.aconc.ncf'
c = mio.cmaq.open_dataset(cmaqfile)
This will return an Dataset.
print(c)
<xarray.Dataset>
Dimensions: (DATE-TIME: 2, VAR: 41, time: 45, x: 80, y: 52, z: 1)
Coordinates:
latitude (y, x) float32 ...
longitude (y, x) float32 ...
* time (time) datetime64[ns] 2018-05-17T12:00:00 2018-05-17T13:00:00 ...
Dimensions without coordinates: DATE-TIME, VAR, x, y, z
Data variables:
TFLAG (time, VAR, DATE-TIME) int32 dask.array<shape=(45, 41, 2), chunksize=(45, 41, 2)>
O3 (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
NO2 (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
NO (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
NO3 (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
N2O5 (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
HNO3 (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
HONO (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
PNA (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
CO (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
FORM (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
ALD2 (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
PAN (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
NTR (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
XO2N (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
SO2 (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
ASO4I (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
ASO4J (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
ANH4I (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
ANH4J (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
ANO3I (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
ANO3J (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
AORGAI (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
AORGAJ (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
AORGPAI (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
AORGPAJ (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
AORGBI (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
AORGBJ (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
AECI (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
AECJ (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
A25I (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
A25J (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
NUMATKN (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
NUMACC (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
SRFATKN (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
SRFACC (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
AH2OI (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
AH2OJ (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
ACLI (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
ACLJ (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
ANAI (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
ANAJ (time, z, y, x) float32 dask.array<shape=(45, 1, 52, 80), chunksize=(45, 1, 52, 80)>
Attributes:
IOAPI_VERSION: $Id: @(#) ioapi library version 3.1 $ ...
EXEC_ID: ???????????????? ...
FTYPE: 1
CDATE: 2018142
CTIME: 135716
WDATE: 2018142
WTIME: 135716
SDATE: 2018137
STIME: 120000
TSTEP: 10000
NTHIK: 1
NCOLS: 80
NROWS: 52
NLAYS: 1
NVARS: 41
GDTYP: 2
P_ALP: 19.0
P_BET: 21.0
P_GAM: -157.5
XCENT: -157.5
YCENT: 20.53
XORIG: -480000.0
YORIG: -312000.0
XCELL: 12000.0
YCELL: 12000.0
VGTYP: 1
VGTOP: 200.0
VGLVLS: [1. 0.089794]
GDNAM: AQF_HI
UPNAM: OPACONC
VAR-LIST: O3 NO2 NO NO3 ...
FILEDESC: Concentration file output ...
HISTORY:
All monetio xarray objects have common coordinate names (latitude and longitude) and dimension names (time, x, y, z). It retains the original attributes of the file and variable names. monetio will precalculate some variables while loading the data in a lazy fashion, i.e. it will not actually do the computation (not stored in memory) until needed:
pm25 = c.PM25
where pm25 is a DataArray as it is a single variable.
Prep-Chem-Sources
A reader and writer was built into
HYSPLIT
An example with HYSPLIT run for 2019 eruption of Reventador Volcano.
import monetio as mio
hysplitfile = monetio.__path__ + '/../data/cdump.bin
hxr = mio.hysplit.open_dataset(hysplitfile)
This will return an Dataset.
print(hxr)
<xarray.Dataset>
Dimensions: (time: 14, x: 47, y: 174, z: 5)
Coordinates:
* time (time) datetime64[ns] 2019-02-25T17:00:00 ... 2019-02-26T06:00:00
* x (x) int64 8987 8988 8989 8990 8991 ... 9029 9030 9031 9032 9033
* y (y) int64 4332 4333 4334 4335 4336 ... 4501 4502 4503 4504 4505
* z (z) int64 2000 3000 4000 5000 6000
longitude (y, x) float64 -77.85 -77.84 -77.83 ... -77.41 -77.4 -77.39
latitude (y, x) float64 -1.774 -1.774 -1.774 ... -0.04456 -0.04456
Data variables:
p006 (time, z, y, x) float32 nan nan nan nan nan ... nan nan nan nan
par2 (time, z, y, x) float32 nan nan nan nan nan ... nan nan nan nan
par3 (time, z, y, x) float32 nan nan nan nan nan ... nan nan nan nan
par4 (time, z, y, x) float32 nan nan nan nan nan ... nan nan nan nan
Attributes:
Starting Locations: [(-0.077, -77.656)]
Source Date: [datetime.datetime(2019, 2, 25, 16, 30)]
Meteorological Model ID: ERA5
Number Start Locations: 2
Number of Levels: 5
Level top heights (m): [2000 3000 4000 5000 6000]
Number of Species: 4
Sampling Time: 1:00:00
sample time hours: 1.0
Species ID: ['p006', 'par2', 'par3', 'par4']
Concentration Grid: {'Number Lat Points': 9001, 'Number Lon Poi...'}
Coordinate time description: Beginning of sampling time
If there are multiple species in the file
hxr2 = hysplit.add_species(hxr)
returns an ‘~xarray.DataArray’ which is total concentration.
To combine multiple cdump files, use the following command.
file1 = (monetio.__path__ + '/../data/cdump.bin, 'S1', 'ERA5e0')
file2 = (monetio.__path__ + '/../data/cdump2.bin, 'S1', 'ERA5e1')
d1 = datetime.datetime(2019,2,25,18)
d2 = datetime.datetime(2019,2,25,20)
hda = hysplit.combine_dataset([file1,file2], drange=[d1,d2])
returns an ‘~xarray.DataArray’ which is total concentration over all species. The data-array has dimensions of x,y,z,time, source, ens. The ens dimension tags what ensemble member the run belongs to. The source dimension tags which source term the run used.
print(hda)
<xarray.DataArray (source: 1, ens: 2, time: 2, z: 3, y: 38, x: 26)>
array([[[[[[0., ..., 0.],
...,
[0., ..., 0.]],
...,
...,
[[0., ..., 0.],
...,
[0., ..., 0.]]]]]], dtype=float32)
Coordinates:
* y (y) int64 4458 4459 4460 4461 4462 ... 4491 4492 4493 4494 4495
* x (x) int64 9000 9001 9002 9003 9004 ... 9021 9022 9023 9024 9025
* time (time) datetime64[ns] 2019-02-25T18:00:00 2019-02-25T19:00:00
* z (z) int64 3000 4000 5000
* ens (ens) <U6 'Era5e0' 'Era5e1'
* source (source) <U2 'S1'
latitude (y, x) float64 -0.5145 -0.5145 -0.5145 ... -0.1445 -0.1445
longitude (y, x) float64 -77.72 -77.71 -77.7 ... -77.49 -77.48 -77.47
Attributes:
sample time hours: 1.0
To calcluate mass loading
massload = hypslit.hysp_massload(hxr, threshold=0, mult=1e10, species=None)
All points with value below or equal to threshold will be returned as 0. mult is a multiplicative factor applied before the thresholding. species can be a list of values from the “Species ID” attribute. If it is None then all species will be used.
To find top heights
massload = hypslit.hysp_hysp_heights(hxr, threshold=0, height_mult=1/1000.0, mult=1e10, mass_load=False)
returns xarray DataArray which gives top height of each level which contains mass loading higher than the given threshold value. mult is a mutiplicative factor applied before thresholding. height_mult is a multiplicative factor used to convert heights from meters to some other unit. In this example heights are converted to km. mass_load is a boolean which indicates whether the height should be determined from the mass loading value (True) or the concentration value (False).