ISH and UFS-CHEM
Our first example will demonstrate the basics available in MELODIES MONET to compare UFS-CHEM results against ISH surface observations for temperature, wind speed, and dewpoint.
ISH is a particularly useful for model evaluations because it is a global dataset. As you will see in the YAML file, we can create plots globally, for individual countries, and states. NOTE: MELODIES-MONET examples that use ISH will take longer to finish running due to the larger amount of data.
First, we import the melodies_monet.driver module.
from melodies_monet import driver
Analysis driver class
Now, lets create an instance of the analysis driver class, melodies_monet.driver.analysis.
It consists of these main parts:
model instances
observation instances
a paired instance of both
an = driver.analysis()
Initially, most of our analysis object’s attributes
are set to None, though some have meaningful defaults:
an
analysis(
control='control.yaml',
control_dict=None,
models={},
obs={},
paired={},
start_time=None,
end_time=None,
time_intervals=None,
download_maps=True,
output_dir=None,
output_dir_save=None,
output_dir_read=None,
debug=False,
save=None,
read=None,
regrid=False,
)
Control file
We set the YAML control file and begin by reading the file.
Note
Check out the Description of All YAML Options for info on how to create and modify these files.
an.control = "control_ish_ufschem-example.yaml"
an.read_control()
an.control_dict
Note: This is the complete file that was loaded.
control_wrfchem_mech-0905_2.yaml
1# General Description:
2# - Any key that is specific for a plot type will begin with `ts` for timeseries, `ty` for taylor.
3# - Some keys/groups are optional.
4# - For now, all plots except time series average over the analysis window.
5# - Setting axis values
6# - If set_axis = True in data_proc section of each plot_grp,
7# the yaxis for the plot will be set based on the values
8# specified in the obs section for each variable.
9# - If set_axis is set to False, then defaults will be used.
10# - 'vmin_plot' and 'vmax_plot' are needed for
11# 'timeseries', 'spatial_overlay', and 'boxplot'.
12# - 'vdiff_plot' is needed for 'spatial_bias' plots
13# - 'ty_scale' is needed for 'taylor' plots.
14# - 'nlevels' or the number of levels used in the contour plot can also optionally be provided for spatial_overlay plot.
15# - If set_axis = True and the proper limits are not provided in the obs section,
16# a warning will print, and the plot will be created using the default limits.
17analysis:
18 start_time: "2019-09-05-06:00:00" # UTC
19 end_time: "2019-09-06-06:00:00" # UTC
20 output_dir: ./output/airnow_wrfchem # relative to the program using this control file
21 # Currently, the directory must exist or plot saving will error and fail.
22 debug: True
23
24model:
25 RACM_ESRL: # model label
26 files: example:wrfchem:racm_esrl
27 mod_type: "wrfchem"
28 mod_kwargs:
29 mech: "racm_esrl_vcp"
30 surf_only_nc: True # specify that we have only one vertical level; WRF-Chem specific
31 radius_of_influence: 12000 # meters
32 mapping: # of _model_ species name to _obs_ species name
33 airnow: # specifically for the obs labeled 'airnow'
34 PM2_5_DRY: "PM2.5"
35 o3: "OZONE"
36 projection: ~
37 plot_kwargs: # optional
38 color: "magenta"
39 marker: "s"
40 linestyle: "-"
41 RACM_ESRL_VCP:
42 files: example:wrfchem:racm_esrl_vcp
43 mod_type: "wrfchem"
44 mod_kwargs:
45 mech: "racm_esrl_vcp"
46 surf_only_nc: True
47 radius_of_influence: 12000
48 mapping:
49 airnow:
50 PM2_5_DRY: "PM2.5"
51 o3: "OZONE"
52 projection: ~
53 plot_kwargs:
54 color: "gold"
55 marker: "o"
56 linestyle: "-"
57
58obs:
59 airnow: # obs label
60 use_airnow: True
61 filename: example:airnow:2019-09
62 obs_type: pt_sfc
63 variables: # optional
64 OZONE:
65 unit_scale: 1
66 # ^ optional; Scaling factor
67 unit_scale_method: "*"
68 # ^ optional; Multiply = '*' , Add = '+', subtract = '-', divide = '/'
69 nan_value: -1.0
70 # ^ optional; When loading data, set this value to NaN
71 ylabel_plot: "Ozone (ppbv)"
72 # optional; set ylabel in order to include units and/or other info
73 vmin_plot: 15.0
74 # ^ optional; Min for y-axis during plotting.
75 # To apply to a plot, change restrict_yaxis = True.
76 vmax_plot: 55.0
77 # ^ optional; Max for y-axis during plotting.
78 # To apply to a plot, change restrict_yaxis = True.
79 vdiff_plot: 20.0
80 # ^ optional; +/- range to use in bias plots.
81 # To apply to a plot, change restrict_yaxis = True.
82 nlevels_plot: 21
83 # ^ optional; number of levels used in colorbar for contourf plot.
84 PM2.5:
85 unit_scale: 1
86 unit_scale_method: "*"
87 # obs_min: 0
88 # ^ optional; set all values less than this value to NaN
89 # obs_max: 100
90 # ^ optional; set all values greater than this value to NaN
91 nan_value: -1.0
92 # Note: The obs_min, obs_max, and nan_values are set to NaN first
93 # and then the unit conversion is applied.
94 ylabel_plot: "PM2.5 (ug/m3)"
95 ty_scale: 2.0 # optional; `ty_` indicates for Taylor diagram plot
96 vmin_plot: 0.0
97 vmax_plot: 22.0
98 vdiff_plot: 15.0
99 nlevels_plot: 23
100
101plots:
102 plot_grp1:
103 type: "timeseries" # plot type
104 fig_kwargs: # optional; to define figure options
105 figsize: [12, 6] # figure size (width, height) in inches
106 default_plot_kwargs:
107 # ^ optional; Define defaults for all plots.
108 # Important: Model kwargs overwrite these.
109 linewidth: 2.0
110 markersize: 10.
111 text_kwargs: # optional
112 fontsize: 24.
113 domain_type: ["all", "state_name", "epa_region"]
114 # ^ List of domain types: 'all' or any domain in obs file.
115 # (e.g., airnow: epa_region, state_name, siteid, etc.)
116 domain_name: ["CONUS", "CA", "R9"]
117 # ^ List of domain names. If domain_type = all,
118 # the domain name is used in the plot title.
119 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
120 # ^ make this a list of pairs in obs_model
121 # where the obs is the obs label and model is the model_label
122 data_proc: # optional??
123 rem_obs_nan: True
124 # ^ True: Remove all points where model or obs variable is NaN.
125 # False: Remove only points where model variable is NaN.
126 ts_select_time: "time_local" # `ts_` indicates this is time series plot-specific
127 # ^ Time used for avg and plotting
128 # Options: 'time' for UTC or 'time_local'
129 ts_avg_window: "h"
130 # ^ Options: None for no averaging, pandas resample rule (e.g., 'h', 'D')
131 set_axis: True
132 # ^ If true, add `vmin_plot` and `vmax_plot` for each variable in obs.
133
134 plot_grp2:
135 type: "taylor"
136 fig_kwargs:
137 figsize: [8, 8]
138 default_plot_kwargs:
139 linewidth: 2.0
140 markersize: 10.
141 text_kwargs:
142 fontsize: 16.
143 domain_type: ["all"]
144 domain_name: ["CONUS"]
145 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
146 data_proc:
147 rem_obs_nan: True
148 set_axis: True
149
150 plot_grp3:
151 type: "spatial_bias"
152 fig_kwargs: # optional; For all spatial plots, specify map_kwargs here too.
153 states: True # such as whether to show the state boundaries
154 figsize: [10, 5]
155 text_kwargs:
156 fontsize: 16.
157 domain_type: ["all",]
158 domain_name: ["CONUS"]
159 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
160 data_proc:
161 rem_obs_nan: True
162 set_axis: True
163
164 plot_grp4:
165 type: "spatial_overlay"
166 fig_kwargs:
167 states: True
168 figsize: [10, 5]
169 text_kwargs:
170 fontsize: 16.
171 domain_type: ["all", "epa_region"]
172 domain_name: ["CONUS", "R9"]
173 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
174 data_proc:
175 rem_obs_nan: True
176 set_axis: True
177
178 plot_grp5:
179 type: "boxplot"
180 fig_kwargs:
181 figsize: [8, 6]
182 text_kwargs:
183 fontsize: 20.
184 domain_type: ["all"]
185 domain_name: ["CONUS"]
186 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
187 data_proc:
188 rem_obs_nan: True
189 set_axis: False
190
191 plot_grp6:
192 type: "scorecard"
193 fig_kwargs:
194 figsize: [15, 10]
195 text_kwargs:
196 fontsize: 20.
197 domain_type: ["all"]
198 domain_name: ["CONUS"]
199 region_name: ['epa_region']
200 region_list: ['R1','R2','R3','R4','R5','R6','R7','R8','R9','R10']
201 urban_rural_name: ['msa_name']
202 urban_rural_differentiate_value: ''
203 better_or_worse_method: 'NME' #support 'RMSE', 'IOA' ,' NMB', 'NME'
204 model_name_list: ['AirNow','RACM_ESRL','RACM_ESRL_VCP']
205 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
206 data_proc:
207 rem_obs_nan: True
208 set_axis: False
209
210 plot_grp7:
211 type: "multi_boxplot"
212 fig_kwargs:
213 figsize: [10, 8]
214 text_kwargs:
215 fontsize: 20.
216 domain_type: ["all"]
217 domain_name: ["CONUS"]
218 region_name: ['epa_region']
219 region_list: ['R1','R2','R3','R4','R5','R6','R7','R8','R9','R10']
220 model_name_list: ['AirNow','RACM_ESRL','RACM_ESRL_VCP']
221 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
222 data_proc:
223 rem_obs_nan: True
224 set_axis: False
225
226 plot_grp8:
227 type: "csi"
228 fig_kwargs:
229 figsize: [10, 8]
230 text_kwargs:
231 fontsize: 20.
232 domain_type: ["all",'epa_region']
233 domain_name: ["CONUS",'R1']
234 threshold_list: [10,20,30,40,50,60,70,80,90,100]
235 score_name: 'Critical Success Index' #can be used 'Critical Success Index' 'False Alarm Rate' 'Hit Rate'
236 model_name_list: ['RACM_ESRL','RACM_ESRL_VCP']
237 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
238 data_proc:
239 rem_obs_nan: True
240 set_axis: False
241
242stats:
243 # Stats require positive numbers, so if you want to calculate temperature use Kelvin!
244 # Wind direction has special calculations for AirNow if obs name is 'WD'
245 stat_list: ["MB", "MdnB", "R2", "RMSE"]
246 # ^ List stats to calculate. Dictionary of definitions included
247 # in submodule `plots/proc_stats`. Only stats listed below are currently working.
248 # Full calc list:
249 # ['STDO', 'STDP', 'MdnNB','MdnNE','NMdnGE',
250 # 'NO', 'NOP', 'NP', 'MO', 'MP', 'MdnO', 'MdnP',
251 # 'RM', 'RMdn', 'MB', 'MdnB', 'NMB', 'NMdnB', 'FB',
252 # 'ME','MdnE','NME', 'NMdnE', 'FE', 'R2', 'RMSE','d1',
253 # 'E1', 'IOA', 'AC']
254 round_output: 2 # optional; defaults to rounding to 3rd decimal place
255 output_table: False
256 # ^ Always outputs a .txt file.
257 # Optional to also output a Matplotlib figure table (image).
258 output_table_kwargs: # optional
259 figsize: [7, 3]
260 fontsize: 12.
261 xscale: 1.4
262 yscale: 1.4
263 edges: "horizontal"
264 domain_type: ["all"]
265 domain_name: ["CONUS"]
266 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
Now, some of our analysis object’s attributes are populated:
an
analysis(
control='control_ish_ufschem-example.yaml',
control_dict=...,
models={},
obs={},
paired={},
start_time=Timestamp('2017-07-01 00:00:00'),
end_time=Timestamp('2017-07-03 00:00:00'),
time_intervals=None,
download_maps=True,
output_dir='./output/ish_ufschem',
output_dir_save='./output/ish_ufschem',
output_dir_read='./output/ish_ufschem',
debug=True,
save=None,
read=None,
regrid=False,
)
Load the model data
The driver will automatically loop through the “models” found in the model section
of the YAML file and create an instance of melodies_monet.driver.model for each
that includes the
label
mapping information
file names (can be expressed using a glob expression)
xarray object
Note: Relevant control file section.
control_wrfchem_mech-0905_2.yaml
1model:
2 RACM_ESRL: # model label
3 files: example:wrfchem:racm_esrl
4 mod_type: "wrfchem"
5 mod_kwargs:
6 mech: "racm_esrl_vcp"
7 surf_only_nc: True # specify that we have only one vertical level; WRF-Chem specific
8 radius_of_influence: 12000 # meters
9 mapping: # of _model_ species name to _obs_ species name
10 airnow: # specifically for the obs labeled 'airnow'
11 PM2_5_DRY: "PM2.5"
12 o3: "OZONE"
13 projection: ~
14 plot_kwargs: # optional
15 color: "magenta"
16 marker: "s"
17 linestyle: "-"
18 RACM_ESRL_VCP:
19 files: example:wrfchem:racm_esrl_vcp
20 mod_type: "wrfchem"
21 mod_kwargs:
22 mech: "racm_esrl_vcp"
23 surf_only_nc: True
24 radius_of_influence: 12000
25 mapping:
26 airnow:
27 PM2_5_DRY: "PM2.5"
28 o3: "OZONE"
29 projection: ~
30 plot_kwargs:
31 color: "gold"
32 marker: "o"
33 linestyle: "-"
an.open_models()
ufs
example:ufschem:2017-07
**** Reading UFS-AQM or UFS-Chem model output...
Performing extra model calculations...
Calculating modeled Dewpoint...
Calculating modeled relative humidity...
Calculating modeled windspeed...
Calculating modeled wind direction...
Applying open_models()
populates the models attribute.
an.models
{'ufschem_v1': model(
model='ufs',
is_global=True,
radius_of_influence=100000,
mod_kwargs={'surf_only': True, 'sfc_varlist': ['tmp2m', 'spfh2m', 'ugrd10m', 'vgrd10m'], 'fname_sfc': ['/home/rschwantes/.cache/pooch/5207e0e702072b5e37fd507da111f78d-2017_07_01_03_ufschemv1_sfc.nc'], 'var_list': ['lat', 'lon', 'phalf', 'tmp', 'pressfc', 'dpres', 'hgtsfc', 'delz']},
file_str='example:ufschem:2017-07',
label='ufschem_v1',
obj=...,
extra_calc={'dewpoint': {'pres_calc': 'surfpres_pa', 'specific_hum': 'spfh2m'}, 'rel_hum': {'pres_calc': 'surfpres_pa', 'specific_hum': 'spfh2m', 'temp_calc': 'tmp2m'}, 'windspeed': {'u_comp': 'ugrd10m', 'v_comp': 'vgrd10m'}, 'winddir': {'u_comp': 'ugrd10m', 'v_comp': 'vgrd10m'}, 'wind_barb': {'u_comp': 'ugrd10m', 'v_comp': 'vgrd10m'}, 'rose_plot': {'model_wdir': 'winddir', 'model_wspd': 'windspeed'}},
mapping={'ish': {'tmp2m': 't', 'windspeed': 'ws', 'dewpoint': 'dpt'}},
variable_dict={'surfpres_pa': 'None', 'spfh2m': 'None', 'tmp2m': 'None', 'ugrd10m': 'None', 'vgrd10m': 'None', 'winddir': 'None', 'windspeed': 'None'},
label='ufschem_v1',
...
)}
We can access the underlying dataset with the
obj attribute.
an.models['ufschem_v1'].obj
<xarray.Dataset> Size: 320MB
Dimensions: (y: 192, x: 384, time: 72, z: 1)
Coordinates:
latitude (y, x) float64 590kB dask.array<chunksize=(192, 384), meta=np.ndarray>
longitude (y, x) float64 590kB dask.array<chunksize=(192, 384), meta=np.ndarray>
* time (time) datetime64[ns] 576B 2017-07-01T01:00:00 ... 2017-07-04
* x (x) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1
* y (y) float64 2kB 89.28 88.36 87.42 ... -87.42 -88.36 -89.28
Dimensions without coordinates: z
Data variables: (12/14)
temperature_k (time, z, y, x) float32 21MB dask.array<chunksize=(1, 1, 192, 384), meta=np.ndarray>
surfpres_pa (time, z, y, x) float32 21MB dask.array<chunksize=(1, 1, 192, 384), meta=np.ndarray>
dp_pa (time, z, y, x) float32 21MB dask.array<chunksize=(1, 1, 192, 384), meta=np.ndarray>
surfalt_m (time, z, y, x) float32 21MB dask.array<chunksize=(1, 1, 192, 384), meta=np.ndarray>
dz_m (time, z, y, x) float32 21MB dask.array<chunksize=(1, 1, 192, 384), meta=np.ndarray>
pres_pa_mid (time, z, y, x) float64 42MB dask.array<chunksize=(1, 1, 192, 384), meta=np.ndarray>
... ...
ugrd10m (time, z, y, x) float32 21MB dask.array<chunksize=(1, 1, 192, 384), meta=np.ndarray>
vgrd10m (time, z, y, x) float32 21MB dask.array<chunksize=(1, 1, 192, 384), meta=np.ndarray>
dewpoint (time, z, y, x) float32 21MB dask.array<chunksize=(1, 1, 192, 384), meta=np.ndarray>
rel_hum (time, z, y, x) float32 21MB dask.array<chunksize=(1, 1, 192, 384), meta=np.ndarray>
windspeed (time, z, y, x) float32 21MB dask.array<chunksize=(1, 1, 192, 384), meta=np.ndarray>
winddir (time, z, y, x) float32 21MB 243.3 244.2 ... 352.7 351.9
Attributes:
grid: gaussian
grid_id: 1
hydrostatic: non-hydrostatic
im: 384
jm: 192
ncnsto: 139
source: FV3GFS
NCO: netCDF Operators version 5.0.7 (Homepage = http://nco.sf.ne...
ak: [0. 0.]
bk: [0.99467119 1. ]
history: Fri Feb 27 09:53:22 2026: ncrcat 20170701_dynf001.nc 201707...Load the observational data
As with the model data, the driver will loop through the “observations” found in
the obs section of the YAML file and create an instance of
melodies_monet.driver.observation for each.
Note: Relevant control file section.
control_wrfchem_mech-0905_2.yaml
1obs:
2 airnow: # obs label
3 use_airnow: True
4 filename: example:airnow:2019-09
5 obs_type: pt_sfc
6 variables: # optional
7 OZONE:
8 unit_scale: 1
9 # ^ optional; Scaling factor
10 unit_scale_method: "*"
11 # ^ optional; Multiply = '*' , Add = '+', subtract = '-', divide = '/'
12 nan_value: -1.0
13 # ^ optional; When loading data, set this value to NaN
14 ylabel_plot: "Ozone (ppbv)"
15 # optional; set ylabel in order to include units and/or other info
16 vmin_plot: 15.0
17 # ^ optional; Min for y-axis during plotting.
18 # To apply to a plot, change restrict_yaxis = True.
19 vmax_plot: 55.0
20 # ^ optional; Max for y-axis during plotting.
21 # To apply to a plot, change restrict_yaxis = True.
22 vdiff_plot: 20.0
23 # ^ optional; +/- range to use in bias plots.
24 # To apply to a plot, change restrict_yaxis = True.
25 nlevels_plot: 21
26 # ^ optional; number of levels used in colorbar for contourf plot.
27 PM2.5:
28 unit_scale: 1
29 unit_scale_method: "*"
30 # obs_min: 0
31 # ^ optional; set all values less than this value to NaN
32 # obs_max: 100
33 # ^ optional; set all values greater than this value to NaN
34 nan_value: -1.0
35 # Note: The obs_min, obs_max, and nan_values are set to NaN first
36 # and then the unit conversion is applied.
37 ylabel_plot: "PM2.5 (ug/m3)"
38 ty_scale: 2.0 # optional; `ty_` indicates for Taylor diagram plot
39 vmin_plot: 0.0
40 vmax_plot: 22.0
41 vdiff_plot: 15.0
42 nlevels_plot: 23
an.open_obs()
Performing extra calculations for obs...
an.obs
{'ish': observation(
obs='ish',
label='ish',
file='example:ish:2017-07',
obj=...,
extra_calc={'rose_plot': {'obs_wdir': 'wdir', 'obs_wspd': 'ws'}},
type='pt_src',
sat_type=None,
sat_method=None,
data_proc=None,
variable_dict={'t': {'unit_scale': 273.15, 'unit_scale_method': '+', 'nan_value': -1.0, 'ylabel_plot': 'Temperature 2m (K)', 'xlabel_plot': 'Temperature 2m (K)', 'vmin_plot': 273.15, 'vmax_plot': 313, 'vdiff_plot': 20.0, 'nlevels_plot': 21}, 'ws': {'unit_scale': 1, 'unit_scale_method': '*', 'nan_value': -1.0, 'ylabel_plot': 'Windspeed 10m (m/s)', 'xlabel_plot': 'Windspeed 10m (m/s)', 'vmin_plot': 0, 'vmax_plot': 12, 'vdiff_plot': 4, 'nlevels_plot': 18}, 'wdir': {'unit_scale': 1, 'unit_scale_method': '*', 'nan_value': -1.0, 'ylabel_plot': 'Wind Direction 10m (deg)', 'xlabel_plot': 'Wind Direction 10m (deg)', 'vmin_plot': 0, 'vmax_plot': 370, 'vdiff_plot': 4, 'nlevels_plot': 18}, 'dpt': {'unit_scale': 273.15, 'unit_scale_method': '+', 'nan_value': -1.0, 'ylabel_plot': 'Dewpoint 2m (K)', 'xlabel_plot': 'Dewpoint 2m (K)', 'vmin_plot': 273.15, 'vmax_plot': 300, 'vdiff_plot': 4, 'nlevels_plot': 18}},
resample=None,
time_var=None,
regrid_method=None,
)}
an.obs['ish'].obj
<xarray.Dataset> Size: 48MB
Dimensions: (time: 49, y: 1, x: 12321)
Coordinates:
* time (time) datetime64[ns] 392B 2017-07-01 ... 2017-07-03
siteid (x) <U11 542kB ...
latitude (x) float64 99kB ...
longitude (x) float64 99kB ...
* x (x) int64 99kB 0 1 2 3 4 5 ... 12316 12317 12318 12319 12320
Dimensions without coordinates: y
Data variables: (12/19)
varlength (time, y, x) float64 5MB ...
wdir (time, y, x) float64 5MB nan nan 320.0 80.0 ... nan nan nan
ws (time, y, x) float64 5MB nan nan 4.0 2.0 ... nan nan nan nan
ceiling (time, y, x) float64 5MB ...
vsb (time, y, x) float64 5MB ...
t (time, y, x) float64 5MB nan nan 271.1 275.1 ... nan nan nan
... ...
icao (y, x) <U4 197kB ...
elevation (y, x) float64 99kB ...
utcoffset (y, x) float64 99kB ...
begin (y, x) datetime64[ns] 99kB ...
end (y, x) datetime64[ns] 99kB ...
time_local (time, y, x) datetime64[ns] 5MB ...Pair model and observational data
Now, we create a melodies_monet.driver.pair for each model–obs pair
using the pair_data() routine.
%%time
an.pair_data()
an.paired
{'ish_ufschem_v1': pair(
type='pt_sfc',
radius_of_influence=1000000.0,
obs='ish',
model='ufschem_v1',
model_vars=['tmp2m', 'windspeed', 'dewpoint'],
obs_vars=['t', 'ws', 'dpt'],
filename='ish_ufschem_v1.nc',
)}
an.paired['ish_ufschem_v1']
pair(
type='pt_sfc',
radius_of_influence=1000000.0,
obs='ish',
model='ufschem_v1',
model_vars=['tmp2m', 'windspeed', 'dewpoint'],
obs_vars=['t', 'ws', 'dpt'],
filename='ish_ufschem_v1.nc',
)
MELODIES-MONET now contains several meteorological capabilities
In addition to previous capabilites and plots, users can now:
Specify wind barbs
Note: users may experience longer wait times if they wish to plot wind barbs
Create rose plots for pollution and wind speed
Rose plots can be aggregated at the Country-, EPA region-, and MSA-scale
Create boxplots on given intervals for any of the supported meteorological variables
Mark statistical significance on boxplots
Plot
The plotting() routine produces plots.
Note: Relevant control file section.
control_wrfchem_mech-0905_2.yaml
1plots:
2 plot_grp1:
3 type: "timeseries" # plot type
4 fig_kwargs: # optional; to define figure options
5 figsize: [12, 6] # figure size (width, height) in inches
6 default_plot_kwargs:
7 # ^ optional; Define defaults for all plots.
8 # Important: Model kwargs overwrite these.
9 linewidth: 2.0
10 markersize: 10.
11 text_kwargs: # optional
12 fontsize: 24.
13 domain_type: ["all", "state_name", "epa_region"]
14 # ^ List of domain types: 'all' or any domain in obs file.
15 # (e.g., airnow: epa_region, state_name, siteid, etc.)
16 domain_name: ["CONUS", "CA", "R9"]
17 # ^ List of domain names. If domain_type = all,
18 # the domain name is used in the plot title.
19 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
20 # ^ make this a list of pairs in obs_model
21 # where the obs is the obs label and model is the model_label
22 data_proc: # optional??
23 rem_obs_nan: True
24 # ^ True: Remove all points where model or obs variable is NaN.
25 # False: Remove only points where model variable is NaN.
26 ts_select_time: "time_local" # `ts_` indicates this is time series plot-specific
27 # ^ Time used for avg and plotting
28 # Options: 'time' for UTC or 'time_local'
29 ts_avg_window: "h"
30 # ^ Options: None for no averaging, pandas resample rule (e.g., 'h', 'D')
31 set_axis: True
32 # ^ If true, add `vmin_plot` and `vmax_plot` for each variable in obs.
33
34 plot_grp2:
35 type: "taylor"
36 fig_kwargs:
37 figsize: [8, 8]
38 default_plot_kwargs:
39 linewidth: 2.0
40 markersize: 10.
41 text_kwargs:
42 fontsize: 16.
43 domain_type: ["all"]
44 domain_name: ["CONUS"]
45 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
46 data_proc:
47 rem_obs_nan: True
48 set_axis: True
49
50 plot_grp3:
51 type: "spatial_bias"
52 fig_kwargs: # optional; For all spatial plots, specify map_kwargs here too.
53 states: True # such as whether to show the state boundaries
54 figsize: [10, 5]
55 text_kwargs:
56 fontsize: 16.
57 domain_type: ["all",]
58 domain_name: ["CONUS"]
59 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
60 data_proc:
61 rem_obs_nan: True
62 set_axis: True
63
64 plot_grp4:
65 type: "spatial_overlay"
66 fig_kwargs:
67 states: True
68 figsize: [10, 5]
69 text_kwargs:
70 fontsize: 16.
71 domain_type: ["all", "epa_region"]
72 domain_name: ["CONUS", "R9"]
73 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
74 data_proc:
75 rem_obs_nan: True
76 set_axis: True
77
78 plot_grp5:
79 type: "boxplot"
80 fig_kwargs:
81 figsize: [8, 6]
82 text_kwargs:
83 fontsize: 20.
84 domain_type: ["all"]
85 domain_name: ["CONUS"]
86 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
87 data_proc:
88 rem_obs_nan: True
89 set_axis: False
90
91 plot_grp6:
92 type: "scorecard"
93 fig_kwargs:
94 figsize: [15, 10]
95 text_kwargs:
96 fontsize: 20.
97 domain_type: ["all"]
98 domain_name: ["CONUS"]
99 region_name: ['epa_region']
100 region_list: ['R1','R2','R3','R4','R5','R6','R7','R8','R9','R10']
101 urban_rural_name: ['msa_name']
102 urban_rural_differentiate_value: ''
103 better_or_worse_method: 'NME' #support 'RMSE', 'IOA' ,' NMB', 'NME'
104 model_name_list: ['AirNow','RACM_ESRL','RACM_ESRL_VCP']
105 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
106 data_proc:
107 rem_obs_nan: True
108 set_axis: False
109
110 plot_grp7:
111 type: "multi_boxplot"
112 fig_kwargs:
113 figsize: [10, 8]
114 text_kwargs:
115 fontsize: 20.
116 domain_type: ["all"]
117 domain_name: ["CONUS"]
118 region_name: ['epa_region']
119 region_list: ['R1','R2','R3','R4','R5','R6','R7','R8','R9','R10']
120 model_name_list: ['AirNow','RACM_ESRL','RACM_ESRL_VCP']
121 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
122 data_proc:
123 rem_obs_nan: True
124 set_axis: False
125
126 plot_grp8:
127 type: "csi"
128 fig_kwargs:
129 figsize: [10, 8]
130 text_kwargs:
131 fontsize: 20.
132 domain_type: ["all",'epa_region']
133 domain_name: ["CONUS",'R1']
134 threshold_list: [10,20,30,40,50,60,70,80,90,100]
135 score_name: 'Critical Success Index' #can be used 'Critical Success Index' 'False Alarm Rate' 'Hit Rate'
136 model_name_list: ['RACM_ESRL','RACM_ESRL_VCP']
137 data: ["airnow_RACM_ESRL", "airnow_RACM_ESRL_VCP"]
138 data_proc:
139 rem_obs_nan: True
140 set_axis: False
NOTE: That ISH data for calm wind sets windspeed = 0 and wind direction = 0. In the wind-rose plots, calm winds are plotted as an inner circle for wind direction and data is removed for pollution rose plots based on the threshold set in your yaml file.
All other plots, it’s up to the user on what to consider for these calm winds. Plots below and corresponding yaml file demonstrates how to remove all calm winds from the calculation for the timeseries plots (second set of plots plot_grp1a) and the spatial overlay plots (second set of plots plot_grp3a). You can see that windspeed biases go down when removing these calm winds. Additional processing and consideration for calm winds is under development.
%%time
an.plotting()
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04
ish vs. ufschem_v1: Custom statistical test, P_val:5.084e-07
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04
ish vs. ufschem_v1: Custom statistical test, P_val:0.000e+00
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04
ish vs. ufschem_v1: Custom statistical test, P_val:9.758e-92
Saving rose plot to ./output/ish_ufschem/plot_grp6.rose_plot.t.2017-07-01_00.2017-07-03_00.state.MI...
Saving rose plot to ./output/ish_ufschem/plot_grp6.rose_plot.t.2017-07-01_00.2017-07-03_00.siteid.72539614815...
Saving rose plot to ./output/ish_ufschem/plot_grp6.rose_plot.ws.2017-07-01_00.2017-07-03_00.state.MI...
Saving rose plot to ./output/ish_ufschem/plot_grp6.rose_plot.ws.2017-07-01_00.2017-07-03_00.siteid.72539614815...
Saving rose plot to ./output/ish_ufschem/plot_grp6.rose_plot.dpt.2017-07-01_00.2017-07-03_00.state.MI...
Saving rose plot to ./output/ish_ufschem/plot_grp6.rose_plot.dpt.2017-07-01_00.2017-07-03_00.siteid.72539614815...
CPU times: user 2min 29s, sys: 32.1 s, total: 3min 1s
Wall time: 2min 59s
%%time
an.stats()
CPU times: user 6.45 s, sys: 11.7 s, total: 18.2 s
Wall time: 18.3 s
The stats routine has produced two files (one for each data variable). This is one of them:
output/airnow_wrfchem/stats.OZONE.all.CONUS.2019-09-05_06.2019-09-06_06.csv
Stat_ID,Stat_FullName,airnow_RACM_ESRL,airnow_RACM_ESRL_VCP
MB,Mean_Bias,3.93,3.23
MdnB,Median_Bias,3.86,3.27
R2,Coefficient_of_Determination_(R2),0.56,0.54
RMSE,Root_Mean_Square_Error,11.64,11.59