Repository for low-level, stand-alone/column canopy parameterizations for testing and application to gridded atmospheric composition/air quality models.

Authors: Patrick Campbell, Zachary Moon, and Wei-Ting Hung

Getting Started

Build

Canopy-App requires NetCDF-Fortran Libraries (i.e., -lnetcdf -lnetcdff) when using the 1D/2D NetCDF I/O Option (i.e., infmt_opt=0). See the included Makefile, which detects NetCDF using nf-config, for an example (on GMU Hopper, you can use the netcdf-c/4.7.4-vh and netcdf-fortran/4.5.3-ff modules).

Compilation options can be controlled with environment variables:

• FC=gfortran (default) or compiler name/path (e.g. FC=ifort, FC=gfortran-11, FC=/usr/bin/gfortran-11)
• DEBUG=0 (off; default) or DEBUG=1 (on) or DEBUG=2 (more flags, including FPE traps and traceback)
• NC=0 (off) or NC=1 (on; default)

Example: a) with compiler set by FC environment variable (falling back to gfortran if unset), Debug flags ON and with NetCDF:

DEBUG=1 NC=1 make -C src

Note: Not supplying FC doesn't necessarily give gfortran, since FC might already be set in the environment (for example, module load situations may do this). In such case do:

DEBUG=1 NC=1 FC=gfortran make -C src

b) with Intel Fortran (ifort), Debug flags ON and with NetCDF:

DEBUG=1 NC=1 FC=ifort make -C src

Modify settings

If necessary, modify the settings in the Fortran namelist file input/namelist.canopy, which is read at runtime.

Run

./canopy

You can also generate global inputs and run with Python.

Components

Current Canopy-App components:

1. In-Canopy Winds and Wind Adjustment Factor (WAF) for wildfire spread and air quality applications. Based on Massman et al. (2017).

   Namelist Option : ifcanwind and/or ifcanwaf Output Variables: ws (m s-1) waf (fraction)

   • canopy_wind_mod.F90
   • canopy_waf_mod.F90

2. In-Canopy vertical diffusion (i.e., eddy diffusivities used to scale resolved model layer 1 diffusion). Based on Massman et al. (2017) and Makar et al. (2017).

   Namelist Option : ifcaneddy Output Variables: kz (m2 s-1)

   • canopy_eddyx_mod.F90

3. In-Canopy photolysis attenuation (i.e., used to scale resolved model layer 1 photolysis). Based on Massman et al. (2017) and Markar et al. (2017).

   Namelist Option : ifcanphot Output Variables: rjcf (fraction)

   • canopy_phot_mod.F90

4. In-Canopy leaf-level biogenic emissions (kg m-3 s-1). Based on MEGANv2 and v3 (Guenther et al., 2012), and using both Clifton et al. (2021) and Silva et al. (2020) parameterizations.

   • Note the emissions here are at leaf-level and the units are in per m3 (in each canopy layer volume using the LAD/biomass distribution) for the respective vegetation type in each grid cell/point. This is different then MEGANv2 or v3, as such models approximate combined activity factors per canopy level, sum them weighted to a given biomass distribution, and use total LAI to calculate the "big-leaf" 2D flux of biogenic emissions to the overlying atmosphere. Thus, to get 2D total flux of biogenic emissions per m2 from Canopy-App, the explicit leaf-level emissions profile must be integrated across the individual canopy layer depths/resolutions (i.e., "modres", see Table 3). Since the modres is constant in Canopy-App, the layers can be directly summed and then multiplied by modres to get kg m-2 s-1. This sum neglects any effects of time integrated losses and/or chemistry that would reduce total biogenic emissions flux from the canopy. When fractional vtypes (i.e., land use) are used, the summed layers can be multiplied by the fractional grid box areal coverage for each vegetation types in the grid cell. However, for the current dominant vtype approach as input to Canopy-App and many UFS applications this multiplicative fraction = 1.

   Namelist Option : ifcanbio Output Variables: see Table 1 below

   • canopy_bioemi_mod.F90

Outputs

Note for Biogenic emissions: When ifcanbio=.TRUE., output will include 3D canopy resolved biogenic emissions for the following species (based on Guenther et al., 2012), which have been mapped from Guenther et al. PFTs to input LU_OPT.

Table 1. Canopy-App Biogenic Emissions Output Variables

Variable Name	Variable Description (Units: kg m-3 s-1)	ID Number
emi_isop	Isoprene	1
emi_myrc	Myrcene	2
emi_sabi	Sabinene	3
emi_limo	Limonene	4
emi_care	3-Carene	5
emi_ocim	t-beta-Ocimene	6
emi_bpin	beta-Pinene	7
emi_apin	alpha-Pinene	8
emi_mono	Other Monoterpenes (34 compounds, Table 1 Guenther et al. (2012)	9
emi_farn	alpha-Farnesene	10
emi_cary	beta-Caryophyllene	11
emi_sesq	Other Sesquiterpene (30 compounds, Table 1 Guenther et al. (2012)	12
emi_mbol	232-MBO emissions	13
emi_meth	Methanol emissions	14
emi_acet	Acetone emissions	15
emi_co	Carbon Monoxide emissions	16
emi_bvoc	Bi-Directional VOC emissions (5 compounds, Table 1 Guenther et al. (2012)	17
emi_svoc	Stress VOC emissions (15 compounds, Table 1 Guenther et al. (2012)	18
emi_ovoc	Other VOC emissions (49 compounds, Table 1 Guenther et al. (2012)	19

Current Canopy-App Output: As discussed above, the current Canopy-App optional outputs includes 3D canopy winds (canwind), canopy vertical/eddy diffusivity values kz), biogenic emissions (see Table 1), and canopy photolysis attenuation correction factors (rjcf), and derived Leaf Area Density (lad) from the foliage shape function. Current 2D fields includes the Wind Adjustment Factor (waf), flame heights (flameh), and canopy heights (canheight). Current 1D fields include the canopy model interface levels (z).

Namelist Option : file_out Prefix string (e.g., 'test') used to name output file (Output is 1D txt when using input 1D data (i.e., infmt_opt=1), or is 2D NetCDF output when 2D NetCDF input is used (i.e., infmt_opt=0)).

Inputs and Settings

Current Canopy-App Input: Typical 1D or 2D (time=1,lat,lon) gridded atmospheric model input variables in 1st layer above canopy. Some 3D inputs are supported (see var3d_opt in Table 3 and associated options).

Namelist Option : file_vars Full name of input file (Supports either text or NetCDF format with following formats: .txt, .nc, .ncf, or .nc4)

• See example file inputs for variables and format (gfs.t12z.20220701.sfcf000.canopy.txt or gfs.t12z.20220701.sfcf000.canopy.nc). Example surface met/land/soil inputs are based on NOAA's UFS-GFSv16 inputs initialized on July 01, 2022 @ 12 UTC (forecast at hour 000). Other external inputs for canopy related and other calculated variables are from numerous sources. See Table 2 below for more information. Note: The example GFSv16 domain has been cut to the southeast U.S. region only in this example for size/time constraints here.
• Canopy-App assumes the NetCDF input files are in CF-Convention and test file is based on UFS-GFSv16; recommend using double or float for real variables. Input data must be valid values.
• Canopy-App can also be run with a single point of 1D input data in a text file (e.g. point_file_20220701.sfcf000.txt).
• The namelist can be modified with f90nml (included with the Canopy-App Conda environment) to insert multiple input filenames at once:

  f90nml -g filenames -v file_vars="$(realpath *.txt | xargs -I {} echo "'{}'")" namelist.canopy namelist.canopy_copy
  

The Canopy-App input data in Table 2 below is based around NOAA's UFS operational Global Forecast System Version 16 (GFSv16) gridded met data, and is supplemented with external canopy data (from numerous sources) and other external and calculated input variables.

Table 2. Canopy-App Required Input Variables

GFS /Met/Land/Soil Variables	Variable Description and Units	Example Data Sources/References (if necessary)
lat	Latitude (degrees)	N/A
lon	Longitude (degrees; from 0-360)	N/A
time	Timestamp (days since YYYY-N-D 0:0:0) (NetCDF Only)	N/A
ugrd10m	U wind at reference height above canopy (m/s), e.g., 10 m	UFS NOAA/GFSv16 *(see below for downloading using AWS)
vgrd10m	V wind at reference height above canopy (m/s), e.g., 10 m	UFS NOAA/GFSv16
vtype	Vegetation type (dimensionless), VIIRS or MODIS	UFS NOAA/GFSv16
fricv	Friction velocity (m/s)	UFS NOAA/GFSv16
sfcr	Total surface roughness length (m)	UFS NOAA/GFSv16
sotyp	Soil type (dimensionless), STATSGO	UFS NOAA/GFSv16
pressfc	Surface pressure (Pa)	UFS NOAA/GFSv16
dswrf	Instantaneous downward shortwave radiation at surface (W/m2)	UFS NOAA/GFSv16
shtfl	Instantaneous sensible heat flux at surface (W/m2)	UFS NOAA/GFSv16
tmpsfc	Surface temperature (K)	UFS NOAA/GFSv16
tmp2m	2-meter temperature (K)	UFS NOAA/GFSv16
spfh2m	2-meter specific humidity (kg/kg)	UFS NOAA/GFSv16
hpbl	Height of the planetary boundary layer (m)	UFS NOAA/GFSv16
prate_ave	Average mass precipitation rate (kg m-2 s-1)	UFS NOAA/GFSv16
soilw1	Volumetric soil moisture in layer 1 (m3 m-3)	UFS NOAA/GFSv16
soilw2	Volumetric soil moisture in layer 2 (m3 m-3)	UFS NOAA/GFSv16
soilw3	Volumetric soil moisture in layer 3 (m3 m-3)	UFS NOAA/GFSv16
soilw4	Volumetric soil moisture in layer 4 (m3 m-3)	UFS NOAA/GFSv16
wilt	Wilting point (proportion)	UFS NOAA/GFSv16
External Canopy Variables	Variable Description and Units	Data Source/Reference (if necessary)
ch	Canopy height (m)	Globally extended GEDI data. Data Period=2020. Data frequency=Annual. (Lang et al., 2023)
clu	Canopy clumping index (dimensionless)	GriddingMachine/MODIS. Data Period=2001-2017 Climatology. Data frequency=Monthly. (Wei et al., 2019). Extended globally for high latitudes using methods described here.
lai	Leaf area index (m2/m2)	VIIRS-NPP. Data Period=2020. Data frequency=Daily, interpolated from original 8-day product. (Myneni 2018). Extended globally for high latitudes using methods described here.
canfrac	Canopy green vegetation fraction (dimensionless)	Based on VIIRS GVF. Data Period=2020. Data frequency=Monthly. Extended globally for high latitudes using methods described here.
pavd	Plant area volume density (m2/m3)	GEDI product from North Arizona University. Data Period=201904-202212 Climatology. Data frequency=Annual. Three dimensional structure of plant area volume density with 14 vertical layers from the surface (0 m) to 70 m above ground level. Data at each layer represents the average pavd within certain height range (e.g. 0 - 5 m for first layer).
lev	Height AGL for levels associated with optional pavd (or other canopy profile) inputs (m)	Same as for GEDI PAVD (or other canopy profile inputs) above
Other External Variables	Variable Description and Units	Data Source/Reference (if necessary)
frp	Total Fire Radiative Power (MW/grid cell area)	NOAA/NESDIS GBBEPx
csz	Cosine of the solar zenith angle (dimensionless)	Based on Python Pysolar
mol	Monin-Obukhov Length (m)	Externally calculated using GFS tmp2m, fricv, and shtfl. (Essa, 1999)
href	Reference height above canopy (m) - 10 m	Assumed constant (i.e., 10 m). Can be taken from NL.
ozone_w126	Ozone W126 index (ppm-hours)	Climatological value during 04/2021-04/2024 based on GFSv16 lowest model layer ozone mixing ratios.

More Information on Data Sources from Table 2:

Global GFS meteorological and canopy files may be provided by request:

Patrick.C.Campbell@noaa.gov

Hourly gridded GFSv16 data is available from March 23, 2021 - Current Day and is supplemented by calculated and canopy parameters shown in Table 2.

GriddingMachine: GriddingMachine is open source database and software for Earth system modeling at global and regional scales. Data is easily accessible in consistent formats for ease of downloading/processing. All available datasets may be found at: https://github.com/CliMA/GriddingMachine.jl. (Wang et al., 2022).

For GMU Hopper users, daily global canopy files for 2022 at 12 UTC are available at

/groups/ESS/whung/canopy_wind/gfsv16_test_data/test_2022

For NOAA Hera users, daily global canopy files for 2022 at 12 UTC are available at

/scratch1/RDARCH/rda-arl-gpu/Wei-ting.Hung/Global_canopy/canopy_app_2022

For NOAA HPSS users (e.g., Hera or WCOSS2), hourly operational GFSv16 meteorology files are archived at (following 07/01/2024)

 /NAGAPE/arl/5year/Patrick.C.Campbell//yyyy_GFSv16_prod/

Otherwise, please contact Patrick.C.Campbell@noaa.gov for other GFSv16 data periods.

Near-real-time hourly GFSv16 outputs are on WCOSS2 at

/lfs/h1/ops/prod/com/gfs/v16.3/gfs.yyyymmdd/12/atmos/

Table 3. Current User Namelist Options

Namelist Option	Namelist Description and Units
	Input model format options
infmt_opt	integer for choosing 1D or 2D text (= 1) or 2D NetCDF input file format (= 0, default)
	Input model grid sizes
nlat	number of latitude cells (must match # of LAT in file_vars above)
nlon	number of longitude cells (must match # of LON in file_vars above)
	Input model run times and interval
time_start	Start/initial time stamp in YYYY-MM-DD-HH:MM:SS.SSSS for simulation/observation inputs
time_end	End time stamp in YYYY-MM-DD-HH:MM:SS.SSSS for simulation/observation inputs
ntime	Number of time steps for simulation/observation inputs
time_intvl	Integer time interval for simulation/observation input time steps in seconds (e.g. 3600 for hourly time stpes and 24*3600 for daily time steps)
	Canopy model vegetation/land use input dataset options
lu_opt	integer for input model land use type (0: VIIRS 17 Cat (default) or 1: MODIS-IGBP 20 Cat (valid LU types 1-10 and 12); input mapped to Massman et al.)
	**Input model 3d NetCDF variable options
var3d_opt	integer for selecting to use 3D variable in NetCDF file (e.g., 'PAVD') or to read supplementary canopy text file inputs (file_canvars). (= 0, default, off) or (= 1, on). file_canvars read only when infmt_opt = 1 and var3d_opt = 1. This is used with the number of levels defined by var3d_set below
var3d_set	integer for selecting number of 3D input levels, only used when setting var3d_opt= 1`, default = 14 (Note: For input text file the max current levels can only be 14, please input according to example data)
	**Options to use observed PAVD profiles and latitude threshold
pavd_opt	integer for choosing to use GEDI 3D input PAVD profiles instead of prescribed plant distribution functions (= 0, default, off) or (= 1, on); Note: To use this option, must set var3d_set= 1, and the 3D pavd variable must be available in the input NetCDF file (i.e., file_vars`) or in new auxilliary 3D PAVD text file
pavd_set	real value for +/- latitude threshold within to use observed GEDI 3D PAVD profiles instead of prescribed plant distribution functions. Used only if pavd_opt=1. Default = 52.0 degrees latitude.
	Canopy model vertical layers
modlays	number of model (below and above canopy) layers. Strongly recommend adjusting this in accordance with modres option below to maintain canopy model column extension above tallest canopies in simulation domain (e.g.,for a 50 meter column simulation, a user could use 1000 modlays @ 0.05 m resolution, 100 modlays @ 0.5 m resolution, 50 modlays @ 1.0 m resolution, etc.
modres	above and below canopy model vertical resolution (m)
	Contiguous canopy model thresholds
lai_thresh	user-set real value of LAI threshold for contiguous canopy (m2/m2)
cf_thresh	user-set real value of canopy fraction threshold for contiguous canopy
ch_thresh	user-set real value of canopy height threshold for contiguous canopy (m)
	Canopy crop and shrub/savanna/grass extension options
ssg_opt	integer for using either input data (= 0, default) or user set shrub/savanna/grass (SSG) vegetation type heights from namelist (= 1). Currently, GEDI FCH input data may not provide canopy heights for very low-lying vegetation such as SSG, and thus FCH=0. This is important for options such as biogenic emissions, as this would then not have any emissions for these areas. Warning: use of ssg_opt=1 will overide potential observations of FCH from GEDI for low-lying SSG (at higher spatial resolution) and cover larger areas of lower resolution vegtype data indicating SSG.
ssg_set	user-set real value of constant SSG vegetation type heights (m) (only used if ssg_opt=1). We recommend setting this to a low value, e.g., ssg_set=0.5 or 1.0 (meters) when ssg_opt=1
crop_opt	integer for using either input data (= 0, default) or user set crop vegetation type heights from namelist (= 1). Currently, GEDI FCH input data only provides canopy heights for forests and not crops. Warning: use of crop_opt=1 will overide typically higher resolution input data (e.g., GEDI) forest canopy heights where the lower resolution vegtype data indicates crops
crop_set	user-set real value of constant crop vegetation type heights (m) (only used if crop_opt=1)
	Canopy physics and wind-specific options
ifcanwind	logical canopy wind option (default: .FALSE.)
href_opt	integer for using href_set in namelist (= 0, default) or array from file (= 1)
href_set	user-set real value of reference height above canopy associated with input wind speed (m) (only used if href_opt=0) ***
z0ghc	ratio of ground roughness length to canopy top height (Massman et al., 2017)
rsl_opt	user-set option for either MOST or unified Roughness SubLayer (RSL) effects above and at canopy top (Uc).(= 0, default: uses MOST and a constant lambdars factor only), (= 1, under development: will use a more unified RSL approach from Bonan et al. (2018) and Abolafia-Rosenzweig et al., 2021)
lambdars	Value representing influence of RSL effects (with rsl_opt=0) (Massman et al., 2017)
pai_opt	integer (0: PAI fixed from Katul et al. 2004 veg types-->default; 1: PAI Massman et al. 2017 Eq. 19 calc; 2: PAI from model LAI (based on PAI=LAI/(1-alpha), where alpha is the "woody-to-total area ratio" and is vegetation type dependent. Based on Figure 1 of Fang et al. (2019) (https://doi.org/10.1029/2018RG000608) and combining Eqs. 10 and 14 in Zou et al., 2009 (https://doi.org/10.1093/treephys/tpp042) ; 3: user-set PAI value)
pai_set	user-set real value of PAI (default: 4.0; only used if pai_opt=3)
z0_opt	integer (0: use model input or 1: vegtype dependent z0 for first estimate)
	Canopy fire/WAF-specific options
ifcanwaf	logical canopy WAF option (default: .FALSE.) **
dx_opt	0: Calculation of dx resolution/distance from lon; 1: user-set dx grid resolution
dx_set	user-set real value of grid resolution (m) only if dx_opt=1
flameh_opt	0: Calculation of vegtype dependent flame height from FRP (i.e., fire intensity); Note: this uses the one of two FRP calculation methods based on flameh_cal below; 1: user-set flameh; 2: FRP calculation where available (active fires), elsewhere user-set flameh; 3: FlameH override, i.e., only uses fraction of canopy height (flameh_set must be <=1.0) as a surrogate for flameh; 4: FRP calculation where available (active fires) and FlameH override elsewhere (same as option 3); 5: FRP/intensity dependent (i.e., sub-canopy vs. crown fires) calculation where available (active fires) and FlameH override elsewhere (same as option 3). If option 5 is used and crowning is calculated, then the total flame height (i.e., top of canopy=FCH) is used instead of 1/2 flame height.
flameh_cal	0: Calculates the vegtype dependent flame height from FRP, based on Table 1 of Alexander and Cruz (2012) and assuming that flame height = flame length (overestimates flame height in high winds and/or slope conditions). 1: Calculates the vegtype dependent flame height from FRP based on Table 2 and Equation 14 of Alexander and Cruz (2012). These relate flame height directly to crown scorch height, which is derived from FRP. This method assumes that the ambient temperature is in the experimental ranges from Table 3 of Alexander and Cruz (2012), and that the lethal temperature for burning foliage is 60.0 C.
flameh_set	user-set real value of flame height (m) if flameh_opt=1 or 2, or flameh = fraction of canopy height (<=1.0), i.e., flameh override, if flameh_opt=3, 4, or 5
frp_fac	user-set real value of tuning factor applied to FRP in calculation of flame height (default: 1.0). Used only if flameh_opt=0, 2, 4, or 5.
	Canopy eddy diffusivity-specific options
ifcaneddy	logical canopy eddy Kz option (default: .FALSE.)
	Canopy radiation/photolysis-specific options
ifcanphot	logical canopy photolysis option (default: .FALSE.)
	Canopy biogenic emissions-specific options
ifcanbio	logical canopy biogenic emissions option (default: .FALSE.)
bio_cce	user-set real value of MEGAN biogenic emissions "canopy environment coefficient" used to tune emissions to model inputs/calculations (default: 0.21, based on Silva et al. 2020)
biospec_opt	user set option to select species for NetCDF biogenic emissions output (0: all species; 1-19: one species selected according to ID number - Table 1) (default: 0; ID number for single species selection only used if infmt_opt=0)
biovert_opt	user set biogenic vertical summing option (0: no sum, full leaf-level biogenic emissions, units=kg/m3/s; 1: MEGANv3-like summing of LAD weighted activity coefficients using the canopy-app plant distributions, caution-- units=kg m-2 s-1 and puts the total emissions in the topmost canopy-app model layer only; 2: Same as in option 1, but instead uses Gaussian/normally weighted activity coefficients acoss all sub-canopy layers -- also units of kg m-2 s-1 in topmost model layer; 3: Same as in option 1, but instead uses evenly weighted activity coefficients acoss all sub-canopy layers -- also units of kg m-2 s-1 in topmost model layer
loss_opt	user set option to apply a canopy loss factor when the vertical summing option is used (biovert_opt >= 1) to facilitate comparison of top-of-canopy BVOC emissions with ground flux observations. (0: no loss factor applied, 1: loss factor calculated based on Eq. 21 of Guenther et al. (2006) based on the formulation and empirical parameters for isoprene, 2: constant user set factor applied with loss_set below, Note: The loss factor can be applied to all or any single biogenic species (using loss_ind below), and caution must be used for other BVOC species besides isoprene. User may adjust the variable chemical lifetime (lifetime, default = 3600 s taken for approximate isoprene lifetime) below and re-run to target other specific BVOC species flux observation comparisons.
loss_set	Set default value for constant canopy loss factor applied used when loss_opt=2 (Default = 0.96 based on Guenther et al. (2006)
loss_ind	Set default integer for applying canopy loss factor to all species (=0) or only specific biogenics species specific indice (> 0) based on Table 1 above (e.g., 1 = Isoprene, 2 = Myrcene, etc.)
lifetime	user-set real value of chemical lifetime (in seconds) used when loss_opt=1. Default = 3600 s based on approximate above canopy isoprene lifeftime of 1 hour.
co2_opt	user-set options for applying a CO2 inhibition factor for biogenic isoprene-only emissions using either the Possell & Hewitt (2011) (= 0, default) or Wilkinson et al. (2009) method (= 1). Use of option = 1 (Possell & Hewitt 2011) is especially recommended for sub-ambient CO2 concentrations. To turn off co2 inhibition factor set co2_opt=2
co2_set	user-set real value of atmospheric co2 concentration (ppmv) (only used if co2_opt=0 or co2_opt=1)
leafage_opt	user-set options for applying leaf-age response to biogenic VOC emissions based on Guenther et al. 2006 (default is off i.e., leafage_opt=1, the corresponding $\gamma$ is set to 1). If turned on (leafage_opt=0), leafage $\gamma$ is calculated and the lai_tstep option needs to be set to ensure correct interpolation in this leafage_opt calculation.
lai_tstep	user-defined options for the number of seconds in the interval at which LAI (Leaf Area Index) data is provided to the model. For instance, if LAI data is given on a daily basis, lai_tstep would be set to the number of seconds in a day (86,400 seconds). If LAI data is provided monthly, then lai_tstep would represent the total number of seconds in that month (e.g., 2,592,000 seconds for a 30-day month). This parameter helps in determining the frequency of LAI input and is crucial for interpolating LAI values to the model's hourly timesteps when the model's timestep (time_intvl) is smaller than the LAI input interval.
hist_opt	user-set option to use historically averaged short-term (24-hr) and long-term (240-hr) rolling averages for leaf temperature and PAR for biogenic emissions (default is off i.e., hist_opt=0) Note: If simulation is </= 24 hours, instantaneous values for leaf temperature and PAR will be used even if historical averaging is turned on (i.e., hist_opt=1). Recommend turning on hist_opt=1 and running at least for 25 hours to create a model spin-up, and use subsequent simulation hours for analysis. Overall a 10-day (240 hr) spinup is optimal for best analysis of biogenic emissions.
soim_opt	user-set options for applying soil moisture response to biogenic VOC emissions based on Guenther et al. 2006, which depends on input soil moisture at depth and the wilting point. This includes additional PFT dependent approach for cumulative root fraction within each soil layer from Zeng (2001). (default is off i.e., soim_opt=1, the corresponding $\gamma$ is set to 1). If turned on (soim_opt=0), which is recommended, soim $\gamma$ is calculated and the prescribed 4-layer soil depths (soild[1-4] below) are used. Four layers are assumed, and are based on GFS Noah and Noah-MP LSM.
soild[1-4]	user-set real values of four level soil depths at centerpoint (cm). Four layers are based on the GFS Noah and Noah-MP LSM, default values are soild1=5.0, soild2=25.0, soild3=70.0, and soild4=150.0.

** If modres >> flameh then some error in WAF calculation will be incurred. Suggestion is to use relative fine modres (at least <= 0.5 m) compared to average flame heights (e.g., ~ 1.0 m) if WAF is required.

*** If href_set becomes small and approaches z0 (or as href_set --> 0), only the sub-canopy wind profile is calculated, recommend href_set = 10 m.

Note: Canopy is parameterized by foliage distribution shape functions and parameters for different vegetation types.

• canopy_profile_mod.F90

Global Canopy-App Example (July 01, 2022 at 1200 UTC)

References

Further references contained within the source code.

• Abolafia-Rosenzweig, R., He, C., Burns, S. P., & Chen, F. (2021). Implementation and evaluation of a unified turbulence parameterization throughout the canopy and roughness sublayer in Noah-MP snow simulations. Journal of Advances in Modeling Earth Systems, 13, e2021MS002665. https://doi.org/10.1029/2021MS002665.

• Bonan, G. B., Patton, E. G., Harman, I. N., Oleson, K. W., Finnigan, J. J., Lu, Y., & Burakowski, E. A. (2018). Modeling canopy-induced turbulence in the Earth system: A unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0). Geoscientific Model Development, 11, 1467–1496. https://doi.org/10.5194/gmd-11-1467-2018.

• Clifton, O. E., Patton, E. G., Wang, S., Barth, M., Orlando, J., & Schwantes, R. H. (2022). Large eddy simulation for investigating coupled forest canopy and turbulence influences on atmospheric chemistry. Journal of Advances in Modeling Earth Systems, 14, e2022MS003078. https://doi.org/10.1029/2022MS003078

• Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012.

• Katul, G.G., Mahrt, L., Poggi, D., and Sanz, C. (2004). One- and two-equation models for canopy turbulence. Boundary-Layer Meteorol. 113: 81–109. https://doi.org/10.1023/B:BOUN.0000037333.48760.e5

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Development

After cloning the repository, set up the pre-commit hooks by invoking

pre-commit install --install-hooks

within the repository (after installing pre-commit). This only has to be done once. The current configuration applies findent -i4 to fix indentation and strips trailing whitespace. Using pre-commit saves you time/energy and reduces diffs.

Pull requests should target the develop branch (current default). The stable branch is periodically updated to reflect the "stable" state of the code, e.g. for users. After updating stable from develop (via GitHub pull request or manual merge), rebase develop so that it doesn't appear to be behind. This can be done locally:

git switch stable
git pull
git switch develop
git rebase stable
git push