Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add pavd and daily LAI #95

Merged
merged 9 commits into from
Oct 19, 2023
Merged

Add pavd and daily LAI #95

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
c692c70
Update global_data_process.py
angehung5 Oct 16, 2023
4f3a8ac
Update Table 2
angehung5 Oct 16, 2023
6a7a33a
Update Table 2
angehung5 Oct 16, 2023
501aab6
Update global_data_process.py
angehung5 Oct 16, 2023
9f409a0
Update global_data_process.py
angehung5 Oct 16, 2023
d4ac4c4
Update global_data_process.py
angehung5 Oct 16, 2023
b4eb77e
Update global_data_process.py
angehung5 Oct 16, 2023
44ae791
Update global_data_process.py
angehung5 Oct 16, 2023
f7856c8
Update README.md
drnimbusrain Oct 17, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 5 additions & 4 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -150,10 +150,11 @@ The Canopy-App input data in [Table 2](#table-2-canopy-app-required-input-variab
| `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 |
| **External Canopy Variables** | **Variable Description and Units** | **Data Source/Reference (if necessary)** |
| `fh` | Forest canopy height (m) | Fused GEDI/Landsat data. Data Period=2020. ([Potapov et al., 2020](https://doi.org/10.1016/j.rse.2020.112165)) |
| `clu` | Forest clumping index (dimensionless) | GriddingMachine/MODIS. Data Period=2001-2017 Climatology. ([Wei et al., 2019](https://doi.org/10.1016/j.rse.2019.111296)). Extended globally for high latitudes using methods described [here](https://gmuedu-my.sharepoint.com/:w:/g/personal/whung_gmu_edu/EdglXmW2kzBDtDj1xV0alGcB1Yo2I8hzdyWGVGB2YOTfgw). |
| `lai` | Leaf area index (m2/m2) | VIIRS-NPP. Data Period=2018-2020 Climatology. ([Myneni 2018](https://doi.org/10.5067/VIIRS/VNP15A2H.001)). Extended globally for high latitudes using methods described [here](https://gmuedu-my.sharepoint.com/:w:/g/personal/whung_gmu_edu/EdglXmW2kzBDtDj1xV0alGcB1Yo2I8hzdyWGVGB2YOTfgw). |
| `ffrac` | Forest fraction (dimensionless) | Based on [VIIRS GVF-NPP](https://www.star.nesdis.noaa.gov/jpss/gvf.php) and GriddingMachine/MODIS FFRAC/FVC from Terra. Data Period=2020. ([DiMiceli et al., 2022](https://doi.org/10.5067/MODIS/MOD44B.061)). Extended globally for high latitudes using methods described [here](https://gmuedu-my.sharepoint.com/:w:/g/personal/whung_gmu_edu/EdglXmW2kzBDtDj1xV0alGcB1Yo2I8hzdyWGVGB2YOTfgw). |
| `fh` | Forest canopy height (m) | Fused GEDI/Landsat data. Data Period=2020. Data frequency=Annual. ([Potapov et al., 2020](https://doi.org/10.1016/j.rse.2020.112165)) |
| `clu` | Forest clumping index (dimensionless) | GriddingMachine/MODIS. Data Period=2001-2017 Climatology. Data frequency=Monthly. ([Wei et al., 2019](https://doi.org/10.1016/j.rse.2019.111296)). Extended globally for high latitudes using methods described [here](https://gmuedu-my.sharepoint.com/:w:/g/personal/whung_gmu_edu/EdglXmW2kzBDtDj1xV0alGcB1Yo2I8hzdyWGVGB2YOTfgw). |
| `lai` | Leaf area index (m2/m2) | VIIRS-NPP. Data Period=2020. Data frequency=Daily, interpolated from original 8-day product. ([Myneni 2018](https://doi.org/10.5067/VIIRS/VNP15A2H.001)). Extended globally for high latitudes using methods described [here](https://gmuedu-my.sharepoint.com/:w:/g/personal/whung_gmu_edu/EdglXmW2kzBDtDj1xV0alGcB1Yo2I8hzdyWGVGB2YOTfgw). |
| `ffrac` | Forest fraction (dimensionless) | Based on [VIIRS GVF-NPP](https://www.star.nesdis.noaa.gov/jpss/gvf.php) and GriddingMachine/MODIS FFRAC/FVC from Terra. Data Period=2020. Data frequency=Annual. ([DiMiceli et al., 2022](https://doi.org/10.5067/MODIS/MOD44B.061)). Extended globally for high latitudes using methods described [here](https://gmuedu-my.sharepoint.com/:w:/g/personal/whung_gmu_edu/EdglXmW2kzBDtDj1xV0alGcB1Yo2I8hzdyWGVGB2YOTfgw). |
| `pavd` | Plant area volume density (m2/m3) | [GEDI product from North Arizona University](https://goetzlab.rc.nau.edu/index.php/gedi/). 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). |
| **Other External Variables** | **Variable Description and Units** | **Data Source/Reference (if necessary)** |
| `frp` | Total Fire Radiative Power (MW/grid cell area) | [NOAA/NESDIS GBBEPx](https://www.ospo.noaa.gov/Products/land/gbbepx/) |
| `csz` | Cosine of the solar zenith angle (dimensionless) | [Based on Python Pysolar](https://pysolar.readthedocs.io/en/latest/) |
Expand Down
102 changes: 83 additions & 19 deletions python/global_data_process.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
"""
Created on Sun Jun 4 15:45:29 2023
Created on Sun Jun 4 2023
Updated on Mon Oct 16 2023: Use daily gfs.canopy files

Author: Wei-Ting Hung
"""
Expand Down Expand Up @@ -65,7 +66,7 @@
f_met = (
path + "/gfs.t" + HHI + "z." + YY + MM + DD + ".sfcf0" + HH + ".nc"
) # gfs met file
f_can = path + "/gfs.canopy.t" + HHI + "z." + YY + MM + "01.sfcf000.nc" # canopy file
f_can = path + "/gfs.canopy.t" + HHI + "z." + YY + MM + DD + ".sfcf000.nc" # canopy file
f_output = (
path + "/gfs.canopy.t" + HHI + "z." + YY + MM + DD + ".global.f0" + HH + ".nc"
) # output file
Expand All @@ -88,7 +89,7 @@
+ ".nc "
)
elif frp_src == 2: # climatological frp
f_frp = path + "/gfs.canopy.t" + HHI + "z." + YY + MM + "01.sfcf000.nc"
f_frp = path + "/gfs.canopy.t" + HHI + "z." + YY + MM + DD + ".sfcf000.nc"


# required variables
Expand All @@ -109,7 +110,7 @@
"hpbl",
"prate_ave",
]
canlist = ["lai", "clu", "ffrac", "fh", "mol", "csz", "frp", "href"]
canlist = ["lai", "clu", "ffrac", "fh", "pavd", "mol", "csz", "frp", "href"]


# constants
Expand Down Expand Up @@ -149,12 +150,35 @@ def mapping(xgrid, ygrid, data, xdata, ydata, map_method, fvalue):
def read_gfs_climatology(filename, lat, lon, varname):
readin = Dataset(filename)

# map to met grids
yt = readin["lat"][:]
xt = readin["lon"][:]
data = np.squeeze(readin[varname][0, :, :])
if varname == "pavd":
# map to met grids
yt = readin["lat"][:]
xt = readin["lon"][:]
data = np.squeeze(readin[varname][:])

DATA = np.empty([data.shape[0], lat.shape[0], lat.shape[1]])

for ll in np.arange(data.shape[0]):
DATA[ll, :, :] = mapping(
lat,
lon,
data[ll, :, :].flatten(),
yt.flatten(),
xt.flatten(),
"linear",
np.nan,
)

else:
# map to met grids
yt = readin["lat"][:]
xt = readin["lon"][:]
data = np.squeeze(readin[varname][0, :, :])

DATA = mapping(
lat, lon, data.flatten(), yt.flatten(), xt.flatten(), "linear", np.nan
)

DATA = mapping(lat, lon, data.flatten(), yt.flatten(), xt.flatten(), "linear", np.nan)
DATA[np.isnan(DATA)] = 0
DATA[DATA < 0] = 0
return DATA
Expand Down Expand Up @@ -226,7 +250,8 @@ def read_frp_local(filename, lat, lon, fill_value):
+ "z."
+ YY
+ MM
+ "01.sfcf000.nc",
+ DD
+ ".sfcf000.nc",
]
)
if os.path.isfile(f_can) is True:
Expand Down Expand Up @@ -262,7 +287,7 @@ def read_frp_local(filename, lat, lon, fill_value):
else:
print("---- No available FRP file. Switch to Climatology FRP...")
frp_src = 2
f_frp = path + "/gfs.canopy.t" + HHI + "z." + YY + MM + "01.sfcf000.nc"
f_frp = path + "/gfs.canopy.t" + HHI + "z." + YY + MM + DD + ".sfcf000.nc"

if frp_src == 2: # 12 month climatology frp
if os.path.isfile(f_frp) is True:
Expand All @@ -281,7 +306,8 @@ def read_frp_local(filename, lat, lon, fill_value):
+ "z."
+ YY
+ MM
+ "01.sfcf000.nc",
+ DD
+ ".sfcf000.nc",
]
)
if os.path.isfile(f_met) is True:
Expand Down Expand Up @@ -354,6 +380,11 @@ def read_frp_local(filename, lat, lon, fill_value):
ATT = ["Canopy height above the surface", "m", fill_value]
DATA = read_gfs_climatology(f_can, lat, lon, "fh")

elif varname == "pavd":
ATTNAME = ["long_name", "units", "missing_value"]
ATT = ["Plant area volume density profile", "m2/m3", fill_value]
DATA = read_gfs_climatology(f_can, lat, lon, "pavd")

elif varname == "mol":
# Reference:
# Essa 1999, ESTIMATION OF MONIN-OBUKHOV LENGTH USING RICHARDSON AND BULK RICHARDSON NUMBER
Expand Down Expand Up @@ -409,15 +440,48 @@ def read_frp_local(filename, lat, lon, fill_value):
print(ATT)

# adding to output file
output = Dataset(f_output, "a")
if varname == "pavd":
output = Dataset(f_output, "a")
output.createDimension("level", DATA.shape[0])

var_bot = output.createVariable("layer_bottom", "i4", ("level",))
var_top = output.createVariable("layer_top", "i4", ("level",))
var = output.createVariable(
varname,
"float",
("time", "level", "grid_yt", "grid_xt"),
fill_value=fill_value,
)

var = output.createVariable(
varname, "float", ("time", "grid_yt", "grid_xt"), fill_value=fill_value
)
write_varatt(var, ATTNAME, ATT)
var[:] = DATA
write_varatt(var, ATTNAME, ATT)
write_varatt(
var_bot,
["long_name", "units"],
["height of the layer bottom above the ground", "m"],
)
write_varatt(
var_top,
["long_name", "units"],
["height of the layer top above the ground", "m"],
)

var_bot[:] = np.arange(0, 65 + 1, 5)
var_top[:] = np.arange(5, 70 + 1, 5)
var[:] = DATA

output.close()
del [var_bot, var_top]

else:
output = Dataset(f_output, "a")

var = output.createVariable(
varname, "float", ("time", "grid_yt", "grid_xt"), fill_value=fill_value
)
write_varatt(var, ATTNAME, ATT)
var[:] = DATA

output.close()
output.close()

print("---- " + varname + " complete!")

Expand Down