Global Fire Emissions Database (GFED)

This shows how to load the GFED dataset into emiproc.

First we need to download the files from the GFED website.

For this purpose we can use the emiproc function.

from pathlib import Path
from emiproc.inventories.gfed import download_gfed5

local_dir = Path("./gfed5")
local_dir.mkdir(exist_ok=True)
year = 2020

download_gfed5(local_dir, year)

Downloaded gfed5 files for year=2020.

Look at the files

Below we open the first file, which is the file for january.

It contains gridded emissions for every day of the mounth and for every grid cell in the domain.

import xarray as xr

ds = xr.open_dataset(local_dir / f"GFED5_Beta_daily_{year}01.nc")
ds

<xarray.Dataset> Size: 5GB
Dimensions:                      (lat: 720, lon: 1440, time: 31)
Coordinates:
  * lat                          (lat) float32 3kB 89.88 89.62 ... -89.62 -89.88
  * lon                          (lon) float32 6kB -179.9 -179.6 ... 179.6 179.9
  * time                         (time) datetime64[ns] 248B 2020-01-01T01:00:...
Data variables: (12/41)
    Daily fraction of emissions  (time, lat, lon) float32 129MB ...
    DM                           (time, lat, lon) float32 129MB ...
    C                            (time, lat, lon) float32 129MB ...
    CO2                          (time, lat, lon) float32 129MB ...
    CO                           (time, lat, lon) float32 129MB ...
    CH4                          (time, lat, lon) float32 129MB ...
    ...                           ...
    HCN                          (time, lat, lon) float32 129MB ...
    HCOOH                        (time, lat, lon) float32 129MB ...
    CH3COOH                      (time, lat, lon) float32 129MB ...
    MEK                          (time, lat, lon) float32 129MB ...
    CH3COCHO                     (time, lat, lon) float32 129MB ...
    HOCH2CHO                     (time, lat, lon) float32 129MB ...
Attributes:
    title:     GFED5 202001
    subtitle:  Global Fire Emissions Database (GFED) version 5 daily trace ga...

xarray.Dataset

• Dimensions:
  • lat: 720
  • lon: 1440
  • time: 31
• Coordinates: (3)
  • lat
    (lat)
    float32
    89.88 89.62 89.38 ... -89.62 -89.88

    units :

    degrees_north

    long_name :

    latitude

    array([ 89.875,  89.625,  89.375, ..., -89.375, -89.625, -89.875],
          dtype=float32)

  • lon
    (lon)
    float32
    -179.9 -179.6 ... 179.6 179.9

    units :

    degrees_east

    long_name :

    longitude

    array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2020-01-01T01:00:00 ... 2020-01-...

    long_name :

    time

    array(['2020-01-01T01:00:00.000000000', '2020-01-02T01:00:00.000000000',
           '2020-01-03T01:00:00.000000000', '2020-01-04T01:00:00.000000000',
           '2020-01-05T01:00:00.000000000', '2020-01-06T01:00:00.000000000',
           '2020-01-07T01:00:00.000000000', '2020-01-08T01:00:00.000000000',
           '2020-01-09T01:00:00.000000000', '2020-01-10T01:00:00.000000000',
           '2020-01-11T01:00:00.000000000', '2020-01-12T01:00:00.000000000',
           '2020-01-13T01:00:00.000000000', '2020-01-14T01:00:00.000000000',
           '2020-01-15T01:00:00.000000000', '2020-01-16T01:00:00.000000000',
           '2020-01-17T01:00:00.000000000', '2020-01-18T01:00:00.000000000',
           '2020-01-19T01:00:00.000000000', '2020-01-20T01:00:00.000000000',
           '2020-01-21T01:00:00.000000000', '2020-01-22T01:00:00.000000000',
           '2020-01-23T01:00:00.000000000', '2020-01-24T01:00:00.000000000',
           '2020-01-25T01:00:00.000000000', '2020-01-26T01:00:00.000000000',
           '2020-01-27T01:00:00.000000000', '2020-01-28T01:00:00.000000000',
           '2020-01-29T01:00:00.000000000', '2020-01-30T01:00:00.000000000',
           '2020-01-31T01:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (41)
  • Daily fraction of emissions
    (time, lat, lon)
    float32
    ...

    units :

    Fraction_of_emissions

    standard_name :

    Fraction of total monthly emissions emitted per day

    [32140800 values with dtype=float32]

  • DM
    (time, lat, lon)
    float32
    ...

    units :

    g DM m^-2 day^-1

    standard_name :

    GFED5 emissions of DM

    [32140800 values with dtype=float32]

  • C
    (time, lat, lon)
    float32
    ...

    units :

    g C m^-2 day^-1

    standard_name :

    GFED5 emissions of C

    [32140800 values with dtype=float32]

  • CO2
    (time, lat, lon)
    float32
    ...

    units :

    g CO2 m^-2 day^-1

    standard_name :

    GFED5 emissions of CO2

    [32140800 values with dtype=float32]

  • CO
    (time, lat, lon)
    float32
    ...

    units :

    g CO m^-2 day^-1

    standard_name :

    GFED5 emissions of CO

    [32140800 values with dtype=float32]

  • CH4
    (time, lat, lon)
    float32
    ...

    units :

    g CH4 m^-2 day^-1

    standard_name :

    GFED5 emissions of CH4

    [32140800 values with dtype=float32]

  • NMOC_g
    (time, lat, lon)
    float32
    ...

    units :

    g NMOC_g m^-2 day^-1

    standard_name :

    GFED5 emissions of NMOC_g

    [32140800 values with dtype=float32]

  • H2
    (time, lat, lon)
    float32
    ...

    units :

    g H2 m^-2 day^-1

    standard_name :

    GFED5 emissions of H2

    [32140800 values with dtype=float32]

  • NOx
    (time, lat, lon)
    float32
    ...

    units :

    g NOx m^-2 day^-1 (as NO)

    standard_name :

    GFED5 emissions of NOx (as NO)

    [32140800 values with dtype=float32]

  • N2O
    (time, lat, lon)
    float32
    ...

    units :

    g N2O m^-2 day^-1

    standard_name :

    GFED5 emissions of N2O

    [32140800 values with dtype=float32]

  • PM2p5
    (time, lat, lon)
    float32
    ...

    units :

    g PM2p5 m^-2 day^-1

    standard_name :

    GFED5 emissions of PM2p5

    [32140800 values with dtype=float32]

  • TPC
    (time, lat, lon)
    float32
    ...

    units :

    g TPC m^-2 day^-1

    standard_name :

    GFED5 emissions of TPC

    [32140800 values with dtype=float32]

  • OC
    (time, lat, lon)
    float32
    ...

    units :

    g OC m^-2 day^-1

    standard_name :

    GFED5 emissions of OC

    [32140800 values with dtype=float32]

  • BC
    (time, lat, lon)
    float32
    ...

    units :

    g BC m^-2 day^-1

    standard_name :

    GFED5 emissions of BC

    [32140800 values with dtype=float32]

  • SO2
    (time, lat, lon)
    float32
    ...

    units :

    g SO2 m^-2 day^-1

    standard_name :

    GFED5 emissions of SO2

    [32140800 values with dtype=float32]

  • NH3
    (time, lat, lon)
    float32
    ...

    units :

    g NH3 m^-2 day^-1

    standard_name :

    GFED5 emissions of NH3

    [32140800 values with dtype=float32]

  • C2H6
    (time, lat, lon)
    float32
    ...

    units :

    g C2H6 m^-2 day^-1

    standard_name :

    GFED5 emissions of C2H6

    [32140800 values with dtype=float32]

  • CH3OH
    (time, lat, lon)
    float32
    ...

    units :

    g CH3OH m^-2 day^-1

    standard_name :

    GFED5 emissions of CH3OH

    [32140800 values with dtype=float32]

  • C2H5OH
    (time, lat, lon)
    float32
    ...

    units :

    g C2H5OH m^-2 day^-1

    standard_name :

    GFED5 emissions of C2H5OH

    [32140800 values with dtype=float32]

  • C3H8
    (time, lat, lon)
    float32
    ...

    units :

    g C3H8 m^-2 day^-1

    standard_name :

    GFED5 emissions of C3H8

    [32140800 values with dtype=float32]

  • C2H2
    (time, lat, lon)
    float32
    ...

    units :

    g C2H2 m^-2 day^-1

    standard_name :

    GFED5 emissions of C2H2

    [32140800 values with dtype=float32]

  • C2H4
    (time, lat, lon)
    float32
    ...

    units :

    g C2H4 m^-2 day^-1

    standard_name :

    GFED5 emissions of C2H4

    [32140800 values with dtype=float32]

  • C3H6
    (time, lat, lon)
    float32
    ...

    units :

    g C3H6 m^-2 day^-1

    standard_name :

    GFED5 emissions of C3H6

    [32140800 values with dtype=float32]

  • C5H8
    (time, lat, lon)
    float32
    ...

    units :

    g C5H8 m^-2 day^-1

    standard_name :

    GFED5 emissions of C5H8

    [32140800 values with dtype=float32]

  • C10H16
    (time, lat, lon)
    float32
    ...

    units :

    g C10H16 m^-2 day^-1

    standard_name :

    GFED5 emissions of C10H16

    [32140800 values with dtype=float32]

  • C7H8
    (time, lat, lon)
    float32
    ...

    units :

    g C7H8 m^-2 day^-1

    standard_name :

    GFED5 emissions of C7H8

    [32140800 values with dtype=float32]

  • C6H6
    (time, lat, lon)
    float32
    ...

    units :

    g C6H6 m^-2 day^-1

    standard_name :

    GFED5 emissions of C6H6

    [32140800 values with dtype=float32]

  • C8H10
    (time, lat, lon)
    float32
    ...

    units :

    g C8H10 m^-2 day^-1

    standard_name :

    GFED5 emissions of C8H10

    [32140800 values with dtype=float32]

  • Toluene_lump
    (time, lat, lon)
    float32
    ...

    units :

    g Toluene_lump m^-2 day^-1

    standard_name :

    GFED5 emissions of Toluene_lump

    [32140800 values with dtype=float32]

  • Higher_Alkenes
    (time, lat, lon)
    float32
    ...

    units :

    g Higher_Alkenes m^-2 day^-1

    standard_name :

    GFED5 emissions of Higher_Alkenes

    [32140800 values with dtype=float32]

  • Higher_Alkanes
    (time, lat, lon)
    float32
    ...

    units :

    g Higher_Alkanes m^-2 day^-1

    standard_name :

    GFED5 emissions of Higher_Alkanes

    [32140800 values with dtype=float32]

  • CH2O
    (time, lat, lon)
    float32
    ...

    units :

    g CH2O m^-2 day^-1

    standard_name :

    GFED5 emissions of CH2O

    [32140800 values with dtype=float32]

  • C2H4O
    (time, lat, lon)
    float32
    ...

    units :

    g C2H4O m^-2 day^-1

    standard_name :

    GFED5 emissions of C2H4O

    [32140800 values with dtype=float32]

  • C3H6O
    (time, lat, lon)
    float32
    ...

    units :

    g C3H6O m^-2 day^-1

    standard_name :

    GFED5 emissions of C3H6O

    [32140800 values with dtype=float32]

  • C2H6S
    (time, lat, lon)
    float32
    ...

    units :

    g C2H6S m^-2 day^-1

    standard_name :

    GFED5 emissions of C2H6S

    [32140800 values with dtype=float32]

  • HCN
    (time, lat, lon)
    float32
    ...

    units :

    g HCN m^-2 day^-1

    standard_name :

    GFED5 emissions of HCN

    [32140800 values with dtype=float32]

  • HCOOH
    (time, lat, lon)
    float32
    ...

    units :

    g HCOOH m^-2 day^-1

    standard_name :

    GFED5 emissions of HCOOH

    [32140800 values with dtype=float32]

  • CH3COOH
    (time, lat, lon)
    float32
    ...

    units :

    g CH3COOH m^-2 day^-1

    standard_name :

    GFED5 emissions of CH3COOH

    [32140800 values with dtype=float32]

  • MEK
    (time, lat, lon)
    float32
    ...

    units :

    g MEK m^-2 day^-1

    standard_name :

    GFED5 emissions of MEK

    [32140800 values with dtype=float32]

  • CH3COCHO
    (time, lat, lon)
    float32
    ...

    units :

    g CH3COCHO m^-2 day^-1

    standard_name :

    GFED5 emissions of CH3COCHO

    [32140800 values with dtype=float32]

  • HOCH2CHO
    (time, lat, lon)
    float32
    ...

    units :

    g HOCH2CHO m^-2 day^-1

    standard_name :

    GFED5 emissions of HOCH2CHO

    [32140800 values with dtype=float32]

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 89.875,  89.625,  89.375,  89.125,  88.875,  88.625,  88.375,  88.125,
            87.875,  87.625,
           ...
           -87.625, -87.875, -88.125, -88.375, -88.625, -88.875, -89.125, -89.375,
           -89.625, -89.875],
          dtype='float32', name='lat', length=720))

  • lon
    PandasIndex

    PandasIndex(Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625, -178.375,
           -178.125, -177.875, -177.625,
           ...
            177.625,  177.875,  178.125,  178.375,  178.625,  178.875,  179.125,
            179.375,  179.625,  179.875],
          dtype='float32', name='lon', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-01-01 01:00:00', '2020-01-02 01:00:00',
                   '2020-01-03 01:00:00', '2020-01-04 01:00:00',
                   '2020-01-05 01:00:00', '2020-01-06 01:00:00',
                   '2020-01-07 01:00:00', '2020-01-08 01:00:00',
                   '2020-01-09 01:00:00', '2020-01-10 01:00:00',
                   '2020-01-11 01:00:00', '2020-01-12 01:00:00',
                   '2020-01-13 01:00:00', '2020-01-14 01:00:00',
                   '2020-01-15 01:00:00', '2020-01-16 01:00:00',
                   '2020-01-17 01:00:00', '2020-01-18 01:00:00',
                   '2020-01-19 01:00:00', '2020-01-20 01:00:00',
                   '2020-01-21 01:00:00', '2020-01-22 01:00:00',
                   '2020-01-23 01:00:00', '2020-01-24 01:00:00',
                   '2020-01-25 01:00:00', '2020-01-26 01:00:00',
                   '2020-01-27 01:00:00', '2020-01-28 01:00:00',
                   '2020-01-29 01:00:00', '2020-01-30 01:00:00',
                   '2020-01-31 01:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (2)

  title :

  GFED5 202001

  subtitle :

  Global Fire Emissions Database (GFED) version 5 daily trace gas and aerosol emissions for 202001. Note this is a Beta version and prone to changes until the accompanying paper is accepted

Load with emiproc

emiproc hopefully makes it easy to load and process the data. You need simply to specify which of the substances you want to load.

from emiproc.inventories.gfed import GFED5

gfed = GFED5(file_dir=local_dir, year=year, substances=["CO2", "CH4"])

from emiproc.plots import plot_inventory
import matplotlib.pyplot as plt

plt.style.use("default")

plot_inventory(gfed, reverse_y=True)

Temporal distribution

The gfed inventory also has a temporal distribution of the emissions.

With emiproc we can get the temporally distributed emissions of the inventory. This is what we do below, where we get for each cell/day/category/substance the emissions.

from emiproc.exports.utils import get_temporally_scaled_array
import pandas as pd

emissions = get_temporally_scaled_array(gfed, time_range=year, sum_over_cells=False)
emissions

<xarray.DataArray (substance: 2, category: 1, cell: 1036800, time: 366)> Size: 6GB
array([[[[0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.],
         ...,
         [0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.]]],


       [[[0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.],
         ...,
         [0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 0.]]]])
Coordinates:
  * substance  (substance) <U3 24B 'CO2' 'CH4'
  * category   (category) <U4 16B 'gfed'
  * cell       (cell) int32 4MB 0 1 2 3 4 ... 1036796 1036797 1036798 1036799
  * time       (time) datetime64[ns] 3kB 2020-01-01 2020-01-02 ... 2020-12-31

xarray.DataArray

• substance: 2
• category: 1
• cell: 1036800
• time: 366

• 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

  array([[[[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]]],
  
  
         [[[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]]]])

• Coordinates: (4)
  • substance
    (substance)
    <U3
    'CO2' 'CH4'

    array(['CO2', 'CH4'], dtype='<U3')

  • category
    (category)
    <U4
    'gfed'

    array(['gfed'], dtype='<U4')

  • cell
    (cell)
    int32
    0 1 2 3 ... 1036797 1036798 1036799

    array([      0,       1,       2, ..., 1036797, 1036798, 1036799])

  • time
    (time)
    datetime64[ns]
    2020-01-01 ... 2020-12-31

    array(['2020-01-01T00:00:00.000000000', '2020-01-02T00:00:00.000000000',
           '2020-01-03T00:00:00.000000000', ..., '2020-12-29T00:00:00.000000000',
           '2020-12-30T00:00:00.000000000', '2020-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (4)
  • substance
    PandasIndex

    PandasIndex(Index(['CO2', 'CH4'], dtype='object', name='substance'))

  • category
    PandasIndex

    PandasIndex(Index(['gfed'], dtype='object', name='category'))

  • cell
    PandasIndex

    PandasIndex(Index([      0,       1,       2,       3,       4,       5,       6,       7,
                 8,       9,
           ...
           1036790, 1036791, 1036792, 1036793, 1036794, 1036795, 1036796, 1036797,
           1036798, 1036799],
          dtype='int32', name='cell', length=1036800))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',
                   '2020-01-05', '2020-01-06', '2020-01-07', '2020-01-08',
                   '2020-01-09', '2020-01-10',
                   ...
                   '2020-12-22', '2020-12-23', '2020-12-24', '2020-12-25',
                   '2020-12-26', '2020-12-27', '2020-12-28', '2020-12-29',
                   '2020-12-30', '2020-12-31'],
                  dtype='datetime64[ns]', name='time', length=366, freq='D'))

• Attributes: (0)

Now we can plot this results.

We see a large correlation between the 2 substances but we can see that the emissions are not the same.

total = emissions.sum(dim="cell") / 365
fig, ax = plt.subplots(nrows=len(gfed.substances), figsize=(10, 5), sharex=True)
for i, substance, ax in zip(range(len(gfed.substances)), gfed.substances, ax):
    ax.plot(total["time"], total.sel(substance=substance).values.ravel())
    ax.set_ylabel(f"{substance} [kg/day]")
ax.set_xlabel("Time")

Text(0.5, 0, 'Time')