Exploring Fire - Active Fire & Fire Radiative Power products available through the C3S

This notebook can be run on free online platforms, such as Binder, Kaggle and Colab, or they can be accessed from GitHub. The links to run this notebook in these environments are provided here, but please note they are not supported by ECMWF.

Introduction

Fire Radiative Power (FRP) and Active Fire data enable us to quantify biomass burning, improve the accuracy of our estimates of greenhouse gas and aerosol emissions, and support real-time monitoring of fire activity (important for climate modeling, air quality forecasting, and land management). When examined as (Intermediate) Climate Data Records they can provide key insights into fire trends and feedbacks within the Earth system, making them useful for understanding and responding to climate change

Learning objectives 🎯

At the end of this notebook, the user will be able to:

• Understand and use key terminology;

• Retrieve data on Fire AF & FRP products from the C3S Climate Data Store, examining, subsetting and re-projecting it using Python code;

• Create an FRP time series for a given location.

• Calculate the start, end and duration of a region’s fire season using FRP data

Key Terms & context

This dataset used in this tutorial provides a set of active fire (AF) and fire radiative power (FRP) products derived from observations made by the Sea and Land Surface Temperature Radiometer (SLSTR) operating concurrently onboard the European Sentinel-3 satellites, currently S3A and S3B. These products are generated from the non-time critical (NTC) Level 2 AF detection and FRP product from European Space Agency (ESA). The AF, FRP and the Burnt Areas are Essential Climate Variables (ECV) from the Global Observing System for Climate (GCOS). Burnt Area is provided through a different catalogue entry.

Active Fire (AF)

A fire that was actively burning when the satellite observations were made. Satellite ‘Active Fire’ products are those that report information on these types of ‘hotspots’ using thermal remote sensing techniques. AF or ‘hotspot’ pixels are pixels classified as containing one or more fires when the observation was made. Generally, most AF or ‘hotspot’ pixels are related to landscape fires, but some are related to other phenomena such as gas flares, volcanos or even other warm industrial targets. A small proportion of AF pixels are also ‘false alarms’ caused for example by unmasked areas of sunglint, land-water boundaries, or solar heated warm ground. Removal of such false alarms is typically attempted prior to final generation of the dataset.

Fire Radiative Power (FRP)

The rate of radiant heat output from a landscape fire, typically expressed in Watts x 106 (MW). FRP is typically very well related to a fire´s combustion rate (how much material is being burned per unit time) and rate of smoke emission, and hence remotely-sensed FRP measures are commonly used to estimate these terms. At the pixel scale, a satellite product typically is reporting the total FRP from all fires burning within that pixel at the time the observation was made.

Gas Flare

A controlled open flame used to burn off excess natural or other combustible gas, typically at oil and gas facilities, chemical plants and natural gas processing plants, or landfills. Sometimes known as a flare stack, flare boom, ground flare, or flare pit.

AF & FRP Products

The AF and the FRP products encompass a Level 2 summary product which provides a text-based table summary of the Level 2 AF detection and FRP data collected over the period of one month across the globe. In addition, three gridded Level 3 ‘synthesis products’ are also provided. Each synthesis product grids the Level 2 AF and FRP data at various spatial and temporal resolutions, and provides information for including or removing adjustments for cloud cover that blocks the fires from the satellite sensors view. The following gridded products are available:

• daily global Level 3 FRP product generated on a global 0.1 degree resolution grid. AF data coming from observations made by both Sentinel-3A (S3A) and -3B (S3B) have separate layers in this product. Initially only night-time land observations were included, with the full daytime dataset provided subsequently once the corresponding NTC Level 2 FRP data became available.

• 27 day global Level 3 FRP product also derived at 0.1 degree grid cell resolution but summarising the FRP data from both Sentinel-3A and -3B. This time period matches the Sentinel-3 satellites standard orbital repeat cycle.

• monthly global Level 3 FRP product which provides global AF detection and FRP data at a grid cell size of 0.25 degrees. This resolution matches the MODIS Climate Modelling Grid (CMG) active fire product.

The purpose of these AF and FRP products is to provide a summarised and ‘easy to use’ version of the Level 2 AF and FRP products, as well as providing the AF and FRP information in a consistently gridded format. The latter in particular is designed to be suitable for use in global modelling, trend analysis and model evaluation.

These AF and FRP were produced on behalf of the Copernicus Climate Change Service and have been designed to be broadly comparable with the equivalent Fire Burnt Area product, since some applications may be likely to use them in combination.

Figure 1: Exemplary Active Fire und Fire Radiative Power Map

References:

• Freeborn, P.H., Cochrane, M.A. and Wooster, M.J., 2014. A decade long, multi-scale map comparison of fire regime parameters derived from three publically available satellite-based fire products: A case study in the Central African Republic. Remote Sensing, 6(5), pp.4061-4089.

• Silva, P., Carmo, M., Rio, J. and Novo, I., 2023. Changes in the seasonality of fire activity and fire weather in Portugal: is the wildfire season really longer? Meteorology, 2(1), pp.74-86.

Prepare your environment

Set up CDSAPI and your credentials

The code below will ensure that the cdsapi package is installed. If you have not setup your ~/.cdsapirc file with your credenials, you can replace None with your credentials that can be found on the how to api page (you will need to log in to see your credentials).

!pip install -q cdsapi
# If you have already setup your .cdsapirc file you can leave this as None
cdsapi_key = None
cdsapi_url = None

[notice] A new release of pip is available: 25.1.1 -> 25.3
[notice] To update, run: python.exe -m pip install --upgrade pip

(Install and) Import libraries

The following code block will import the python modules required for this notebook. We will be using earthkit-data and xarray for handling the data, and matplotlib and cartopy for plotting. The code block also includes a command to install earthkit-data form pip.

import os

# Modules related to data retrieving
!pip install -q "earthkit-data[all]"
import earthkit as ek
import cdsapi

# Modules related to plot and EO data manipulation
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import geopandas as gpd

[notice] A new release of pip is available: 25.1.1 -> 25.3
[notice] To update, run: python.exe -m pip install --upgrade pip

Specify data directory

Below we define a directory to store the files locally, and create this directory if it does not already exist

# Directory to store data
# Please ensure that data_dir is a location where you have write permissions
DATADIR = './data_dir/'
# Create this directory if it doesn't exist
os.makedirs(DATADIR, exist_ok=True)

Explore data

The Copernicus Climate Change Service (C3S) provides Intermediate Climate Data Records (ICDRs) for many Essential Climate Variables (ECVs), amongst which is active fire (AF) and fire radiative power (FRP) - [C3S Fire AF & FRP dataset]. The C3S AF & FRP products are generated from observations made by the Sea and Land Surface Temperature Radiometer (SLSTR) operating onboard the Sentinel-3 satellites, currently (2023) Sentinel-3A and Sentinel-3B.

Sentinel-3 SLSTR Level 1 data are used to generate a set of granule-based Level 2 Active Fire (AF) Detection and FRP products. Each stores essential information about pixels believed to contain actively burning fires identified in each 3 minute Universal Time SLSTR Level 1 granule. This includes their location, fire radiative power (FRP in MW) and FRP uncertainty, along with a SUMMARY FLAG dataset providing information on every pixel in the granule related primarily to the output of the active fire detection tests. These SUMMARY FLAG data are stored as a 2D array bit mask. Each Level 2 FRP product corresponds to a single SLSTR data granule and contains a very large amount of information across a set of eleven measurement and annotation data files, plus an XML manifest (xfdumanifest.xml) that describes the content and metadata of the package. The manifest is the entry point for accessing and processing the package. Whilst these Level 2 FRP product files are extremely comprehensive in the information they contain, use of them at the global scale and over long time periods results in potentially very large numbers of data files. Therefore, the C3S FRP products provide both a Level 2 global summary FRP product that provides a text-based summary of the Level 2 Active Fire Detection and FRP product data collected over the period of one month at the locations of all detected active fire (AF) pixels. In addition, they provide three Level 3 ‘synthesis products’ which summarize the Level 2 AF detection and FRP data at various spatial and temporal resolutions, and provide some adjustments for cloud cover variations. These C3S products are designed for ease of access and use of the key information held within the Level 2 granule-based FRP products, and for global modelling, trend analysis and model evaluation.

Data description

DATA	DESCRIPTION
Data type	Gridded for the gridded product Point data for the summary product
Horizontal coverage	Global
Horizontal resolution	0.1°x 0.1° and 0.25°x 0.25° for the gridded product Point data (based on 1km observations) for the summary product
Vertical coverage	Surface
Vertical resolution	Single level
Temporal coverage	From March 2020 to present
Temporal resolution	Daily, 27 days (Sentinel-3 repeat cycle) and monthly for the gridded product Individual time stamps of active fire events, monthly files for the summary product
File format	NetCDF4 for the gridded product CSV for the summary product
Conventions	Climate and Forecast (CF) Metadata Convention v1.7 and ESA CCI Data Standards [DSWG 2015] for the gridded product
Versions	v1.0 - March 2020 - February 2021 v1.2 March 2021 to present
Update frequency	Yearly

Main variables

	MAIN VARIABLES
Name	Units	Description
Active pixels	Count	Total number of active fire pixels in a grid cell for either Sentinel-3A or Sentinel-3B.
Fire radiative power	MW	Fire Radiative Power (FRP) represents the rate of outgoing thermal radiative energy coming from a burning landscape fire, integrated over all emitted wavelengths and over the hemisphere above the fire. It is expressed in Watts (Joules per Sec). Within a single pixels field of view there can be many landscape fires burning and thus the FRP recorded is that of all fires within the pixel, and is typically expressed in MWatts (MW). The FRP is measured at day and night either by Sentinel-3A or -3B and when derived from MWIR (middle infrared) channel observations, which are those most suitable for landscape fire FRP estimation. NetCDF files also provides FRP_SWIR, which is the same FRP metric as represented by FRP_MWIR, but now derived using SWIR channel observations. This form of FRP derivation is more suited to phenomena of higher temperature than landscape fires, most notably industrial gas flares.
Mean fire radiative power	MW	Rate of radiant heat output from a fire. This variable is measured during day or night time either by Sentinel-3A or Sentinel-3B.

Note

Due to an error in the processing chain of the ESA SLSTR L2 FRP NTC products (currently under correction) sometimes fires that are temporarily categorised as ‘unclassified’ are not reclassified correctly (e.g. as vegetation fires; gas flares; etc.) The user should use the classification flag in the C3S AF & FRP L2 summary product derived from the ESA SLSTR L2 FRP NTC products with caution and also consider the unclassified fires.

Download the data

There are different ways to download data in the Climate Data Store. You can do it manually from the Download tab, or you can download the data using the CDS-API as we do in this notebook. To write a new request, the easiest way is to select your data parameters on the Download tab, then click on “Show API request”, and copy/paste it in a file (or directly in a notebook code block).

Warning

Please remember to accept the terms and conditions of the dataset, at the bottom of the CDS download form.

Active Fire & Fire Radiative Power summary product

In this first example we request and download the tabular summary of the Active Fire and Fire Radiative Power products for April 2020.

summary_file = f"{DATADIR}/frp_summary_04_2020.zip"

# Downloading ba-grid product
c = cdsapi.Client(url=cdsapi_url, key=cdsapi_key)
dataset = "satellite-fire-radiative-power"
request = {
    "variable": "all",
    "product_type": "table_summary",
    "time_aggregation": "month",
    "year": "2020",
    "month": ["04"],
    "day": ["01"],
    "version": ["1_0"]
}

if not os.path.exists(summary_file):
    c.retrieve(dataset, request).download(target=summary_file)

# We then open this downloaded file with EarthKit 
frp_summary_data = ek.data.from_source("file", summary_file)

Active Fire & Fire Radiative Power gridded products

Now we’ll take a look at the gridded data product and plot an example for April 2020. The following code block will request and download the gridded (0.25°x0.25°), monthly aggregated, Active Fire and Fire Radiative Power products for April 2020.

gridded_data_file = f"{DATADIR}/frp_month_202004.zip"

request = {
    "variable": "all",
    "product_type": "gridded",
    "time_aggregation": "month",
    "horizontal_aggregation": "0_25_degree_x_0_25_degree",
    "year": "2020",
    "month": ["04"],
    "day": ["01"],
    "version": ["1_0"]
}

if not os.path.exists(gridded_data_file):
    c.retrieve(dataset, request).download(target=gridded_data_file)

# We then open this downloaded file with EarthKit 
frp_data_042020 = ek.data.from_source("file", gridded_data_file)

Visualise the C3S Fire AF & FRP dataset

Active Fire & Fire Radiative Power summary product

We can use the earthkit methods to convert open the summary table file with pandas, we then convert this to a geopandas to make plotting easier. The output of the code block below shows the geopandas Table, as well as the data and auxiliary property columns. There is a geometry column which makes the plotting step in the next clode block much simpler.

frp_summary_df = frp_summary_data.to_pandas(pandas_read_csv_kwargs={"sep":"\t", "header": 0, "na_values":"NaN", "index_col": False})

frp_summary_df_geo=gpd.GeoDataFrame(frp_summary_df, geometry = gpd.points_from_xy(frp_summary_df.Longitude, frp_summary_df.Latitude))

frp_summary_df_geo

	Column	Row	Date	Time	Latitude	Longitude	sat_zenith	FRP_MWIR	FRP_MWIR_uncertainty	FRP_SWIR	FRP_SWIR_uncertainty	Confidence	F1_flag	Day_flag	Area	Platform	Land/Ocean	Hotspot_class	geometry
0	1145	638	20200401	10344	-22.90556	-43.73040	12.73245	4.029664	1.119233	0.000000	-1.000000	0.000000	0	0	1207521.500	S3B	1	1	POINT (-43.7304 -22.90556)
1	1146	638	20200401	10344	-22.90909	-43.73986	12.79676	5.546079	1.109867	0.000000	-1.000000	0.000000	0	0	1208348.875	S3B	1	1	POINT (-43.73986 -22.90909)
2	1477	1057	20200401	10504	-19.82693	-47.70098	34.50563	6.409650	1.660399	27.854242	3.030277	1.000000	0	0	1900658.625	S3B	1	1	POINT (-47.70098 -19.82693)
3	776	1168	20200401	10506	-17.48359	-41.43576	17.64586	5.053819	0.538410	11.881440	3.061714	1.000000	0	0	1299196.625	S3B	1	1	POINT (-41.43576 -17.48359)
4	914	221	20200401	10540	-15.53118	-43.23381	8.94689	4.431545	0.464394	7.341445	1.933330	1.000000	0	0	1174161.875	S3B	1	1	POINT (-43.23381 -15.53118)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
247221	402	223	20200430	225415	13.74195	-9.91895	41.52260	4.836735	1.124969	12.944501	3.629281	1.000000	0	0	2481530.500	S3A	1	1	POINT (-9.91895 13.74195)
247222	439	260	20200430	225417	14.00411	-10.31795	39.41111	11.186954	1.862848	0.000000	-1.000000	0.727656	0	0	2285302.000	S3A	1	1	POINT (-10.31795 14.00411)
247223	417	265	20200430	225420	14.08317	-10.13093	40.67306	3.822167	1.029658	0.000000	-1.000000	0.000000	0	0	2397918.750	S3A	1	1	POINT (-10.13093 14.08317)
247224	127	282	20200430	225456	14.73717	-7.52490	55.16823	27.987770	3.016432	0.000000	-1.000000	0.607388	0	0	5011016.000	S3A	1	1	POINT (-7.5249 14.73717)
247225	126	282	20200430	225456	14.74019	-7.51452	55.21109	27.085037	2.956677	0.000000	-1.000000	0.565830	0	0	5011016.000	S3A	1	1	POINT (-7.51452 14.74019)

247226 rows × 19 columns

We can then plot this tabular summary with cartopy and geopandas.

title = 'Active Fire - 04/2020'

ax = plt.axes(projection=ccrs.PlateCarree())
# We can now plot the data using the inbuilt geopandas plot function:
fig = frp_summary_df_geo.plot(ax=ax, color='red', marker='.', markersize=0.001)
gl = ax.gridlines(draw_labels=True, linewidth=1, color='gray', alpha=0.5, linestyle='--')
gl.ylabels_right = False
gl.xlabels_top=False
ax.coastlines()
ax.set_xlim(-180, 180)
ax.set_ylim(-90, 90)
ax.set_title(title)

Text(0.5, 1.0, 'Active Fire - 04/2020')

Figure 2: Map of the Active Fire for April 2020 retrieved from the C3S Fire AF & FRP Night-Time Product

Now we will take a look at the gridded data product and plot an example for April 2020.

Now we can use earthkit to convert the data to an xarray object and inspect the contents. The output of the code block below shows the data in an Xarray.Dataset format. You can explore the data variables, their dimensionality and any attributes attached.

data_frp_all_month = frp_data_042020.to_xarray()
data_frp_all_month

<xarray.Dataset> Size: 91MB
Dimensions:                                           (lon: 1440, lat: 720,
                                                       time: 1, bounds: 2)
Coordinates:
  * lon                                               (lon) float32 6kB -179....
  * lat                                               (lat) float32 3kB 89.88...
  * time                                              (time) datetime64[ns] 8B ...
Dimensions without coordinates: bounds
Data variables: (12/17)
    lon_bounds                                        (lon, bounds) float32 12kB dask.array<chunksize=(1440, 2), meta=np.ndarray>
    lat_bounds                                        (lat, bounds) float32 6kB dask.array<chunksize=(720, 2), meta=np.ndarray>
    time_bounds                                       (time, bounds) datetime64[ns] 16B dask.array<chunksize=(1, 2), meta=np.ndarray>
    s3a_night_fire                                    (time, lat, lon) float64 8MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3a_night_frp                                     (time, lat, lon) float32 4MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3a_night_pixel                                   (time, lat, lon) float64 8MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    ...                                                ...
    s3b_night_FRP_related_surface_conditions_flag     (time, lat, lon) float64 8MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3b_night_FRP_related_atmospheric_condition_flag  (time, lat, lon) float64 8MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3a_night_related_atmospheric_condition_fraction  (time, lat, lon) float32 4MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3b_night_related_atmospheric_condition_fraction  (time, lat, lon) float32 4MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3a_night_fire_weighted                           (time, lat, lon) float32 4MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3b_night_fire_weighted                           (time, lat, lon) float32 4MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
Attributes: (12/39)
    title:                      ECMWF C3S Gridded OLCI Fire Radiative Power p...
    institution:                King's College London, Brockmann Consult GmbH
    source:                     ESA Sentinel-3 A+B SLSTR FRP
    history:                    Created on 20210325T075328Z
    references:                 See https://climate.copernicus.eu/
    tracking_id:                f25465fd-dd1a-44da-9b5c-9093ec610e44
    ...                         ...
    sensor:                     SLSTR
    spatial_resolution:         0.25 degrees
    geospatial_lon_units:       degrees_east
    geospatial_lat_units:       degrees_north
    geospatial_lon_resolution:  0.25
    geospatial_lat_resolution:  0.25

xarray.Dataset

• Dimensions:
  • lon: 1440
  • lat: 720
  • time: 1
  • bounds: 2
• Coordinates: (3)
  • lon
    (lon)
    float32
    -179.9 -179.6 ... 179.6 179.9

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    bounds :

    lon_bounds

    array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],
          shape=(1440,), dtype=float32)

  • lat
    (lat)
    float32
    89.88 89.62 89.38 ... -89.62 -89.88

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    bounds :

    lat_bounds

    array([ 89.875,  89.625,  89.375, ..., -89.375, -89.625, -89.875],
          shape=(720,), dtype=float32)

  • time
    (time)
    datetime64[ns]
    2020-04-01

    standard_name :

    time

    long_name :

    time

    bounds :

    time_bounds

    array(['2020-04-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (17)
  • lon_bounds
    (lon, bounds)
    float32
    dask.array<chunksize=(1440, 2), meta=np.ndarray>

    Array Chunk Bytes 11.25 kiB 11.25 kiB Shape (1440, 2) (1440, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lat_bounds
    (lat, bounds)
    float32
    dask.array<chunksize=(720, 2), meta=np.ndarray>

    Array Chunk Bytes 5.62 kiB 5.62 kiB Shape (720, 2) (720, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • time_bounds
    (time, bounds)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    Array Chunk Bytes 16 B 16 B Shape (1, 2) (1, 2) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • s3a_night_fire
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of S3A nighttime active fire pixels

    Array Chunk Bytes 7.91 MiB 506.25 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s3a_night_frp
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    MW

    long_name :

    Mean Fire Radiative Power measured by S3A during nighttime

    Array Chunk Bytes 3.96 MiB 253.12 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float32 numpy.ndarray

  • s3a_night_pixel
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of S3A nighttime pixels

    Array Chunk Bytes 7.91 MiB 506.25 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s3a_night_FRP_related_surface_conditions_flag
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of pixels unprocessed by the AF detection algorithm due to them being considered unsuitable surfaces, e.g. permanent water

    Array Chunk Bytes 7.91 MiB 506.25 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s3a_night_FRP_related_atmospheric_condition_flag
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of pixels unprocessed by the AF detection algorithm due to them being considered to have unsuitable atmospheric conditions for FRP product processing, e.g. certain types of cloud

    Array Chunk Bytes 7.91 MiB 506.25 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s3b_night_fire
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of S3B nighttime active fire pixels

    Array Chunk Bytes 7.91 MiB 506.25 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s3b_night_frp
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    MW

    long_name :

    Mean Fire Radiative Power measured by S3B during nighttime

    Array Chunk Bytes 3.96 MiB 253.12 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float32 numpy.ndarray

  • s3b_night_pixel
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of S3B nighttime pixels

    Array Chunk Bytes 7.91 MiB 506.25 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s3b_night_FRP_related_surface_conditions_flag
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of pixels unprocessed by the AF detection algorithm due to them being considered unsuitable surfaces, e.g. permanent water

    Array Chunk Bytes 7.91 MiB 506.25 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s3b_night_FRP_related_atmospheric_condition_flag
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of pixels unprocessed by the AF detection algorithm due to them being considered to have unsuitable atmospheric conditions for FRP product processing, e.g. certain types of cloud

    Array Chunk Bytes 7.91 MiB 506.25 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s3a_night_related_atmospheric_condition_fraction
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Mean unsuitable atmospheric condition fraction of S3A land pixels in a macro pixel of 1.25 degrees

    Array Chunk Bytes 3.96 MiB 253.12 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float32 numpy.ndarray

  • s3b_night_related_atmospheric_condition_fraction
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Mean unsuitable atmospheric condition fraction of S3B land pixels in a macro pixel of 1.25 degrees

    Array Chunk Bytes 3.96 MiB 253.12 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float32 numpy.ndarray

  • s3a_night_fire_weighted
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Number of S3A nighttime active fire pixels weighted by atmospheric condition fraction

    Array Chunk Bytes 3.96 MiB 253.12 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float32 numpy.ndarray

  • s3b_night_fire_weighted
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Number of S3B nighttime active fire pixels weighted by atmospheric condition fraction

    Array Chunk Bytes 3.96 MiB 253.12 kiB Shape (1, 720, 1440) (1, 45, 1440) Dask graph 16 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • 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))

  • 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))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-04-01'], dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (39)

  title :

  ECMWF C3S Gridded OLCI Fire Radiative Power product

  institution :

  King's College London, Brockmann Consult GmbH

  source :

  ESA Sentinel-3 A+B SLSTR FRP

  history :

  Created on 20210325T075328Z

  references :

  See https://climate.copernicus.eu/

  tracking_id :

  f25465fd-dd1a-44da-9b5c-9093ec610e44

  Conventions :

  CF-1.7

  summary :

  The Copernicus Climate Change Service issues three Level 3 Fire Radiative Power (FRP) Products, each generated from Level 2 Sentinel-3 Active Fire Detection and FRP Products issued in NTC mode, which themselves are based on Sentinel 3 SLSTR data. The global Level 3 Monthly Summary FRP Products synthesise global data from the Level 2 AF Detection and FRP Product granules at 0.25 degree spatial and at 1 month temporal resolution, and also provide some adjustments for unsuitable atmospheric condition since e.g clouds can mask actively burning fires from view. These products are primarily designed for ease of use of the key information coming from individual granule-based Level 2 Products, for example in global modelling, trend analysis and model evaluation.

  keywords :

  Fire Radiative Power, Climate Change, ESA, C3S, GCOS

  id :

  20200401-C3S-L3-FRP-SLSTR-P1M-0.25deg-fv1.0.nc

  naming_authority :

  org.esa-cci

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science keywords

  cdm_data_type :

  Grid

  comment :

  These data were produced as part of the Copernicus Climate Change Service programme.

  date_created :

  20210325T075328Z

  creator_name :

  Brockmann Consult GmbH

  creator_url :

  https://www.brockmann-consult.de

  creator_email :

  info@brockmann-consult.de

  contact :

  http://copernicus-support.ecmwf.int

  project :

  EC C3S Fire Radiative Power

  geospatial_lat_min :

  -90

  geospatial_lat_max :

  90

  geospatial_lon_min :

  -180

  geospatial_lon_max :

  180

  geospatial_vertical_min :

  0

  geospatial_vertical_max :

  0

  time_coverage_start :

  20200401T000000Z

  time_coverage_end :

  20200430T000000Z

  time_coverage_duration :

  P1M

  time_coverage_resolution :

  P1M

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  license :

  EC C3S FRP Data Policy

  platform :

  Sentinel-3

  sensor :

  SLSTR

  spatial_resolution :

  0.25 degrees

  geospatial_lon_units :

  degrees_east

  geospatial_lat_units :

  degrees_north

  geospatial_lon_resolution :

  0.25

  geospatial_lat_resolution :

  0.25

Now we will plot the total night time fire radiative power for the month of April. First, we define a colormap for visualising the Fire Radiative Power data.

import matplotlib as mpl

cmap = mpl.colors.ListedColormap([
    ( 49/256.,  57/256., 149/256.),    # <1         # <5
    ( 57/256.,  94/256., 196/256.),    # 1-5        # 5-10
    ( 96/256., 143/256., 204/256.),    # 5-10       # 10-25
    (171/256., 217/256., 233/256.),    # 10-25      # 25-50
    (255/256., 255/256., 191/256.),    # 25-50      # 50-100
    (253/256., 174/256.,  97/256.),    # 50-100     # 100-250
    (215/256.,  48/256.,  39/256.),    # 100-200    # 250-500
    (165/256.,   0/256.,  38/256.),    # 200-300    # 500-750
    (135/256.,   0/256.,   0/256.),    # >300       # >750
])

bounds = [2, 4, 6, 8, 10, 20, 30, 40, 50, 60]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N) 

plot_date = '2020-04-01'

data_frp_month_2D=data_frp_all_month['s3a_night_frp']
data_fire_month_2D=data_frp_all_month['s3a_night_fire']
# Calculate the total FRP for the month as the product of the mean FRP and the number of fires
data_total_frp_month_2D = data_frp_month_2D * data_fire_month_2D

# Set plot title
title = f'Total Fire Radiative Power - S3A - Night-Time - {plot_date}'

# Create the figure
ax = plt.axes(projection=ccrs.PlateCarree())
data_total_frp_month_2D.sel(time=plot_date).plot(cmap=cmap, norm=norm)
ax.gridlines(
    draw_labels=["bottom", "left"], linewidth=1, color='gray', alpha=0.5, linestyle='--'
)
ax.coastlines()
ax.set_title(title)

Text(0.5, 1.0, 'Total Fire Radiative Power - S3A - Night-Time - 2020-04-01')

Figure 3: Map of the Fire Radiative Power for April 2020 retrieved from the C3S Fire AF & FRP Night-Time Product

Other projection

Let’s visualise the April of the year 2020 once again, but in a different projection!

ax = plt.axes(projection=ccrs.Orthographic(15, 45))
ax.set_global()
data_total_frp_month_2D[0].plot(ax=ax, transform=ccrs.PlateCarree(),cmap=cmap, norm=norm )
ax.coastlines()
plt.title(title)

Text(0.5, 1.0, 'Total Fire Radiative Power - S3A - Night-Time - 2020-04-01')

Figure 4: Map of the Fire Radiative Power for April 2020 retrieved from the C3S Fire AF & FRP Night-Time Product - orthographic projection

Use case I - Explore C3S Fire AF & FRP dataset over time

Active Fire & Fire Radiative Power gridded products

We will now download a longer time-period and extract the data for a given location and plot a time-series.

frp_gridded_data_multiple_month_file = f"{DATADIR}/frp_month_03_12_2020.zip"
request = {
    "variable": "all",
    "product_type": "gridded",
    "time_aggregation": "month",
    "horizontal_aggregation": "0_25_degree_x_0_25_degree",
    "year": "2020",
    "month": [
        "03", "04", "05",
        "06", "07", "08",
        "09", "10", "11",
        "12"
    ],
    "day": ["01"],
    "version": ["1_0"]
}

if not os.path.exists(frp_gridded_data_multiple_month_file):
    c.retrieve(dataset, request).download(target=frp_gridded_data_multiple_month_file)

# We then open this downloaded file with EarthKit 
frp_gridded_data_multiple_month = ek.data.from_source("file", frp_gridded_data_multiple_month_file)

First we convert the earthkit object to an xarray.dataset. The output of the code block below shows the data as an xarray.dataset, you can see that we have 10 time steps corresponding to the months from February to December of 2020.

data_grid=frp_gridded_data_multiple_month.to_xarray()
data_grid

<xarray.Dataset> Size: 913MB
Dimensions:                                           (time: 10, lon: 1440,
                                                       bounds: 2, lat: 720)
Coordinates:
  * lon                                               (lon) float32 6kB -179....
  * lat                                               (lat) float32 3kB 89.88...
  * time                                              (time) datetime64[ns] 80B ...
Dimensions without coordinates: bounds
Data variables: (12/17)
    lon_bounds                                        (time, lon, bounds) float32 115kB dask.array<chunksize=(1, 1440, 2), meta=np.ndarray>
    lat_bounds                                        (time, lat, bounds) float32 58kB dask.array<chunksize=(1, 720, 2), meta=np.ndarray>
    time_bounds                                       (time, bounds) datetime64[ns] 160B dask.array<chunksize=(1, 2), meta=np.ndarray>
    s3a_night_fire                                    (time, lat, lon) float64 83MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3a_night_frp                                     (time, lat, lon) float32 41MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3a_night_pixel                                   (time, lat, lon) float64 83MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    ...                                                ...
    s3b_night_FRP_related_surface_conditions_flag     (time, lat, lon) float64 83MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3b_night_FRP_related_atmospheric_condition_flag  (time, lat, lon) float64 83MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3a_night_related_atmospheric_condition_fraction  (time, lat, lon) float32 41MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3b_night_related_atmospheric_condition_fraction  (time, lat, lon) float32 41MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3a_night_fire_weighted                           (time, lat, lon) float32 41MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
    s3b_night_fire_weighted                           (time, lat, lon) float32 41MB dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>
Attributes: (12/39)
    title:                      ECMWF C3S Gridded OLCI Fire Radiative Power p...
    institution:                King's College London, Brockmann Consult GmbH
    source:                     ESA Sentinel-3 A+B SLSTR FRP
    history:                    Created on 20210325T072251Z
    references:                 See https://climate.copernicus.eu/
    tracking_id:                047362b6-a953-4c05-857b-e908ba2c3386
    ...                         ...
    sensor:                     SLSTR
    spatial_resolution:         0.25 degrees
    geospatial_lon_units:       degrees_east
    geospatial_lat_units:       degrees_north
    geospatial_lon_resolution:  0.25
    geospatial_lat_resolution:  0.25

xarray.Dataset

• Dimensions:
  • time: 10
  • lon: 1440
  • bounds: 2
  • lat: 720
• Coordinates: (3)
  • lon
    (lon)
    float32
    -179.9 -179.6 ... 179.6 179.9

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    bounds :

    lon_bounds

    array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],
          shape=(1440,), dtype=float32)

  • lat
    (lat)
    float32
    89.88 89.62 89.38 ... -89.62 -89.88

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    bounds :

    lat_bounds

    array([ 89.875,  89.625,  89.375, ..., -89.375, -89.625, -89.875],
          shape=(720,), dtype=float32)

  • time
    (time)
    datetime64[ns]
    2020-03-01 ... 2020-12-01

    standard_name :

    time

    long_name :

    time

    bounds :

    time_bounds

    array(['2020-03-01T00:00:00.000000000', '2020-04-01T00:00:00.000000000',
           '2020-05-01T00:00:00.000000000', '2020-06-01T00:00:00.000000000',
           '2020-07-01T00:00:00.000000000', '2020-08-01T00:00:00.000000000',
           '2020-09-01T00:00:00.000000000', '2020-10-01T00:00:00.000000000',
           '2020-11-01T00:00:00.000000000', '2020-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (17)
  • lon_bounds
    (time, lon, bounds)
    float32
    dask.array<chunksize=(1, 1440, 2), meta=np.ndarray>

    Array Chunk Bytes 112.50 kiB 11.25 kiB Shape (10, 1440, 2) (1, 1440, 2) Dask graph 10 chunks in 31 graph layers Data type float32 numpy.ndarray

  • lat_bounds
    (time, lat, bounds)
    float32
    dask.array<chunksize=(1, 720, 2), meta=np.ndarray>

    Array Chunk Bytes 56.25 kiB 5.62 kiB Shape (10, 720, 2) (1, 720, 2) Dask graph 10 chunks in 31 graph layers Data type float32 numpy.ndarray

  • time_bounds
    (time, bounds)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    Array Chunk Bytes 160 B 16 B Shape (10, 2) (1, 2) Dask graph 10 chunks in 21 graph layers Data type datetime64[ns] numpy.ndarray

  • s3a_night_fire
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of S3A nighttime active fire pixels

    Array Chunk Bytes 79.10 MiB 506.25 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float64 numpy.ndarray

  • s3a_night_frp
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    MW

    long_name :

    Mean Fire Radiative Power measured by S3A during nighttime

    Array Chunk Bytes 39.55 MiB 253.12 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float32 numpy.ndarray

  • s3a_night_pixel
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of S3A nighttime pixels

    Array Chunk Bytes 79.10 MiB 506.25 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float64 numpy.ndarray

  • s3a_night_FRP_related_surface_conditions_flag
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of pixels unprocessed by the AF detection algorithm due to them being considered unsuitable surfaces, e.g. permanent water

    Array Chunk Bytes 79.10 MiB 506.25 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float64 numpy.ndarray

  • s3a_night_FRP_related_atmospheric_condition_flag
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of pixels unprocessed by the AF detection algorithm due to them being considered to have unsuitable atmospheric conditions for FRP product processing, e.g. certain types of cloud

    Array Chunk Bytes 79.10 MiB 506.25 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float64 numpy.ndarray

  • s3b_night_fire
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of S3B nighttime active fire pixels

    Array Chunk Bytes 79.10 MiB 506.25 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float64 numpy.ndarray

  • s3b_night_frp
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    MW

    long_name :

    Mean Fire Radiative Power measured by S3B during nighttime

    Array Chunk Bytes 39.55 MiB 253.12 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float32 numpy.ndarray

  • s3b_night_pixel
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of S3B nighttime pixels

    Array Chunk Bytes 79.10 MiB 506.25 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float64 numpy.ndarray

  • s3b_night_FRP_related_surface_conditions_flag
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of pixels unprocessed by the AF detection algorithm due to them being considered unsuitable surfaces, e.g. permanent water

    Array Chunk Bytes 79.10 MiB 506.25 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float64 numpy.ndarray

  • s3b_night_FRP_related_atmospheric_condition_flag
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of pixels unprocessed by the AF detection algorithm due to them being considered to have unsuitable atmospheric conditions for FRP product processing, e.g. certain types of cloud

    Array Chunk Bytes 79.10 MiB 506.25 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float64 numpy.ndarray

  • s3a_night_related_atmospheric_condition_fraction
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Mean unsuitable atmospheric condition fraction of S3A land pixels in a macro pixel of 1.25 degrees

    Array Chunk Bytes 39.55 MiB 253.12 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float32 numpy.ndarray

  • s3b_night_related_atmospheric_condition_fraction
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Mean unsuitable atmospheric condition fraction of S3B land pixels in a macro pixel of 1.25 degrees

    Array Chunk Bytes 39.55 MiB 253.12 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float32 numpy.ndarray

  • s3a_night_fire_weighted
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Number of S3A nighttime active fire pixels weighted by atmospheric condition fraction

    Array Chunk Bytes 39.55 MiB 253.12 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float32 numpy.ndarray

  • s3b_night_fire_weighted
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 45, 1440), meta=np.ndarray>

    units :

    1

    long_name :

    Number of S3B nighttime active fire pixels weighted by atmospheric condition fraction

    Array Chunk Bytes 39.55 MiB 253.12 kiB Shape (10, 720, 1440) (1, 45, 1440) Dask graph 160 chunks in 21 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • 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))

  • 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))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01',
                   '2020-07-01', '2020-08-01', '2020-09-01', '2020-10-01',
                   '2020-11-01', '2020-12-01'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (39)

  title :

  ECMWF C3S Gridded OLCI Fire Radiative Power product

  institution :

  King's College London, Brockmann Consult GmbH

  source :

  ESA Sentinel-3 A+B SLSTR FRP

  history :

  Created on 20210325T072251Z

  references :

  See https://climate.copernicus.eu/

  tracking_id :

  047362b6-a953-4c05-857b-e908ba2c3386

  Conventions :

  CF-1.7

  summary :

  The Copernicus Climate Change Service issues three Level 3 Fire Radiative Power (FRP) Products, each generated from Level 2 Sentinel-3 Active Fire Detection and FRP Products issued in NTC mode, which themselves are based on Sentinel 3 SLSTR data. The global Level 3 Monthly Summary FRP Products synthesise global data from the Level 2 AF Detection and FRP Product granules at 0.25 degree spatial and at 1 month temporal resolution, and also provide some adjustments for unsuitable atmospheric condition since e.g clouds can mask actively burning fires from view. These products are primarily designed for ease of use of the key information coming from individual granule-based Level 2 Products, for example in global modelling, trend analysis and model evaluation.

  keywords :

  Fire Radiative Power, Climate Change, ESA, C3S, GCOS

  id :

  20200301-C3S-L3-FRP-SLSTR-P1M-0.25deg-fv1.0.nc

  naming_authority :

  org.esa-cci

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science keywords

  cdm_data_type :

  Grid

  comment :

  These data were produced as part of the Copernicus Climate Change Service programme.

  date_created :

  20210325T072251Z

  creator_name :

  Brockmann Consult GmbH

  creator_url :

  https://www.brockmann-consult.de

  creator_email :

  info@brockmann-consult.de

  contact :

  http://copernicus-support.ecmwf.int

  project :

  EC C3S Fire Radiative Power

  geospatial_lat_min :

  -90

  geospatial_lat_max :

  90

  geospatial_lon_min :

  -180

  geospatial_lon_max :

  180

  geospatial_vertical_min :

  0

  geospatial_vertical_max :

  0

  time_coverage_start :

  20200301T000000Z

  time_coverage_end :

  20200331T000000Z

  time_coverage_duration :

  P1M

  time_coverage_resolution :

  P1M

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  license :

  EC C3S FRP Data Policy

  platform :

  Sentinel-3

  sensor :

  SLSTR

  spatial_resolution :

  0.25 degrees

  geospatial_lon_units :

  degrees_east

  geospatial_lat_units :

  degrees_north

  geospatial_lon_resolution :

  0.25

  geospatial_lat_resolution :

  0.25

Now we extract the point data and plot as a time series.

# Point selection 
lat = 27.70 # 43.0
lon = 84.58 # 75.0

data_grid_subset = data_grid.sel(lat=lat, lon=lon, method='nearest')
data_total_frp_timeseries=data_grid_subset['s3a_night_frp'] * data_grid_subset['s3a_night_fire']

title = 'Total Fire Radiative Power - S3A - Night-Time'
data_total_frp_timeseries.coords['time'].values
# Create the figure
fig, ax = plt.subplots()
ax.plot(
    data_total_frp_timeseries.coords['time'].values, data_total_frp_timeseries,
    marker="D", linestyle='None', color ='red'
)
ax.set_xlim(np.datetime64('2020-01'), np.datetime64('2021-01'))

ax.set_title(title + ' - Point lat:' + str(lat) + ' lon:' + str(lon))

Text(0.5, 1.0, 'Total Fire Radiative Power - S3A - Night-Time - Point lat:27.7 lon:84.58')

Figure 5: Fire Radiative Power on a selected location in Nepal for March to December 2020 retrieved from the C3S Fire AF & FRP Night-Time Product

Use case II - Identify the fire season

The start, end and duration of a regions fire season can be calculated in different ways, including via thresholding the cumulative FRP distribution that essentially brackets the continuous time of year that an area experiences the majority of fire activity (see Freeborn et al., 2014). Here we produce the cumulative distribution of FRP generated by night-time fires burning in Australia, expressing this as a % of the annual total. From such a graph, the fire season start and end can be easily identified – for example as the times when the 10th and 90th percentiles of the cumulative distribution of FRP are reached. The fire season duration is the time between these dates, and in future such data might be used for example to test whether fire season lengths appear to be changing – potentially in response to a changing climate (e.g. Silva et al., 2022).

First we will loop over the months March to December 2021 and download daily aggregated data.

base_request = {
    "variable": "all",
    "product_type": "gridded",
    "time_aggregation": "day",
    "horizontal_aggregation": "0_1_degree_x_0_1_degree",
    "year": "2020",
    "month": ["03"],
    "day": [
        "01", "02", "03",
        "04", "05", "06",
        "07", "08", "09",
        "10", "11", "12",  
        "13", "14", "15",
        "16", "17", "18",
        "19", "20", "21",
        "22", "23", "24",
        "25", "26", "27",
        "28", "29", "30",
        "31"
    ],
    "version": ["1_0"]
}

download_files = []
for month in [f"{i:02d}" for i in range(3, 13)]:
    download_file = f"{DATADIR}/frp_gridded_2020{month}.zip"
    download_files.append(download_file)
    if not os.path.exists(download_file):
        request = base_request.copy()
        request["month"] = [month]
        c.retrieve(dataset, request).download(target=download_file)

We can then use earthkit-data to open all our files into a single xarray object, which will slice to our area of interest. The output of the code block below shows the xarray. Dataset of the Fire Radiative Power product, subset over Australia.

# Spatial subset selection - Australia
min_lon = 110
min_lat = -10
max_lon = 155
max_lat = -40

frp_gridded_data_2020 = ek.data.from_source("file", download_files)
data_grid_2020 = frp_gridded_data_2020.to_xarray()

data_grid_2020 = data_grid_2020.sel(lat=slice(min_lat,max_lat), lon=slice(min_lon,max_lon))

data_grid_2020

<xarray.Dataset> Size: 4GB
Dimensions:                                           (time: 306, lon: 450,
                                                       bounds: 2, lat: 300)
Coordinates:
  * lon                                               (lon) float32 2kB 110.1...
  * lat                                               (lat) float32 1kB -10.0...
  * time                                              (time) datetime64[ns] 2kB ...
Dimensions without coordinates: bounds
Data variables: (12/19)
    lon_bounds                                        (time, lon, bounds) float32 1MB dask.array<chunksize=(1, 450, 2), meta=np.ndarray>
    lat_bounds                                        (time, lat, bounds) float32 734kB dask.array<chunksize=(1, 300, 2), meta=np.ndarray>
    time_bounds                                       (time, bounds) datetime64[ns] 5kB dask.array<chunksize=(1, 2), meta=np.ndarray>
    s3a_night_fire                                    (time, lat, lon) float64 330MB dask.array<chunksize=(1, 8, 450), meta=np.ndarray>
    s3a_night_frp                                     (time, lat, lon) float32 165MB dask.array<chunksize=(1, 8, 450), meta=np.ndarray>
    s3a_night_frp_unc                                 (time, lat, lon) float32 165MB dask.array<chunksize=(1, 8, 450), meta=np.ndarray>
    ...                                                ...
    s3b_night_FRP_related_surface_conditions_flag     (time, lat, lon) float64 330MB dask.array<chunksize=(1, 8, 450), meta=np.ndarray>
    s3b_night_FRP_related_atmospheric_condition_flag  (time, lat, lon) float64 330MB dask.array<chunksize=(1, 8, 450), meta=np.ndarray>
    s3a_night_related_atmospheric_condition_fraction  (time, lat, lon) float32 165MB dask.array<chunksize=(1, 8, 450), meta=np.ndarray>
    s3b_night_related_atmospheric_condition_fraction  (time, lat, lon) float32 165MB dask.array<chunksize=(1, 8, 450), meta=np.ndarray>
    s3a_night_fire_weighted                           (time, lat, lon) float32 165MB dask.array<chunksize=(1, 8, 450), meta=np.ndarray>
    s3b_night_fire_weighted                           (time, lat, lon) float32 165MB dask.array<chunksize=(1, 8, 450), meta=np.ndarray>
Attributes: (12/39)
    title:                      ECMWF C3S Gridded OLCI Fire Radiative Power p...
    institution:                King's College London, Brockmann Consult GmbH
    source:                     ESA Sentinel-3 A+B SLSTR FRP
    history:                    Created on 20210325T065258Z
    references:                 See https://climate.copernicus.eu/
    tracking_id:                c7854f27-0c49-48f4-a17b-8b0c948ae900
    ...                         ...
    sensor:                     SLSTR
    spatial_resolution:         0.1 degrees
    geospatial_lon_units:       degrees_east
    geospatial_lat_units:       degrees_north
    geospatial_lon_resolution:  0.1
    geospatial_lat_resolution:  0.1

xarray.Dataset

• Dimensions:
  • time: 306
  • lon: 450
  • bounds: 2
  • lat: 300
• Coordinates: (3)
  • lon
    (lon)
    float32
    110.1 110.2 110.2 ... 154.9 154.9

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    bounds :

    lon_bounds

    array([110.05, 110.15, 110.25, ..., 154.75, 154.85, 154.95],
          shape=(450,), dtype=float32)

  • lat
    (lat)
    float32
    -10.05 -10.15 ... -39.85 -39.95

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    bounds :

    lat_bounds

    array([-10.050003, -10.150002, -10.25    , ..., -39.75    , -39.850006,
           -39.949997], shape=(300,), dtype=float32)

  • time
    (time)
    datetime64[ns]
    2020-03-01 ... 2020-12-31

    standard_name :

    time

    long_name :

    time

    bounds :

    time_bounds

    array(['2020-03-01T00:00:00.000000000', '2020-03-02T00:00:00.000000000',
           '2020-03-03T00:00:00.000000000', ..., '2020-12-29T00:00:00.000000000',
           '2020-12-30T00:00:00.000000000', '2020-12-31T00:00:00.000000000'],
          shape=(306,), dtype='datetime64[ns]')

• Data variables: (19)
  • lon_bounds
    (time, lon, bounds)
    float32
    dask.array<chunksize=(1, 450, 2), meta=np.ndarray>

    Array Chunk Bytes 1.05 MiB 3.52 kiB Shape (306, 450, 2) (1, 450, 2) Dask graph 306 chunks in 920 graph layers Data type float32 numpy.ndarray

  • lat_bounds
    (time, lat, bounds)
    float32
    dask.array<chunksize=(1, 300, 2), meta=np.ndarray>

    Array Chunk Bytes 717.19 kiB 2.34 kiB Shape (306, 300, 2) (1, 300, 2) Dask graph 306 chunks in 920 graph layers Data type float32 numpy.ndarray

  • time_bounds
    (time, bounds)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    Array Chunk Bytes 4.78 kiB 16 B Shape (306, 2) (1, 2) Dask graph 306 chunks in 613 graph layers Data type datetime64[ns] numpy.ndarray

  • s3a_night_fire
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of S3A nighttime active fire pixels

    Array Chunk Bytes 315.17 MiB 63.28 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float64 numpy.ndarray

  • s3a_night_frp
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    MW

    long_name :

    Mean Fire Radiative Power measured by S3A during nighttime

    Array Chunk Bytes 157.59 MiB 31.64 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float32 numpy.ndarray

  • s3a_night_frp_unc
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    MW

    long_name :

    Mean Fire Radiative Power uncertainty measured by S3A during nighttime

    Array Chunk Bytes 157.59 MiB 31.64 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float32 numpy.ndarray

  • s3a_night_pixel
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of S3A nighttime pixels

    Array Chunk Bytes 315.17 MiB 63.28 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float64 numpy.ndarray

  • s3a_night_FRP_related_surface_conditions_flag
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of pixels unprocessed by the AF detection algorithm due to them being considered unsuitable surfaces, e.g. permanent water

    Array Chunk Bytes 315.17 MiB 63.28 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float64 numpy.ndarray

  • s3a_night_FRP_related_atmospheric_condition_flag
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of pixels unprocessed by the AF detection algorithm due to them being considered to have unsuitable atmospheric conditions for FRP product processing, e.g. certain types of cloud

    Array Chunk Bytes 315.17 MiB 63.28 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float64 numpy.ndarray

  • s3b_night_fire
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of S3B nighttime active fire pixels

    Array Chunk Bytes 315.17 MiB 63.28 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float64 numpy.ndarray

  • s3b_night_frp
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    MW

    long_name :

    Mean Fire Radiative Power measured by S3B during nighttime

    Array Chunk Bytes 157.59 MiB 31.64 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float32 numpy.ndarray

  • s3b_night_frp_unc
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    MW

    long_name :

    Mean Fire Radiative Power uncertainty measured by S3B during nighttime

    Array Chunk Bytes 157.59 MiB 31.64 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float32 numpy.ndarray

  • s3b_night_pixel
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of S3B nighttime pixels

    Array Chunk Bytes 315.17 MiB 63.28 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float64 numpy.ndarray

  • s3b_night_FRP_related_surface_conditions_flag
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of pixels unprocessed by the AF detection algorithm due to them being considered unsuitable surfaces, e.g. permanent water

    Array Chunk Bytes 315.17 MiB 63.28 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float64 numpy.ndarray

  • s3b_night_FRP_related_atmospheric_condition_flag
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    1

    long_name :

    Total number of pixels unprocessed by the AF detection algorithm due to them being considered to have unsuitable atmospheric conditions for FRP product processing, e.g. certain types of cloud

    Array Chunk Bytes 315.17 MiB 63.28 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float64 numpy.ndarray

  • s3a_night_related_atmospheric_condition_fraction
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    1

    long_name :

    Mean unsuitable atmospheric condition fraction of S3A land pixels in a macro pixel of 1.1 degrees

    Array Chunk Bytes 157.59 MiB 31.64 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float32 numpy.ndarray

  • s3b_night_related_atmospheric_condition_fraction
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    1

    long_name :

    Mean unsuitable atmospheric condition fraction of S3B land pixels in a macro pixel of 1.1 degrees

    Array Chunk Bytes 157.59 MiB 31.64 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float32 numpy.ndarray

  • s3a_night_fire_weighted
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    1

    long_name :

    Number of S3A nighttime active fire pixels weighted by atmospheric condition fraction

    Array Chunk Bytes 157.59 MiB 31.64 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float32 numpy.ndarray

  • s3b_night_fire_weighted
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 8, 450), meta=np.ndarray>

    units :

    1

    long_name :

    Number of S3B nighttime active fire pixels weighted by atmospheric condition fraction

    Array Chunk Bytes 157.59 MiB 31.64 kiB Shape (306, 300, 450) (1, 18, 450) Dask graph 5508 chunks in 614 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lon
    PandasIndex

    PandasIndex(Index([110.05000305175781,  110.1500015258789,             110.25,
            110.3499984741211, 110.44999694824219, 110.55000305175781,
            110.6500015258789,             110.75,  110.8499984741211,
           110.94999694824219,
           ...
            154.0500030517578, 154.14999389648438,             154.25,
           154.35000610351562,  154.4499969482422,  154.5500030517578,
           154.64999389648438,             154.75, 154.85000610351562,
            154.9499969482422],
          dtype='float32', name='lon', length=450))

  • lat
    PandasIndex

    PandasIndex(Index([-10.050003051757812, -10.150001525878906,              -10.25,
           -10.349998474121094, -10.449996948242188, -10.550003051757812,
           -10.650001525878906,              -10.75, -10.849998474121094,
           -10.949996948242188,
           ...
            -39.05000305175781, -39.149993896484375,              -39.25,
           -39.350006103515625,  -39.44999694824219,  -39.55000305175781,
           -39.649993896484375,              -39.75, -39.850006103515625,
            -39.94999694824219],
          dtype='float32', name='lat', length=300))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-03-01', '2020-03-02', '2020-03-03', '2020-03-04',
                   '2020-03-05', '2020-03-06', '2020-03-07', '2020-03-08',
                   '2020-03-09', '2020-03-10',
                   ...
                   '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=306, freq=None))

• Attributes: (39)

  title :

  ECMWF C3S Gridded OLCI Fire Radiative Power product

  institution :

  King's College London, Brockmann Consult GmbH

  source :

  ESA Sentinel-3 A+B SLSTR FRP

  history :

  Created on 20210325T065258Z

  references :

  See https://climate.copernicus.eu/

  tracking_id :

  c7854f27-0c49-48f4-a17b-8b0c948ae900

  Conventions :

  CF-1.7

  summary :

  The Copernicus Climate Change Service issues three Level 3 Fire Radiative Power (FRP) Products, each generated from Level 2 Sentinel-3 Active Fire Detection and FRP Products issued in NTC mode, which themselves are based on Sentinel 3 SLSTR data. The global Level 3 Daily FRP Products synthesise global data from the Level 2 AF Detection and FRP Product granules at 0.1 degree spatial and at 1-day temporal resolution, and also provide some adjustments for unsuitable atmospheric condition since e.g clouds can mask actively burning fires from view. These products are primarily designed for ease of use of the key information coming from individual granule-based Level 2 Products, for example in global modelling, trend analysis and model evaluation.

  keywords :

  Fire Radiative Power, Climate Change, ESA, C3S, GCOS

  id :

  20200301-C3S-L3-FRP-SLSTR-P1D-0.1deg-fv1.0.nc

  naming_authority :

  org.esa-cci

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science keywords

  cdm_data_type :

  Grid

  comment :

  These data were produced as part of the Copernicus Climate Change Service programme.

  date_created :

  20210325T065258Z

  creator_name :

  Brockmann Consult GmbH

  creator_url :

  https://www.brockmann-consult.de

  creator_email :

  info@brockmann-consult.de

  contact :

  http://copernicus-support.ecmwf.int

  project :

  EC C3S Fire Radiative Power

  geospatial_lat_min :

  -90

  geospatial_lat_max :

  90

  geospatial_lon_min :

  -180

  geospatial_lon_max :

  180

  geospatial_vertical_min :

  0

  geospatial_vertical_max :

  0

  time_coverage_start :

  20200301T000000Z

  time_coverage_end :

  20200301T000000Z

  time_coverage_duration :

  P1D

  time_coverage_resolution :

  P1D

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  license :

  EC C3S FRP Data Policy

  platform :

  Sentinel-3

  sensor :

  SLSTR

  spatial_resolution :

  0.1 degrees

  geospatial_lon_units :

  degrees_east

  geospatial_lat_units :

  degrees_north

  geospatial_lon_resolution :

  0.1

  geospatial_lat_resolution :

  0.1

We then calculate the cumulative sum of the Night-time Fire Radiative Power over the Australian region of interest, as a percentage of the total Night-time Fire Radiative Power for the whole time period.

# Calculate the total FRP
data_grid_2020['s3a_night_total_frp'] = data_grid_2020['s3a_night_frp'] * data_grid_2020['s3a_night_fire']

# Sum the data set via the dimensions lat, lon and time for the selected variables
data_total_frp_yearly = data_grid_2020['s3a_night_total_frp'].sum(dim=["lat", "lon", "time"], skipna=True).compute()

# Sum the data in the lat and lon dimensions to create our time series dataset
data_total_frp_timeseries = data_grid_2020['s3a_night_total_frp'].sum(dim=["lat", "lon"], skipna=True).compute()

# Calculate cumulative FRP in the time dimension
data_total_frp_cumulative_timeseries = data_total_frp_timeseries.cumsum(dim='time')

# We convert this to a percentage of the total FRP for the time-period, and updte the units
data_total_frp_cumulative_timeseries_percent = data_total_frp_cumulative_timeseries *100 / data_total_frp_yearly
data_total_frp_cumulative_timeseries_percent = data_total_frp_cumulative_timeseries_percent.assign_attrs(
    units='%'
)
data_total_frp_cumulative_timeseries_percent.to_dataset()

<xarray.Dataset> Size: 5kB
Dimensions:              (time: 306)
Coordinates:
  * time                 (time) datetime64[ns] 2kB 2020-03-01 ... 2020-12-31
Data variables:
    s3a_night_total_frp  (time) float64 2kB 0.02114 0.02928 ... 99.96 100.0

xarray.Dataset

• Dimensions:
  • time: 306
• Coordinates: (1)
  • time
    (time)
    datetime64[ns]
    2020-03-01 ... 2020-12-31

    standard_name :

    time

    long_name :

    time

    bounds :

    time_bounds

    array(['2020-03-01T00:00:00.000000000', '2020-03-02T00:00:00.000000000',
           '2020-03-03T00:00:00.000000000', ..., '2020-12-29T00:00:00.000000000',
           '2020-12-30T00:00:00.000000000', '2020-12-31T00:00:00.000000000'],
          shape=(306,), dtype='datetime64[ns]')

• Data variables: (1)
  • s3a_night_total_frp
    (time)
    float64
    0.02114 0.02928 ... 99.96 100.0

    units :

    %

    array([2.11448511e-02, 2.92823047e-02, 6.55451645e-02, 2.46658867e-01,
           2.50238266e-01, 2.56638123e-01, 3.00917076e-01, 3.39240400e-01,
           4.28738931e-01, 5.13340118e-01, 6.00065051e-01, 7.13997741e-01,
           7.61082896e-01, 9.20914926e-01, 1.03454632e+00, 1.17320548e+00,
           1.29475633e+00, 1.39908336e+00, 1.58905848e+00, 1.84494819e+00,
           1.84775921e+00, 1.94465382e+00, 2.06170375e+00, 2.16391607e+00,
           2.23237886e+00, 2.34150818e+00, 2.44563998e+00, 2.56340229e+00,
           2.61385742e+00, 2.72673235e+00, 2.92703788e+00, 3.04705658e+00,
           3.10032032e+00, 3.26631343e+00, 3.41642778e+00, 3.49500254e+00,
           3.63170085e+00, 3.82470484e+00, 3.97838391e+00, 4.04344942e+00,
           4.07755523e+00, 4.23616031e+00, 4.31342872e+00, 4.34274936e+00,
           4.44031068e+00, 4.57988454e+00, 4.70627758e+00, 4.81841203e+00,
           4.90116587e+00, 4.97371010e+00, 5.04457804e+00, 5.15869675e+00,
           5.30469083e+00, 5.56978323e+00, 5.70354955e+00, 5.84153663e+00,
           5.96540976e+00, 6.15189619e+00, 6.39240879e+00, 6.81491647e+00,
           7.04634845e+00, 7.45330846e+00, 8.78964823e+00, 9.00989226e+00,
           9.29969116e+00, 9.81699982e+00, 1.00552942e+01, 1.01799505e+01,
           1.03291492e+01, 1.04399491e+01, 1.05530313e+01, 1.08806163e+01,
           1.13361420e+01, 1.13361420e+01, 1.13361420e+01, 1.13361420e+01,
           1.13361420e+01, 1.13361420e+01, 1.13361420e+01, 1.13361420e+01,
    ...
           4.80681016e+01, 4.84268994e+01, 4.89230531e+01, 4.92415950e+01,
           4.95121145e+01, 5.04229067e+01, 5.10455253e+01, 5.13113330e+01,
           5.15212095e+01, 5.17822014e+01, 5.19353245e+01, 5.22026037e+01,
           5.22765771e+01, 5.29598449e+01, 5.34699993e+01, 5.37364177e+01,
           5.43226276e+01, 5.53992286e+01, 5.66338066e+01, 5.68669550e+01,
           5.82912945e+01, 6.23343032e+01, 6.39560457e+01, 6.44031458e+01,
           6.53320549e+01, 6.65747559e+01, 6.75101275e+01, 6.80709882e+01,
           6.84248887e+01, 6.88400821e+01, 6.95947118e+01, 7.02491749e+01,
           7.25142412e+01, 7.59464687e+01, 7.64811337e+01, 7.72302696e+01,
           7.88139712e+01, 8.02919056e+01, 8.07993685e+01, 8.12002348e+01,
           8.27858001e+01, 8.35086491e+01, 8.37145498e+01, 8.42943864e+01,
           8.47662050e+01, 8.55924607e+01, 8.58651529e+01, 8.68325981e+01,
           8.81414985e+01, 8.85955842e+01, 8.88039194e+01, 8.98194766e+01,
           9.18530535e+01, 9.25900541e+01, 9.27881718e+01, 9.33726520e+01,
           9.35889062e+01, 9.36448088e+01, 9.44887743e+01, 9.48211760e+01,
           9.49966069e+01, 9.50402032e+01, 9.50820122e+01, 9.53885867e+01,
           9.58136031e+01, 9.60023430e+01, 9.62587048e+01, 9.70572493e+01,
           9.86700168e+01, 9.86846757e+01, 9.88076177e+01, 9.92544766e+01,
           9.95728178e+01, 9.96833817e+01, 9.97261787e+01, 9.99393914e+01,
           9.99598814e+01, 1.00000000e+02])

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-03-01', '2020-03-02', '2020-03-03', '2020-03-04',
                   '2020-03-05', '2020-03-06', '2020-03-07', '2020-03-08',
                   '2020-03-09', '2020-03-10',
                   ...
                   '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=306, freq=None))

• Attributes: (0)

Finally we plot the cummulative time-series of the FRP over Australia for March to December 2020.

title = 'Total Fire Radiative Power - S3A - Night-Time - Australia'
# Create the figure
fig_time, ax_time = plt.subplots()
data_total_frp_cumulative_timeseries_percent.plot(ax=ax_time, marker="X", linestyle='None', color ='red')
ax_time.set_ylabel("Cumulative percentage [%]")
ax_time.set_title(title)

Text(0.5, 1.0, 'Total Fire Radiative Power - S3A - Night-Time - Australia')

Figure 6: Time series of the total global daily FRP for Australia calculated from the C3S Fire AF & FRP Night-Time Products

The timeseries of the total global daily FRP of the products C3S (AF & FRP) shows the fire season for Australia.

Take home messages 📌

This tutorial demonstrated how Sentinel-3 Active Fire and Fire Radiative Power products available through the C3S can be explored. By working on a timeseries of the total global daily FRP of the products C3S (AF & FRP), we produced information on Australia’s fire season. We have downloaded, visualised, subset, and analysed the data, to produce meaningful and informative visuals and tables. In addition, we derived and visualised an additional aspect - fire season - from the data.