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McArthur's Fire Danger Rating System

McArthur's Fire Danger Rating System is a widely used fire danger assessment system in Australia. It provides a quantitative rating of fire danger based on weather and fuel moisture conditions. The system was developed by Dr. Richard McArthur in the 1960s and has undergone several revisions since then.The purpose of McArthur's Fire Danger Rating System is to assess and communicate the potential risk and behavior of bushfires and wildfires. It helps fire agencies, land managers, and the public make informed decisions regarding fire prevention, preparedness, and response strategies. There are several components of McArthur's Fire Danger Rating System:

a. Fire Danger Index (FDI):
The Fire Danger Index represents the overall fire danger rating. It considers weather variables such as temperature, relative humidity, wind speed, and rainfall, as well as fuel moisture content. The FDI ranges from 1 to 100, with higher values indicating more severe fire weather conditions and increased fire danger.

b. Grassland Fire Danger Index (GFDI) (not provided):
The Grassland Fire Danger Index specifically assesses fire danger in grassland areas. It takes into account weather conditions and fuel moisture content relevant to grassland fuels. The GFDI ranges from 1 to 100, with higher values indicating higher fire danger in grassland environments.

c. Forest Fire Danger Index (FFDI) (not provided):
The Forest Fire Danger Index is designed to evaluate fire danger in forested areas. It considers weather conditions and fuel moisture content specific to forest fuels. The FFDI ranges from 1 to 100, with higher values indicating higher fire danger in forested environments. 

McArthur's Fire Danger Rating System uses mathematical equations to calculate the Fire Danger Index (FDI), Grassland Fire Danger Index (GFDI), and Forest Fire Danger Index (FFDI). These calculations incorporate observed weather data, such as temperature, humidity, wind speed, and rainfall, along with fuel moisture information. The resulting numerical values can be interpreted as follows:

- FDI, GFDI, and FFDI values below 12 generally indicate low fire danger.
- FDI, GFDI, and FFDI values between 12 and 24 indicate moderate fire danger.
- FDI, GFDI, and FFDI values above 24 indicate high fire danger.
- Higher values within each range indicate increasing severity of fire danger.

It is important to note that specific interpretations and thresholds may vary based on regional standards, local practices, and fire management agency guidelines.

McArthur's Fire Danger Rating System has various applications, including:

- Fire management and suppression: It assists fire agencies in assessing fire danger levels, predicting fire behavior, and allocating firefighting resources effectively.
- Fire permits and restrictions: The rating system helps determine the need for fire permits and implement fire restrictions based on the assessed fire danger.
- Public awareness and education: The system aids in communicating fire danger to the public and promoting awareness of fire safety measures during periods of elevated fire risk.

While McArthur's Fire Danger Rating System is a valuable tool, it has certain limitations:

- It does not consider local variations in topography, which can significantly influence fire behavior.
- The accuracy of the system relies on the availability and quality of weather data and fuel moisture measurements.
- The rating system does not account for local fire history or specific fire-prone vegetation types.

References:
- McArthur, A. G. (1973). Forest Fire Danger Meter. Australian Forestry, 36(1), 39-45.
- McArthur, A. G., Noble, I. R., & Bary, G. A. V. (1982). Forest fire danger meter-1977. Forest and Timber Bureau, Canberra, Australia.

- Bureau of Meteorology (Australia). (n.d.). Fire Danger Rating and the McArthur Forest Fire Danger Index. 

Usage notes 

Download global fire danger forecast maps

Downloading these data is rather straight forward using the CDS web interface. The registered user needs to tick a few boxes to specify the index, period of interest and type of data, then click on a `Download' button. For larger data requests, the use of the CDS API is recommended. Below an example script is provided.

import cdsapi
c = cdsapi.Client()
c.retrieve(
    'cems-fire-seasonal',
    {
        'product_type': 'ensemble_mean',
        'variable': 'danger_severity_rating',
        'year': '2019',
        'month': [
            '01', '07',
        ],
        'leadtime': '1_month',
        'format': 'zip',
    },
    'download.zip')

Plotting data using the CDS toolbox

To harness the power of the CDS, users are invited to familiarise with the CDS Toolbox. This is an interactive environment that allows to process and plot data without necessarily downloading them. This is particularly useful for users with limited bandwidth and/or unstable connections. The toolbox is designed to develop python applications that can be shared with other users, hence streamlining collaborative research and development. The script below can be pasted in the toolbox editor to generate a static map of the Fire Weather Index (as they are shown in the EFFIS and GWIS platform) that can be exported and used for reports and publications. 

import cdstoolbox as ct
# Magics plot configuration dictionary
MAP_CONFIG = {    
    'contour': {
        'contour_level_selection_type': 'level_list',
        'contour_level_list': [0, 5.2, 11.2, 21.3, 38, 50, 150],
        'contour_shade': 'on',
        'contour_label': 'off',
        'contour_shade_method': 'area_fill',
        'contour_shade_colour_method': 'list',
        'contour_shade_colour_list': ['#84F07F', '#FFEB3C', '#FFB00C',
                                      '#FA4F00', '#B40000', '#280923'],
        'contour': 'off',
        'legend': 'on',
    },
    'legend': {
        'legend_text_colour': 'black',
        'legend_text_font_size': 0.4,
        'legend_display_type':'continuous',
    }
}
# Initialise the application
@ct.application(title='Static map', fullscreen=True)
@ct.output.figure()
def application():
    # Retrieve full resolution FWI data for a single date
    data = ct.catalogue.retrieve(
        'cems-fire-historical',
        {
            'product_type': 'reanalysis',
            'variable': 'fire_weather_index',
            'version': '3.1',
            'dataset': 'Intermediate dataset',
            'year': '2020',
            'month': '07',
            'day': '01',
        }
    )
    
    # Plot the data using the defined configuration MAP_CONFIG on a dynamic map
    plot = ct.map.plot(data, **MAP_CONFIG)
    
    return plot