30 Aug 2023
 | 30 Aug 2023
Status: this preprint is currently under review for the journal ESD.

Regionally optimized fire parameterizations using feed-forward neural networks

Yoo-Geun Ham, Seon-Ho Nam, and Jin-Soo Kim

Abstract. The fire weather index (FWI) is a widely used metric for fire danger based on meteorological observations. However, due to its empirical formulation based on a specific regional relationship between the meteorological observations and fire intensity, the ability of the FWI to accurately represent global satellite-derived fire intensity observations is limited. In this study, we propose a fire parameterization method using feed-forward neural networks (FFNNs) for individual grids. These FFNNs for each grid point utilize four daily meteorological variables (2-meter relative humidity (RH2m), precipitation, 2-meter temperature, and wind speed) as inputs. The outputs of the FFNNs are satellite-derived fire radiative power (FRP) values. Applying the proposed FFNNs for fire parameterization during the 2001–2020 period revealed a marked enhancement in cross-validated skill compared to parameterization solely based on the FWI. This improvement was particularly notable across East Asia, Russia, the eastern US, southern South America, and central Africa. The sensitivity experiments demonstrated that the RH2m is the most critical variable in estimating the FRP and its regional differences via the FFNNs. Conversely, the FWI-based estimations were primarily influenced by precipitation. The FFNNs accurately captured the observed nonlinear correlations between FRP and RH2m, as well as precipitation. In contrast, FWI-based estimations exhibit an excessively negative relationship between FRP and precipitation.

Yoo-Geun Ham et al.

Status: open (until 03 Nov 2023)

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Yoo-Geun Ham et al.

Yoo-Geun Ham et al.


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Short summary
Fires are inflicting substantial ecological and socio-economic impacts on a global scale. For the real-time monitoring the risk of fire incidents at an early stage, we developed a fire intensity estimation method based on the well-monitored meteorological variables. We utilized feed-forward neural networks (FFNNs) which uses four meteorological variables to estimate a fire radiative power. The estimation accuracy of FFNNs revealed a marked increase compared to a previous method.