Generation and evaluation of ensemble simulations of wildfire spread for probabilistic forecast

Allaire, Frédéric; al., et

doi:10.14195/978-989-26-16-506_6

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Furthermore, if the produced LLDP is moving nonrelativistically and has a long lifetime, a significant time delay Δt [247] (with respect to the expected arrival time of a relativistic particle) can be measured by the precision timing detectors proposed to be installed at LHCb [21], CMS [246] and ATLAS [248,249]. A time delay signal from the LLDPs model [243] has been investigated using the future timing detector at CMS (MTD CMS) which has a ∼30 ps timing resolution [246].…”

Section: Dark Photonsmentioning

confidence: 99%

Unleashing the full power of LHCb to probe stealth new physics

et al. 2022

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In this paper, we describe the potential of the LHCb experiment to detect Stealth physics. This refers to dynamics beyond the Standard Model that would elude searches that focus on energetic objects or precision measurements of known processes. Stealth signatures include long-lived particles and light resonances that are produced very rarely or together with overwhelming backgrounds. We will discuss why LHCb is equipped to discover this kind of physics at the Large Hadron Collider and provide examples of well-motivated theoretical models that can be probed with great detail at the experiment.

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Section: Dark Photonsmentioning

confidence: 99%

Unleashing the full power of LHCb to probe stealth new physics

et al. 2022

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“…Uncertainties in prediction by fire simulators are statistically related to the simulators' dependence on the required parameters, such that sensitivity analysis is considered for improving the reliability of predictions (Trucchia 2019, Asensio 2020) and also on the regional-scale weather prediction. Thus, such uncertainties are covered by probabilistic predictions in terms of ensemble forecasts through a proper distribution of input parameters (Allaire 2018, Allaire 2020, Allaire 2021.…”

Section: Introductionmentioning

confidence: 99%

From the probability density function of the rate of spread to that of the corresponding burned area

Crespo-Santiago¹,

Pagnini²

2022

Advances in Forest Fire Research 2022

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Land managers use models to understand potential fire behaviour, which provide insight into the social, economic and environmental implications of fire. Although combustion is a fundamental process, numerous fire behaviour models exist across the world, each with model-specific inputs and outputs. While these are useful for local-level fire management, vegetation- or country-specific fire behaviour models limits knowledge-sharing between jurisdictions â€“ largely because model inputs (particularly fuel arguments) and outputs differ, meaning they cannot be readily compared. At the same time, advancements in remote sensing techniques have resulting in accurate methods to estimate fuel for current fire behaviour models that have not been fully integrated. This project has two key aims: a) to utilise remote sensing and field-based measurement approaches to develop a comprehensive set of model parameters that can be used to universally characterise fuel attributes fundamental to fire behaviour, and b) based on knowledge gaps identified, undertake expert elicitation to identify fuel attributes for fire behaviour not currently utilised in models, and propose methods to estimate these values using remote sensing. To achieve this, we reviewed 25 fire behaviour models to identify similarities and differences in model inputs and outputs. We then subset model inputs to the fuel parameters, and linked each to current remote sensing methods. Following the review, and in conjunction with an expert elicitation process, we developed a list of fundamental fuel attributes missing from current fire behaviour models. From this list, we developed novel fuel parameters that fully utilise information contained within remote sending datasets. The final step in this process is to validate the importance of each fuel argument using further expert elicitation and field burning experiments*. We found many common parameters, including a fuel value, short- and long-term fuel moisture, temperature and wind variables, however, the physical fuel parameters are typically where models diverge. Models across the world use fuel inputs that describe the amount, horizontal and vertical arrangement of fuel, bark hazard and the importance of crown fuels. Preliminary expert elicitation identified shared and divergent opinions related to fuel characteristics that drive fire behaviour. While common parameters related to horizontal and vertical arrangement of fuel were important, gaps exist in our capacity to describe ladder fuels. From these results, we have developed novel remote sensing metrics to describe vertical and horizontal connectivity. Throughout 2022, we aim to validate these preliminary results through further expert elicitation and field observations of fire behaviour. Through this work, we have developed a complete list of fuel parameters for fire behaviour and related these to remote sensing methods. This provides a framework for current models to shift to remotely sensed inputs, and a basis for future fire behaviour models. Overall, we argue that this work provides the foundation for a universal fuel assessment methodology to be developed, that is scalable and captures change over time, that can link physical fuel structure and remote sending techniques to fire behaviour models of the future. * Note that this work is planned for late 2022.

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