Front shape similarity measure for data-driven simulations of wildland fire spread based on state estimation: Application to the RxCADRE field-scale experiment

Zhang, Cong; Collin, Annabelle; Moireau, Philippe; Trouvé, Arnaud; Rochoux, Mélanie C.

doi:10.1016/j.proci.2018.07.112

Cited by 19 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the present study, we leverage one advantageous satellite overpass to provide high‐resolution validation, but in the future, we hope to provide more extensive validation and error quantification using sporadically available aircraft IR, or, ideally, field campaign observations with collocated instrumentation. Such data would facilitate better perimeter skill score quantification (e.g., Skok & Roberts, 2016) and fire ROS estimation and validation (e.g., Zhang et al., 2019).…”

Section: Discussionmentioning

confidence: 99%

Tracking Wildfires With Weather Radars

et al. 2022

View full text Add to dashboard Cite

There is a need for nowcasting tools to provide timely and accurate updates on the location and rate of spread (ROS) of large wildfires, especially those impacting communities in the wildland urban interface. In this study, we demonstrate how fixed‐site weather radars can be used to fill this gap. Specifically, we develop and test a radar‐based fire‐perimeter tracking tool that leverages the tendency for local maxima in the radar reflectivity to be collocated with active fire perimeters. Reflectivity maxima are located using search radials from points inside a fire polygon, and perimeters are updated at intervals of ∼10 min. The algorithm is tested using publicly available Next Generation Weather Radar radar data for two large and destructive wildfires, the Camp and Bear Fires, both occurring in northern California, USA. The radar‐based fire perimeters are compared with available, albeit limited, satellite and airborne infrared observations, showing good agreement with conventional fire‐tracking tools. The radar data also provide insights into fire ROS, revealing the importance of long‐range spotting in generating ROS that exceeds conventional estimates. One limitation of this study is that high‐resolution fire perimeter validation data are sparsely available, precluding detailed error quantification for the radar estimates drawn from samples spanning a range of environmental conditions and radar configurations. Nevertheless, the radar tracking approach provides the basis for improved situational awareness during high‐impact fires.

show abstract

Section: Discussionmentioning

confidence: 99%

Tracking Wildfires With Weather Radars

et al. 2022

View full text Add to dashboard Cite

show abstract

“…This approach is taken by Zhang et al [48], where they use a data-driven wildland fire spread model (FIREFLY) introduced by da Silva et al [49]. Zhang et al [50,51] used data assimilation to estimate state parameters and spread from the 2012 RxCADRE controlled burn experiment. Progress on data assimilation and machine learning techniques requires building relevant physical and statistical fundamentals into the methodology.…”

Section: Discussionmentioning

confidence: 99%

Fine-Scale Fire Spread in Pine Straw

Sagel

Speer

Pokswinski

et al. 2021

Fire

View full text Add to dashboard Cite

Most wildland and prescribed fire spread occurs through ground fuels, and the rate of spread (RoS) in such environments is often summarized with empirical models that assume uniform environmental conditions and produce a unique RoS. On the other hand, representing the effects of local, small-scale variations of fuel and wind experienced in the field is challenging and, for landscape-scale models, impractical. Moreover, the level of uncertainty associated with characterizing RoS and flame dynamics in the presence of turbulent flow demonstrates the need for further understanding of fire dynamics at small scales in realistic settings. This work describes adapted computer vision techniques used to form fine-scale measurements of the spatially and temporally varying RoS in a natural setting. These algorithms are applied to infrared and visible images of a small-scale prescribed burn of a quasi-homogeneous pine needle bed under stationary wind conditions. A large number of distinct fire front displacements are then used statistically to analyze the fire spread. We find that the fine-scale forward RoS is characterized by an exponential distribution, suggesting a model for fire spread as a random process at this scale.

show abstract

“…Mandel et al rst applied the idea of DA to the eld of forest re spread prediction (Mandel et al 2008). Subsequently, scholars such as Rochoux and Trouvé conducted extensive research on ensemble Kalman lter (EnKF) algorithms (Rochoux et al 2014(Rochoux et al , 2015Zhang et al 2019), which became one of the mainstream correction methods in the eld of forest re spread prediction. In recent studies, Zhou et al rst applied the ensemble transform Kalman lter (ETKF) to forest re spread prediction, improving the correction effect (Zhou et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Correction of Forest Fire Spread Prediction using Observation Error Covariance Matrix Estimation Technique based on FLC-GRU

Wu,

zhang,

Zhu

et al. 2024

Preprint

View full text Add to dashboard Cite

Background Data assimilation (DA) techniques have played a significant role in improving the prediction accuracy of forest fire spread. This dynamic correction technique enhances the analytical values that better reflect the fire situation by weighting the predicted values and observed values. The weighted importance of each contribution is determined by the magnitude of its associated error. However, as a crucial parameter affecting prediction accuracy, the covariance matrix of observation errors is often inaccurate and neglects its own temporal correlation. This is unfriendly to spread prediction results. To address this issue, we proposed a targeted technique for estimating the observation error covariance matrix (R matrix) based on the Fire Line Convolutional Gated Recurrent Unit (FLC-GRU). Results We integrated this method into the DA framework and validated its applicability and accuracy using Observing System Simulation Experiment (OSSE). Through comparisons with traditional methods, the results indicate that using the FLC-GRU estimated R matrix for correction calculations leads to wildfire prediction locations that are closer to the true values. Conclusions The proposed approach learns the covariance matrix directly from time-series observed fire line data, without requiring any prior knowledge or assumptions about the error distribution, in contrast to classical posterior tuning methods. The proposed method significantly improves the rationality and accuracy of R matrix estimation, enhances the utility of observational data, and thereby improves the correction accuracy of forest fire spread predictions. Moreover, the study also demonstrates the applicability of the proposed method within the DA framework.

show abstract

Front shape similarity measure for data-driven simulations of wildland fire spread based on state estimation: Application to the RxCADRE field-scale experiment

Cited by 19 publications

References 20 publications

Tracking Wildfires With Weather Radars

Tracking Wildfires With Weather Radars

Fine-Scale Fire Spread in Pine Straw

Dynamic Correction of Forest Fire Spread Prediction using Observation Error Covariance Matrix Estimation Technique based on FLC-GRU

Contact Info

Product

Resources

About