Assimilation of Perimeter Data and Coupling with Fuel Moisture in a Wildland Fire–Atmosphere DDDAS

Mandel, Jan; Beezley, Jonathan D.; Kochanski, Adam K.; Kondratenko, Volodymyr Y.; Kim, Minjeong

doi:10.1016/j.procs.2012.04.119

Cited by 35 publications

(24 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Kondratenko et al (2011), the fire arrival times inside a given perimeter were approximated based on the distance from a known ignition point to the perimeter, while use of the re-initialization equation was proposed in Mandel et al (2012). Our current approach consists of reversing the direction of time in a fire-spread method, thus shrinking the fire to one or more ignition points.…”

Section: Initialization From a Fire Perimetermentioning

confidence: 99%

See 1 more Smart Citation

Recent advances and applications of WRF–SFIRE

Mandel

Amram

Beezley

et al. 2014

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Coupled atmosphere-fire models can now generate forecasts in real time, owing to recent advances in computational capabilities. WRF-SFIRE consists of the Weather Research and Forecasting (WRF) model coupled with the fire-spread model SFIRE. This paper presents new developments, which were introduced as a response to the needs of the community interested in operational testing of WRF-SFIRE. These developments include a fuel-moisture model and a fuel-moisture-data-assimilation system based on the Remote Automated Weather Stations (RAWS) observations, allowing for fire simulations across landscapes and time scales of varying fuel-moisture conditions. The paper also describes the implementation of a coupling with the atmospheric chemistry and aerosol schemes in WRF-Chem, which allows for a simulation of smoke dispersion and effects of fires on air quality. There is also a data-assimilation method, which provides the capability of starting the fire simulations from an observed fire perimeter, instead of an ignition point. Finally, an example of operational deployment in Israel, utilizing some of the new visualization and datamanagement tools, is presented.

show abstract

Section: Initialization From a Fire Perimetermentioning

confidence: 99%

“…The following simple approach Mandel et al, 2012) models the evolution of fuel-moisture response by a first-order differential equation, running at each node of the surface mesh independently.…”

Section: Fuel-moisture Modelmentioning

confidence: 99%

Recent advances and applications of WRF–SFIRE

Mandel

Amram

Beezley

et al. 2014

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…WRF-SFIRE simulates continuous drop-torch ignition at steady speeds along straight lines, developed for the simulation of the FireFlux experiment [17]. A generalization of this scheme to complex ignition patterns is unwieldy, so, in this project, we have developed a new ignition mechanism by building on the earlier work on perimeter ignitions [18,19]: instead of modeling the fire spread until the time of the perimeter is reached, perimeter ignition in WRF-SFIRE prescribes the fire arrival time so that an appropriate atmospheric circulation due to the fire forcing can develop. The new scheme allows the user to specify the latest fire arrival time at the nodes (grid points) of the fire simulation mesh as an additional input array.…”

Section: Coupled Atmosphere-fire Simulationmentioning

confidence: 99%

“…These values were entered as initial moisture values and did not change with time. See [1,19] for a further description of the fuel moisture in WRF-SFIRE. 2.…”

Section: Sampling Setupmentioning

confidence: 99%

Experimental Design of a Prescribed Burn Instrumentation

2018

Self Cite

View full text Add to dashboard Cite

Abstract:Observational data collected during experiments, such as the planned Fire and Smoke Model Evaluation Experiment (FASMEE), are critical for evaluating and transitioning coupled fire-atmosphere models like WRF-SFIRE and WRF-SFIRE-CHEM into operational use. Historical meteorological data, representing typical weather conditions for the anticipated burn locations and times, have been processed to initialize and run a set of simulations representing the planned experimental burns. Based on an analysis of these numerical simulations, this paper provides recommendations on the experimental setup such as size and duration of the burns, and optimal sensor placement. New techniques are developed to initialize coupled fire-atmosphere simulations with weather conditions typical of the planned burn locations and times. The variation and sensitivity analysis of the simulation design to model parameters performed by repeated Latin Hypercube Sampling is used to assess the locations of the sensors. The simulations provide the locations for the measurements that maximize the expected variation of the sensor outputs with varying the model parameters.

show abstract

“…Forest fire modelling is computed at high spatial resolution scale, in the order of meters. When using complex models that take into account as many parameters as possible, including the interaction of the atmosphere, the computing cost and the initialization becomes a not easy task [6]. Several research communities try to simplify the problem and provide tools to assess the fire behavior in real-time [7] [8].…”

Section: Introductionmentioning

confidence: 99%

Non-supervised method for early forest fire detection and rapid mapping

Artés¹,

Roberto²,

Giorgio³

et al. 2017

Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017)

View full text Add to dashboard Cite

Natural hazards are a challenge for the society. Scientific community efforts have been severely increased assessing tasks about prevention and damage mitigation. The most important points to minimize natural hazard damages are monitoring and prevention. This work focuses particularly on forest fires. This phenomenon depends on small-scale factors and fire behavior is strongly related to the local weather. Forest fire spread forecast is a complex task because of the scale of the phenomena, the input data uncertainty and time constraints in forest fire monitoring. Forest fire simulators have been improved, including some calibration techniques avoiding data uncertainty and taking into account complex factors as the atmosphere. Such techniques increase dramatically the computational cost in a context where the available time to provide a forecast is a hard constraint. Furthermore, an early mapping of the fire becomes crucial to assess it. In this work, a nonsupervised method for forest fire early detection and mapping is proposed. As main sources, the method uses daily thermal anomalies from MODIS and VIIRS combined with land cover map to identify and monitor forest fires with very few resources. This method relies on a clustering technique (DBSCAN algorithm) and on filtering thermal anomalies to detect the forest fires. In addition, a concave hull (alpha shape algorithm) is applied to obtain rapid mapping of the fire area (very coarse accuracy mapping). Therefore, the method leads to a potential use for high-resolution forest fire rapid mapping based on satellite imagery using the extent of each early fire detection. It shows the way to an automatic rapid mapping of the fire at high resolution processing as few data as possible.

show abstract

Assimilation of Perimeter Data and Coupling with Fuel Moisture in a Wildland Fire–Atmosphere DDDAS

Cited by 35 publications

References 36 publications

Recent advances and applications of WRF–SFIRE

Recent advances and applications of WRF–SFIRE

Experimental Design of a Prescribed Burn Instrumentation

Non-supervised method for early forest fire detection and rapid mapping

Contact Info

Product

Resources

About