Forest Fire Spread Prediction and Assimilation Using the Deterministic Ensemble Kalman Filter
Authors:
- 6 January 2025
- journal article
- research article
- Published by Springer Nature in Fire Technology
- Vol. 61 (4) , 2467-2492
Abstract
No abstract availableKeywords
Funding Information
- Anhui Provincial Science and Technology Major Project (No. 202203a07020017)
- National Natural Science Foundation of China (No. 32471866)
- Key Science and Technology Program of the Ministry of Emergency Management of China (No. 2024EMST010101)
This publication has 47 references indexed in Scilit:
- Adjusting the rate of spread of fire simulations in real-timeEcological Modelling, 2019
- Data assimilation in the geosciences: An overview of methods, issues, and perspectivesWires Climate Change, 2018
- An integrated approach for tactical monitoring and data-driven spread forecasting of wildfiresFire Safety Journal, 2017
- Assimilating synthetic hyperspectral sounder temperature and humidity retrievals to improve severe weather forecastsAtmospheric Research, 2017
- A global classification of vegetation based on NDVI, rainfall and temperatureInternational Journal of Climatology, 2016
- Wildland fire modeling with an Eulerian level set method and automated calibrationFire Safety Journal, 2013
- Comparison of deterministic ensemble Kalman filters for assimilating hydrogeological dataAdvances in Water Resources, 2009
- The Ensemble Kalman Filter: theoretical formulation and practical implementationOcean Dynamics, 2003
- An elliptical growth model of forest fire fronts and its numerical solutionInternational Journal for Numerical Methods in Engineering, 1990
- A forest-fire model and some thoughts on turbulencePhysics Letters A, 1990