Data Assimilation of Dead Fuel Moisture Observations from Remote automated Weather Stations

0459808 - ÚI 2017 RIV AU eng J - Článek v odborném periodiku
Vejmelka, Martin - Kochanski, A. - Mandel, Jan
Data Assimilation of Dead Fuel Moisture Observations from Remote automated Weather Stations.
International Journal of Wildland Fire. Roč. 25, č. 5 (2016), s. 558-568. ISSN 1049-8001. E-ISSN 1448-5516
Grant CEP: GA ČR GA13-34856S
Grant ostatní: National Science Foundation(US) AGS-0835579 and DMS-1216481; NASA(US) NNX12AQ85G and NNX13AH9G.
Institucionální podpora: RVO:67985807
Klíčová slova: data assimilation * dead fuel moisture * equilibrium * Kalman filter * remote automated weather stations * time lag model * trend surface model
Kód oboru RIV: DG - Vědy o atmosféře, meteorologie
Impakt faktor: 2.748, rok: 2016

Fuel moisture has a major influence on the behaviour of wildland fires and is an important underlying factor in fire risk assessment. We propose a method to assimilate dead fuel moisture content (FMC) observations from remote automated weather stations (RAWS) into a time lag fuel moisture model. RAWS are spatially sparse and a mechanism is needed to estimate fuel moisture content at locations potentially distant from observational stations. This is arranged using a trend surface model (TSM), which allows us to account for the effects of topography and atmospheric state on the spatial variability of FMC. At each location of interest, the TSM provides a pseudo-observation, which is assimilated via Kalman filtering. The method is tested with the time lag fuel moisture model in the coupled weather-fire code WRF–SFIRE on 10-h FMC observations from Colorado RAWS in 2013. Using leave-one-out testing we show that the TSM compares favourably with inverse squared distance interpolation as used in the Wildland Fire Assessment System. Finally, we demonstrate that the data assimilation method is able to improve on FMC estimates in unobserved fuel classes.
Trvalý link: http://hdl.handle.net/11104/0259967