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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F16%3A00459808" target="_blank" >RIV/67985807:_____/16:00459808 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1071/WF14085" target="_blank" >http://dx.doi.org/10.1071/WF14085</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1071/WF14085" target="_blank" >10.1071/WF14085</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

DG - Vědy o atmosféře, meteorologie

OECD FORD obor

—

Projekt

<a href="/cs/project/GA13-34856S" target="_blank" >GA13-34856S: Pokročilé metody náhodných polí v asimilaci dat pro krátkodobou předpověď počasí</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Wildland Fire

ISSN

1049-8001

e-ISSN

—

Svazek periodika

25

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

AU - Austrálie

Počet stran výsledku

11

Strana od-do

558-568

Kód UT WoS článku

000375877900006

EID výsledku v databázi Scopus

2-s2.0-84968835226