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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

DG - Atmospheric sciences, meteorology

OECD FORD branch

—

Project

<a href="/en/project/GA13-34856S" target="_blank" >GA13-34856S: Advanced random field methods in data assimilation for short-term weather prediction</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2016

Confidentiality

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

Name of the periodical

International Journal of Wildland Fire

ISSN

1049-8001

e-ISSN

—

Volume of the periodical

25

Issue of the periodical within the volume

5

Country of publishing house

AU - AUSTRALIA

Number of pages

11

Pages from-to

558-568

UT code for WoS article

000375877900006

EID of the result in the Scopus database

2-s2.0-84968835226