Data assimilation of dead fuel moisture observations from remote automated weather stations

TL;DR

This paper presents a method to improve dead fuel moisture content estimates by assimilating remote weather station data into a spatial model and a time-lag fuel moisture model, enhancing fire risk assessment accuracy.

Contribution

It introduces a trend surface model combined with Kalman filtering to estimate and assimilate fuel moisture data at unobserved locations, improving spatial predictions.

Findings

TSM outperforms inverse squared distance interpolation

Data assimilation improves fuel moisture estimates in unobserved areas

Method effectively integrates remote observations into fire behavior models

Abstract

Fuel moisture has a major influence on the behavior of wildland fires and is an important underlying factor in fire risk assessment. We propose a method to assimilate dead fuel moisture content 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 fuel moisture content. 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-hr fuel moisture content observations from Colorado RAWS in 2013. We show using leave-one-out testing that the TSM compares favorably 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 fuel moisture content estimates in unobserved fuel classes.