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  1. Free, publicly-accessible full text available July 1, 2024
  2. null (Ed.)
    With the increase in commercially available small unmanned aircraft systems (UAS), new observations in extreme environments are becoming more obtainable. One such application is the fire environment, wherein measuring both fire and atmospheric properties are challenging. The Fire and Smoke Model Evaluation Experiment offered the unique opportunity of a large controlled wildfire, which allowed measurements that cannot generally be taken during an active wildfire. Fire–atmosphere interactions have typically been measured from stationary instrumented towers and by remote sensing systems such as lidar. Advances in UAS and compact meteorological instrumentation have allowed for small moving weather stations that can move with the fire front while sampling. This study highlights the use of DJI Matrice 200, which was equipped with a TriSonica Mini Wind and Weather station sonic anemometer weather station in order to sample the fire environment in an experimental and controlled setting. The weather station was mounted on to a carbon fiber pole extending off the side of the platform. The system was tested against an RM-Young 81,000 sonic anemometer, mounted at 6 and 2 m above ground levelto assess any bias in the UAS platform. Preliminary data show that this system can be useful for taking vertical profiles of atmospheric variables, in addition to being used in place of meteorological tower measurements when suitable. 
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  3. null (Ed.)
    The November 2018 Camp Fire quickly became the deadliest and most destructive wildfire in California history. In this case study, we investigate the contribution of meteorological conditions and, in particular, a downslope windstorm that occurred during the 2018 Camp Fire. Dry seasonal conditions prior to ignition led to 100-h fuel moisture contents in the region to reach record low levels. Meteorological observations were primarily made from a number of remote automatic weather stations and a mobile scanning Doppler lidar deployed to the fire on 8 November 2018. Additionally, gridded operational forecast models and high-resolution meteorological simulations were synthesized in the analysis to provide context for the meteorological observations and structure of the downslope windstorm. Results show that this event was associated with mid-level anti-cyclonic Rossby wave breaking likely caused by cold air advection aloft. An inverted surface trough over central California created a pressure gradient which likely enhanced the downslope winds. Sustained surface winds between 3–6 m s−1 were observed with gusts of over 25 m s−1 while winds above the surface were associated with an intermittent low-level jet. The meteorological conditions of the event were well forecasted, and the severity of the fire was not surprising given the fire danger potential for that day. However, use of surface networks alone do not provide adequate observations for understanding downslope windstorm events and their impact on fire spread. Fire management operations may benefit from the use of operational wind profilers to better understand the evolution of downslope windstorms and other fire weather phenomena that are poorly understood and observed. 
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  4. The FireFlux II experiment was conducted in a tall grass prairie located in south-east Texas on 30 January 2013 under a regional burn ban and high fire danger conditions. The goal of the experiment was to better understand micrometeorological aspects of fire spread. The experimental design was guided by the use of a coupled fire–atmosphere model that predicted the fire spread in advance. Preliminary results show that after ignition, a surface pressure perturbation formed and strengthened as the fire front and plume developed, causing an increase in wind velocity at the fire front. The fire-induced winds advected hot combustion gases forward and downwind of the fire front that resulted in acceleration of air through the flame front. Overall, the experiment collected a large set of micrometeorological, air chemistry and fire behaviour data that may provide a comprehensive dataset for evaluating and testing coupled fire–atmosphere model systems. 
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