Verification of WRF modelled fire weather in the 2009–10 New Zealand fire season

Abstract

Abstract. The Weather Research and Forecasting (WRF) mesoscale model was used to simulate the fire weather conditions for the 2009-10 wildland fire season in New Zealand. The suitability of WRF to simulate the high-end fire weather conditions for this period was assessed through direct comparison with observational data taken from 23 surface and two upper-air stations located across New Zealand. The weather variables and fire weather indices considered in the verification were the 1200 hours NZST air temperature, relative humidity, wind speed and direction, 24-h rainfall, New Zealand Fire Weather Index (FWI) and Continuous Haines Index (CHI). On observed high-end fire weather days, the model under-predicted the air temperatures and relative humidities, and over-predicted the wind speeds and 24-h rainfall at most weather stations. The results demonstrated that although WRF is suitable for modelling the air temperatures, there are issues with modelling the wind speeds and rainfall quantities. The model error in the wind speeds and 24-h rainfall contributed significantly towards the model under-prediction of the FWI on observed high-end fire weather days. In addition, the model was not suitable for predicting the number of high-end fire weather days at most weather stations, which represents a serious operational limitation of the WRF model for fire management applications. Finally, the modelled CHI values were only in moderate agreement with the observed values, principally due to the model error in the dew point depression at 850 hPa.

Assessment of WRF/Chem Simulated Vertical Distributions of Particulate Matter from the 2009 Minto Flats South Wildfire in Interior Alaska by CALIPSO Total Backscatter and Depolarization Measurements

“…While the performances of various air-quality models in predicting anthropogenic and wildfire-caused aerosol distributions have been evaluated by means of data from surface and aircraft measurements [4], such evaluations are nonexistant for Interior Alaska. Here, the lack of data from aircraft field campaigns and the sparse availability of air-quality surface observations has limited the assessment of model performance.…”

Assessment of the 2006-2012 Climatological Fields and Mesoscale Features from Regional Downscaling of CESM Data by WRF-Chem over Southeast Alaska

“…Regional downscaling serves to capture local terrainintroduced effects such as pollution channeling through valleys, accumulation under inversions, or redistribution by mesoscale circulations. Various studies showed WRF's capability to predict mesoscale features when driven with analysis data [19] [61]. Therefore, we compared monthly mean diurnal course of 2m temperature, 2m relative humidity, 10m wind speed, and wind direction over the entire domain, over land, ocean, coastal areas, and Glacier Bay obtained by EXP with those of CON to assess whether the climatologies from downscaling provide similar magnitudes in response to these regional/local features.…”

“…CESM used a mosaic approach for the various plant-functional types [75]. Any mosaic approach simplifies and smoothens the heterogeneity of the landscape [61] [76], for which the likelihood for overestimation of wind speed increases.…”

The application of high-resolution atmospheric modelling to weather and climate variability in vineyard regions

“…Physics-based mesoscale atmospheric numerical models are tools that can be used to provide a good understanding of the finescale variability of weather and climate across a vineyard area, even in regions of complex terrain (Bonnardot and Cautenet, 2009;Soltanzadeh et al, 2016). These models have been used to address a range of other applied problems, including dust and air pollution dispersion, wild fire behaviour and wind energy resource assessment (Purcell and Gilbert, 2015 ;Alizadeh Choobari et al, 2012;Simpson et al, 2013;Sturman et al, 2011;Titov et al, 2007).…”