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Reconstructed weather datasets, such as reanalyses based on model output with data assimilation, often show systematic biases in magnitude when compared with observations. Postprocessing approaches can help adjust the distribution so that the reconstructed data resemble the observed data as closely as possible. In this study, we have compared various statistical bias-correction approaches based on quantile–quantile matching to correct the data from the Twentieth Century Reanalysis, version 2c (20CRv2c), with observation-based data. Methods included in the comparison utilize a suite of different approaches: a linear model, a median-based approach, a nonparametric linear method, a spline-based method, and approaches that are based on the lognormal and Weibull distributions. These methods were applied to daily data in the Australian region for rainfall, maximum temperature, relative humidity, and wind speed. Note that these are the variables required to compute the forest fire danger index (FFDI), widely used in Australia to examine dangerous fire weather conditions. We have compared the relative errors and performances of each method across various locations in Australia and applied the approach with the lowest mean-absolute error across multiple variables to produce a reliable long-term bias-corrected FFDI dataset across Australia. The spline-based data correction was found to have some benefits relative to the other methods in better representing the mean FFDI values and the extremes from the observed records for many of the cases examined here. It is intended that this statistical bias-correction approach applied to long-term reanalysis data will help enable new insight on climatological variations in hazardous phenomena, including dangerous wildfires in Australia extending over the past century. [ABSTRACT FROM AUTHOR]

Lightning is a natural hazard that can lead to the ignition of wildfires, disruption and damage to power and telecommunication infrastructures, human and livestock injuries and fatalities, and disruption to airport activities. This paper examines the ability of six statistical and machine-learning classification techniques to distinguish between nonlightning and lightning days at the coarse spatial and temporal scales of current general circulation models and reanalyses. The classification techniques considered were 1) a combination of principal component analysis and logistic regression, 2) classification and regression trees, 3) random forests, 4) linear discriminant analysis, 5) quadratic discriminant analysis, and 6) logistic regression. Lightning-flash counts at six locations across Australia for 2004-13 were used, together with atmospheric variables from the ERA-Interim dataset. Tenfold cross validation was used to evaluate classification performance. It was found that logistic regression was superior to the other classifiers considered and that its prediction skill is much better than using climatological values. The sets of atmospheric variables included in the final logistic-regression models were primarily composed of spatial mean measures of instability and lifting potential, along with atmospheric water content. The memberships of these sets varied among climatic zones. [ABSTRACT FROM AUTHOR]

Long-term variations in fire weather conditions are examined throughout Australia from gridded daily data from 1950 to 2016. The McArthur forest fire danger index is used to represent fire weather conditions throughout this 67-yr period, calculated on the basis of a gridded analysis of observations over this time period. This is a complementary approach to previous studies (e.g., those based primarily on model output, reanalysis, or individual station locations), providing a spatially continuous and long-term observations-based dataset to expand on previous research and produce climatological guidance information for planning agencies. Long-term changes in fire weather conditions are apparent in many regions. In particular, there is a clear trend toward more dangerous conditions during spring and summer in southern Australia, including increased frequency and magnitude of extremes, as well as indicating an earlier start to the fire season. Changes in fire weather conditions are attributable at least in part to anthropogenic climate change, including in relation to increasing temperatures. The influence of El Niño-Southern Oscillation (ENSO) on fire weather conditions is found to be broadly consistent with previous studies (indicating more severe fire weather in general for El Niño conditions than for La Niña conditions), but it is demonstrated that this relationship is highly variable (depending on season and region) and that there is considerable potential in almost all regions of Australia for long-range prediction of fire weather (e.g., multiweek and seasonal forecasting). It is intended that improved understanding of the climatological variability of fire weather conditions will help lead to better preparedness for risks associated with dangerous wildfires in Australia. [ABSTRACT FROM AUTHOR]

Lightning accompanied by inconsequential rainfall (i.e., 'dry' lightning) is the primary natural ignition source for wildfires globally. This paper presents a machine-learning and statistical-classification analysis of dry and 'wet' thunderstorm days in relation to associated atmospheric conditions. The study is based on daily data for lightning-flash count and precipitation from ground-based sensors and gauges and a comprehensive set of atmospheric variables that are based on ERA-Interim for the period from 2004 to 2013 at six locations in Australia. These locations represent a wide range of climatic zones (temperate, subtropical, and tropical). Quadratic surface representations and low-dimensional summary statistics were used to characterize the main features of the atmospheric fields. Four prediction skill scores were considered, and 10-fold cross validation was used to evaluate the performance of each classifier. The results were compared with those obtained by adopting the approach used in an earlier study for the U.S. Pacific Northwest. It was found that both approaches have prediction skill when tested against independent data, that mean atmospheric field quantities proved to be the most influential variables in determining dry-lightning activity, and that no single classifier or set of atmospheric variables proved to be consistently superior to its counterpart for the six sites examined here. [ABSTRACT FROM AUTHOR]

A systematic examination is presented of the relationship between lightning occurrence and fires attributed to lightning ignitions. Lightning occurrence data are matched to a database of fires attributed to lightning ignition over southeastern Australia and are compared with atmospheric and fuel characteristics at the time of the lightning occurrence. Factors influencing the chance of fire per lightning stroke are examined, including the influence of fuel moisture and weather parameters, as well as seasonal and diurnal variations. The fuel moisture parameters of the Canadian Fire Weather Index System are found to be useful in indicating whether a fire will occur, given the occurrence of lightning. The occurrence of 'dry lightning' (i.e., lightning that occurs without significant rainfall) is found to have a large influence on the chance of fire per lightning stroke. Through comparison of the results presented here with the results of studies from other parts of the world, a considerable degree of universality is shown to exist in the characteristics of lightning fires and the atmospheric conditions associated with them, suggesting the potential for these results to be applied more widely than just in the area of the study. [ABSTRACT FROM AUTHOR]