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R. F. Griffiths


Experiments are described in which ice samples resembling natural precipitation elements were exposed to an electric field of sufficient strength to produce corona at the extremities of the particles. Measurements were made of the residual charge QR left on the particles after the field had been turned off.

Values of QR lay typically in the range ∼0.1−0.5 nC at 1000 mb, falling roughly linearly with pressure. Possible applications of this process in thunderclouds are discussed.

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R. F. Griffiths and J. Latham


The calculations of Gay et al. of the terminal velocities of charged hydrormeteors in the presence of electric fields have formed the basis of computations of the charging current density J flowing through a thunder-cloud as a result of the operation of a precipitative mechanism of cloud electrification. Values of J were calculated for a range of values of field strength E, precipitation rate p O, precipitation content L, cloud water content C, charge distribution, total separated charge, and the fraction of the small particles that have undergone a charging event.

It is found that the estimated field required for the initiation of a lightning stroke (σ3.5 kV cm−1 can be achieved only over a narrow range of conditions. The ease with which precipitative mechanisms can produce breakdown fields is considerably increased, however, if account is taken of spatial inhomogenities in the field.

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H. F. Dacre, B. R. Crawford, A. J. Charlton-Perez, G. Lopez-Saldana, G. H. Griffiths, and J. Vicencio Veloso


The 2016/17 wildfire season in Chile was the worst on record, burning more than 600,000 ha. While wildfires are an important natural process in some areas of Chile, supporting its diverse ecosystems, wildfires are also one of the biggest threats to Chile’s unique biodiversity and its timber and wine industries. They also pose a danger to human life and property because of the sharp wildland–urban interface that exists in many Chilean towns and cities. Wildfires are, however, difficult to predict because of the combination of physical (meteorology, vegetation, and fuel condition) and human (population density and awareness level) factors. Most Chilean wildfires are started because of accidental ignition by humans. This accidental ignition could be minimized if an effective wildfire warning system alerted the population to the heightened danger of wildfires in certain locations and meteorological conditions. Here, we demonstrate the design of a novel probabilistic wildfire prediction system. The system uses ensemble forecast meteorological data together with a long time series of fire products derived from Earth observation to predict not only fire occurrence but also how intense wildfires could be. The system provides wildfire risk estimation and associated uncertainty for up to six days in advance and communicates it to a variety of end users. The advantage of this probabilistic wildfire warning system over deterministic systems is that it allows users to assess the confidence of a forecast and thus make more informed decisions regarding resource allocation and forest management. The approach used in this study could easily be adapted to communicate other probabilistic forecasts of natural hazards.

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