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K. J. Tory and J. D. Kepert


Pyrocumulonimbus (pyroCb) clouds are difficult to predict and can produce extreme and unexpected wildfire behavior that can be very hazardous to fire crews. Many forecasters modify conventional thunderstorm diagnostics to predict pyroCb potential, by adding temperature (Δθ) and moisture increments (Δq) to represent smoke plume thermodynamics near the expected plume condensation level. However, estimating these Δθ and Δq increments is a highly subjective process that requires expert knowledge of all factors that might influence future fire size and intensity. In this paper, instead of trying to anticipate these Δθ and Δq increments for a particular fire, the minimum firepower required to generate pyroCb for a given atmospheric environment is considered. This concept, termed the pyroCb firepower threshold (PFT) requires only atmospheric information, removing the need for subjective estimates of the fire contribution. A simple approach to calculating PFT is presented that incorporates only basic plume-rise physics, yielding an analytic solution that offers important insight into plume behavior and pyroCb formation. Minimum increments of Δθ and Δq required for deep, moist convection, plus a minimum cloud-base height (z fc), are diagnosed on a thermodynamic diagram. Briggs’s plume rise equations are used to convert Δθ, z fc, and a mean horizontal wind speed U to a measure of the PFT: the minimum heat flux entering the base of the plume. This PFT is proportional to the product of U, Δθ, and the square of z fc. Plume behavior insights provided by the Briggs’s equations are discussed, and a selection of PFT examples presented.

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Eric W. Schulz, Jeffrey D. Kepert, and Diana J. M. Greenslade


A method for routinely verifying numerical weather prediction surface marine winds with satellite scatterometer winds is introduced. The marine surface winds from the Australian Bureau of Meteorology’s operational global and regional numerical weather prediction systems are evaluated. The model marine surface layer is described. Marine surface winds from the global and limited-area models are compared with observations, both in situ (anemometer) and remote (scatterometer). A 2-yr verification shows that wind speeds from the regional model are typically underestimated by approximately 5%, with a greater bias in the meridional direction than the zonal direction. The global model also underestimates the surface winds by around 5%–10%. A case study of a significant marine storm shows that where larger errors occur, they are due to an underestimation of the storm intensity, rather than to biases in the boundary layer parameterizations.

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