One of the first significant developments in wildfire modeling research was to introduce heat flux as wildfire line intensity (kW·m -1 ). This idea could be adapted to using weather station measurements, topography, and fuel properties to estimate rate of fire spread, shape, and intensity. This review will present, in an accessible manner, the next evolution in wildfire models. The new generation models use mechanistic combustion models and large-eddy simulation (LES) to define the flaming combustion and the mechanism of rate of spread. These wildfire models are then coupled to a computational fluid dynamics (CFD) or mesoscale weather model. In other words, wildfire models become weather and climate models with add-in fuel and terrain models. These coupled models can use existing fire and weather physics or developed noncoupled models with a coupling mechanism. These models are tailored for specific spatial and temporal scales.Résumé : Un des premiers développements en recherche sur la modélisation des feux de forêt a été d'introduire le flux de chaleur en tant qu'intensité de la ligne de feu (kW·m -1 ). Cette idée pouvait être adaptée pour utiliser les mesures des stations météorologiques, la topographie et les propriétés des combustibles pour estimer la forme, l'intensité et le taux de propagation du feu. Cette synthèse présente de façon accessible la prochaine évolution dans les modèles de feux de forêt. La nouvelle génération de modèles utilise des modèles de combustion mécanistes et la simulation de grands écoulements tourbillonnants (LES) pour définir la combustion accompagnée de flammes et le mécanisme du taux de propagation. Ces modèles de feux de forêt sont ensuite couplés à un modèle de dynamique des fluides numérique (CFD) ou un modèle météorologique méso-échelle. En d'autres mots, les modèles de feux de forêt deviennent des modèles météorologiques et climatiques avec des modèles complémentaires de combustibles et de terrain. Ces modèles couplés peuvent utiliser un feu en cours et la physique météorologique ou des modèles non couplés avec un mécanisme de couplage. Ces modèles sont adaptés à des échelles spatiales et temporelles spécifiques. [Traduit par la Rédaction]Mots-clés : feu de forêt, combustible, comportement du feu, modèles de simulation WRF-FIRE, FIRETEC, WFDS, CAWFE, ForeFire/ Meso-NH, ARPS/DEVS-FIRE, différence finie, volume fini.

A method called gene-expression programming (GEP), which uses symbolic regression to form a nonlinear combination of ensemble NWP forecasts, is introduced. From a population of competing and evolving algorithms (each of which can create a different combination of NWP ensemble members), GEP uses computational natural selection to find the algorithm that maximizes a weather verification fitness function. The resulting best algorithm yields a deterministic ensemble forecast (DEF) that could serve as an alternative to the traditional ensemble average.Motivated by the difficulty in forecasting montane precipitation, the ability of GEP to produce biascorrected short-range 24-h-accumulated precipitation DEFs is tested at 24 weather stations in mountainous southwestern Canada. As input to GEP are 11 limited-area ensemble members from three different NWP models at four horizontal grid spacings. The data consist of 198 quality controlled observation-forecast date pairs during the two fall-spring rainy seasons of October 2003-March 2005 Comparing the verification scores of GEP DEF versus an equally weighted ensemble-average DEF, the GEP DEFs were found to be better for about half of the mountain weather stations tested, while ensembleaverage DEFs were better for the remaining stations. Regarding the multimodel multigrid-size ''ensemble space'' spanned by the ensemble members, a sparse sampling of this space with several carefully chosen ensemble members is found to create a DEF that is almost as good as a DEF using the full 11-member ensemble. The best GEP algorithms are nonunique and irreproducible, yet give consistent results that can be used to good advantage at selected weather stations.

A B S T R A C T Quantitative precipitation estimation and forecasting (QPE and QPF) are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF) model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale bandlike precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data.

Two noniterative approximations are presented for saturated pseudoadiabats (also known as moist adiabats). One approximation determines which moist adiabat passes through a point of known pressure and temperature, such as through the lifting condensation level on a skew T or tephigram. The other approximation determines the air temperature at any pressure along a known moist adiabat, such as the final temperature of a rising cloudy air parcel. The method used to create these statistical regressions is a relatively new variant of genetic programming called gene-expression programming. The correlation coefficient between the resulting noniterative approximations and the iterated data such as plotted on thermodynamic diagrams is over 99.97%. The mean absolute error is 0.28°C, and the root mean square error is 0.44 within a thermodynamic domain bounded by −30° < θw ≤ 40°C, P > 20 kPa, and −60° ≤ T ≤ 40°C, where θw, P, and T are wet-bulb potential temperature, pressure, and air temperature.

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