Abstract. We describe the physical model, numerical algorithms, and software structure of a model consisting of the Weather Research and Forecasting (WRF) model, coupled with the fire-spread model (SFIRE) module. In every time step, the fire model inputs the surface wind, which drives the fire, and outputs the heat flux from the fire into the atmosphere, which in turn influences the atmosphere. SFIRE is implemented by the level set method, which allows a submesh representation of the burning region and a flexible implementation of various kinds of ignition. The coupled model is capable of running on a cluster faster than real time even with fine resolution in dekameters. It is available as a part of the Open Wildland Fire Modeling (OpenWFM) environment at http://openwfm.org, which contains also utilities for visualization, diagnostics, and data processing, including an extended version of the WRF Preprocessing System (WPS). The SFIRE code with a subset of the features is distributed with WRF 3.3 as WRF-Fire.

Observations of the scale dependence of height-resolved temperature T and water vapor q variability are valuable for improved subgrid-scale climate model parameterizations and model evaluation. Variance spectral benchmarks for T and q obtained from the Atmospheric Infrared Sounder (AIRS) are compared to those generated by state-of-the-art numerical weather prediction ''analyses'' and ''free-running'' climate model simulations with spatial resolution comparable to AIRS. The T and q spectra from both types of models are generally too steep, with small-scale variance up to several factors smaller than AIRS. However, the two model analyses more closely resemble AIRS than the two free-running model simulations. Scaling exponents obtained for AIRS column water vapor (CWV) and height-resolved layers of q are also compared to the superparameterized Community Atmospheric Model (SP-CAM), highlighting large differences in the magnitude of CWV variance and the relative flatness of height-resolved q scaling in SP-CAM. Height-resolved q spectra obtained from aircraft observations during the Variability of the American Monsoon Systems OceanCloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) demonstrate changes in scaling exponents that depend on the observations' proximity to the base of the subsidence inversion with scale breaks that occur at approximately the dominant cloud scale (;10-30 km). This suggests that finer spatial resolution requirements must be considered for future satellite observations of T and q than those currently planned for infrared and microwave satellite sounders.

We employ dynamical downscaling and pseudo global warming methodologies to evaluate climate change impact on the roles of temperature and precipitation in spring snowpack (S) variability across the western United States (U.S.). The negative correlation between S and temperature weakens linearly with elevation, whereas the correlation between S and precipitation increases asymptotically with elevation. The curvilinear relationship in the latter case was not visible in prior studies because of the observation networks' limited range. In our historical validation, there is a range of threshold elevations (1580–2181 m) across six mountainous regions, above which precipitation is the main driver of snowpack variability and below which temperature is the main driver. Under a moderate end‐of‐century climate change scenario, these thresholds increase by 191 to 432 m. These rising thresholds indicate increasing spatial and elevational vulnerability of western U.S. spring snowpack along with associated impacts to hydrologic and ecologic systems.

Abstract. Coupled atmosphere-fire models can now generate forecasts in real time, owing to recent advances in computational capabilities. WRF-SFIRE consists of the Weather Research and Forecasting (WRF) model coupled with the fire-spread model SFIRE. This paper presents new developments, which were introduced as a response to the needs of the community interested in operational testing of WRF-SFIRE. These developments include a fuel-moisture model and a fuel-moisture-data-assimilation system based on the Remote Automated Weather Stations (RAWS) observations, allowing for fire simulations across landscapes and time scales of varying fuel-moisture conditions. The paper also describes the implementation of a coupling with the atmospheric chemistry and aerosol schemes in WRF-Chem, which allows for a simulation of smoke dispersion and effects of fires on air quality. There is also a data-assimilation method, which provides the capability of starting the fire simulations from an observed fire perimeter, instead of an ignition point. Finally, an example of operational deployment in Israel, utilizing some of the new visualization and datamanagement tools, is presented.

In this study, we describe how WRF-Sfire is coupled with WRF-Chem to construct WRFSC, an integrated forecast system for wildfire and smoke prediction. The integrated forecast system has the advantage of not requiring a simple plume-rise model and assumptions about the size and heat release from the fire in order to determine fire emissions into the atmosphere. With WRF-Sfire, wildfire spread, plume and plume-top heights are predicted directly, at every WRF time-step, providing comprehensive meteorology and fire emissions to the chemical transport model WRF-Chem.Evaluation of WRFSC was based on comparisons between available observations to the results of two WRFSC simulations.The study found overall good agreement between forecasted and observed fire spread and smoke transport for the Witch-Guejito fire. Also the simulated PM2.5 (fine particulate matter) peak concentrations matched the observations. However, the NO and ozone levels were underestimated in the simulations and the peak concentrations were mistimed. Determining the terminal or plume-top height is one of the most important aspects of simulating wildfire plume transport, and the study found overall good agreement between simulated and observed plume-top heights, with some (10% or less) underestimation by the simulations. One of the most promising results of the study was the agreement between passive-tracer modeled plumetop heights for the Barker Canyon fire simulation and observations. This simulation took only 13h, with the first 24h forecast ready in almost 3h, making it a possible operational tool for providing emission profiles for external chemical transport models.

Horizontal motions from 25 sodium cloud experiments are examined in the altitude range from 70 to 190 km. The outstanding characteristics of the apparent motion are pronounced velocity oscillations in the 70‐ to 130‐km layer; they reach a maximum near 105 km and attenuate at greater heights. A quiescent zone appears from 140 to 190 km, where, despite an increase of speed with height, the rate at which velocity changes with elevation is small. An attempt is made to resolve the apparent motion into various constituents by assuming that the observed drift represents a sum of three types of motion superimposed on one another: a general drift, tidal components, and internal gravity waves. The derived quantities seem to explain vertical shear distribution and other phenomena. It is estimated that in the 90‐ to 125‐km layer the contributions of the three constituents to the observed motion are: gravity waves, 40% general drift, 34% tidal components, 26%. Above 180 km, the term representing a sum of the general drift and tidal components assumes a still more dominant role, and at 160 km its contribution to the observed motion is 85%.

During the summer of 2015, a number of large wildfires burned across Northern California in areas of localized topographic relief. Persistent valley smoke hindered fire‐fighting efforts, delayed helicopter operations, and exposed communities to extreme concentrations of particulate matter. It was hypothesized that smoke from the wildfires reduced the amount of incoming solar radiation reaching the ground, which resulted in near‐surface cooling, while smoke aerosols resulted in warming aloft. As a result of increased inversion‐like conditions, smoke from wildfires was trapped within mountain valleys adjacent to active wildfires. In this study, wildfire smoke‐induced inversion episodes across Northern California were examined using a modeling framework that couples an atmospheric, chemical, and fire spread model. Modeling results examined in this study indicate that wildfire smoke reduced incoming solar radiation during the afternoon, which lead to local surface cooling by up to 3 °C, which agrees with cooling observed at nearby surface stations. Direct heating from the fire itself did not significantly enhance atmospheric stability. However, midlevel warming (+0.5 °C) and pronounced surface cooling was observed in the smoke layer, indicating that smoke aerosols significantly enhanced atmospheric stability. A positive feedback associated with the presence of smoke was observed, where local smoke‐enhanced inversions inhibited the growth of the planetary boundary layer, and reduced surface winds, which resulted in smoke accumulation that further reduced near‐surface temperatures. This work suggests that the inclusion of fire‐smoke‐atmosphere feedback in a coupled modeling framework such as WRF‐SFIRE‐CHEM can forecast the dispersion of wildfire smoke and its radiative feedback, and potentially provide decision‐support for wildfire operations.

Heating from wildfires adds buoyancy to the overlying air, often producing plumes that vertically distribute fire emissions throughout the atmospheric column over the fire. The height of the rising wildfire plume is a complex function of the size of the wildfire, fire heat flux, plume geometry, and atmospheric conditions, which can make simulating plume rises difficult with coarser-scale atmospheric models. To determine the altitude of fire emission injection, several plume rise parameterizations have been developed in an effort estimate the height of the wildfire plume rise. Previous work has indicated the performance of these plume rise parameterizations has generally been mixed when validated against satellite observations. However, it is often difficult to evaluate the performance of plume rise parameterizations due to the significant uncertainties associated with fire input parameters such as fire heat fluxes and area. In order to reduce the uncertainties of fire input parameters, we applied an atmospheric modeling framework with different plume rise parameterizations to a well constrained prescribed burn, as part of the RxCADRE field experiment. Initial results found that the model was unable to reasonably replicate downwind smoke for cases when fire emissions were emitted at the surface and released at the top of the plume. However, when fire emissions were distributed below the plume top following a Gaussian distribution, model results were significantly improved.