There is an increasing need for simulating the evolution of wildland fires. The realism of the simulation increases by accounting for feedbacks between the fire and the atmosphere. These coupled models combine a fire behavior model with a regional numerical weather prediction model and have been used for fire research during the last few decades. This is the case, for instance, for the widely adopted Weather Research and Forecasting model with fire extensions (WRF-Fire). Typically, the coupling includes specific code for the particular models being coupled such as interpolation procedures to exchange variables between the atmospheric grid to the fire grid, and vice versa. However, having a fire modeling framework that can be coupled to different atmospheric models is advantageous to foster collaborations and joint developments. With this aim, we have created, for the first time, a fire behavior model that can be connected to other atmospheric models without the need for developing specific low-level procedures for the particular atmospheric model being used. To this end, the fire behavior model, referred to as the Community Fire Behavior model (CFBM), makes use of the Earth System Modeling Framework (ESMF) library to communicate information between the fire and the atmosphere. More specifically, CFBM is available as a national unified operational prediction capability (NUOPC) component which allows for an agnostic coupling to atmospheric models and other Earth system components. CFBM closely follows WRF-Fire version 4.3.3 methods in its version 0.2.0 which allows us to verify the adequacy of our implementation. The CFBM can be also run offline using an existing WRF simulation to propagate the fires in what we refer to as the standalone model. Herein we describe CFBM and its implementation in the Unified Forecast System (UFS). Simulations of the Cameron Peak Fire performed with UFS and WRF-Fire are presented to compare results from both models in order to verify our implementation. Results from both models, as well as with the standalone version, are consistent indicating a proper development of the CFBM and its coupling to the UFS-Atmosphere. This is the starting point to go beyond WRF-Fire methods and improve the realism of the simulations in the future. The possibility of using the fire behavior model with other atmospheric models provides an attractive collaborative framework to further improve the realism of the CFBM model in order to meet the growing demand for accurate wildland fire simulations.

This study develops, implements, and evaluates the Firebrand Spotting parameterization within the WRF-Fire coupled fire–atmosphere modeling system. Fire spotting is an important mechanism characterizing fire spread in wind-driven events. It can accelerate the rate of spread and enable the fire to spread over streams and barriers such as highways. Without the capability to simulate fire spotting, wind-driven fire simulations cannot accurately represent fire behavior. In the Firebrand Spotting parameterization, firebrands are generated with a set of fixed properties, from locations vertically aligned with the leading fire line. Firebrands are transported using a Lagrangian framework accounting for particle burnout (combustion) through an MPI-compatible implementation within WRF-Fire. Fire spots may occur when firebrands land on unburned grid points. The parameterization is verified through idealized simulations and its application is demonstrated for the 2021 Marshall Fire, Colorado. The simulations are assessed using the observed fire perimeter and time of arrival at multiple locations identified from social media footage and official documents. All simulations using a range of ignition thresholds outperform the control without spotting. Simulations accounting for fire spots show more accurate fire arrival times (i.e., reflecting a better fire rate of spread), despite producing a generally larger fire area. The Heidke Skill Score (Cohen’s Kappa) for the burn area ranges between 0.62 and 0.78 for simulations with fire spots compared to 0.47 for the control. These results show that the parameterization consistently improves the fire forecast verification metrics, while also underscoring future work priorities, including advancing the generation and ignition components.

This study introduces the firebrand spotting parameterization implemented in WRF-Fire and applies it to the Marshall Fire, Colorado (2021) to demonstrate that, without fire spotting, wind-driven fire simulations cannot accurately represent the fire behavior. Spotting can be a dominant fire spread mechanism in wind-driven events, particularly those that occur in the wildland-urban interface (WUI), such as the Marshall Fire. To simulate these fires, the model’s ability to spot is critical, in that it accelerates the rate of spread and enables the fire to spread over streams and urban features such as highways. The firebrand spotting parameterization was implemented in WRF-Fire to improve simulations of wind-driven fires in a fire-atmosphere coupled system. In the parameterization, particles are generated with a set of fixed firebrand properties, from locations vertically aligned with the fire front. Firebrands are transported using a Lagrangian framework and firebrand physics is represented by a burnout (combustion) parameterization. Fire spots may occur when firebrands land on unburned grid points. The parameterization components are illustrated through idealized simulations and its application is demonstrated through simulations of a devastating real case - the Marshall Fire (Colorado, 2021). The simulations were verified using time of arrival and contingency table metrics. Our metrics show that when fire spots were included in the simulations, fire rate of spread and burn area consistently improved.

Predicting the evolution of burned area, smoke emissions, and energy release from wildfires is crucial to air quality forecasting and emergency response planning yet has long posed a significant scientific challenge. Here we compare predictions of burned area and fire radiative power from the coupled weather/fire‐spread model WRF‐Fire (Weather and Research Forecasting Tool with fire code), against simpler methods typically used in air quality forecasts. We choose the 2019 Williams Flats Fire as our test case due to a wealth of observations and ignite the fire on different days and under different configurations. Using a novel re‐gridding scheme, we compare WRF‐Fire's heat output to geostationary satellite data at 1‐hr temporal resolution. We also evaluate WRF‐Fire's time‐resolved burned area against high‐resolution imaging from the National Infrared Operations aircraft data. Results indicate that for this study, accounting for containment efforts in WRF‐Fire simulations makes the biggest difference in achieving accurate results for daily burned area predictions. When incorporating novel containment line inputs, fuel density increases, and fuel moisture observations into the model, the error in average daily burned area is 30% lower than persistence forecasting over a 5‐day forecast. Prescribed diurnal cycles and those resolved by WRF‐Fire simulations show a phase offset of at least an hour ahead of observations, likely indicating the need for dynamic fuel moisture schemes. This work shows that with proper configuration and input data, coupled weather/fire‐spread modeling has the potential to improve smoke emission forecasts.

On 30 December 2021, the Marshall Fire devastated the Boulder, Colorado region. The fire initiated in fine fuels in open space just southeast of Boulder and spread rapidly due to the strong, downslope winds that penetrated into the Boulder Foothills. Despite the increasing occurrence of wildland-urban interface (WUI) disasters, many questions remain about how fires progress through vegetation and the built environment. To help answer these questions for the Marshall Fire, we use a coupled fire-atmosphere model and Doppler on Wheels (DOW) observations to study the fire's progression as well as examine the physical drivers of its spread. Evaluation of the model using the DOW suggests that the model is able to capture general characteristics of the flow field; however, it does not produce as robust of a hydraulic jump as the one observed. Our results highlight limitations of the model that should be addressed for successful WUI simulations.

This study uses a newly-developed firebrand spotting parameterization in simulations of the Marshall Fire (2021) to demonstrate that without fire spotting, wind-driven fire simulations cannot reproduce the behavior of some fires. The Marshall Fire, the most destructive in Colorado’s history, took mere hours to cause nearly half a billion dollars in damage and destroy over 1000 homes. In wind-driven events that occur in the wildland-urban interface, the model’s ability to spot is critical for modeling fire spread over water streams and urban features such as highways. Without ignition of fire spots, the simulated Marshall Fire cannot advance. In cases when spotting significantly contributes to fire spread, the process’ nonlinear nature is a source of uncertainty to modeling fire behavior that can broaden the model’s ensemble spread and possibly produce a more realistic probability of outcomes. The results in this study corroborate the importance of representing fire spotting in atmosphere-fire behavior coupled models, such as WRF-Fire.

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]A Fire-Spotting Parameterization Coupled with the WRF-Fire ModelAuthorsMariaFredianiiDTimothy WJulianoiDAmyDeCastroBrankoKosovicJasonKnievelSee all authors Maria FredianiiDCorresponding Author• Submitting AuthorNational Center for Atmospheric ResearchiDhttps://orcid.org/0000-0002-7604-9455view email addressThe email was not providedcopy email addressTimothy W JulianoiDNational Center for Atmospheric ResearchiDhttps://orcid.org/0000-0003-0417-0886view email addressThe email was not providedcopy email addressAmy DeCastroNational Center for Atmospheric Researchview email addressThe email was not providedcopy email addressBranko KosovicNational Center for Atmospheric Researchview email addressThe email was not providedcopy email addressJason KnievelNCARview email addressThe email was not providedcopy email address

Current state-of-the art regional numerical weather forecasts are run at horizontal grid spacings of a few kilometers, which permits medium- to large-scale convective systems to be represented explicitly in the model. With the convection parameterization no longer active, much uncertainty in the formulation of subgrid-scale processes moves to other areas such as the cloud microphysical, turbulence, and land surface parameterizations. The goal of this study is to investigate experiments with stochastically perturbed parameters (SPP) within a microphysics parameterization and the model’s horizontal diffusion coefficients. To estimate the “true” uncertainty due to parameter uncertainty, the magnitudes of the perturbations are chosen as realistically as possible and not with a purposeful intent of maximal forecast impact as some prior work has done. Spatial inhomogeneities and temporal persistence are represented using a random perturbation pattern with spatial and temporal correlations. The impact on the distributions of various hydrometeors, precipitation characteristics, and solar and longwave radiation are quantified for a winter case and a summer case. In terms of upscale error growth, the impact is relatively small and consists primarily of triggering atmospheric instabilities in convectively unstable regions. In addition, small in situ changes with potentially large socioeconomic impacts are observed in the precipitation characteristics such as maximum hail size. Albeit the impact of introducing physically based parameter uncertainties within the bounds of aerosol uncertainties is small, their influence on the solar and longwave radiation balances may still have important implications for global model simulations of future climate scenarios.

In this study, infrared brightness temperatures (BTs) are used to examine how applying stochastic perturbed parameter (SPP) methodology to the widely used Thompson-Eidhammer cloud microphysics scheme impacts the cloud field in high-resolution forecasts. Modifications are made to add stochastic perturbations to three parameters controlling cloud generation and dissipation processes. Two five-member ensembles are generated, one using the microphysics parameter perturbations (SPP-MP) and another where white noise perturbations were added to potential temperature fields at initialization time (Control). The impact of the SPP method was assessed using simulated and observed GOES-16 BTs. This analysis uses pixel-based and object-based methods to assess the impact on the cloud field. Pixel-based methods revealed that the SPP-MP BTs are slightly more accurate than the Control BTs. However, too few pixels with a BT lower than 270 K result in a positive bias compared to the observations. A negative bias compared to the observations is observed when only analyzing lower BTs. The spread of the ensemble BTs was analyzed using the continuous ranked probability score differences, with the SPP-MP ensemble BTs having less (more) spread during May (January) compared to the Control. Object-based analysis using the Method for Object-Based Diagnostic Evaluation revealed the upper-level cloud objects are smaller in the SPP-MP ensemble than the Control but a lower bias exists in the SPP-MP BTs compared to the Control BTs when overlapping matching objects. However, there is no clear distinction between the SPP-MP and Control ensemble members during the evolution of objects, Overall, the SPP-MP perturbations result in lower BTs compared to the Control ensemble and more cloudy pixels.

This paper introduces new developments in an outage prediction model (OPM) for an electric distribution network in the Northeastern United States and assesses their significance to the OPM performance. The OPM uses regression tree models fed by numerical weather prediction outputs, spatially distributed information on soil, vegetation, electric utility assets, and historical power outage data to forecast the number and spatial distribution of outages across the power distribution grid. New modules introduced hereby consist in 1) a storm classifier based on weather variables; 2) a multimodel optimization of regression tree output; and 3) a post-processing routine for more accurately describing tree-leaf conditions. Model implementations are tested through leave-one-storm-out cross-validations performed on 120 storms of varying intensity and characteristics. The results show that the median absolute percentage error of the new OPM version decreased from 130% to 59% for outage predictions at the service territory level, and the OPM skills for operational forecasts are consistent with the skills based on historical storm analyses.

Hurricane Sandy (2012, referred to as Current Sandy) was among the most devastating storms to impact Connecticut's overhead electric distribution network, resulting in over 15 000 outage locations that affected more than 500 000 customers. In this paper, the severity of tree-caused outages in Connecticut is estimated under future-climate Hurricane Sandy simulations, each exhibiting strengthened winds and heavier rain accumulation over the study area from large-scale thermodynamic changes in the atmosphere and track changes in the year similar to 2100 (referred to as Future Sandy). Three machine-learning models used five weather simulations and the ensemble mean of Current and Future Sandy, along with land-use and overhead utility infrastructure data, to predict the severity and spatial distribution of outages across the Eversource Energy service territory in Connecticut. To assess the influence of increased precipitation from Future Sandy, two approaches were compared: an outage model fit with a full set of variables accounting for both wind and precipitation, and a reduced set with only wind. Future Sandy displayed an outage increase of 42%-64% when using the ensemble of WRF simulations fit with three different outage prediction models. This study is a proof of concept for the assessment of increased outage risk resulting from potential changes in tropical cyclone intensity associated with late-century thermodynamic changes driven by the IPCC AR4 A2 emissions scenario.

Analogs are used as a forecast postprocessing technique, in which a statistical forecast is derived from past prognostic states. This study proposes a method to identify analogs through spatial objects, which are then used to create forecast ensembles. The object-analog technique preserves the field's spatial relationships, reduces spatial dimensionality, and consequently facilitates the use of artificial intelligence algorithms to improve analog selection. Forecast objects are created with a three-step object selection, combining standard image processing algorithms. The resulting objects are used to find similar forecasts in a training set with a similarity measure based on object area intersection and magnitude. Storm-induced power outages in the Northeast United States motivated the method's validation for 10-m AGL wind speed forecasts. The training set comprises reforecasts and reanalyses of events that caused damages to the utility infrastructure. The corresponding reanalyses of the best reforecast analogs are used to produce the object-analog ensemble forecasts. The forecasts are compared with other analog forecast methods. Analogs representing lower and upper predictability limits provide references to distinguish the method's ability (to find good analogs) from the training set's ability (to provide good analogs) to generate skillful ensemble forecasts. The object-analog forecasts are competitively skillful compared to simpler analog techniques with an advantage of lower spatial dimensionality, while generating reliable ensemble forecasts, with reduced systematic and random errors, maintaining correlation, and improving Brier scores.

This article compares two nonparametric tree‐based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estimates and prediction intervals of outage predictions for both models using high‐resolution weather, infrastructure, and land use data for 89 storm events (including hurricanes, blizzards, and thunderstorms). We found that spatially BART predicted more accurate point estimates than QRF. However, QRF produced better prediction intervals for high spatial resolutions (2‐km grid cells and towns), while BART predictions aggregated to coarser resolutions (divisions and service territory) more effectively. We also found that the predictive accuracy was dependent on the season (e.g., tree‐leaf condition, storm characteristics), and that the predictions were most accurate for winter storms. Given the merits of each individual model, we suggest that BART and QRF be implemented together to show the complete picture of a storm's potential impact on the electric distribution network, which would allow for a utility to make better decisions about allocating prestorm resources.

This study identifies conditions that determine errors in numerical simulations of 10-m wind speed over moderately complex terrain, emphasizing winds that lead to overhead power-line damage over a subregion of the northeast United States. Simulations with the Mellor-Yamada-Janjic (MYJ) scheme, the Yonsei University (YSU) scheme, and a subgrid-scale topographic drag correction (Topo) applied to YSU are used to investigate error components. The wind speed distribution is dominated by low speeds, which are well depicted by Topo, but are underestimated by the MYJ and YSU schemes. Conversely, moderate and high speeds are underestimated by Topo, and MYJ and YSU perform better across specific ranges. Verification samples are conditioned by season, diurnal cycle, topography, and spatial patterns obtained with a clustering analysis. The systematic error is characterized by a positive bias in low speeds, and as speed increases the biases become more negative. Quantile comparisons, along with systematic and random errors, indicate that beyond the dependence on wind speed itself, errors also depend on seasonal characteristics, indirectly defined by scheme stability profiles. The positive relationship between absolute bias and speed originates in the friction velocity parameterization, and the correction for drag in the Topo scheme exacerbates the effect. The Topo scheme adjusts the total bias and sharpens the bias spread but penalizes moderate and high winds. Clusters reveal that in Topo the bias is primarily driven by wind direction. Excessive correction occurs on terrain-interacting flows, and oceanic flow modulates the adjustment, enhancing the scheme's performance.