Atmospheric Convection and Aerosol Absorption Boost Wildfire Smoke Injection

Abstract. High frequency of agricultural fires is observed every year during the summer months over SW Russia and Eastern Europe. This study investigates the initial injection height of aerosol generated by the fires over these regions during the biomass burning season, which determines the potential for long-range transport of the smoke. This information is critical for aerosol transport modeling, as it determines the smoke plume evolution. The study focuses on the period 2006–2008, and is based on observations made by the CALIOP instrument on board the NASA CALIPSO satellite. MODIS data are synergistically used for the detection of the fires and the characterization of their intensity. CALIPSO aerosol vertical distributions generated by the active fires are analyzed to investigate the aerosol top height which is considered dependent on the heat generated by the fires and can be associated with the initial injection height. Aerosol top heights of the vertically homogenous smoke layers are found to range between 1.6 and 5.9 km. Smoke injection heights from CALIPSO are compared with mixing layer heights taken by the European Centre for Medium-range Weather Forecast (ECMWF), to investigate the direct injection of smoke particles into the free troposphere. Our results indicate that the aerosol plumes are observed within the boundary layer for the 50% of the cases examined. For the rest of the cases, the strong updrafts generated by the fires resulted to smoke injection heights greater than the ECMWF estimated mixing layer by 0.5 to 3.0 km, indicating a direct smoke injection into the free troposphere. The smoke injection height showed a dependence on the MODIS-Land Fire Radiative Power product which is indicative of the fire intensity, especially in the cases of lower static stability in the upper part of the boundary layer and the free troposphere.

Smoke injection height is a key driving factor for plume transport, which determines the lifetime of smoke aerosol in the atmosphere, transport path and diffusion along with the wind, and impacts on atmospheric environment. In this study, raw data obtained from the latest multi-angle imaging spectroradiometer (MISR) plume height project was extracted and analzyed. The variation of smoke injection heights of wildfire in China was investigated with statistical analysis methods. The effects of fire characteristics (combustion biomass type and fire radiative power) on the smoke injection height were explored. Meanwhile, the influence of smoke injection heights on the atmospheric environment was discussed based on the proportion of higher injection height plumes and the value of smoke aerosol optical depth (AOD). The results showed that smoke injection heights from wildfire ranged from 345 to 7719 m, with 57.1% of which ranging from 500 to 1000 m. Except for an abnormally high value of smoke injection height from a large grassland fire, the rest of smoke injection heights were lower than 3000 m. The biomass type for combustion was an important factor affecting smoke injection heights. The injection heights of the plume caused by forest fire were the highest and had the greatest variability. Smoke injection heights increased with the fire radiation power, but with obvious dispersion (R2=0.19). By setting a simple threshold, the proportion of higher injection plumes which might cause long-distance transportation of air pollutants in China was 10.5%. Combined with the analysis of smoke AOD, it was found that the average smoke injection height from cropland burning was the lowest, but their smoke caused the highest regional air pollution. In contrast, although forest fires could produce the highest smoke injection height, their smoke had a lower average value of AOD, which indicated a relatively weak impact of forest fires on regional air quality.

We present an analysis of over 23,000 globally distributed wildfire smoke plume injection heights derived from Multi-angle Imaging SpectroRadiometer (MISR) space-based, multi-angle stereo imaging. Both pixel-weighted and aerosol optical depth (AOD)-weighted results are given, stratified by region, biome, and month or season. This offers an observational resource for assessing first-principle plume-rise modelling, and can provide some constraints on smoke dispersion modelling for climate and air quality applications. The main limitation is that the satellite is in a sun-synchronous orbit, crossing the equator at about 10:30 a.m. local time on the day side. Overall, plumes occur preferentially during the northern mid-latitude burning season, and the vast majority inject smoke near-surface. However, the heavily forested regions of North and South America, and Africa produce the most frequent elevated plumes and the highest AOD values; some smoke is injected to altitudes well above 2 km in nearly all regions and biomes. Planetary boundary layer (PBL) versus free troposphere injection is a critical factor affecting smoke dispersion and environmental impact, and is affected by both the smoke injection height and the PBL height; an example assessment is made here, but constraining the PBL height for this application warrants further work.

Smoke injection height (SIH) determines the distance and direction of smoke transport, thus impacting the atmospheric environment. In this study, we used Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations data coupled with Moderate Resolution Imaging Spectroradiometer (MODIS) data and the Hybrid Single-Particle Lagrangian Integrated Trajectory model to derive the SIH values during the peak forest and grassland fire seasons from 2012 to 2017 in Southwest China. The results suggest that the SIH values ranged from 2500 m to 2890 m. An analysis of the dependence of SIH on fire characteristics revealed no obvious correlation between SIH and fire radiative power (FRP) because other factors in addition to FRP have an important impact on SIH. Moreover, MODIS FRP data has a drawback in representing the energy released by real fires, also leading to this result. The topographic variables of forest and grassland fires in Southwest China are very different. Complex topography affects SIH by affecting fire intensity and interactions with wind. A comparison of the SIHs with boundary layer height reveals that 75% of the SIHs are above the boundary layer. Compared with other areas, a higher percentage of free troposphere injection occurs in Southwest China, indicating that smoke can cause air pollution over large ranges. Our work provides a better understanding of the transport and vertical distribution of smoke in Southwest China.

Every year, smoke is still a significant problem and challenge in Indonesia. Forest and land fires cause vast amounts of smoke, negatively affecting society, such as health by decreased air quality index and transportation through reduced visibility. Factors that affect the spread of smoke from a fire source include wind, smoke injection height, and atmospheric conditions. This study examines the smoke dispersion that occurred on 23 and 31 August 2019; and 18 September 2019 in Riau province, where smoke was identified by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The results showed that the height of the smoke injection was relatively high, ranging from 1.6 to 2.5 km, the majority of which are above the Planetary Boundary Layer (PBL). Winds that move from the southeast and southwest with a speed of 3-5 m/s support the growth of the smoke column and the transportation of smoke from the fires to the downtown area of Pekanbaru. A secondary stable layer in the lower layer supports the fumigation process, which resulted in a decrease in the air quality index in Pekanbaru at the event to an unhealthy level and visibility up to 2 km.

Inverse modeling methods have been widely used for model performance improvement and parameter estimation. For air quality studies, inverse modeling is often used for emission inversion as emissions are associated with significant amount of uncertainties. NOx emission sources are estimated through a four dimensional variational (4D-Var) inverse modeling approach using satellite and ground-level observations. In the non-temporal set-ups, NO_x emissions are adjusted by assimilating NO_2 columns from satellite (OMI) and NO_2 from ground based observations separately. The results indicate that the average scaling factors vary from 0.41 to 1.74 or from 0.45 to 2.52 when ground observations and OMI observations are used, respectively. The average scaling factors are in the range of 0.43 to 1.79 when both types of observations were used simultaneously. The total amount of emissions increase 3.5% when inversion is based on ground observations only, 17.3% when inversion includes only OMI observations and 13.6% when both observations are used simultaneously. Under the temporal set-up, NOx emissions are adjusted by using OMI and modified surface observations to estimate hourly profiles of emissions at each location. These results indicate that inverting emissions on an hourly basis leads to reduced distance between observations and modeled concentrations. The majority of scaling factors are higher than one. Lightning is one of the more uncertain sources of Nitrogen dioxides. While inverted emissions included point, mobile, biogenic and other sources in the air quality model (CMAQ), lightening as one of the most significant natural sources of NOx was excluded in previous inversion setups. To account for the impact of this source, lightning emissions are parameterized as input for CMAQ model from the observations of Lightning Detection Networks flash rates. We filtered the places and observations that are dominated by anthropogenic sources. NO emission inversion in filtered areas resulted in reduction in bias and root mean square error between vertical column densities calculated by CMAQ and observed by OMI. However, scarcity of data prevents these results from being extended to all lightning events across the domain and episode.

In Nigeria, particularly in urban areas like Lagos, flooding is a frequent natural hazard. In 2011, Lagos experienced one of its worst floods resulting in significant economic losses and displacement of people. In recent years, Lagos has continued to grapple with flooding challenges, with an equally significant flood episode occurring in 2021. This study focuses on predicting floods and forecasting extremely heavy rainfall in West Africa's equatorial zone using the Weather Research and Forecasting (WRF) model, particularly in humid tropical environments like Lagos. The study discusses the need to review existing flood models and adopt alternative flood models to address the limitations of flood prediction. As potential causes of these rainfall episodes, the interconnections between synoptic systems such as tropical easterly waves, southwesterly winds related to the West African Monsoon, and local topography and oceanic conditions are investigated. Three key metrics: root mean square error (RMSE), mean bias (MB), and mean absolute error (MAE) are used to assess the effectiveness of the computational model. Results indicate that the WRF model, specifically when using the Thompson parameterisation, can estimate the amount of rainfall accumulated over a 24-h period. This suggests that the model can predict the size of daily precipitation during intense rain events. The Thompson scheme shows better performance compared to the WSM6 scheme while evaluating the stations and episodes. During the rainfall episode on July 10, 2011, Thompson's spatial rainfall predictions were better than WSM6, resulting in a decrease in root mean square error (RMSE) of 15–31% depending on the area. Simulations of the July 2021 episode also show better performance, with a decrease in RMSE of 11–25% when comparing Thompson to WSM6 scheme. The Thompson scheme’s improved ability is directly linked to a more accurate depiction of the microphysical mechanisms that control the rainfall formation. By explicitly simulating the dynamics of ice crystals and graupel, it is possible to accurately replicate the processes of orographic lifting and moist convection that are responsible for driving intense monsoon precipitation. In addition, Thompson scheme shows a reduced degree of systemic bias in comparison to WSM6, with a 75% reduction in the average bias in rainfall accumulation over the research area. The combination of the advanced Thompson microphysics method and WRF's atmospheric dynamics shows a high level of accuracy in predicting intense rainfall and the risk of floods in this area with diverse tropical topography.

The Pyrocumulonimbus (pyroCb) events over British Columbia in 2017 were observed in the lower stratosphere for about 8–10 months after the smoke injections. Several previous studies used global climate models to investigate the physical parameters for the 2017 pyroCb events, but the conclusions show strong model dependency. In this study, we use the Energy Exascale Earth System Model (E3SM) and complete an ensemble of runs exploring three injection parameters: smoke aerosol mass, the percentage of black carbon within the smoke aerosols, and plume injection height. Additionally, we consider the heterogeneous reaction of ozone and primary organic matter. According to the satellite daily observed aerosol optical depth (AOD), we find that the best ensemble member is the simulation with 0.4 Tg of smoke, 3% of which is black carbon, a 13.5 km smoke injection height, and a 10−5 probability factor of the heterogeneous reaction. Besides AOD, we examine the ensemble score based on the metrics used in previous studies: the metrics of the extinction coefficient at 18 km altitude and the maximum plume height. The conclusion of the best estimate of the injection parameters for 2017 pyroCb events shows strong not only model but also evaluation metric dependency. We use the Random Forest machine learning technique to quantify the relative importance of each parameter in accurately simulating the 2017 pyroCb events and find that the injection height is the most critical feature, no matter which metric is used to score the ensemble members.

Abstract. The buoyant rise and the resultant vertical distribution of wildfire smoke in the atmosphere have a strong influence on downwind pollutant concentrations at the surface. The amount of smoke injected vs. height is a key input into chemical transport models and smoke modelling frameworks. Due to scarcity of model evaluation data as well as the inherent complexity of wildfire plume dynamics, smoke injection height predictions have large uncertainties. In this work we use the coupled fire–atmosphere model WRF-SFIRE configured in large-eddy simulation (LES) mode to develop a synthetic plume dataset. Using this numerical data, we demonstrate that crosswind integrated smoke injection height for a fire of arbitrary shape and intensity can be modelled with a simple energy balance. We introduce two forms of updraft velocity scales that exhibit a linear dimensionless relationship with the plume vertical penetration distance through daytime convective boundary layers. Lastly, we use LES and prescribed burn data to constrain and evaluate the model. Our results suggest that the proposed simple parameterization of mean plume rise as a function of vertical velocity scale offers reasonable accuracy (30 m errors) at little computational cost.

During the Australian fire season 2019/2020, an unprecedented amount of smoke aerosol was not only released, but also transported upwards and injected into the tropopause region by so-called pyro-cumulonimbus clouds (pyroCb). The resulting lower stratospheric aerosol loads in early 2020 were comparable to those of the largest volcanic eruptions of the twentieth century. PyroCbs have been identified as the main pathway for biomass burning aerosol into the stratosphere. To study the phenomenon of PyroCbs, simulations of the so-called Australian New Year Super Outbreak are performed with the numerical weather model ICON. Simulations were run in a nested, limited area mode setup, with the smallest domain reaching down to a horizontal grid spacing of 500 m. Within the domain, an idealised fire perturbation was applied for which an additional constant surface sensible heat and water vapour flux was introduced to represent the thermodynamical impacts of the fire. Simulations with this setup were successful in producing fire-induced deep convection with subsequent smoke injection into the lower stratosphere. Preliminary sensitivity experiments show a high sensitivity of the PyroCb properties to initial and boundary conditions. We can show, that especially water vapour emissions, which would originate from evaporating surface water as well as from combustion of organic materials, have a decisive, enhancing impact on the pyro-convection. Moreover, besides the fire intensity, the plume characteristics and smoke injection heights are also closely linked to the background meteorology, in particular. In the long term, the goal is to incorporate the effects of extreme biomass burning emission into large scale climate simulations by taking into account PyroCb activity. However, this will require a very deep understanding of wildfire triggered convection and PyroCb dynamics.  

Abstract. Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from 12 state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, US, August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within 1 d are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models, with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by fire radiative power (FRP)-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center's Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper, with a day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models, with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported smoke that is 1 d old. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated for by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

The elevation at which wildfire smoke is injected into the atmosphere has a strong influence on how the smoke is dispersed, and is a key input to aerosol transport models. Aerosol layer height is derived with great precision from space‐borne lidar, but horizontal sampling is very poor on a global basis. Aerosol height derived from space‐borne stereo imaging is limited to source plumes having discernable features. But coverage is vastly greater, and captures the cores of major fires, where buoyancy can be sufficient to lift smoke above the near‐surface boundary layer. Initial assessment of smoke injection from the Alaska‐Yukon region during summer 2004 finds at least about 10% of wildfire smoke plumes reached the free troposphere. Modeling of smoke environmental impacts can benefit from the combined strengths of the stereo and lidar observations.

Accurately estimating aboveground biomass (AGB) in tropical forests is vital for managing the threats posed by deforestation, degradation, and climate change. However, challenges persist in accurately estimating AGB in high AGB regions. This study aims to accurately estimate the AGB of regions with high AGB by using spatial statistical analyses based on AGB estimates made by machine‐learning fusion of multisource data. We hypothesize that incorporating dominant auxiliary factors in the analysis increases the estimation accuracy. This study focuses on tropical forests located in Longyan, Fujian Province, China, covering an area of 19,028 km2. Multisource data are used, including airborne laser scanning, the Shuttle Radar Topography Mission digital elevation model, the Landsat Operational Land Imager, and the National Forest Inventory. Based on GeogDetector's spatial covariance matrix and the spatial similarity principle, we identify key auxiliary factors (dominant tree species, canopy closure, and herbaceous cover) and investigated how auxiliary variables can improve estimation accuracy. Empirical Bayesian kriging regression prediction introduces the main auxiliary factors to refine AGB estimates. These refinements significantly enhance the accuracy of AGB estimates, particularly for high AGB, resulting in a 0.1 increase in R2, a 7.0% reduction in root mean square error, a 13.5% reduction in mean square error, and a 6.6% reduction in mean absolute error when compared with the AGB estimates obtained by using machine learning to fuse multisource data. Thus, incorporating spatial statistical analysis into the integration of multisource data and machine learning for AGB estimation can enhance the accuracy of high‐AGB estimates in intricate forest structures, resulting in precise AGB maps.

Highly frequent wildfires are observed every year in Yunnan province, China. This study investigated the smoke injection height in a wildfire event using multi-source remote sensing data in Yunnan. The wildfire event was identified and characterized by combining MODIS and Landsat-8 data. The smoke injection height of the event and the vertical profile of smoke aerosol were inferred from CALIPSO data. The results showed that although the burned areas were only 167.67 ha, the maximum smoke injection height of the wildfire event can reach approximately 4.63 km above sea level. In addition, by comparing the smoke injection height with that of other types of fire events, we found that the plumes of the wildfire event in Yunnan were the most likely candidates for injecting smoke into the free troposphere and producing smoke transport. This study has significant implications for understanding the impact of wildfire smoke plumes on air quality in Yunnan.

The impact of wildfire smoke is largely determined by the height where they are injected into the atmosphere. Current plume rise models tend to underestimate the high smoke injection heights because the previous models and configurations were mainly constrained and validated by the plume height observation from Multi‐angle Imaging SpectroRadiometer (MISR), of which most cases inject low within the planetary boundary layer (PBL). Here we retrieve smoke injection heights from intense pyro‐convections based on pyrocumulonimbus satellite images in PYROCAST data set alongside meteorological reanalysis. It largely augments the MISR data set with smoke injection heights up to the upper troposphere and lower stratosphere (UTLS). Constrained by both MISR and PYROCAST, we show that a scaling down of factor 0.2 to the entrainment efficiency parameterized in the one‐dimensional plume‐rise model (1‐D PRM, Freitas et al. (2010, https://doi.org/10.5194/acp‐10‐585‐2010)) significantly improves the model performance for high injection cases without compromising the accuracy of low injection cases. We also found that the fire intensity input can be obtained through a simplified dependence on the biome and biomass burning emission flux. While being unable to represent high cases before, the improved 1‐D PRM model predicts similarly well in injection heights both low near the PBL height and high into the UTLS. The improved 1‐D PRM is then coupled into Community Atmosphere Model with Chemistry (CAM‐chem). The coupled CAM‐chem‐PRM, when predicting injection heights in tests imitating real BB emission, exhibited consistent predictive capabilities with the standalone 1‐D PRM while saw a mere 15% increase of computation time.