scholarly journals Review of "Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary observations" by Kim et al.

2020 ◽  
Author(s):  
Meelis Zidikheri
Keyword(s):  
Inverse Modeling ◽  
Dispersion Model ◽  
Smoke Plume ◽  
Fire Emissions ◽  
Plume Transport

scholarly journals Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary satellite observations

2020 ◽  
Vol 20 (17) ◽  
pp. 10259-10277
Author(s):  
Hyun Cheol Kim ◽  
Tianfeng Chai ◽  
Ariel Stein ◽  
Shobha Kondragunta
Keyword(s):  
Root Mean Square Error ◽  
Cost Function ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Inverse Modeling ◽  
Dispersion Model ◽  
Satellite Observations ◽  
Mean Square ◽  
Fire Emissions ◽  
Wildfire Smoke

Abstract. Smoke forecasts have been challenged by high uncertainty in fire emission estimates. We develop an inverse modeling system, the HYSPLIT-based Emissions Inverse Modeling System for wildfires (or HEIMS-fire), that estimates wildfire emissions from the transport and dispersion of smoke plumes as measured by satellite observations. A cost function quantifies the differences between model predictions and satellite measurements, weighted by their uncertainties. The system then minimizes this cost function by adjusting smoke sources until wildfire smoke emission estimates agree well with satellite observations. Based on HYSPLIT and Geostationary Operational Environmental Satellite (GOES) Aerosol/Smoke Product (GASP), the system resolves smoke source strength as a function of time and vertical level. Using a wildfire event that took place in the southeastern United States during November 2016, we tested the system's performance and its sensitivity to varying configurations of modeling options, including vertical allocation of emissions and spatial and temporal coverage of constraining satellite observations. Compared with currently operational BlueSky emission predictions, emission estimates from this inverse modeling system outperform in both reanalysis (21 out of 21 d; −27 % average root-mean-square-error change) and hindcast modes (29 out of 38 d; −6 % average root-mean-square-error change) compared with satellite observed smoke mass loadings.

scholarly journals Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary satellite observations

2020 ◽  
Author(s):  
Hyun Cheol Kim ◽  
Tianfeng Chai ◽  
Ariel Stein ◽  
Shobha Kondragunta
Keyword(s):  
Cost Function ◽  
Inverse Modeling ◽  
Dispersion Model ◽  
Satellite Observations ◽  
Satellite Measurements ◽  
Fire Emissions ◽  
Wildfire Smoke ◽  
Smoke Plumes ◽  
In Fire ◽  
Vertical Level

Abstract. Smoke forecasts have been challenged by high uncertainty in fire emission estimates. We develop an inverse modeling system, the HYSPLIT-based Emissions Inverse Modeling System for wildfires (or HEIMS-fire), that estimates wildfire emissions from the transport and dispersion of smoke plumes as measured by satellite observations. A cost function quantifies the differences between model predictions and satellite measurements, weighted by their uncertainties. The system then minimizes this cost function by adjusting smoke sources until wildfire smoke emission estimates agree well with satellite observations. Based on NOAA’s HYSPLIT and GOES Aerosol/Smoke Product (GASP), the system resolves smoke source strength as a function of time and vertical level. Using a wildfire event that took place in the Southeastern United States during November 2016, we tested the system’s performance and its sensitivity to varying configurations of modeling options, including vertical allocation of emissions and spatial and temporal coverage of constraining satellite observations. Compared with currently operational BlueSky emission predictions, emission estimates from this inverse modeling system outperform in both reanalysis (21 out of 21 days; −27 % average RMSE change) and hindcast modes (29 out of 38 days; −6 % average RMSE change).

scholarly journals Tropical biomass burning smoke plume size, shape, reflectance, and age based on 2001–2009 MISR imagery of Borneo

2012 ◽  
Vol 12 (7) ◽  
pp. 3437-3454 ◽  
Author(s):  
C. S. Zender ◽  
A. G. Krolewski ◽  
M. G. Tosca ◽  
J. T. Randerson
Keyword(s):  
Optical Properties ◽  
Tropical Forests ◽  
Optical Depth ◽  
Biomass Burning ◽  
Single Scattering ◽  
Particle Dispersion ◽  
Hygroscopic Growth ◽  
Smoke Plume ◽  
Fire Emissions ◽  
Plume Geometry

Abstract. Land clearing for crops, plantations and grazing results in anthropogenic burning of tropical forests and peatlands in Indonesia, where images of fire-generated aerosol plumes have been captured by the Multi-angle Imaging SpectroRadiometer (MISR) since 2001. Here we analyze the size, shape, optical properties, and age of distinct fire-generated plumes in Borneo from 2001–2009. The local MISR overpass at 10:30 a.m. misses the afternoon peak of Borneo fire emissions, and may preferentially sample longer plumes from persistent fires burning overnight. Typically the smoke flows with the prevailing southeasterly surface winds at 3–4 m s−1, and forms ovoid plumes whose mean length, height, and cross-plume width are 41 km, 708 m, and 27% of the plume length, respectively. 50% of these plumes have length between 24 and 50 km, height between 523 and 993 m and width between 18% and 30% of plume length. Length and cross-plume width are lognormally distributed, while height follows a normal distribution. Borneo smoke plume heights are similar to previously reported plume heights, yet Borneo plumes are on average nearly three times longer than previously studied plumes. This could be due to sampling or to more persistent fires and greater fuel loads in peatlands than in other tropical forests. Plume area (median 169 km2, with 25th and 75th percentiles at 99 km2 and 304 km2, respectively) varies exponentially with length, though for most plumes a linear relation provides a good approximation. The MISR-estimated plume optical properties involve greater uncertainties than the geometric properties, and show patterns consistent with smoke aging. Optical depth increases by 15–25% in the down-plume direction, consistent with hygroscopic growth and nucleation overwhelming the effects of particle dispersion. Both particle single-scattering albedo and top-of-atmosphere reflectance peak about halfway down-plume, at values about 3% and 10% greater than at the origin, respectively. The initially oblong plumes become brighter and more circular with time, increasingly resembling smoke clouds. Wind speed does not explain a significant fraction of the variation in plume geometry. We provide a parameterization of plume shape that can help atmospheric models estimate the effects of plumes on weather, climate, and air quality. Plume age, the age of smoke furthest down-plume, is lognormally distributed with a median of 2.8 h (25th and 75th percentiles at 1.3 h and 4.0 h), different from the median ages reported in other studies. Intercomparison of our results with previous studies shows that the shape, height, optical depth, and lifetime characteristics reported for tropical biomass burning plumes on three continents are dissimilar and distinct from the same characteristics of non-tropical wildfire plumes.

scholarly journals Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign

2020 ◽  
Vol 13 (5) ◽  
pp. 2169-2184
Author(s):  
Li Pan ◽  
HyunCheol Kim ◽  
Pius Lee ◽  
Rick Saylor ◽  
YouHua Tang ◽  
...  
Keyword(s):  
Air Quality ◽  
Evaluation Methods ◽  
Hazard Mapping ◽  
Data Sets ◽  
Observation Data ◽  
Field Campaign ◽  
Smoke Plume ◽  
Fire Emissions ◽  
Air Quality Forecast ◽  
Multiple Data Sets

Abstract. Multiple observation data sets – Interagency Monitoring of Protected Visual Environments (IMPROVE) network data, the Automated Smoke Detection and Tracking Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and aircraft acetonitrile (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign – are used to evaluate the HMS–BlueSky–SMOKE (Sparse Matrix Operator Kernel Emission)–CMAQ (Community Multi-scale Air Quality Model) fire emissions and smoke plume prediction system. A similar configuration is used in the US National Air Quality Forecasting Capability (NAQFC). The system was found to capture most of the observed fire signals. Usage of HMS-detected fire hotspots and smoke plume information was valuable for deriving both fire emissions and forecast evaluation. This study also identified that the operational NAQFC did not include fire contributions through lateral boundary conditions, resulting in significant simulation uncertainties. In this study we focused both on system evaluation and evaluation methods. We discussed how to use observational data correctly to retrieve fire signals and synergistically use multiple data sets. We also addressed the limitations of each of the observation data sets and evaluation methods.

scholarly journals Regional CO<sub>2</sub> inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0

2021 ◽  
Vol 14 (6) ◽  
pp. 3383-3406
Author(s):  
Guillaume Monteil ◽  
Marko Scholze
Keyword(s):  
Inverse Modeling ◽  
Transport Model ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Model Errors ◽  
Inversion Algorithm ◽  
In Situ Data ◽  
Increasing Demand ◽  
Modular Inversion

Abstract. Atmospheric inversions are used to derive constraints on the net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. The resolution and accuracy that the fluxes can be estimated with depends, among other factors, on the quality and density of the observational coverage, on the precision and accuracy of the transport model used by the inversion to relate fluxes to observations, and on the adaptation of the statistical approach to the problem studied. In recent years, there has been an increasing demand from stakeholders for inversions at higher spatial resolution (country scale), in particular in the framework of the Paris agreement. This step up in resolution is in theory enabled by the growing availability of observations from surface in situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the bottom-up modeling community (vegetation models, fossil fuel emission inventories, etc.). However, it calls for new developments in the inverse models: diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency. In this context, we developed LUMIA, the Lund University Modular Inversion Algorithm. LUMIA is a Python library for inverse modeling built around the central idea of modularity: it aims to be a platform that enables users to construct and experiment with new inverse modeling setups while remaining easy to use and maintain. It is in particular designed to be transport-model-agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. We have constructed a first regional inversion setup using the LUMIA framework to conduct regional CO2 inversions in Europe using in situ data from surface and tall-tower observation sites. The inversions rely on a new offline coupling between the regional high-resolution FLEXPART Lagrangian particle dispersion model and the global coarse-resolution TM5 transport model. This test setup is intended both as a demonstration and as a reference for comparison with future LUMIA developments. The aims of this paper are to present the LUMIA framework (motivations for building it, development principles and future prospects) and to describe and test this first implementation of regional CO2 inversions in LUMIA.

scholarly journals Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign

2017 ◽  
Author(s):  
Li Pan ◽  
Hyun Cheol Kim ◽  
Pius Lee ◽  
Rick Saylor ◽  
YouHua Tang ◽  
...  
Keyword(s):  
Air Quality ◽  
Evaluation Methods ◽  
Hazard Mapping ◽  
Data Sets ◽  
Observation Data ◽  
Field Campaign ◽  
Smoke Plume ◽  
Fire Emissions ◽  
Air Quality Forecast ◽  
Multiple Data Sets

Abstract. Multiple observation data sets, including Interagency Monitoring of Protected Visual Environments (IMPROVE) network data, Automated Smoke Detection and Tracking Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and aircraft acetonitrile (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign are used to evaluate the HMS-BlueSky-SMOKE-CMAQ fire emissions and smoke plume prediction system. A similar configuration is used in the National Air Quality Forecasting Capability (NAQFC). The system was found to capture signatures of most of the observed fire signals. Use of HMS-detected fire hotspots and smoke plume information are valuable for both initiating fire emissions and evaluating model simulations. However, we also found that the current system does not include fire contributions through lateral boundary condition and missed fires that are not associated with visible smoke plumes resulting in significant simulation uncertainties. In this study we focused not only on model evaluation but also on evaluation methods. We discuss how to use observational data correctly to filter out fire signals and synergistic use of multiple data sets together. We also address the limitations of each of the observation data sets and of the evaluation methods.

scholarly journals Capturing Plume Rise and Dispersion with a Coupled Large-Eddy Simulation: Case Study of a Prescribed Burn

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 579
Author(s):  
Nadya Moisseeva ◽  
Roland Stull
Keyword(s):  
Large Eddy Simulation ◽  
Numerical Models ◽  
Real Life ◽  
Plume Rise ◽  
Atmospheric Conditions ◽  
Prescribed Burn ◽  
Smoke Plume ◽  
Fire Emissions ◽  
Eddy Simulation ◽  
Large Eddy

Current understanding of the buoyant rise and subsequent dispersion of smoke due to wildfires has been limited by the complexity of interactions between fire behavior and atmospheric conditions, as well as the uncertainty in model evaluation data. To assess the feasibility of using numerical models to address this knowledge gap, we designed a large-eddy simulation of a real-life prescribed burn using a coupled semi-emperical fire–atmosphere model. We used observational data to evaluate the simulated smoke plume, as well as to identify sources of model biases. The results suggest that the rise and dispersion of fire emissions are reasonably captured by the model, subject to accurate surface thermal forcing and relatively steady atmospheric conditions. Overall, encouraging model performance and the high level of detail offered by simulated data may help inform future smoke plume modeling work, plume-rise parameterizations and field experiment designs.

Fire emission uncertainties and their effect on smoke dispersion predictions: a case study at Eglin Air Force Base, Florida, USA

2015 ◽  
Vol 24 (2) ◽  
pp. 276 ◽  
Author(s):  
Aika Y. Davis ◽  
Roger Ottmar ◽  
Yongqiang Liu ◽  
Scott Goodrick ◽  
Gary Achtemeier ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Air Force ◽  
Prescribed Burning ◽  
Dispersion Model ◽  
Field Measurements ◽  
Plume Dispersion ◽  
Prescribed Burn ◽  
Fire Emissions ◽  
Smoke Dispersion ◽  
Air Force Base

Prescribed burning is practiced to benefit ecosystems but the resulting emissions can adversely affect air quality. A better understanding of the uncertainties in emission estimates and how these uncertainties affect smoke predictions is critical for model-based decision making. This study examined uncertainties associated with estimating fire emissions and how they affected smoke concentrations downwind from a prescribed burn that was conducted at Eglin Air Force Base in Florida, US. Estimated variables used in the modelled emission calculation were compared with field measurements. Fuel loadings, fuel consumption and emission factors were simulated using Photo Series, Consume, and previously published values. A plume dispersion model was used to study the effect of uncertainty in emissions on ground concentration prediction. The fire emission models predicted fuel loading, fuel consumption and emission factor within 15% of measurements. Approximately 18% uncertainty in field measurements of PM2.5 emissions and 36% uncertainty attributed to variability in emission estimating models resulted respectively in 20% and 42% ground level PM2.5 concentration uncertainties in dispersion modelling using Daysmoke. Uncertainty in input emissions influences the concentrations predicted by the smoke dispersion model to the same degree as does the model’s inherent uncertainty due to turbulence.

scholarly journals A case study of pyro-convection using transport model and remote sensing data

2006 ◽  
Vol 6 (1) ◽  
pp. 173-185 ◽  
Author(s):  
R. Damoah ◽  
N. Spichtinger ◽  
R. Servranckx ◽  
M. Fromm ◽  
E. W. Eloranta ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Transport Model ◽  
Dispersion Model ◽  
Deep Convection ◽  
Particle Dispersion ◽  
Convective Transport ◽  
Yukon Territory ◽  
Pollutant Emissions ◽  
Fire Emissions

Abstract. Summer 2004 saw severe forest fires in Alaska and the Yukon Territory that were mostly triggered by lightning strikes. The area burned (>2.7×106 ha) in the year 2004 was the highest on record to date in Alaska. Pollutant emissions from the fires lead to violation of federal standards for air quality in Fairbanks. This paper studies deep convection events that occurred in the burning regions at the end of June 2004. The convection was likely enhanced by the strong forest fire activity (so-called pyro-convection) and penetrated into the lower stratosphere, up to about 3 km above the tropopause. Emissions from the fires did not only perturb the UT/LS locally, but also regionally. POAM data at the approximate location of Edmonton (53.5° N, 113.5° W) show that the UT/LS aerosol extinction was enhanced by a factor of 4 relative to unperturbed conditions. Simulations with the particle dispersion model FLEXPART with the deep convective transport scheme turned on showed transport of forest fire emissions into the stratosphere, in qualitatively good agreement with the enhancements seen in the POAM data. A corresponding simulation with the deep convection scheme turned off did not result in such deep vertical transport. Lidar measurements at Wisconsin on 30 June also show the presence of substantial aerosol loading in the UT/LS, up to about 13 km. In fact, the FLEXPART results suggest that this aerosol plume originated from the Yukon Territory on 25 June.

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