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NASA NTRS2026

Timing the Flames: Geostationary Satellite Detection of Diurnally Shifting Stubble Burning in Northwestern India

Post-monsoon open-field stubble burning in northwestern (NW) India—a key agricultural region known as the “breadbasket”—is a longstanding practice used to clear fields. Satellite observations spanning over two decades have revealed significant upward trends in crop production, vegetative greenness, and the frequency of post-harvest fires, with this last contributing to hazardous air quality during the peak burning season (mid-October to mid-November). Since 2022, thermal anomaly data from Aqua-MODIS and SNPP-VIIRS sensors have shown a sharp decline in reported fire events—an observation that contrasts starkly with the concurrent rise in regional aerosol loading detected from space. This apparent discrepancy became particularly pronounced in 2024–2025, prompting a closer examination using high-temporal-resolution imagery from the Advanced Meteorological Imager (AMI) on the geostationary satellite GEO-KOMPSAT-2A. These observations revealed a clear spike in fire-related signals occurring around and after 4:00 p.m. local time, i.e., outside the typical noon to 2:00 p.m. detection window of the MODIS and VIIRS. A fire detection algorithm exploiting the fire-sensitive shortwave-infrared 3.8 μm signal and its contrast to 11.2 μm infrared observations is designed to adopt AMI observations and applied to its multi-year observations (2019–2025). The resulting fire dataset unambiguously shows a gradual shift in stubble burning activity toward the late afternoon hours beginning in 2022 which is underreported by polar-orbiting satellites. The orbital drift of NASA’s MODIS sensor on the Aqua platform allows detection of some of the gradually shifting fires during afternoon hours, but the MODIS still misses a large number of fires occurring around and after 4 p.m. The AMI’s relatively coarse spatial resolution (~4 km), a consequence of its slant viewing geometry over NW India, imposes inherent limitations on quantifying the full extent of fire occurrences. The operational air quality forecasting models currently assimilate satellite fire detections predominantly captured during early afternoon overpasses of the MODIS and VIIRS. The temporal shift in fire activity complicates such forecast, leading to a substantial underestimation of emissions. Intense stubble burning and the resulting air pollution highlight the need for effective crop residue management practices for mitigating the frequency of open biomass burning and thereby reducing episodic degradation of air quality and its associated public health and economic impacts.

post-monsoon stubble burning; northwestern India;↗

NASA NTRS1996

Examination of Biomass Fire Temperature and Areal Extent from Calibrated AVIRIS Spectra

Biomass burning is an important process on the Earth at the local, regional and global scales. To investigate issues related to biomass burning, a range of remotely acquired data were measured as part of the NASA Smoke Cloud Aerosol and Radiation experiment in Brazil, 1995. As part of this experiment, images of calibrated spectral radiance from 400 to 2500 nm at 10 nm intervals were acquired by AVIRIS. To investigate the expression of biomass fires in AVIRIS spectra, a model of the upwelling radiance from a burning fire was developed. This spectral model accounts for four components in the 20 by 20 m AVIRIS spatial resolution element. These are: (1) the atmospheric path radiance, (2) the solar reflected radiance from unburnt vegetation and soil, (3) the apparent temperature and area of a primary fire, and (4) the apparent temperature and area of a secondary fire. A nonlinear least squares spectral fitting algorithm was developed to invert this model for the AVIRIS spectra. The derived biomass burning parameters from this algorithm are presented for AVIRIS spectral images acquired over Cuiaba, Brazil on 25 August 1995.

Green Robert O.↗

NASA NTRS2015

Assessing Eruption Column Height in Ancient Flood Basalt Eruptions

A buoyant plume model is used to explore the ability of flood basalt eruptions to inject climate-relevant gases into the stratosphere. An example from the 1986 Izu-Oshima basaltic fissure eruption validates the model's ability to reproduce the observed maximum plume heights of 12-16 km above sea level, sustained above fire-fountains. The model predicts maximum plume heights of 13-17 km for source widths of between 4-16 m when 32% (by mass) of the erupted magma is fragmented and involved in the buoyant plume (effective volatile content of 6 wt%). Assuming that the Miocene-age Roza eruption (part of the Columbia River Basalt Group) sustained fire-fountains of similar height to Izu-Oshima (1.6 km above the vent), we show that the Roza eruption could have sustained buoyant ash and gas plumes that extended into the stratosphere at approximately 45 deg N. Assuming 5 km long active fissure segments and 9000 Mt of SO2 released during explosive phases over a 10-15 year duration, the approximately 180 km of known Roza fissure length could have supported approximately 36 explosive events/phases, each with a duration of 3-4 days. Each 5 km fissure segment could have emitted 62 Mt of SO2 per day into the stratosphere while actively fountaining, the equivalent of about three 1991 Mount Pinatubo eruptions per day. Each fissure segment could have had one to several vents, which subsequently produced lava without significant fountaining for a longer period within the decades-long eruption. Sensitivity of plume rise height to ancient atmospheric conditions is explored. Although eruptions in the Deccan Traps (approximately 66 Ma) may have generated buoyant plumes that rose to altitudes in excess of 18 km, they may not have reached the stratosphere because the tropopause was substantially higher in the late Cretaceous. Our results indicate that some flood basalt eruptions, such as Roza, were capable of repeatedly injecting large masses of SO2 into the stratosphere. Thus sustained flood basalt eruptions could have influenced climate on time scales of decades to centuries but the location (i.e., latitude) of the province and relevant paleoclimate is important and must be considered.

climate↗

DOE OSTI2024

A One-dimensional Heat Transfer Model for a Cooled Airfoil in a Direct-fired Supercritical CO 2 Turbine

A one-dimensional (1-D) heat transfer model was developed to predict the performance of a cooled airfoil for a supercritical carbon dioxide (sCO 2 ) turbine. The model included the effects of film cooling effectiveness, thermal barrier coating thickness, and internal Nusselt number augmentation. Trends for overall cooling effectiveness versus heat load parameter were obtained for over twelve unique thermal management schemes. The study found that adding a thermal barrier coating was the most effective scheme for increasing the airfoil overall cooling effectiveness. Internal cooling technologies have diminishing returns as the heat transfer coefficient augmentation increases past a factor of five, however the sensitivity of overall cooling effectiveness to heat transfer augmentation scales by a factor of 1.3 when a thermal barrier coating is introduced. Film cooling is seen as a long-term research area which will be inevitably explored as direct sCO 2 systems develop a market presence. However, the model indicates that realizable film cooling effectiveness levels yield a smaller improvement in overall cooling effectiveness than a thermal barrier coating.

42 ENGINEERING↗

NASA NTRS1980

An assessment of the risk arising from electrical effects associated with the release of carbon fibers from general aviation aircraft fires

A Poisson type model was developed and exercised to estimate the risk of economic losses through 1993 due to potential electric effects of carbon fibers released from United States general aviation aircraft in the aftermath of a fire. Of the expected 354 annual general aviation aircraft accidents with fire projected for 1993, approximately 88 could involve carbon fibers. The average annual loss was estimated to be about $250 (1977 dollars) and the likelihood of exceeding $107,000 (1977 dollars) in annual loss in any one year was estimated to be at most one in ten thousand.

Rosenfield, D.↗

DOE OSTI2025

A New Framework for Interstellar Medium Emission Line Models: Connecting Multiscale Simulations across Cosmological Volumes

The James Webb Space Telescope (JWST) and Atacama Large Millimeter/submillimeter Array have detected emission lines from the ionized interstellar medium (ISM) in some of the first galaxies at z ≳ 6. These measurements present an opportunity to better understand galaxy assembly histories and may allow important tests of state-of-the-art galaxy formation simulations. It is challenging, however, to model these lines in their proper cosmological context. In order to meet this challenge, we introduce a novel subgrid line emission modeling framework. The framework uses the high-z zoom-in simulation suite from the Feedback in Realistic Environments (FIRE) collaboration. The line emission signals from H II regions within each simulated FIRE galaxy are modeled using the semianalytic HIIL INES code. A machine learning approach is then used to determine the conditional probability distribution for the line luminosity to stellar-mass ratio from the H II regions around each simulated stellar particle. This conditional probability distribution can then be applied to predict the line luminosities around stellar particles in lower-resolution, yet larger volume cosmological simulations. As an example, we apply this approach to the IllustrisTNG simulations at z = 6. The resulting predictions for the [O II ], [O III ], and Balmer line luminosities as a function of star formation rate agree well with current observations. Our predictions differ, however, from related works in the literature, which lack detailed subgrid ISM models. This highlights the importance of our multiscale simulation modeling framework. Finally, we provide forecasts for future line luminosity function measurements from the JWST and quantify the cosmic variance in such surveys.

(ISM:) H II regions↗

NASA NTRS2021

The Response of the Amazon Ecosystem to the Photosynthetically Active Radiation Fields: Integrating Impacts of Biomass Burning Aerosol and Clouds in the NASA GEOS Earth System Model

The Amazon experiences fires every year, and the resulting biomass burning aerosols, together with cloud particles, influence the penetration of sunlight through the atmosphere, increasing the ratio of diffuse to direct photosynthetically active radiation (PAR) reaching the vegetation canopy and thereby potentially increasing ecosystem productivity. In this study, we use the NASA Goddard Earth Observing System (GEOS) model with coupled aerosol, cloud, radiation, and ecosystem modules to investigate the impact of Amazon biomass burning aerosols on ecosystem productivity, as well as the role of the Amazon’s clouds in tempering this impact. The study focuses on a seven-year period (2010-2016) during which the Amazon experienced a variety of dynamic environments (e.g., La Niña, normal years, and El Niño). The direct radiative impact of biomass burning aerosols on ecosystem productivity—called here the aerosol diffuse radiation fertilization effect —is found to increase Amazonian Gross Primary Production (GPP) by 2.6% via a 3.8% increase in diffuse PAR (DFPAR) despite a 5.4% decrease in direct PAR (DRPAR) on multiyear average during burning seasons. On a monthly basis, this increase in GPP can be as large as 9.9% (occurring in August 2010). Consequently, the net primary production (NPP) in Amazon is increased by 1.5%, or ~92 Tg C a-1– equivalent to ~37% of the average carbon lost due to Amazon fires over the seven years considered. Clouds, however, strongly regulate the effectiveness of the aerosol diffuse radiation fertilization effect. The efficiency of this fertilization effect is the highest in cloud-free conditions and linearly decreases with increasing cloud amount until the cloud fraction reaches ~0.8, at which point the aerosol-influenced light changes from being a stimulator to an inhibitor of plant growth. Nevertheless, interannual changes in the overall strength of the aerosol diffuse radiation fertilization effect are primarily controlled by the large interannual changes in biomass burning aerosols rather than by changes in cloudiness during the studied period.

biomass burning aerosols on ecosystem productivity↗

NASA NTRSDate not supplied

Determining the Cause of Reduced Concurrent Flame Spread over Thin Solid Fuels in Low Pressure and Low Gravity

The spread of flames over the surface of solid combustible materials is affected by the environmental conditions. Variables such as flow velocity, oxygen concentration, ambient pressure, partial gravity or microgravity, may change the material flammability and influence the fire dynamics. This is an important fire safety issue for space exploration vehicles and space habitats which will very likely have different environments than those currently used on the International Space Station, such as reduced pressure and enriched oxygen concentration (SEA). However, testing experimentally the materials to be used and qualified for space exploration under these conditions is a difficult and expensive task. Here, numerical modeling is used to understand the dominant physico-chemical processes on the concurrent flame spread over thin fabrics under reduced ambient pressure (and in turn, buoyancy) and variable gravitational conditions. The numerical model uses the Fire Dynamics Simulator (FDS6) code with a single-step Arrhenius reaction rate for the solid phase decomposition. Different models are tested for the gas phase combustion kinetics. The model results are validated with experimental results obtained at similar reduced ambient pressure and flow conditions at 1 g. It is shown that as ambient pressure is reduced the flame spread rate over a thin fabric is also reduced, both experimentally and numerically. Numerical results are compared to an analytical approach previously developed to explain the experimental trends. Further interpretation of the model results provides information regarding the physics of the process and how they are affected by the lower pressure environments and gravity conditions. The results of this work provide guidance for potential on-earth testing for fire safety design in spacecraft and space habitats.

Fire Safety↗

NASA NTRSDate not supplied

Determining the Cause of Reduced Concurrent Flame Spread over Thin Solid Fuels in Low Pressure and Low Gravity

microgravity↗

NASA NTRS1993

Importance of aggregation and small ice crystals in cirrus clouds, based on observations and an ice particle growth model

The 1 November 1986 FIRE I case study was used to test an ice particle growth model which predicts bimodal size spectra in cirrus clouds. The model was developed from an analytically based model which predicts the height evolution of monomodal ice particle size spectra from the measured ice water content (IWC). Size spectra from the monomodal model are represented by a gamma distribution, N(D) = N(sub o)D(exp nu)exp(-lambda D), where D = ice particle maximum dimension. The slope parameter, lambda, and the parameter N(sub o) are predicted from the IWC through the growth processes of vapor diffusion and aggregation. The model formulation is analytical, computationally efficient, and well suited for incorporation into larger models. The monomodal model has been validated against two other cirrus cloud case studies. From the monomodal size spectra, the size distributions which determine concentrations of ice particles less than about 150 mu m are predicted.

Mitchell, David L.↗

DOE OSTI2025

Enhancing fire emissions inventories for acute health effects studies: integrating high spatial and temporal resolution data

Daily fire progression information is crucial for public health studies that examine the relationship between population-level smoke exposures and subsequent health events. Issues with remote sensing used in fire emissions inventories (FEI) lead to the possibility of missed exposures that impact the results of acute health effects studies. This paper provides a method for improving an FEI dataset with readily available information to create a more robust dataset with daily fire progression. High temporal and spatial resolution burned area information from two FEI products are combined into a single dataset, and a linear regression model fills gaps in daily fire progression. The combined dataset provides up to 71% more PM 2.5 emissions, 69% more burned area, and 367% more fire days per year than using a single source of burned area information. The FEI combination method results in improved FEI information with no gaps in daily fire emissions estimates. The combined dataset provides a functional improvement to FEI data that can be achieved with currently available data.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

NASA NTRS1974

A directional microphone array for acoustic studies of wind tunnel models

An end-fire microphone array that utilizes a digital time delay system has been designed and evaluated for measuring noise in wind tunnels. The directional response of both a four- and eight-element linear array of microphones has enabled substantial rejection of background noise and reverberations in the NASA Ames 40- by 80-foot wind tunnel. In addition, it is estimated that four- and eight-element arrays reject 6 and 9 dB, respectively, of microphone wind noise, as compared with a conventional omnidirectional microphone with nose cone. Array response to two types of jet engine models in the wind tunnel is presented. Comparisons of array response to loudspeakers in the wind tunnel and in free field are made.

Soderman, P. T.↗

DOE OSTI2025

Data and code from: Multivariate bayesian regression model for predicting disposed ash composition at U.S. coal fired power stations

This dataset contains the code and data files needed for implementation of a Multivariate Bayesian Regression model, described in Jin et al. (2025), for the historical prediction of the chemical composition of disposed coal ash at U.S. coal fired power plants as a function of annualized coal purchase data. The integrated coal supply data file (CoalSupplyDataset.csv) represents a compilation of monthly fuel purchase records for the period 1973-2022 at major U.S. power stations. These records were obtained from the U.S. Energy Information Administration. The CSV file also contains, for each coal purchase record, the coal region of the mine as defined by the U.S. Geological Survey. Data entry errors and data gaps in the EIA records were corrected as described in Jin et al. This CSV file represents the integrated coal supply data after corrections were made. The model structure and fitting parameters are encoded in pickle file format (Bayesian.pkl). The model was developed with the coal supply data and coal ash composition data, apportioned according to the Stratified Shuffle Split for training and testing subsets. The model was built using Python and the PyMC library. Reference Publication: Jin, Z.; Huang, J.; Hower, J.C.; Hsu-Kim, H.(2025). Predictive Assessment of the Chemical Composition of Coal Ash in Reserve at U.S. Disposal Sites. Environmental Science & Technology.

Coal ash composition↗

NASA NTRS1958

The Explorer Rocket Research Program

Since September of 1956, nine Jupiter-C missiles have been launched from the firing pad at Cape Canaveral. The first Jupiter-C firing tested the propulsion system, air frame, and guidance components of the missile, and the second and third firings tested a model of the Jupiter nose cone under realistic re-entry conditions. The remaining six Jupiter-C missiles were used as the launching vehicles for EXPLORER satellites I through VI (Fig. 1). Of the six satellite firings, EXPLORERs I, III, and IV achieved satisfactory orbits. The Jupiter-C missile was designed and developed as a joint program under the technical direction of the Jet Propulsion Laboratory and the Army Ballistic Missile Agency. The Jet Propulsion Laboratory developed the three high-speed stages, and the Army Ballistic Missile Agency handled the development, construction, and operation of the first-stage booster rocket and the guidance system. Many other organizations have contributed to the success of the EXPLORER satellite program, most notably the State University of Iowa, the Air Force Cambridge Research Center, and the satellite tracking teams of the Vanguard organization.

Robillard, G.↗

DOE OSTI2024

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F 1 -score of 0.846 ± 0.012, surpassing process-driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Multiscale Modeling of the Uptake of Hydrogen Chloride on Anodized Aluminum in Relevance to Spacecraft Fire Safety

Fire safety is a critical component of space missions. An electrical fire within the spacecraft often produces gaseous hydrogen chloride (HCl) from the smoldering of wire insulation. These gases tend to stick to the interior materials of the spacecraft, which is often comprised of treated aluminum. Determining the rate of uptake for HCl by aluminum helps the safe design of the spacecraft to reduce catastrophic mission risk. Our previous studies showed that a one-step global reaction model for adsorption of HCl on aluminum is incapable of predicting the experimentally measured HCl uptake of anodized aluminum, compared to other treatments with thinner oxide layers. X-ray photoelectron spectroscopy revealed that aluminum chloride is found deep in the oxide layer of the anodized aluminum, implying that the transport of HCl within the oxide layer covering the aluminum surface must be considered. This work uses a multiscale modeling approach to predict the uptake of HCl. A pore model is developed wherein the HCl reacts with the aluminum oxide pore walls to create an aluminum chloride layer. The HCl then diffuses through that layer for further reaction, until the diffusion resistance grows too large, and the surface is said to be saturated. The pore model is then coupled with the reactor-scale model to predict overall HCl uptake. Results show that this new multiscale approach predicts the uptake of HCl far more accurately than previously used single-scale models.

Modeling↗

NASA NTRS2024

Evaluation of Machine Learning and Deep Learning Algorithms for Fire Prediction in Southeast Asia

Vegetation fires are prevalent in South/Southeast Asian countries, making fire prediction crucial due to their potential environmental, economic, and social impacts. Accurate predictions of fires facilitate timely interventions, helping to mitigate uncontrolled fires that can lead to biodiversity loss and air quality issues. In this study, we utilize VIIRS satellite-derived fire data alongside six machine learning and deep learning models—Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and ConvLSTM—to determine the most effective fire prediction model, using Root Mean Square Error (RMSE) as the metric. Our results indicate that the CNN model is the most reliable in regions with spatial dependencies, such as Brunei, Indonesia, Malaysia, the Philippines, Timor-Leste, and Thailand. Conversely, the ConvLSTM model excels in countries with complex spatiotemporal dynamics like Laos, Myanmar, and Vietnam. The CNN-LSTM hybrid model also performed well in Cambodia, suggesting a need for a balanced approach in areas requiring both spatial and temporal feature extraction. Furthermore, simpler models like Persistence and MLP showed limitations in capturing dynamic patterns and temporal dependencies. Our findings highlight the importance of evaluating models before implementing any decision support systems (DSS) in fire management. By tailoring models to specific regional fire data, we can enhance prediction accuracy and responsiveness, ultimately improving fire risk management in Southeast Asia and beyond.

Deep learning↗

NASA NTRS1993

Analysis of the tropospheric water distribution during FIRE 2

The Penn State/NCAR mesoscale model, as adapted for use at ARC, was used as a testbed for the development and validation of cloud models for use in General Circulation Models (GCM's). This modeling approach also allows us to intercompare the predictions of the various cloud schemes within the same dynamical framework. The use of the PSU/NCAR mesoscale model also allows us to compare our results with FIRE-II (First International Satellite Cloud Climatology Project Regional Experiment) observations, instead of climate statistics. Though a promising approach, our work to date revealed several difficulties. First, the model by design is limited in spatial coverage and is only run for 12 to 48 hours at a time. Hence the quality of the simulation will depend heavily on the initial conditions. The poor quality of upper-tropospheric measurements of water vapor is well known and the situation is particularly bad for mid-latitude winter since the coupling with the surface is less direct than in summer so that relying on the model to spin-up a reasonable moisture field is not always successful. Though one of the most common atmospheric constituents, water vapor is relatively difficult to measure accurately, especially operationally over large areas. The standard NWS sondes have little sensitivity at the low temperatures where cirrus form and the data from the GOES 6.7 micron channel is difficult to quantify. For this reason, the goals of FIRE Cirrus II included characterizing the three-dimensional distribution of water vapor and clouds. In studying the data from FIRE Cirrus II, it was found that no single special observation technique provides accurate regional distributions of water vapor. The Raman lidar provides accurate measurements, but only at the Hub, for levels up to 10 km, and during nighttime hours. The CLASS sondes are more sensitive to moisture at low temperatures than are the NWS sondes, but the four stations only cover an area of two hundred kilometers on a side. The aircraft give the most accurate measurements of water vapor, but are limited in spatial and temporal coverage. This problem is partly alleviated by the use of the MAPS analyses, a four-dimensional data assimilation system that combines the previous 3-hour forecast with the available observations, but its upper-level moisture analyses are sometimes deficient because of the vapor measurement problem. An attempt was made to create a consistent four-dimensional description of the water vapor distribution during the second IFO by subjectively combining data from a variety of sources, including MAPS analyses, CLASS sondes, SPECTRE sondes, NWS sondes, GOES satellite analyses, radars, lidars, and microwave radiometers.

Westphal, Douglas L.↗