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DOE OSTI2026

Multi-ignition fire complexes drive extreme fire years and impacts

Climate change is intensifying fire behavior, with the largest and fastest-spreading fires causing the greatest impacts on people and ecosystems. Yet the mechanisms driving variability and trends in large fires remain poorly understood. Using 12-hour satellite-derived fire tracking data from 2012 to 2023, we show that the merging of separate ignitions into multi-ignition complexes is a key process amplifying fire size and destructive potential across temperate and boreal ecoregions. Multi-ignition fires account for 31% of the burned area in California and 59% in the Arctic-boreal domain, spread faster and persist longer than single-ignition fires, and disproportionately contribute to extreme fire years in California, Canada, and Siberia. They also generate stronger atmospheric feedbacks, produce more pyrocumulonimbus events, and strain firefighting capacity by dispersing suppression resources. Recognizing and accounting for fire-merging dynamics are critical for improving wildfire prediction, risk assessment, and management.

Environmental sciences↗

NASA NTRS2010

Effects of Lightning and Other Meteorological Factors on Fire Activity in the North American Boreal Forest: Implications for Fire Weather Forecasting

The effects of lightning and other meteorological factors on wildfire activity in the North American boreal forest are statistically analyzed during the fire seasons of 2000-2006 through an integration of the following data sets: the MODerate Resolution Imaging Spectroradiometer (MODIS) level 2 fire products, the 3-hourly 32-kin gridded meteorological data from North American Regional Reanalysis (NARR), and the lightning data collected by the Canadian Lightning Detection Network (CLDN) and the Alaska Lightning Detection Network (ALDN). Positive anomalies of the 500 hPa geopotential height field, convective available potential energy (CAPE), number of cloud-to-ground lightning strikes, and the number of consecutive dry days are found to be statistically important to the seasonal variation of MODIS fire counts in a large portion of Canada and the entirety of Alaska. Analysis of fire occurrence patterns in the eastern and western boreal forest regions shows that dry (in the absence of precipitation) lightning strikes account for only 20% of the total lightning strikes, but are associated with (and likely cause) 40% of the MODIS observed fire counts in these regions. The chance for ignition increases when a threshold of at least 10 dry strikes per NARR grid box and at least 10 consecutive dry days is reached. Due to the orientation of the large-scale pattern, complex differences in fire and lightning occurrence and variability were also found between the eastern and western sub-regions. Locations with a high percentage of dry strikes commonly experience an increased number of fire counts, but the mean number of fire counts per dry strike is more than 50% higher in western boreal forest sub-region, suggesting a geographic and possible topographic influence. While wet lightning events are found to occur with a large range of CAPE values, a high probability for dry lightning occurs only when 500 hPa geopotential heights are above 5700m and CAPE values are near the maximum observed level, underscoring the importance of low-level instability to boreal fire weather forecasts-

Peterson, D.↗

NASA NTRSDate not supplied

Future Arctic (Wild) Fires

Wildland fires in the boreal and Arctic are increasing in frequency and severity, with extreme fire seasons documented across the Pan-Arctic and boreal in five of the last seven years – including an early start to extreme fires in 2023.Human-caused and wildland fires above 60°N were larger sources of black carbon and methane than current modeled estimates of anthropogenic sectors like energy extraction and transportation. Further, these large boreal and Arctic fires have the potential to release large amounts of carbon dioxide and subsequent methane emissions from degraded permafrost and peat fires, as well as negatively impacting air quality thousands of miles south. By end of this century, current extreme fire years in the Arctic will likely be a normal fire year for much of Greenland, North America, Northern Europe, and Eurasia. As a new horizon in global wildland fire science, novel scientific pathways must be undertaken to improve our understanding and management of wildfires in the High Northern Latitudes – including understanding how NASA and U.S. researchers can enhance collaboration and develop near-real-time fire and smoke monitoring and modeling between North American and European ecology, fire, and climate scientists and stakeholders. Issues of inclusion, diversity, and equity are inherent to understanding fire ecology, impacts, and management in the Arctic and boreal, including centering and deferring to Indigenous and local communities when constructing best management practices.

Future↗

NASA NTRS2020

Estimating Wildfire-Generated Ozone Over North America Using Ozonesonde Profiles and a Differential Back Trajectory Technique

An objective method, employing HYSPLIT back-trajectories and Moderate Resolution Imaging Spectroradiometer (MODIS) fire observations, is developed to estimate ozone enhancement in air transported from regions of active forest fires at 18 ozone sounding sites located across North America. The Differential Back Trajectory (DBT) method compares mean differences between ozone concentrations associated with fire-affected and fire-unaffected parcels. It is applied to more than 1100 ozonesonde profiles collected from these sites during the summer months June to August, 2006, 2008, 2010 and 2011. Layers of high ozone associated with low humidity were first removed from the ozonesonde profiles to minimize the potential effects of stratospheric intrusions on the calculations. No significant influence on average ozone levels by North American fires was found for stations located at Arctic latitudes. The ozone enhancement for stations nearer large fires, such as Trinidad Head and Bratt’s Lake, was up to 324.8% of the TTOC (Total Tropospheric Ozone Column). Fire ozone accounted for up to 8.3% of TTOC at downwind sites such as Yarmouth, Sable Island, Narragansett, and Walsingham. The results are consistent with other studies that have reported an increase in ozone production with the age of the smoke plume.

Moderate Resolution Imaging Spectroradiometer (MOD↗

NASA NTRS2016

Radiative Characteristics of Clouds Embedded in Smoke Derived from Airborne Multiangular Measurements

Clouds in the presence of absorbing aerosols result in their apparent darkening, observed at the top of atmosphere (TOA), which is associated with the radiative effects of aerosol absorption. Owing to the large radiative effect and potential impacts on regional climate, above-cloud aerosols have recently been characterized in multiple satellite-based studies. While satellite data are particularly useful in showing the radiative impact of above-cloud aerosols at the TOA, recent literature indicates large uncertainties in satellite retrievals of above-cloud aerosol optical depth (AOD) and single scattering albedo (SSA), which are among the most important parameters in the assessment of associated radiative effects. In this study, we analyze radiative characteristics of clouds in the presence of wildfire smoke using airborne data primarily from NASA's Cloud Absorption Radiometer, collected during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites campaign in Canada during the 2008 summer season. We found a strong positive reflectance (R) gradient in the UV-visible (VIS)-near infrared (NIR) spectrum for clouds embedded in dense smoke, as opposed to an (expected) negative gradient for cloud-free smoke and a flat spectrum for smoke-free cloud cover. Several cases of clouds embedded in thick smoke were found, when the aircraft made circular/spiral measurements, which not only allowed the complete characterization of angular distribution of smoke scattering but also provided the vertical distribution of smoke and clouds (within 0.5-5 km). Specifically, the largest darkening by smoke was found in the UV/VIS, with R(sub 0.34 microns) reducing to 0.2 (or 20%), in contrast to 0.8 at NIR wavelengths (e.g., 1.27 microns). The observed darkening is associated with large AODs (0.5-3.0) and moderately low SSA (0.85-0.93 at 0.53 microns), resulting in a significantly large instantaneous aerosol forcing efficiency of 254 +/- 47 W/sq m/τ. Our observations of smoke-cloud radiative interactions were found to be physically consistent with theoretical plane-parallel 1-D and Monte Carlo 3-D radiative transfer calculations, capturing the observed gradient across UV-VIS-NIR. Results from this study offer insights into aerosol-cloud radiative interactions and may help in better constraining satellite retrieval algorithms.

absorbing aerosols↗

DOE OSTI2024

Modeling PAH Mixture Interactions in a Human In Vitro Organotypic Respiratory Model

One of the most significant challenges in human health risk assessment is to evaluate hazards from exposure to environmental chemical mixtures. Polycyclic aromatic hydrocarbons (PAHs) are a class of ubiquitous contaminants typically found as mixtures in gaseous and particulate phases in ambient air pollution associated with petrochemicals from Superfund sites and the burning of fossil fuels. However, little is understood about how PAHs in mixtures contribute to toxicity in lung cells. To investigate mixture interactions and component additivity from environmentally relevant PAHs, two synthetic mixtures were created from PAHs identified in passive air samplers at a legacy creosote site impacted by wildfires. The primary human bronchial epithelial cells differentiated at the air–liquid interface were treated with PAH mixtures at environmentally relevant proportions and evaluated for the differential expression of transcriptional biomarkers related to xenobiotic metabolism, oxidative stress response, barrier integrity, and DNA damage response. Component additivity was evaluated across all endpoints using two independent action (IA) models with and without the scaling of components by toxic equivalence factors. Both IA models exhibited trends that were unlike the observed mixture response and generally underestimated the toxicity across dose suggesting the potential for non-additive interactions of components. Overall, this study provides an example of the usefulness of mixture toxicity assessment with the currently available methods while demonstrating the need for more complex yet interpretable mixture response evaluation methods for environmental samples.

3D in vitro models↗

NASA NTRS1970

Potentially efficient forest and range applications of remote sensing using earth orbital space craft, circa 1980

Sixteen remote sensing applications or groups of related applications judged to be most important of any in the forestry and range disciplines were evaluated. In one application, major land classification, large amounts of useful data are anticipated to be contributed by space sensors in 1980. In four applications moderate amounts are anticipated to be so contributed. These are timber inventory, range inventory, fire weather forecasting, and monitoring snowfields. In the following seven applications small but significant amounts of data are anticipated to be contributed by space sensors: (1) detailed land classification; (2) inventory of wildlife habitat; (3) recreation resource inventory; (4) detecting stresses on the vegetation (5) monitoring air pollution caused by wildfires and prescribed burning; (6) monitoring water cycle, (7) pollution and erosion; and (8) evaluating damage to forests and ranges.

Wilson, R. C.↗

NASA NTRSDate not supplied

A Comparison of Stratospheric Smoke Events Seen by SAGE II (1984-2005) and SAGE III (2017-2021)

The ongoing Stratospheric Aerosol and Gas Experiment aboard the International Space Station (SAGE III/ISS) has mostly occurred during a period of relatively low aerosol loading. At the same time, it has almost observed a number of small to moderate injections of volcanic material in the stratosphere from the Aoba/Ambae eruption of 2018 to that of Karymsky in 2022. In addition, two of the largest known smoke intrusions into the stratosphere have occurred during the SAGE III/ISS mission (the British Columbia fire of August 2017 and the Australian Wildfire Event of 2019/2020). At least two other much smaller smoke events have also been observed in the SAGE III data (California Creek in September 2020 and McKay Creek in June 2021). Particularly with the two large events, it is tempting to speculate that these events are increasing in intensity and frequency possibly due to ongoing climate change. Realistically, change is difficult to assess since instrument data sets that can be used for quantifying historical events can be quite different in what and where they provide measurements. This complicates comparing one event to another potentially separated by many years. An exception to this issues are the measurements by SAGE III/ISS and those of its predecessor SAGE II (1984-2005). Both use the same measurement strategy, measure aerosol extinction coefficient at 1020 nm, and have similar spatial/temporal coverage. Both instruments are known to observe smoke events in the stratosphere. However, both have the same low observation density characteristic of solar occultation such that near-global coverage takes almost a month and characterizing effectively instant events like smoke and volcanic events take a matter of weeks depending on the vagaries of where the observations are being made at a critical juncture unlike instruments with high temporal/spatial resolution like limb-scatter instruments such as OMPS and OSIRIS. The goal of this work will be to use the same analysis approach to both the SAGE II and SAGE III data and compare both the frequency and intensity of smoke events observed by these instruments. While we do not expect to be able to make conclusions about change between the periods, the analysis is a first step in interpreting changes in the contribution of smoke to the stratospheric aerosol budget between the time frames encompassed by these instruments.

L. W. Thomason↗

NASA NTRS2009

Assessing Satellite-Based Fire Data for use in the National Emissions Inventory

Biomass burning is significant to emission estimates because: (1) it can be a major contributor of particulate matter and other pollutants; (2) it is one of the most poorly documented of all sources; (3) it can adversely affect human health; and (4) it has been identified as a significant contributor to climate change through feedbacks with the radiation budget. Additionally, biomass burning can be a significant contributor to a regions inability to achieve the National Ambient Air Quality Standards for PM 2.5 and ozone, particularly on the top 20% worst air quality days. The United States does not have a standard methodology to track fire occurrence or area burned, which are essential components to estimating fire emissions. Satellite imagery is available almost instantaneously and has great potential to enhance emission estimates and their timeliness. This investigation compares satellite-derived fire data to ground-based data to assign statistical error and helps provide confidence in these data. The largest fires are identified by all satellites and their spatial domain is accurately sensed. MODIS provides enhanced spatial and temporal information, and GOES ABBA data are able to capture more small agricultural fires. A methodology is presented that combines these satellite data in Near-Real-Time to produce a product that captures 81 to 92% of the total area burned by wildfire, prescribed, agricultural and rangeland burning. Each satellite possesses distinct temporal and spatial capabilities that permit the detection of unique fires that could be omitted if using data from only one satellite.

Soja, Amber J.↗

DOE OSTI2022

Resilience in soil bacterial communities of the boreal forest from one to five years after wildfire across a severity gradient

Wildfires can represent a major disturbance to ecosystems, including soil microbial communities belowground. Furthermore, fire regimes are changing in many parts of the world, altering and often increasing fire severity, frequency, and size. The boreal forest and taiga plains ecoregions of northern Canada are characterized by naturally-occurring stand-replacing wildfires on a 40–350 year basis. We previously studied the effects of wildfire on soil microbial communities one year post-fire across 40 sites, spanning a range of burn severity. Here, we return to the same sites five years post-fire to test a series of hypotheses about the effects of fire on bacterial community composition. We ask questions on two themes: which factors control bacterial community composition during post-fire recovery, and how does the importance of different fire-responsive traits change during post-fire recovery? We find the following: Five years post-fire, vegetation community, moisture regime, pH, total carbon, texture, and burned/unburned all remained significant predictors of bacterial community composition with similar predictive value (R 2 ). Bacterial communities became more similar to unburned sites five years post-fire, across the range of severity, suggesting resilience, while general structure of co-occurrence networks remained similar one and five years post-fire. Fast growth potential, as estimated using predicted 16S rRNA copy numbers, was no longer significantly correlated with burn severity five years post-fire, indicating the importance of this trait for structuring bacterial community composition may be limited to relatively short timescales. Many taxa that were enriched in burned sites one year post-fire remained enriched five years post-fire, although the degree to which they were enriched generally decreased. Specific taxa of interest from the genera Massilia, Blastococcus, and Arthrobacter all remained significantly enriched, suggesting that they may have traits that allow them to continue to flourish in the post-fire environment, such as tolerance to increased pH or ability to degrade pyrogenic organic matter. This hypothesis-based work expands our understanding of the post-fire recovery of soil bacterial communities and raises new hypotheses to test in future studies.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2014

Wildfire Emissions and Their Interaction with Urban and Rural Pollution: Data and Simulations

In recent years NASA has conducted a series of airborne campaigns (e. g. SEAC4RS*, ARCTAS, INTEX-A/B) over North America using an instrumented DC-8 aircraft equipped to measure a very large number of gaseous and aerosol constituents including several unique tracers. In these campaigns wild fires were extensively sampled near source as well as downwind after aging. The data provided detailed information on the composition and chemistry of fire emissions under a variety of atmospheric conditions as well as their interactions with rural and urban air pollution. Major fires studied including the California Rim fire in 2013 (SEAC4RS), the 2008 California wildfires (ARCTAS), and the Alaskan fires downwind over eastern US (INTEX-A). Although some fire plumes contained virtually no O3 enhancement, others showed significant ozone formation. Over Los Angeles, the highest O3 mixing ratios were observed in fire influenced urban air masses. Attempts to simulate these interactions using state of the art models were only minimally successful and indicated several shortcomings in simulating fire emission influences on urban smog formation. A variety of secondary oxidation products (e. g. O3, PAN, HCHO) were substantially underestimated. We will discuss the data collected in fire influenced air masses and their potential air quality implications.

FIre Emissions↗

DOE OSTI2025

A Fortran–Python interface for integrating machine learning parameterization into earth system models

Abstract. Parameterizations in earth system models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation, and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran–Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and scikit-learn. We demonstrate the interface's modularity and reusability through two cases: an ML trigger function for convection parameterization and an ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A Fortran-Python Interface for Integrating Machine Learning Parameterization into Earth System Models

Parameterizations in Earth System Models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran-Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and Scikit-learn. We demonstrate the interface's modularity and reusability through two cases: a ML trigger function for convection parameterization and a ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2010

Use of Current 2010 Forest Disturbance Monitoring Products for the Conterminous United States in Aiding a National Forest Threat Early Warning System

This presentation discusses contributions of near real time (NRT) MODIS forest disturbance detection products for the conterminous United States to an emerging national forest threat early warning system (EWS). The latter is being developed by the USDA Forest Service s Eastern and Western Environmental Threat Centers with help from NASA Stennis Space Center and the Oak Ridge National Laboratory. Building off work done in 2009, this national and regional forest disturbance detection and viewing capability of the EWS employs NRT MODIS NDVI data from the USGS eMODIS group and historical NDVI data from standard MOD13 products. Disturbance detection products are being computed for 24 day composites that are refreshed every 8 days. Products for 2010 include 42 dates of the 24 day composites. For each compositing date, we computed % change in forest maximum NDVI products for 2010 with respect to each of three historical baselines of 2009, 2007-2009, and 2003-2009,. The three baselines enable one to view potential current, recent, and longer term forest disturbances. A rainbow color table was applied to each forest change product so that potential disturbances (NDVI drops) were identified in hot color tones and growth (NDVI gains) in cold color tones. Example products were provided to end-users responsible for forest health monitoring at the Federal and State levels. Large patches of potential forest disturbances were validated based on comparisons with available reference data, including Landsat and field survey data. Products were posted on two internet mapping systems for US Forest Service internal and collaborator use. MODIS forest disturbance detection products were computed and posted for use in as little as 1 day after the last input date of the compositing period. Such products were useful for aiding aerial disturbance detection surveys and for assessing disturbance persistence on both inter- and intra-annual scales. Multiple 2010 forest disturbance events were detected across the nation, including damage from ice storms, tornadoes, caterpillars, bark beetles, and wildfires. This effort enabled improved NRT forest disturbance monitoring capabilities for this nation-wide forest threat EWS.

Spruce, Joseph P.↗

DOE OSTI2021

America Resilient Climate Conference

On April 14, 2021, scientists, policymakers, and other interested parties from research institutes, academia, and other organizations gathered together virtually at the America Resilient Climate Conference to discuss one of the most pressing challenges of the 21st century: building resilience to climate change. Climate change affects the security and health of all Americans. Coastal areas are enduring more frequent and severe flooding due to sea level rise and storm surge; western states and Alaska have experienced increasingly devastating wildfires, driven in part by hotter, drier, and longer fire seasons; and communities across the nation have suffered through extreme precipitation events and heat waves. Even if emissions are reduced aggressively in the near future, the world—and the United States—will continue to feel the impacts of climate change for decades to come, due to the continued accumulation of greenhouse gasses in the atmosphere. Consequently, it is essential to act now to protect natural and human assets from the gradual—as well as extreme—impacts of a changing climate. To build resilient communities, leaders and community members need science-based information about the potential impacts climate change will have decades into the future and for specific regions. Therefore, it is essential to develop high-resolution climate models that can project both various climate impacts and the interactions between earth system variables and humans down to regional and local scales. Collecting and curating data for such models and their computational requirements poses large challenges. In the future, artificial intelligence will be needed to increase their accuracy and reduce associated uncertainties. The investments in Earth system science and artificial intelligence made by the U.S. Department of Energy and other federal entities will be essential in addressing these challenges.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

A Fire Community Observatory: Interdisciplinary, AI-informed Post-Fire Rapid Response for Improved Water Cycle Science at Watershed Scale

Wildfire is an ecological disturbance that disrupts the hydrological cycle. In the past few years, a record number of multiple-and-compounding fires have occurred across urban-wildland gradients in the Western United States. Changes to watershed hydrological partitioning in response to fires (infiltration, runoff, evapotranspiration) presents unprecedented challenges to “Water-in-the-West” through negative impacts to water supply and its quality, and is a direct threat to downstream communities, groundwater, and drinking water supply infrastructure. While much work is being done to advance Artificial Intelligence and Machine Learning (AI/ML) use during fires for emergency response (i.e. predict fire movement, direct evacuations), significant potential exists to use AI/ML to address three scientific grand challenges that are rarely addressed in a convergent science context: 1) how to enhance the potential resiliency of a landscape before fire(s), 2) how to cost-effectively and optimally monitor watershed changes after fires, and 3) how to predict future hydrological and biogeochemical trajectories in fire-impacted watershed given climate change. This whitepaper addresses DOE Focal Area 1: Data acquisition and assimilation enabled by machine learning, AI, and advanced methods.

58 GEOSCIENCES↗

DOE OSTI2022

The Press and Pulse of Climate Change: Extreme Events in the Colorado River Basin

Extremes in temperature and precipitation are associated with damaging floods, prolonged drought, destructive wildfires, agricultural challenges, compromised human health, vulnerable infrastructure, and threatened ecosystems and species. Often, the steady and progressive trends (or presses) of rising global temperature are the central focus in how climate impacts are described. However, observations of extreme weather events (or pulses) increasingly show that the intensity, duration and/or frequency of acute events are also changing, resulting in greater impacts on communities and the environment. Describing how the influence of extreme events may shape water management in the Colorado River Basin in clear terms is critical to sound future planning and efforts to manage risk. Three scenario planning workshops in 2019 and 2020 were held as part of a Colorado River Conversations series, identifying potential impacts from multiple intersecting extreme events. Water managers identified climate-related events of concern in the Colorado River Basin that necessitate greater attention and adaptive responses. To support efforts to include consideration of climate-change-driven extremes in water management and planning, we explore the current state of knowledge at the confluence of long-term climate shifts and extreme weather in the Colorado River Basin related to the events of concern that were identified by scenario planning participants.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2019

Joint 3D-Wind Retrievals with Stereoscopic Views from MODIS and GOES

Atmospheric motion vectors (AMVs), derived by tracking patterns, represent the winds in a layer characteristic of the pattern. AMV height (or pressure), important for applications in atmospheric research and operational meteorology, is usually assigned using observed IR brightness temperatures with a modeled atmosphere and can be inaccurate. Stereoscopic tracking provides a direct geometric height measurement of the pattern that an AMV represents. We extend our previous work with multi-angle imaging spectroradiometer (MISR) and GOES to moderate resolution imaging spectroradiometer (MODIS) and the GOES-R series advanced baseline imager (ABI). MISR is a unique satellite instrument for stereoscopy with nine angular views along track, but its images have a narrow (380 km) swath and no thermal IR channels. MODIS provides a much wider (2330 km) swath and eight thermal IR channels that pair well with all but two ABI channels, o e ring a rich set of potential applications. Given the similarities between MODIS and VIIRS, our methods should also yield similar performance with VIIRS. Our methods, as enabled by advanced sensors like MODIS and ABI, require high-accuracy geographic registration in both systems but no synchronization of observations. AMVs are retrieved jointly with their heights from the disparities between triplets of ABI scenes and the paired MODIS granule. We validate our retrievals against MISR-GOES retrievals, operational GOES wind products, and by tracking clear-sky terrain. We demonstrate that the 3D-wind algorithm can produce high-quality AMV and height measurements for applications from the planetary boundary layer (PBL) to the upper troposphere, including cold-air outbreaks, wildfire smoke plumes, and hurricanes.

Carr, James L.↗