Publications

2025
Dworak E, Peterson DA, Saide PE, Thapa L, Bortnik J. Impact of Smoke Aerosol Loading on Lightning Characteristics of Pyrocumulonimbus Compared With Other High-Based Thunderstorms. Journal of Geophysical Research: Atmospheres [Internet]. 2025;130 (12) :e2024JD042285. Publisher's VersionAbstract
Abstract Pyrocumulonimbus (pyroCb) is a form of deep convection that is generated by the heating from large wildfires and specific meteorology known for producing lightning. We study the lightning characteristics of five pyroCb events in British Columbia, Canada, from June 29 to July 1 of 2021, and compare them to other clean and smoke-filled high-based thunderstorms in the same region and season using ground-based lightning detection data, satellite retrievals, meteorological and atmospheric composition reanalysis, and observed thermodynamic profiles. One large pyroCb event over the Sparks Lake fire that generated persistent overshooting tops had a remarkable amount of lightning activity, with 5,600 total lightning strikes, while the rest of the pyroCb events corresponded with lower injection altitudes and minimal to no observed lightning activity. The cloud-to-cloud (CC) to cloud-to-ground (CG) lightning ratio (CC:CG) in this Sparks Lake pyroCb was significantly higher than in other high-based storms but displayed similar lightning density and slightly lower peak current distributions. All clean and smoke-filled thunderstorms produced significant levels of lightning activity, regardless of their cloud-top altitudes. However, ingestion of smoke significantly reduced the percentage of positive polarity CG strikes when compared to clean cases. These results set a reference for identifying the characteristics of pyrogenic lightning and improved predictions of lightning-caused fire ignitions, which will aid in understanding pyroCb activity and related impacts.
Christopoulos JA, Saide PE, Ferrare R, Collister B, Barton-Grimley RA, Scarino AJ, Collins J, Hair JW, Nehrir A. Improving Planetary Boundary Layer Height Estimation From Airborne Lidar Instruments. Journal of Geophysical Research: Atmospheres [Internet]. 2025;130 (9) :e2024JD042538. Publisher's VersionAbstract
Abstract The height of the planetary boundary layer (PBLH) influences processes such as pollutant distributions, convection, and cloud formation within the troposphere. Aerosol observables play a critical role in deriving the mixed layer height (MLH) using retrieval techniques like the Haar wavelet covariance transform (WCT), which employs gradients in aerosol backscatter to estimate MLH. Currently, backscatter-only approaches struggle with identifying very shallow stable boundary layers, distinguishing PBL from lofted residual or other aerosol layers, and profiles with very low aerosol loading. Here, we reflect on the WCT method's performance and evaluate different approaches to improve PBLH estimations. We aggregate lidar observables from recent NASA airborne field campaigns and compute MLHs based on the WCT method. Machine learning (ML) approaches are explored to produce PBLH estimates by training lidar information on thermodynamically derived PBLHs over marine and land settings. A linear model is found suitable for producing PBLH estimates in marine settings (improving mean bias by 71 m), while an ensemble tree method proves more suitable for PBLH types over land, as indicated by improved biases (13 m mean bias), errors (179 m mean error and 391 m RMSE), and correlations (+0.3) for the models explored. The algorithms are additionally tested on “unseen” data to gauge differences between MLH and PBLH estimates produced from each of the models. The PBLH estimates, composed of information from lidar and thermodynamic profiles, further support the use of ML for an automated method of PBLH prediction. Overall, these improved predictions will help evaluate models and deepen our understanding of PBL-aerosol interactions.
Neyra-Nazarrett OA, Miyazaki K, Bowman KW, Saide PE. An Assessment of TROPESS CrIS and TROPOMI CO Retrievals and Their Synergies for the 2020 Western U.S. Wildfires. Remote Sensing [Internet]. 2025;17 (11). Publisher's VersionAbstract
The 2020 wildfire season in the Western U.S. was historic in its intensity and impact on the land and atmosphere. This study aims to characterize satellite retrievals of carbon monoxide (CO), a tracer of combustion and signature of those fires, from two key satellite instruments: the Cross-track Infrared Sounder (CrIS) and the Tropospheric Monitoring Instrument (TROPOMI). We evaluate them during this event and assess their synergies. These two retrievals are matched temporally, as the host satellites are in tandem orbit and spatially by aggregating TROPOMI to the CrIS resolution. Both instruments show that the Western U.S. displayed significantly higher daily average CO columns compared to the Central and Eastern U.S. during the wildfires. TROPOMI showed up to a factor of two larger daily averages than CrIS during the most intense fire period, likely due to differences in the vertical sensitivity of the two instruments and representative of near-surface CO abundance near the fires. On the other hand, there was excellent agreement between the instruments in downwind free tropospheric plumes (scatter plot slopes of 0.96–0.99), consistent with their vertical sensitivities and indicative of mostly lofted smoke. Temporally, TROPOMI CO column peaks were delayed relative to the Fire Radiative Power (FRP), and CrIS peaks were delayed with respect to TROPOMI, particularly during the intense initial weeks of September, suggesting boundary layer buildup and ventilation. Satellite retrievals were evaluated using ground-based CO column estimates from the Network for the Detection of Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON), showing Normalized Mean Errors (NMEs) for CrIS and TROPOMI below 32% and 24%, respectively, when compared to all stations studied. While Normalized Mean Bias (NMB) was typically low (absolute value below 15%), there were larger negative biases at Pasadena, likely associated with sharp spatial gradients due to topography and proximity to a large city, which is consistent with previous research. In situ CO profiles from AirCore showed an elevated smoke plume for 15 September 2020, highlighted consistency between TROPOMI and CrIS CO columns for lofted plumes. This study demonstrates that both CrIS and TROPOMI provide complementary information on CO distribution. CrIS’s sensitivity in the middle and lower free troposphere, coupled with TROPOMI’s effectiveness at capturing total columns, offers a more comprehensive view of CO distribution during the wildfires than either retrieval alone. By combining data from both satellites as a ratio, more detailed information about the vertical location of the plumes can potentially be extracted. This approach can enhance air quality models, improve vertical estimation accuracy, and establish a new method for assessing lower tropospheric CO concentrations during significant wildfire events.
Fakoya AA, Redemann J, Saide PE, Gao L, Mitchell LT, Howes C, Dobracki A, Chang I, Ferrada GA, Pistone K, et al. Atmospheric processing and aerosol aging responsible for observed increase in absorptivity of long-range transported smoke over the southeast Atlantic. EGUsphere [Internet]. 2025;2025 :1–45. Publisher's Version
2024
He Q, Cao J, Saide PE, Ye T, Wang W. Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning. Environmental Science & Technology [Internet]. 2024;58 (36) :15938-15948. Publisher's Version
Thapa LH, Saide PE, Bortnik J, Berman MT, Da Silva A, Peterson DA, Li F, Kondragunta S, Ahmadov R, James E, et al. Forecasting Daily Fire Radiative Energy Using Data Driven Methods and Machine Learning Techniques. Journal of Geophysical Research: Atmospheres [Internet]. 2024;129 (16) :e2023JD040514. Publisher's VersionAbstract
Abstract Increasing impacts of wildfires on Western US air quality highlights the need for forecasts of smoke emissions based on dynamic modeled wildfires. This work utilizes knowledge of weather, fuels, topography, and firefighting, combined with machine learning and other statistical methods, to generate 1- and 2-day forecasts of fire radiative energy (FRE). The models are trained on data covering 2019 and 2021 and evaluated on data for 2020. For the 1-day (2-day) forecasts, the random forest model shows the most skill, explaining 48% (25%) of the variance in observed daily FRE when trained on all available predictors compared to the 2% (<0%) of variance explained by persistence for the extreme fire year of 2020. The random forest model also shows improved skill in forecasting day-to-day increases and decreases in FRE, with 28% (39%) of observed increase (decrease) days predicted, and increase (decrease) days are identified with 62% (60%) accuracy. Error in the random forest increases with FRE, and the random forest tends toward persistence under severe fire weather. Sensitivity analysis shows that near-surface weather and the latest observed FRE contribute the most to the skill of the model. When the random forest model was trained on subsets of the training data produced by agencies (e.g., the Canadian or US Forest Services), comparable if not better performance was achieved (1-day R2 = 0.39–0.48, 2-day R2 = 0.13–0.34). FRE is used to compute emissions, so these results demonstrate potential for improved fire emissions forecasts for air quality models.
Krishna M, Saide PE, Ye X, Turney FA, Hair JW, Fenn M, Shingler T. Evaluation of Wildfire Plume Injection Heights Estimated from Operational Weather Radar Observations Using Airborne Lidar Retrievals. Journal of Geophysical Research: Atmospheres [Internet]. 2024;129 (9) :e2023JD039926. Publisher's VersionAbstract
Abstract The vertical distribution of wildfire smoke aerosols is important in determining its environmental impacts but existing observations of smoke heights generally do not possess the temporal resolution required to fully resolve the diurnal behavior of wildfire smoke injection. We use Weather Surveillance Radar-1988 Doppler (WSR-88D) dual polarization data to estimate injection heights of Biomass Burning Debris (BBD) generated by fires. We detect BBD as a surrogate for smoke aerosols, which are often collocated with BBD near the fire but are not within the size range detectable by these radars. Injection heights of BBD are derived for 2–10 August 2019, using WSR-88D reflectivity (Z ≥ 10 dBZ) and dual polarization correlation coefficients (0.2 < C.C < 0.9) to study the Williams Flats fire. Results show the expected diurnal cycles with maximum injection heights present during the late afternoon period when the fire's intensity and convective mixing are maximized. WSR-88D and airborne lidar injection height comparisons reveal that this method is sensitive to outliers and generally overpredicts maximum heights by 40%, though mean and median heights are better captured (<20% mean error). WSR-88D heights between the 75th and 90th percentile seem to accurately represent the maximum heights, with the exception of heights estimated during the occurrence of a pyro-cumulonimbus. Location specific mapping of WSR-88D and lidar injection heights reveal that they diverge further away from the fire as expected due to BBD settling. Most importantly, WSR-88D-derived injection height estimates provide near continuous smoke height information, allowing for the study of diurnal variability of smoke injections.
2023
Howes C, Saide PE, Coe H, Dobracki A, Freitag S, Haywood JM, Howell SG, Gupta S, Uin J, Kacarab M, et al. Biomass-burning smoke's properties and its interactions with marine stratocumulus clouds in WRF-CAM5 and southeastern Atlantic field campaigns. Atmospheric Chemistry and Physics [Internet]. 2023;23 (21) :13911–13940. Publisher's Version
Saide PE, Krishna M, Ye X, Thapa LH, Turney F, Howes C, Schmidt CC. Estimating Fire Radiative Power Using Weather Radar Products for Wildfires. Geophysical Research Letters [Internet]. 2023;50 (21) :e2023GL104824. Publisher's VersionAbstract
Abstract Satellite-based Fire radiative power (FRP) retrievals are used to track wildfire activity but are sometimes not possible or have large uncertainties. Here, we show that weather radar products including composite and base reflectivity and equivalent rainfall integrated in the vicinity of the fires show strong correlation with hourly FRP for multiple fires during 2019–2020. Correlation decreases when radar beams are blocked by topography and when there is significant ground clutter (GC) and anomalous propagation (AP). GC/AP can be effectively removed using a machine learning classifier trained with radar retrieved correlation coefficient, velocity, and spectrum width. We find a power-law best describes the relationship between radar products and FRP for multiple fires combined (0.67–0.76 R2). Radar-based FRP estimates can be used to fill gaps in satellite FRP created by cloud cover and show great potential to overcome satellite FRP biases occurring during extreme fire events.
Pagonis D, Selimovic V, Campuzano-Jost P, Guo H, Day DA, Schueneman MK, Nault BA, Coggon MM, DiGangi JP, Diskin GS, et al. Impact of Biomass Burning Organic Aerosol Volatility on Smoke Concentrations Downwind of Fires. Environmental Science & Technology. 2023.
Turney FA, Saide PE, Jimenez Munoz PA, Muñoz-Esparza D, Hyer EJ, Peterson DA, Frediani ME, Juliano TW, DeCastro AL, Kosović B, et al. Sensitivity of Burned Area and Fire Radiative Power Predictions to Containment Efforts, Fuel Density, and Fuel Moisture Using WRF-Fire. Journal of Geophysical Research: Atmospheres [Internet]. 2023;128 (18) :e2023JD038873. Publisher's VersionAbstract
Abstract Predicting the evolution of burned area, smoke emissions, and energy release from wildfires is crucial to air quality forecasting and emergency response planning yet has long posed a significant scientific challenge. Here we compare predictions of burned area and fire radiative power from the coupled weather/fire-spread model WRF-Fire (Weather and Research Forecasting Tool with fire code), against simpler methods typically used in air quality forecasts. We choose the 2019 Williams Flats Fire as our test case due to a wealth of observations and ignite the fire on different days and under different configurations. Using a novel re-gridding scheme, we compare WRF-Fire's heat output to geostationary satellite data at 1-hr temporal resolution. We also evaluate WRF-Fire's time-resolved burned area against high-resolution imaging from the National Infrared Operations aircraft data. Results indicate that for this study, accounting for containment efforts in WRF-Fire simulations makes the biggest difference in achieving accurate results for daily burned area predictions. When incorporating novel containment line inputs, fuel density increases, and fuel moisture observations into the model, the error in average daily burned area is 30% lower than persistence forecasting over a 5-day forecast. Prescribed diurnal cycles and those resolved by WRF-Fire simulations show a phase offset of at least an hour ahead of observations, likely indicating the need for dynamic fuel moisture schemes. This work shows that with proper configuration and input data, coupled weather/fire-spread modeling has the potential to improve smoke emission forecasts.
Tang B, Saide PE, Gao M, Carmichael GR, Stanier CO. WRF-Chem quantification of transport events and emissions sensitivity in Korea during KORUS-AQ. Elementa: Science of the Anthropocene [Internet]. 2023;11 (1). Publisher's VersionAbstract
To quantify the relative roles of long-range transport (LRT) versus locally emitted aerosol and ozone precursors during polluted periods in Korea, high-resolution (4 km) Weather Research and Forecasting with Chemistry model simulations were performed. The model was evaluated using surface and airborne observations collected during the KORea and United States Air Quality campaign. Ozone above 40 ppb had mean bias of −5.9 ppb. PM2.5 was biased high (8.2 µg/m3), with a relative bias of 30\% given the mean observed value of 26.8 µg/m3. The absolute amounts and shifts between phases for all PM2.5 species except nitrate reasonably match observations across all 4 phases. Notable limitations include an underestimation of nighttime planetary boundary layer height. Transport versus domestic emissions influence was studied by model runs with perturbed emissions and by comparing east-west fluxes over the Yellow Sea to Korean emissions and other normalization metrics. Domestic anthropogenic emission contributions to surface air quality were quantified by location across Korea, segregated by synoptic meteorological phase. The largest contributions from Korean emissions were found under high-pressure stagnant conditions and the smallest for conditions with strong westerly winds. For example, at Seoul, domestic contributions of PM2.5 averaged 49\% and 29\% in the aforementioned meteorological phases, respectively. Surface concentrations of NOx and toluene in Seoul were over 85\% due to domestic emissions. CO and black carbon had both local and remote contributions. Nitrate and ammonium contributions varied greatly by phases in Seoul, with 7\%–51\% nitrate and 42\%–70\% of ammonium from remote sources. Variation in direction (west-to-east vs. east-to-west) and magnitude of fluxes support the model sensitivity results. Analysis using fluxes facilitates the quantification of source contributions for secondary species and, in many cases, can be done using a single model run or reanalysis result. The analysis presented shows the importance of using models with high spatial resolution to capture pollutant transport and mixing around Korea. However, there remain uncertainties in secondary aerosol production mechanisms and indications that local production at times could be higher than those modeled in this analysis. Therefore, the results presented here should be viewed as an upper limit on the importance of LRT.
Yu J, Song CH, Lee D, Lee S, Kim HS, Han KM, Park S, Im J, Park S-Y, Jeon M, et al. Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2. 5 predictability. npj Climate and Atmospheric Science. 2023;6 (1) :41.
Berman M, Ye X, Thapa L, Peterson DA, Hyer E, Soja A, Gargulinski E, Csiszar I, Schmidt C, Saide P. Quantifying burned area of wildfires in the Western United States from polar-orbiting and geostationary satellite active-fire detections. International Journal of Wildland Fire [Internet]. 2023. Publisher's Version
Dobracki A, Zuidema P, Howell SG, Saide P, Freitag S, Aiken AC, Burton SP, Sedlacek III AJ, Redemann J, Wood R. An attribution of the low single-scattering albedo of biomass burning aerosol over the southeastern Atlantic. Atmospheric Chemistry and Physics [Internet]. 2023;23 (8) :4775–4799. Publisher's Version
Lenhardt ED, Gao L, Redemann J, Xu F, Burton SP, Cairns B, Chang I, Ferrare RA, Hostetler CA, Saide PE, et al. Use of lidar aerosol extinction and backscatter coefficients to estimate cloud condensation nuclei (CCN) concentrations in the southeast Atlantic. Atmospheric Measurement Techniques [Internet]. 2023;16 (7) :2037–2054. Publisher's Version
Chang I, Gao L, Flynn CJ, Shinozuka Y, Doherty SJ, Diamond MS, Longo KM, Ferrada GA, Carmichael GR, Castellanos P, et al. On the differences in the vertical distribution of modeled aerosol optical depth over the southeastern Atlantic. Atmospheric Chemistry and Physics [Internet]. 2023;23 (7) :4283–4309. Publisher's Version
Warneke C, Schwarz JP, Dibb J, Kalashnikova O, Frost G, Al-Saad J, Brown SS, Brewer WA, Soja A, Seidel FC, et al. Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ). Journal of Geophysical Research: Atmospheres [Internet]. 2023;128 (2) :e2022JD037758. Publisher's VersionAbstract
Abstract The NOAA/NASA Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) experiment was a multi-agency, inter-disciplinary research effort to: (a) obtain detailed measurements of trace gas and aerosol emissions from wildfires and prescribed fires using aircraft, satellites and ground-based instruments, (b) make extensive suborbital remote sensing measurements of fire dynamics, (c) assess local, regional, and global modeling of fires, and (d) strengthen connections to observables on the ground such as fuels and fuel consumption and satellite products such as burned area and fire radiative power. From Boise, ID western wildfires were studied with the NASA DC-8 and two NOAA Twin Otter aircraft. The high-altitude NASA ER-2 was deployed from Palmdale, CA to observe some of these fires in conjunction with satellite overpasses and the other aircraft. Further research was conducted on three mobile laboratories and ground sites, and 17 different modeling forecast and analyses products for fire, fuels and air quality and climate implications. From Salina, KS the DC-8 investigated 87 smaller fires in the Southeast with remote and in-situ data collection. Sampling by all platforms was designed to measure emissions of trace gases and aerosols with multiple transects to capture the chemical transformation of these emissions and perform remote sensing observations of fire and smoke plumes under day and night conditions. The emissions were linked to fuels consumed and fire radiative power using orbital and suborbital remote sensing observations collected during overflights of the fires and smoke plumes and ground sampling of fuels.
2022
Ye X, Deshler M, Lyapustin A, Wang Y, Kondragunta S, Saide P. Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. Remote Sensing [Internet]. 2022;14 (23). Publisher's VersionAbstract
Satellite remote sensing of aerosol optical depth (AOD) is essential for detection, characterization, and forecasting of wildfire smoke. In this work, we evaluate the AOD (550 nm) retrievals during the extreme wildfire events over the western U.S. in September 2020. Three products are analyzed, including the Moderate-resolution Imaging Spectroradiometers (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) product collections C6.0 and C6.1, and the NOAA-20 Visible Infrared Imaging Radiometer (VIIRS) AOD from the NOAA Enterprise Processing System (EPS) algorithm. Compared with the Aerosol Robotic Network (AERONET) data, all three products show strong linear correlations with MAIAC C6.1 and VIIRS presenting overall low bias (<0.06). The accuracy of MAIAC C6.1 is found to be substantially improved with respect to MAIAC C6.0 that drastically underestimated AOD over thick smoke, which validates the effectiveness of updates made in MAIAC C6.1 in terms of an improved representation of smoke aerosol optical properties. VIIRS AOD exhibits comparable uncertainty with MAIAC C6.1 with a slight tendency of increased positive bias over the AERONET AOD range of 0.5–3.0. Averaging coincident retrievals from MAIAC C6.1 and VIIRS provides a lower root mean square error and higher correlation than for the individual products, motivating the benefit of blending these datasets. MAIAC C6.1 and VIIRS are further compared to provide insights on their retrieval strategy. When gridded at 0.1° resolution, MAIAC C6.1 and VIIRS provide similar monthly AOD distribution patterns and the latter exhibits a slightly higher domain average. On daily scale, over thick plumes near fire sources, MAIAC C6.1 reports more valid retrievals where VIIRS tends to have retrievals designated as low or medium quality, which tends to be due to internal quality checks. Over transported smoke near scattered clouds, VIIRS provides better retrieval coverage than MAIAC C6.1 owing to its higher spatial resolution, pixel-level processing, and less strict cloud masking. These results can be used as a guide for applications of satellite AOD retrievals during wildfire events and provide insights on future improvement of retrieval algorithms under heavy smoke conditions.
Thapa LH, Ye X, Hair JW, Fenn MA, Shingler T, Kondragunta S, Ichoku C, Dominguez RA, Ellison L, Soja AJ, et al. Heat flux assumptions contribute to overestimation of wildfire smoke injection into the free troposphere. Communications Earth & Environment [Internet]. 2022;3 (1) :1–11. Publisher's Version

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