About

Pablo Saide is an Associate Professor in the Department of Atmospheric & Oceanic Sciences and serves as the Chair of the B.S. in Environmental Science at the Institute of the Environment and Sustainability at UCLA. His research focuses on atmospheric chemistry, air quality, and environmental monitoring, with recent work including the assessment of remote airborne monitoring to control sulfur emissions from ocean-going vessels and the forecasting of urban pollution episodes. Saide has contributed to understanding how fires can generate their own weather and has been featured on VOA News for his insights. His publications include studies on data assimilation in atmospheric chemistry models, health impacts of haze events, and urban pollution forecasting. Saide's work integrates atmospheric science with environmental health and policy, contributing to UCLA's efforts in addressing climate change and air quality challenges.

Research topics

• Geography
• Environmental science
• Meteorology
• Climatology
• Geology
• Atmospheric sciences
• Oceanography
• Remote sensing
• Chemistry
• Physics
• Cartography

Selected publications

• Assessing Consistency in Fuel Consumed Between Activity‐Based Wildfire Emission Estimates

  Geophysical Research Letters · 2026-04-16

  articleOpen access1st authorCorresponding

  Abstract Wildfire emission inventories exhibit large variability that complicates assessments of smoke impacts. Here we compare fuel consumed (in mass per burned area units) from multiple burn area‐based and energy‐based approaches for fires in the western US during 2020. Average fuel consumed can vary by up to factors of 2–16 between approaches across burn severity classes and fuel types. Fuel consumed estimates typically increase with burn severity, except for the energy‐based approaches for forest land cover, where it decreases for high burn severity. Also, in contrast to other approaches, energy‐based estimates decrease for tree cover greater than 40% regardless of burn severity class. This implies that corrections to the energy‐based approach are likely needed across burn severity categories to account for canopy and smoke shading. The methodological recommendations provided would likely result in greater consistency between wildfire emission estimates and highlight the need to better constrain fuel loading and consumption.

  PublisherDOI

• NO _x Emissions Constraints from GEMS NO ₂ Retrievals: Inversion Methodology and Air Quality Model Evaluation in Bangkok using ASIA-AQ Multi-Platform Observations

  2026-02-10

  articleOpen access

  Abstract. Nitrogen Dioxide (NO2) is a key component of tropospheric chemistry and air quality, yet large uncertainties persist in regional NOx emissions across rapidly developing megacities in Southeast Asia. Observations from the Geostationary Emissions Monitoring Spectrometer (GEMS) provide new constraints on anthropogenic NO2 variability, while the 2024 NASA Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign, offers an extensive, independent dataset for model evaluation. We examine air quality in Bangkok using coarse (20 km) and high-resolution (4 km) WRF-Chem simulations during ASIA-AQ. We develop a top-down framework that uses hourly GEMS NO2 columns to derive constraints on the daytime cycle of NOx emissions. Emissions are first estimated from GEMS using a Cross-Sectional Flux (CSF) inversion and then incorporated into WRF-Chem through a novel optimization that reshapes the magnitude and daytime structure of NOx while accounting for lifetime and satellite vertical sensitivity. GEMS-constrained NOx emissions for March 2024 are estimated at 2.7 kT month–1 over Bangkok, approximately 75 % lower than EDGAR v5. Re-running WRF-Chem with the updated emissions leads to substantial improvements in modeled NO2 magnitude and temporal variability when evaluated against independent ground-based, Pandora, and airborne measurements. Remaining negative biases are consistent with a systematic low bias in the GEMS v3 NO2 product that cannot be diagnosed using satellite data alone, highlighting the importance of multi-platform evaluation. Together, these results demonstrate the value of hourly geostationary observations combined with high-resolution modeling as a scalable pathway for improving urban NOx emissions estimates and air quality simulations in Southeast Asia.

  PublisherOA PDFDOI

• Comment on egusphere-2026-472

  2026-03-30

  peer-reviewOpen access

  <strong class="journal-contentHeaderColor">Abstract.</strong> Nitrogen Dioxide (NO₂) is a key component of tropospheric chemistry and air quality, yet large uncertainties persist in regional NO_x emissions across rapidly developing megacities in Southeast Asia. Observations from the Geostationary Emissions Monitoring Spectrometer (GEMS) provide new constraints on anthropogenic NO₂ variability, while the 2024 NASA Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign, offers an extensive, independent dataset for model evaluation. We examine air quality in Bangkok using coarse (20 km) and high-resolution (4 km) WRF-Chem simulations during ASIA-AQ. We develop a top-down framework that uses hourly GEMS NO₂ columns to derive constraints on the daytime cycle of NO_x emissions. Emissions are first estimated from GEMS using a Cross-Sectional Flux (CSF) inversion and then incorporated into WRF-Chem through a novel optimization that reshapes the magnitude and daytime structure of NO_x while accounting for lifetime and satellite vertical sensitivity. GEMS-constrained NO_x emissions for March 2024 are estimated at 2.7 kT month^&ndash;1 over Bangkok, approximately 75 % lower than EDGAR v5. Re-running WRF-Chem with the updated emissions leads to substantial improvements in modeled NO₂ magnitude and temporal variability when evaluated against independent ground-based, Pandora, and airborne measurements. Remaining negative biases are consistent with a systematic low bias in the GEMS v3 NO₂ product that cannot be diagnosed using satellite data alone, highlighting the importance of multi-platform evaluation. Together, these results demonstrate the value of hourly geostationary observations combined with high-resolution modeling as a scalable pathway for improving urban NO_x emissions estimates and air quality simulations in Southeast Asia.

  PublisherDOI

• Geostationary Himawari-9 Reveals Missing Agricultural Burning Outside LEO Satellite Detection Windows and Improves Burned-Area Estimates in Mainland Southeast Asia

  2026-01-09

  article

  Biomass burning emissions in Southeast Asia (SE Asia) remain highly uncertain due to limited ground data and persistent model-observation discrepancies. A major source of uncertainty arises from differences in how emission inventories estimate burned area. The Fire Inventory from the National Center for Atmospheric Research version 2.5 (FINNv2.5) relies on low Earth orbit (LEO) Terra/Aqua MODIS (1km) and SNPP VIIRS (375m) active-fire detections, assuming full-pixel burns. Because these sensors pass over the region only twice daily, they miss late-afternoon and evening agricultural fires that often dominate burning activity in this region. Himawari-9, a geostationary (GEO) satellite, provides hourly observations and captures diurnal burning patterns but at a coarser 2 km resolution. To leverage this temporal coverage, we compared burned areas from Himawari-9 and SNPP VIIRS during their overlapping daytime overpass (~12-2 pm local time) and derived a corrected Himawari-9 pixel area of 0.14 km², equivalent to a VIIRS pixel. This correction enables a combined GEO-LEO approach that retains VIIRS-level spatial detail. We integrated Himawari-9 detections with corrected pixel sizes into FINNv2.5, removed tropical duplicate-pixel routine which can inflate burned-area estimates, particularly given MODIS’s coarse 1-km pixel size, and developed additional configurations using NOAA-20 and NOAA-21 VIIRS. Across February-March 2024, the configuration using Himawari-9 and all VIIRS sensors performed best when evaluated against independent burned-area datasets (GISTDA LANDSAT8/9 for Thailand, Copernicus Sentinel-3, and NASA VNP64A1). Relative to default FINNv2.5, this configuration detected substantially more small distinct fires: +73-319% in croplands, +59-113% in grass/savanna, and +59-93% in woody shrubs across Vietnam, Laos, Myanmar, Thailand, and Cambodia. Against March 2024 GISTDA burned areas in Thailand, correlations improved across crop types, with the largest gains in rice (r = 0.29 to 0.41) and sugarcane (r = 0.19 to 0.31). These results demonstrate how combining high-temporal GEO fire detections with fine-spatial VIIRS observations can enhance fire detection and strengthen emission inventories in SE Asia and beyond.

  PublisherDOI

• Comment on egusphere-2026-472

  2026-03-09

  peer-reviewOpen access

  <strong class="journal-contentHeaderColor">Abstract.</strong> Nitrogen Dioxide (NO₂) is a key component of tropospheric chemistry and air quality, yet large uncertainties persist in regional NO_x emissions across rapidly developing megacities in Southeast Asia. Observations from the Geostationary Emissions Monitoring Spectrometer (GEMS) provide new constraints on anthropogenic NO₂ variability, while the 2024 NASA Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign, offers an extensive, independent dataset for model evaluation. We examine air quality in Bangkok using coarse (20 km) and high-resolution (4 km) WRF-Chem simulations during ASIA-AQ. We develop a top-down framework that uses hourly GEMS NO₂ columns to derive constraints on the daytime cycle of NO_x emissions. Emissions are first estimated from GEMS using a Cross-Sectional Flux (CSF) inversion and then incorporated into WRF-Chem through a novel optimization that reshapes the magnitude and daytime structure of NO_x while accounting for lifetime and satellite vertical sensitivity. GEMS-constrained NO_x emissions for March 2024 are estimated at 2.7 kT month^&ndash;1 over Bangkok, approximately 75 % lower than EDGAR v5. Re-running WRF-Chem with the updated emissions leads to substantial improvements in modeled NO₂ magnitude and temporal variability when evaluated against independent ground-based, Pandora, and airborne measurements. Remaining negative biases are consistent with a systematic low bias in the GEMS v3 NO₂ product that cannot be diagnosed using satellite data alone, highlighting the importance of multi-platform evaluation. Together, these results demonstrate the value of hourly geostationary observations combined with high-resolution modeling as a scalable pathway for improving urban NO_x emissions estimates and air quality simulations in Southeast Asia.

  PublisherDOI

• Supplementary material to "NO _x Emissions Constraints from GEMS NO ₂ Retrievals: Inversion Methodology and Air Quality Model Evaluation in Bangkok using ASIA-AQ Multi-Platform Observations"

  2026-02-10

  articleOpen access
  PublisherDOI

• Constraints on the modeled vertical distribution of smoke during the 2020 western US wildfires from satellite data

  npj Clean Air · 2025-12-04 · 1 citations

  articleOpen access

  As wildfires increase in frequency and intensity, accurately representing the vertical distribution of smoke in numerical models is critical for assessing impacts to air quality, but remains highly uncertain. In this study, we leverage satellite retrievals of total column carbon monoxide (CO) and aerosol layer height (ALH) to evaluate two state-of-the-art regionals and global models, one using a plume rise parameterization to estimate smoke injection height (RAP-Chem) and another placing smoke at the surface (MOMO-Chem). We introduce a novel metric that utilizes the differing vertical sensitivities of two satellite sensors observing CO (TROPOMI and CrIS) to infer the vertical distribution of wildfire smoke using a joint CO column ratio. We find that RAP-Chem better captures the distribution of CO and ALH related to the 2020 western US megafire event than MOMO-Chem. However, RAP-Chem underestimates surface CO concentrations, revealing that current plume rise parameterizations are limited in their ability to partition smoke correctly in the vertical column. These results show that synergistic use of satellite data can provide additional constraints on the vertical distribution of smoke, thus providing insights into the strengths and limitations of current plume rise parameterizations and a pathway to improvement.

  PublisherOA PDFDOI

• Improving Planetary Boundary Layer Height Estimation From Airborne Lidar Instruments

  Journal of Geophysical Research Atmospheres · 2025-05-02 · 5 citations

  articleOpen access

  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.

  PublisherOA PDFDOI

• Aerosol Fine Mode Fraction Retrievals for the Marine Boundary Layer From Airborne Lidar

  Journal of Geophysical Research Atmospheres · 2025-10-27

  articleOpen access1st authorCorresponding

  Abstract Separating contributions of the fine and coarse modes is important for characterizing aerosols and assessing their impacts. This work develops retrievals of fine mode fraction (FMF) from lidar observables for the marine boundary layer (MBL) using data collected during the ACTIVATE field campaign. First, we calculate multiwavelength backscatter and extinction and derived metrics for spherical particles derived from measured size distributions (combining in situ aerosol and cloud probes) and hygroscopicity estimates. The calculations show reasonable skill when compared to airborne High Spectral Resolution Lidar—generation 2 (HSRL‐2) retrievals, displaying low biases and explaining up to 87% of the variability in backscattering. While slopes are generally close to 1:1 for lidar ratios and Angstrom exponents (AEs), the variability within HSRL‐2 data is only well captured for lidar ratios (50%–67%). Having established that the calculated optical properties are representative of remotely sensed ones in the marine environment, they are used together with in situ aircraft particle size data to train multilinear regression models to estimate FMF proxies (extinction FMF, PM 1 /PM 10 and PM 2.5 /PM 10 ratios). When tested with HSRL‐2 observations as inputs, these models can represent up to 67%–78% of the variability of the observed FMF proxies with biases at high FMFs that depend on the accuracy of the coarse mode aerosol size measurements. The regression retrievals are tested for lidar transects and show expected gradients due to continental influence on the MBL and differential hygroscopicity of fine versus coarse mode aerosol with height. These results are encouraging for their application for various lidar systems.

  PublisherDOI

• Advancing high-resolution hourly PM2.5 forecasting with a hybrid framework integrating numerical simulation and machine learning

  Atmospheric Research · 2025-10-15

  articleCorresponding
  PublisherDOI

Recent grants

• CAREER: Advancing Modeling of Wildfire Smoke

  NSF · $735k · 2023–2028

• Collaborative Research: Understanding Predictions of Wildfire Smoke Emissions for Air Quality Models

  NSF · $632k · 2020–2024

Frequent coauthors

• Gregory R. Carmichael

  University of Iowa

  59 shared

• Xinxin Ye

  56 shared

• Marta A. Fenn

  Coherent (United States)

  43 shared

• J. Redemann

  University of Oklahoma

  42 shared

• Robert Wood

  University of Washington

  42 shared

• Paquita Zuidema

  41 shared

• Arlindo da Silva

  Goddard Space Flight Center

  35 shared

• Johnathan W. Hair

  35 shared

Education

• Ph.D., Environmental Science and Engineering

  University of California, Los Angeles

  2010

• M.S., Atmospheric and Oceanic Sciences

  University of California, Los Angeles

  2005

• B.S., Atmospheric and Oceanic Sciences

  University of California, Los Angeles

  2003