NOAA Center for Satellite Applications and Research

Abstract. A variety of chemical and dynamical processes in the troposphere and stratosphere affect tropical total column ozone (TCO), the net effect of which may cause changes in surface UV radiation and impact human and ecosystem health. We use dynamical linear modeling to estimate tropical trends in TCO and partial column ozone (PCO) in the troposphere and three stratospheric layers to assess agreement between satellite observational composites and chemistry–climate model (CCM) simulations from two multi-model experiments (CCMI-1 and CCMI-2022). While both model experiments show tropical TCO increases over 2000–2021, CCMI–2022 trends (+2.5 DU) agree slightly better with observations than CCMI-1 (+1.6 DU). However, this overall agreement obscures multiple systematic differences in PCO trends between the models and observations across atmospheric layers. For example, since 2000 tropical tropospheric PCO increased significantly in CCMI-2022 (+1.5 DU) but not in CCMI–1 (+0.3 DU), largely explaining the difference in TCO trends. Also, despite nearly identical stratospheric PCO trends, CCMI-2022 trends are slightly more negative in the lower stratospheric (by ~0.5 DU), compensated by more positive middle/upper stratosphere trends compared to CCMI-1. Crucially, substantial differences exist across observational PCO trends, particularly in the troposphere and middle/upper stratosphere, and these disagreements limit the ability to evaluate CCM fidelity. Furthermore, while the inter-model correlation between late and early 21st century trends is suggestive of a potential emergent constraint on future ozone trends, the spread in observational trends precludes its observational implementation.

<strong class="journal-contentHeaderColor">Abstract.</strong> We present an updated evaluation of stratospheric ozone profile trends in the 60&deg;S&ndash;60&deg;N latitude range using long-term ground-based and satellite climate data records, as well as simulations by chemistry-climate models. The trends are evaluated using the LOTUS (Long-term Ozone Trends and Uncertainties in the Stratosphere) regression model. Analyses of satellite data confirm statistically significant positive ozone trends in the period 2000&ndash;2024 in the upper stratosphere of ~1&ndash;3% decade^-1, with larger trends at mid-latitudes compared to the tropics. The trends are slightly positive or close to zero in the middle stratosphere, and mostly negative, &minus;1&ndash;2% decade^-1, in the lower stratosphere; but they are not statistically significant. The ozone trends are similar to previous analyses (2000&ndash;2020 trends). Ozone trends in 2000&ndash;2024 predicted by climate model simulations are in good agreement with combined satellite trends. In the upper stratosphere, models predict a slightly stronger ozone recovery than observations. In the lower stratosphere, both models and satellite observations report negative trends of in the tropics, while modelled ozone trends are slightly positive at mid-latitudes. Ozone profile trends over several stations estimated from ground-based records capture the same overall vertical pattern of ozone trends as merged gridded satellite datasets. Analyses of regional ozone trends in 2003&ndash;2024 using merged satellite datasets confirmed the previous observations of a longitudinal structure in ozone trends in the NH mid-latitude stratosphere, with positive trends over Scandinavia and negative trends over Siberia. However, the magnitude of this dipole-like structure is reduced compared to previous analyses.

Abstract Blowing dust is a significant hazard in southwestern North America, impacting air quality, visibility, and, consequently, human health and safety. Numerous studies have examined blowing dust in this region to better understand where, when, and how these events occur, as well as their impacts and long-term trends. Most of this research has relied on ground-based in situ observations or polar-orbiting satellite data. However, few have leveraged the high spatial and temporal resolution of geostationary satellite imagery. This study develops a 20-yr record (2001–20) of blowing dust events across southwestern North America using manually analyzed Geostationary Operational Environmental Satellite (GOES) infrared imagery. Human analysts identified dust plumes based on their spectral and spatiotemporal characteristics and recorded their initiation times and locations. The analysis reveals a strong springtime peak in activity, regional hotspots such as northern Chihuahua, west Texas, and northwest Nevada, and elevated dust frequencies during major drought periods like 2002/03 and 2011/12. The resulting dataset provides a unique perspective on the geographic, seasonal, and interannual patterns of dust activity in the region. By consistently identifying when and where dust plumes initiate, this GOES-based record adds valuable temporal and spatial context that complements other surface- and satellite-based climatologies. Significance Statement Blowing dust poses a significant threat to air quality and transportation safety across southwestern North America. This study provides a 20-yr record (2001–20) of blowing dust events using geostationary satellite imagery, offering new insights into seasonal patterns, geographic hotspots, and meteorological drivers. The results show that dust activity peaks in the spring, aligns with drought periods, and is most frequent in the Chihuahuan Desert and agricultural regions of west Texas. These findings can be used to improve air quality forecasting, hazard mitigation, and long-term environmental monitoring.

The launch of the Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission in 2023 marked a new era in air quality monitoring by providing high-frequency, geostationary observations of column NO2 across most of North America. In this study, we present the first implementation of a TEMPO NO2 data assimilation system using the Joint Effort for Data assimilation Integration (JEDI) framework. Leveraging a four-dimensional ensemble variational (4DEnVar) approach and an Ensemble of Data Assimilations (EDA), we demonstrate a novel capability to assimilate hourly NO2 retrievals from TEMPO alongside polar-orbiting TROPOspheric Monitoring Instrument (TROPOMI) data into NASA’s GEOS Composition Forecast (GEOS-CF) model. The system is evaluated over the CONUS region for August 2023, using a suite of independent measurements including Pandora spectrometers, AirNow surface stations, and aircraft-based observations from Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and Synergistic TEMPO Air Quality Science (STAQS) field campaigns. Results show that the assimilation system successfully integrates geostationary NO2 observations, improves model performance in the column, and captures diurnal variability. However, assimilation also leads to systematic reductions in NO2 levels, which improves agreement with some datasets (e.g., Pandora, AEROMMA) but degrades comparisons with others (e.g., STAQS). These findings highlight the importance of joint evaluation across platforms and motivate further development of dual-concentration emission assimilation schemes. While the system imposes high computational costs, primarily from the forecast model, ongoing efforts to integrate AI-based model emulators offer a promising path toward scalable, real-time assimilation of geostationary atmospheric composition data.

Abstract Satellite-based global cloud data records play an essential role in understanding the impact of changing regional weather as well as global climate due to the direct and indirect radiative forcing of clouds. In this research, the Pathfinder Atmospheres–Extended (PATMOS-x) version 6.0 dataset, developed as a collaboration between NOAA National Centers for Environmental Information and the University of Wisconsin–Madison with a focus on providing reliable climate records from NOAA’s POES and EUMETSAT’s MetOp satellite series, is used to investigate long-term changes in maritime clouds from 60°S to 60°N. Monthly mean cloudiness is calculated using generalized additive model backfitting and observational weighting to consider orbital drift and different local overpass times between satellites. Our results show that mean fractional cloudiness is decreasing by 0.86% per decade, with maxima over the central equatorial Pacific Ocean at 3% per decade. Over the western Pacific Ocean and the west coast of South America, our record shows increasing trends in ice clouds and water clouds, respectively. Changes in cloud fractions associated with cloud phase are connected to changes in cloud-top heights. Cloud-top height increases over the western Pacific Ocean at 0.4 km per decade. Decreases in cloud-top heights happen near the southeastern Pacific Ocean as well as at other regions where marine stratocumulus clouds are dominant. These changes in cloud fraction and cloud-top assignments both contribute to the cloud radiative effect so that changes in radiative transfer processes and Earth’s radiation budget should be investigated.

Abstract The conductive heat flux (CHF) from the ocean to the snow/sea ice‐atmosphere interface through sea ice is a crucial component of the surface energy budget over the sea ice‐covered Arctic Ocean. The CHF is influenced by surface skin temperature, sea ice thickness, and depth of snow on sea ice. This study uses monthly mean surface skin temperature from ERA5 reanalysis, and sea ice thickness and snow depth from the Pan‐Arctic Ice‐Ocean Modeling and Assimilation System to derive the means and changes of CHF over the Arctic Ocean from 1979 to 2022. The findings reveal that CHFs are generally positive (from flux to the surface from below) from November to March and negative from June to August. CHFs are large in peripheral seas with thin sea ice and low snow depth, and small over pack ice with thick sea ice and snow. The thinning of sea ice and snow on sea ice contributes to increasing CHF, while rising surface skin temperatures lead to decreasing CHF. Overall, CHF increases from 1979 to 2022, primarily driven by the thinning of sea ice. The magnitude of CHF changes during this period is comparable to or higher than those in sensible heat flux, latent heat flux, and net longwave/thermal radiation flux at the surface from November to March over the peripheral seas. A better representation of sea ice thickness and snow depth over sea ice in the reanalysis would possibly improve the depiction of CHF and surface temperature changes.

In this study, we explore using satellite observations to assess community recovery from natural disasters such as fires and hurricanes, supplementing the Resilience Inference Model (RIM). The RIM model has been successfully used to quantify recoveries from hurricanes along the Gulf Coast, but it relies on long-term population changes over years or decades. Our approach integrates satellite observations to enhance recovery assessment with a shorter latency of weeks or months. Using fire, vegetation, and night light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) with daily global observations, Sentinel-2, Landsat-8, and Geostationary Operational Environmental Satellite/Advanced Baseline Imager, we quantitatively evaluate fire intensity, light outage, and urban greenness changes, along with subsequent recovery, focusing on the 2023 Maui fire and selected hurricane cases along the Gulf Coast. This approach complements the RIM model by introducing quantifiable physical parameters with shorter latency, particularly beneficial in areas where census data are either unavailable or unreliable.

Monitoring chlorophyll-a concentration (Chl-a) is essential for assessing aquatic ecosystem health, yet its retrieval using remote sensing remains challenging in turbid coastal waters because of the intricate optical characteristics of these environments. Elevated levels of colored (chromophoric) dissolved organic matter (CDOM) and suspended sediments (aka total suspended solids, TSS) interfere with satellite-based Chl-a estimates, necessitating alternative approaches. One potential solution is machine learning, indirectly including non-Chl-a signals into the models. In this research, we develop machine learning models to predict Chl-a concentrations in the Chesapeake Bay, one of the largest estuaries on North America’s East Coast. Our approach leverages the Extra-Trees (ET) algorithm, a tree-based ensemble method that offers predictive accuracy comparable to that of other ensemble models, while significantly improving computational efficiency. Using the entire ocean color datasets acquired by the satellite sensors MODIS-Aqua (&gt;20 years) and VIIRS-SNPP (&gt;10 years), we generated long-term Chl-a estimates covering the entire Chesapeake Bay area. The models achieve a multiplicative absolute error of approximately 1.40, demonstrating reliable performance. The predicted spatiotemporal Chl-a patterns align with known ecological processes in the Chesapeake Bay, particularly those influenced by riverine inputs and seasonal variability. This research emphasizes the potential of machine learning to enhance satellite-based water quality monitoring in optically complex coastal waters, providing valuable insights for ecosystem management and conservation.

Abstract. Offshore wind speed data around wind turbine hub heights are fairly limited, available through in situ observations from wind masts, sonic detection and ranging (sodar) instruments, or floating light detection and ranging (lidar) buoys at selected locations or as forecasting-model-based output from reanalysis products. In situ wind profiles have sparse geospatial coverage and are costly to obtain en masse, whereas satellite-derived 10 m wind speeds have vast coverage at high resolution. In this study, we show the benefit of deploying machine learning techniques, in particular random forest regression (RFR), over conventional methods for accurately estimating offshore wind speed profiles on a high-resolution (0.25°) grid at 6-hourly resolution from 1987 to the present using satellite-derived surface wind speeds from the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) Blended Seawinds version 2.0 (NBSv2.0) product. We use wind profiles from five publicly available lidar datasets over the northeastern US and Californian coasts to train and validate an RFR model to extrapolate wind speed profiles up to 200 m. A single extrapolation model applicable to the coastal regions of the contiguous US and Hawai`i is developed instead of site-specific models attempted in previous studies. The model outperforms conventional extrapolation methods at the training locations as well as at two additional lidar and six National Offshore Wind (NOW)-23 stations that are independent of the training locations, especially under conditions of high vertical wind shear and at wind turbine hub heights (∼ 100 m). The final model is applied to the NBSv2.0 data from 1987 to the present to create 6-hourly wind speed profiles over the coastal regions of the contiguous US and Hawai`i on a 0.25° grid, which are shown to outperform NOW-23 and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) at 100 m using a correlated triple-collocation method over 5 years of matchup data (2015–2019). Gridded maps of wind profiles in the marine boundary layer over US coastal waters will enable the development of a suite of wind energy resources and will help stakeholders in their decision-making related to wind-based renewable energy development.