NOAA National Environmental Satellite Data and Information Service

<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.

<strong class="journal-contentHeaderColor">Abstract.</strong> The age of smoke, meaning the time elapsed since it was produced in a fire, is an important parameter for interpreting measurements of evolving smoke composition. This study describes the smoke age estimates developed for large plumes sampled in the 2019 NASA-NOAA FIREX-AQ field experiment. Smoke ages are computed using two methods and applied to observations from two aircraft: the NASA DC-8 and a NOAA Twin Otter. The first method uses measurements of mean horizontal wind speed, as observed by the sampling aircraft, and distance to the fire to provide a single age estimate for each plume-crossing performed by the aircraft. While this "mean-wind method" uses accurate wind measurements, it can be systematically biased by assumptions that plume rise time is negligible and that winds are homogeneous horizontally and in time during the plume transport. Wind inhomogeneities due to terrain effects and day-to-night transition, among other factors, affected some plumes during FIREX-AQ. The mean-wind method therefore performs best for short-range transport over level terrain with steady winds. The second method relies on upwind air parcel trajectories and plume rise computed with multiple high-resolution meteorological datasets. This "trajectory-based method" quantifies age uncertainty from the meteorological ensemble, plume rise speed, wind speed errors, and fire location. The second method also resolves age differences from the center to edge of a transect. Still, it is susceptible to errors in the meteorological model. With careful comparison of the simulated trajectories to smoke transport observed from geostationary satellite imagery described here, we filter out many trajectory errors and improve the smoke age estimates. The two age methods are strongly correlated (<em>R</em> = 0.93) for the periods during FIREX-AQ when both ages are available. The mean-wind age is systematically 14&thinsp;% younger than the trajectory-based age and the median absolute difference between them is 19&thinsp;% (23&thinsp;% for mean). The favorable agreement between the two age methods reflects that the mean-wind method was selectively applied to plumes with little wind variability. Trajectory-based ages are available for more of the FIREX-AQ smoke observations than the mean-wind ages. The median trajectory-based age uncertainty during FIREX-AQ is 24&thinsp;% and the mean uncertainty is 37&thinsp;%, due to a long-tailed distribution. The main source of age uncertainty is spread within the meteorological ensemble, followed by discrepancy between measured and modeled wind speed, then other factors like plume rise. The age uncertainty variable enables the user to identify periods with high or low confidence in the age estimate, thereby informing studies of smoke aging.

The Ozone Mapping and Profiler Suite (OMPS) and the Geostationary Environment Monitoring Spectrometer (GEMS) are satellite instruments designed to monitor atmospheric composition, primarily ozone. OMPS onboard SNPP/NOAA-20/NOAA-21 satellites are polar orbiting instruments, providing global coverage once a day. However, GEMS, onboard South Korea's GEO-KOMPSAT-2B satellite, is a geostationary satellite instrument providing more frequent observations over East Asia and adjacent areas. This difference in their orbits defines their unique capabilities and limitations, making intercomparison an essential exercise for harmonizing their datasets and validating their respective ozone retrievals. OMPS provides global coverage, delivering ozone profiles and trace gases with a lower temporal resolution but comprehensive spatial coverage. GEMS complements this by offering high temporal resolution data, allowing the monitoring of diurnal variations in ozone. The intercomparison between OMPS and GEMS helps to evaluate radiometric calibration consistency of measurements. This study focuses on quantifying the radiometric consistency between NOAA-21 OMPS Nadir Mapper (NM) radiance and reflectance measurements with GEMS. One of the benefits of GEO-LEO intercomparison is the satellites have nearly frequent simultaneous overlapping observations. The sensor intercomparison is performed near nadir for OMPS and GEMS, less than 8° view zenith angle. The radiometric bias results provide a good understanding of how well NOAA-21 OMPS agree with GEMS. The results could help to provide independent validation of OMPS data quality. The study could be further expanded to perform intercomparison of GEMS with SNPP and NOAA-20, resulting in opportunities to evaluate the OMPS radiometric consistency through double difference. This could help to further confirm the SNPP, NOAA-20 and, NOAA-21 OMPS radiometric agreements from the past studies performed using 32-day global average and using deep convective clouds. The results presented in this study are valuable for user communities to better understand how well the OMPS and GEMS agree with each other.

Abstract Editors note: For easy download the posted pdf of the State of the Climate in 2024 is a low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

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.