SearcharxivSearch

arXiv · 2411.15518

Developing Global Aerosol Models based on the Analysis of 30-Year Ground Measurements by AERONET (AEROEX models) and Implication on Satellite based Aerosol Retrievals

Abstract

The AErosol RObotic NETwork (AERONET), established in 1993 with limited global sites, has grown to over 900 locations, providing three decades of continuous aerosol data. While earlier studies based on shorter time periods (10-12 years) and fewer sites (approximately 250) made significant contributions to aerosol research, the vast AERONET dataset (1993-2023) calls for a comprehensive reevaluation to refine global aerosol models and improve satellite retrievals. This is particularly important in light of major environmental changes such as industrialization, land use shifts, and natural events like wildfires and dust storms. In this study, a set of fine and coarse aerosol models called AERONET-Extended (AEROEX) models are developed based on cluster analysis of 30-years AERONET data, analyzing over 202,000 samples using Gaussian Mixture Models to classify aerosol types by season and region. Aerosols are categorized into spherical, spheroidal, and mixed types using particle linear depolarization ratio and fine mode fraction. Four fine-mode aerosol models were derived based on differences in scattering and absorption properties, revealing regional/seasonal variations, particularly in North America, Europe and Asia. Additionally, two coarse-mode aerosol models were identified, separated by their absorbing properties in dust-prone and polluted regions. We performed simulation analysis showing that the new models significantly improve satellite-based aerosol optical depth retrievals compared to widely used dark target aerosol models. A global aerosol model map, generated at 1x1 degree resolution for each season using Random Forest and expert refinement, provides valuable insights for climate and atmospheric studies, improving satellite-based aerosol retrievals at global scale.

Explore related subjects

Keep this discovery

BibTeXRIS

Manoj K Mishra, Shameela S F, Pradyuman Singh Rathore. 2024-11-23. Developing Global Aerosol Models based on the Analysis of 30-Year Ground Measurements by AERONET (AEROEX models) and Implication on Satellite based Aerosol Retrievals. https://doi.org/10.1016/j.scitotenv.2025.180301

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph