Ozone is a crucial atmospheric trace gas that protects life on Earth from harmful ultraviolet radiation. However, at low altitudes in the troposphere, ozone adversely impacts climate, human health, and ecosystems. Therefore, monitoring atmospheric ozone concentration and spatial distribution is essential. Ground-based monitoring sites are sparse and do not provide comprehensive coverage. Satellites have been used for global ozone monitoring for decades. Polar-orbiting satellites have the disadvantage of low temporal resolution, providing data from one overpass per day under cloud-free conditions. Better coverage is obtained by using geostationary data offering multiple observations daily. This study proposes a neural network for ozone retrieval using data from the Himawari-8 (H8) geostationary satellite (NNO3-G), which has a strong ozone absorption spectral band at$9.6~\mu $m. ERA5 total column ozone (TCO) data is used to train a fully connected neural network (FCNN) model to retrieve TCO in cloud-free areas of H8 images. FCNN shows best accuracy compared with other machine learning models. The retrieved TCO product has a spatial resolution of$2\times 2$km and a temporal resolution of 10 min. The NNO3-G-retrieved TCO data are well-correlated with Pandora ground-based measurements, with Pearson’s correlation coefficient R of 0.95,${R} ^{2}$of 0.89, and mean absolute error (MAE) of 8.88 DU. The retrieval accuracy is better in low-latitude regions than in high-latitude regions, with the best performance in summer. The major outcome of this study is the use of one geostationary satellite for retrieving TCO, offering both high time resolution and high precision.

Monitoring aerosols over the oceans is critical for understanding Earth’s climate and air quality. Although polarization can substantially reduce uncertainty in aerosol retrievals, current algorithms rely mainly on multi-view polarimeters, and no dedicated algorithm is available for single-view polarimeters over the ocean. Here, we present the first ocean algorithm for a spaceborne single-view polarimeter, demonstrated with the Particulate Observing Scanning Polarimeter (POSP) onboard the GF-5(02) satellite. Our algorithm combines multi-spectral polarization with machine-learning-accelerated radiative transfer calculation and seasonally clustered global aerosol models. Validation with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data demonstrates high accuracy, with RMSEs of 0.061, 0.479, and 0.037 for AOD550, AE670-870, and SSA550using AERONET, and 0.030 and 0.259 for AOD550and AE670-870using MAN, respectively. Comparison with retrievals from the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm confirms that our algorithm performs comparably to GRASP products. These results underscore the necessity and feasibility of developing specialized aerosol retrieval algorithms for single-view polarimeters, and pave the way for global aerosol over the ocean monitoring.

Single-angle sensors typically use radiative transfer simulations based on Lambertian surface, even though the surface reflectance obtained exhibits directional characteristics. Fully accounting for the contribution of surface directional characteristics to the top of atmosphere (TOA) reflectances can further improve the accuracy of aerosol retrievals. In this study, we propose an enhanced aerosol retrieval algorithm for the Particulate Observing Scanning Polarimeter (POSP), by further considering the surface directional characteristics. Combined with an updated aerosol model, this approach achieves high-accuracy retrievals. We used historical bidirectional reflectance distribution function (BRDF) products to construct stable surface constraints. By exploring the strong empirical statistical relationships between adjacent blue bands, we have realized the joint inversion of multiple blue bands. In addition, we used an optimization algorithm that incorporates boundary constraints, simultaneously accounting for errors in the surface constraint model, and satellite observation errors. The global aerosol optical thickness (AOD) at 550 nm over land was retrieved from November 2021 to April 2022. Validation of POSP AOD versus AErosol RObotic NETwork (AERONET) data shows a high consistency, with correlation coefficient (R) of 0.93, root mean square error (RMSE) of 0.086, bias of 0.004, fraction within expected error (EE) of 80.8%, and fraction within Global Climate Observing System (GCOS) of 52.1%. Comparison with MODIS aerosol products shows that the accuracy of POSP AOD is better than that of MODIS AOD. According to the matching results, for DB, R of 0.936/0.907 and fraction within EE of 82.2%/74.9% (POSP/MODIS DB); for DT, R of 0.937/0.915 and fraction within EE of 83.5%/ 72.0% (POSP/MODIS DT). The spatial distribution difference between POSP AOD and DB AOD is small, indicating good consistency, and POSP AOD captured the intensity of aerosol pollution well. In summary, the enhanced aerosol algorithm achieves reliable high-precision AOD retrieval and because of its generality could also be applied to other sensors.

Large-scale and high spatiotemporal resolution sea surface temperature (SST) products can provide important support for monitoring dynamic changes in the marine environment, energy development, and assimilation models. We develop an algorithm to obtain the high spatiotemporal resolution SST product by fusion of the high spatial resolution SST product from the medium resolution spectral imager (MERSI) on the Fengyun-3E (FY-3E) satellite and the high temporal SST product from the advanced geosynchronous radiation imager (AGRI) on the Fengyun-4E (FY-4E) satellite. During data preprocessing, MERSI products have large data volumes stored in chunks, and we use the multicore computer for batch re-projection to generate the matrix of temperature values and satellite observation times. The matrix has high spatial coverage and removes the anomalous data, increasing the stability of the algorithm. AGRI data have gaps due to cloud cover, and we build a pixel-by-pixel linear fitting model to extract more valid change information from multihours data, which also improve the coverage of fusion results. During the fusion process, we calculate the spectral and temporal weighting factors in real time, divide data into regular blocks for multicores fusion to get 1 km/1 h SST fusion product covering most areas of the Eastern Hemisphere. Using Argo buoy data to verify the accuracy of the results for 15 days, the root mean square error (RMSE) is$1.089~^{\circ }$C and the average deviation is$0.867~^{\circ }$C. Multicore parallel computing makes algorithm fast and efficient. The spatiotemporal resolution of results is improved significantly and the effect of missing original data of the results is reduced.

This letter reports uncertainties in the Aqua-Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 dark target (DT), deep blue (DB), and multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) during the COVID-19 lockdown period (February–May 2020) compared to the pre-COVID-19 period (February–May 2019). Validation of AOD retrievals was conducted against AErosol RObotic NETwork (AERONET) Version 3 Level 1.5 AOD data obtained from three sites located in urban (Beijing_CAMS and Beijing_RADI) and suburban (XiangHe) areas of China. The results show the poor performance of the DT and DB algorithms compared to the MAIAC algorithm, which performed better during the lockdown period. Overall, all MODIS algorithms overestimated the AOD and showed higher positive bias under high aerosol loading conditions during lockdown than during prelockdown. This is mainly attributed to the overestimation of the aerosol single-scattering albedo (SSA), which was found higher during lockdown than during the same period in 2019.

Satellite-based whitecap fraction (W) data have been used to predict sea spray aerosol (SSA) emission rates. This allows to evaluate how an account for natural variability of whitecaps in the W parameterization would affect SSA mass flux predictions when using a sea spray source function (SSSF) based on the whitecap method. Data set containing W data for 2006 together with matching wind speed U10and sea surface temperature (SST) T has been used. Whitecap fraction W was estimated from observations of the ocean surface brightness temperature TBby satellite-borne radiometers at two frequencies (10 and 37 GHz). A global scale assessment of the data set yielded approximately quadratic correlation between W and U10. A regional scale analysis yielded a new W(U10, T) parameterization which explicitly accounted for the effect of SST on W. The analysis of W values obtained with the new W(U10) and W(U10, T) parameterizations indicates that the influence of secondary factors on W is for the largest part embedded in the exponent of the wind speed dependence. In addition, the W(U10, T) parameterization is capable to model the spread (or variability) of the satellite-based W data. The satellite-based parameterization W(U10, T) was applied in an SSSF to estimate the global SSA emission rate. The thus obtained SSA production rate is within previously reported estimates, however with distinctly different spatial distribution.

CESAR, the Cabauw Experimental Site for Atmospheric Research, is the Dutch focal point for collaboration on climate monitoring and atmospheric research and is situated on the KNMI meteorological research site near Cabauw in the Netherlands. CESAR addresses challenging topics in atmospheric research, especially the questions that are related to the interaction between clouds, aerosols and radiation and questions dealing with land-atmosphere interaction. These topics are approached via process studies, model evaluations, climate monitoring, development of new experimental techniques and supporting activities for satellite missions. For each of these approaches, specific demands are put on the instrumentation, mode of operation and overall infrastructure. This paper gives an overview of CESAR that was recently augmented with a scanning drizzle radar (IDRA) and a multi-wavelength Raman lidar for aerosols, clouds and water vapor (CAELI).

To acquire daily estimates of PM2.5 distributions based on satellite data one depends critically on a well established relation between AOD and ground level PM2.5. In this study we aimed to experimentally establish the AOD-PM2.5 relationship for the Netherlands. For that purpose an experiment was set-up at the AERONET site Cabauw. The average PM2.5 concentration during this ten month study was 18 mug/ m3, which confirms that the Netherlands are characterized by a high PM burden. A first inspection of the AERONET level 1.5 (L1.5) AOD and PM2.5 data at Cabauw showed a low correlation between the two properties. However, after screening for cloud contamination in the AERONET L1.5 data, the correlation improved substantially. When also constraining the dataset to data points acquired around noon, the correlation between AOD and PM2.5 amounted to R2=0.6 for situations with fair weather. This indicates that AOD data contain information about the temporal evolution of PM2.5. We used lidar observations to detect residual cloud contamination in the AERONET L1.5 data. Comparison of our cloud-screed L1.5 with AERONET L2 data that became available near the end of the study showed favorable agreement. The final relation found for Cabauw is PM2.5 = 124.5* AOD - 0.34 (with PM2.5 in mug/m3) and is valid for fair weather conditions. The relationship determined between MODIS AOD and ground level PM2.5 at Cabauw is very similar to that based on the much larger dataset from the sun photometer data, after correcting for a systematic overestimation of the MODIS data of 0.05. We applied the relationship to a MODIS composite map to assess the PM2.5 distribution over the Netherlands for the first time based on MODIS data only.