Due to sensor and algorithmic constraints, satellite aerosol optical depth (AOD) retrievals are spatially incomplete and have gaps caused by clouds and bright surfaces. These gaps represent a barrier in characterizing daily aerosol loadings, which is important for air quality applications. In particular, recent studies in aerosol studies have shown satellite AOD to be a useful predictor of particulate matter, but are often limited to monthly or longer temporal resolution because of missing AOD retrievals. In this study, we propose using a UNet 3+ to fill gaps in satellite AOD retrievals. We tested the hypothesis that UNet 3+ trained on deep blue (DB) AOD and supplemental data sets (e.g., Modern‐Era Retrospective analysis for Research and Applications, Version 2 reanalysis AOD, meteorological and land‐use variables from North American Mesoscale Forecast System, and Hazard Mapping System smoke polygons) will improve the availability of AOD data accurately. We created spatiotemporal data sets of daily, gap‐filled DB AOD from 2012 to 2023 over the CONtinental United States (CONUS) at a 12 × 12 km 2 resolution. We were able to train the model and perform the gap‐filling in ∼10 hr, resulting in an increase of AOD data availability by 281%. We demonstrated that our approach is feasible over CONUS through quantitative and qualitative evaluations against AERONET and DB AOD. In statistical evaluations, our gap‐filled AOD data set attained an RMSE ∼ 0.09 and a r ∼ 0.87 against collocated AERONET retrievals, compared to an RMSE ∼ 0.11 and a r ∼ 0.86 that the original DB AOD retrievals scored against AERONET. We plan to use this data set for future air quality and health investigations.

We previously established a derivative-based approach to generate a pixel-level spectral error covariance matrix in satellite-retrieved remote sensing reflectance, ∑Rrs. However, one practical issue is the delivery of the products without increasing the file size by an order of magnitude or more, considering that for N sensor spectral bands, there are N × (N+1)/2 covariance matrix elements to be specified at each pixel. The issue becomes more pertinent for hyperspectral imaging spectroradiometers such as the Ocean Color Instrument (OCI) on NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem mission (PACE), which has 286 bands, resulting in ∼40,000 unique elements in ∑Rrs per pixel that would lead to a ∼60 GB Level-2 file for one 5-min granule. As a first step to tackle the issue, we took OCI and Moderate Resolution Imaging Spectroradiometer (MODIS) data to explore the possibility of approximating ∑Rrs using a third-degree polynomial, thereby decreasing the memory overhead to 4×N numbers. We found that ∑Rrs derived from the polynomial fitting matches well with the original value, with the difference smaller than 5%. We then compared the relative uncertainty in two derived ocean color data products (chla and Kd(490)) calculated using the original fully computed ∑Rrs and then using the polynomial model approximation for ∑Rrs, finding the absolute difference between the two approaches to be smaller than 0.5%. These evaluations suggest the polynomial approximation of ∑Rrs is suitable without degrading the scientific quality. By including the coefficients derived from polynomial fitting instead of the full error covariance matrix, a typical 5-min Level-2 file for OCI decreases from ∼60 GB to a more practical ∼1.7 GB.

We present two new open-source codes, in the C language, for simulation of the line-by-line molecular (gas) absorption in the solar spectral region with wavelengths up to similar to 2500 (nm). The first one, gcell, simulates absorption spectroscopy in a gas cell for a given length of the cell, temperature, and pressure. The second one, aspect, is for spectroscopy in Earth's atmosphere - a common need for remote sensing applications. Both use the HITRAN database for line shape (Voigt) modeling. Aspect adapts height variations of the thermodynamic parameters (profiles) from MODTRAN. Separate discussion of the gas cell and the atmospheric modes simplifies software development, documentation, and support, and ultimately the transfer of knowledge between generations of scientists. These are the main goals of the current paper. Despite the existence of numerous computer programs for absorption spectroscopy, the code development process is poorly covered in literature. As a result, it is difficult for a non-developer to confidently modify an existing code or create a new tool within a reasonable amount of time.

The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission launched from Kennedy Space Center in the early morning of February 8, 2024. Just 63 days later, data from NASA's newest Earth-observing satellite became available to the public. These data will extend and improve upon NASA's 20+ years of global satellite observation of our living oceans, atmospheric aerosols, and cloud and initiate an advanced set of climate-relevant data records. Ultimately, PACE is the first mission to provide daily, global measurements that will enable prediction of the "boom-bust" cycle of fisheries, the appearance of harmful algae, and other factors that affect commercial and recreational industries. PACE also observes clouds and tiny airborne particles known as aerosols that influence air quality and absorb and reflect sunlight, thus warming and cooling the atmosphere. In the months since launch and initial data release, the PACE Project pursued instrument temporal and system vicarious calibrations, executed cross-instrument comparisons, conducted performance assessments, explored synergies with other missions, and released advanced science data products. In parallel, the PACE Validation Science Team left for the field and the Post-launch Airborne eXperiment (PACE-PAX) prepared for its mission. And, most importantly, preliminary science results were realized. Here, we present a snapshot of these activities and their impacts and outcomes, encompassing the first half year of the PACE mission.

Abstract. The performance and consistency of satellite observations in characterizing the saline dust emission from the newly formed Aralkum Desert have remained poorly understood. We address this knowledge gap by providing a review of satellite techniques capable of detecting the presence, column burden, and vertical height of airborne dust over desert surfaces. Then we evaluate the consistency between different aerosol products in observing an intense Aralkum dust storm in 2018, via synergistic analyses of the ultraviolet aerosol index (UVAI) from OMPS, TROPOMI and EPIC, aerosol optical depth (AOD) from MODIS and VIIRS, and aerosol optical centroid height (AOCH) from CALIOP and EPIC. The UVAI products consistently delineate the areal extent of the freshly emitted dust plume if the dynamic range of each product is considered. The heavy dust plume is however erroneously masked as clouds in the AOD products. All UVAI products show large positive values over the Garabogazköl gulf and northern Caspian Sea due to enhanced UV absorption by turbid and saline waters, suggesting that caution must be taken to avoid misinterpreting the surface effect as dust signal over ephemeral or dried lakes. The AOD products show generally good agreement in observing the total and coarse-mode AOD associated with the dust outflow to Caspian Sea. Over-land AOD retrievals show strong non-linear relationships between aerosol algorithms. The NOAA Enterprise Processing System (EPS) product yields significantly lower AOD than other algorithms, likely due to the misuse of an urban aerosol optical model for dust retrieval. The EPIC AOCH retrieval shows the best agreement with CALIOP over heavy dust burden areas, with both mean bias and RMSE below 0.5 km. This study reveals significant inconsistency between satellite aerosol products and the potential of multi-sensor approaches for identifying the product biases and limitations in Central Asia.

The spring 2015 deployment of a suite of instrumentation at Doi Ang Khang (DAK) in northwestern Thailand enabled the characterization of air masses containing smoke aerosols from burning predominantly in Myanmar. Aerosol Robotic Network (AERONET) Sun photometer data were used to validate Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 'Deep Blue' aerosol optical depth (AOD) retrievals; MODIS Terra and Aqua provided results of similar quality, with correlation coefficients of 0.93–0.94 and similar agreement within expected uncertainties to global-average performance. Scattering and absorption measurements were used to compare surface and total column aerosol single scatter albedo (SSA); while the two were well-correlated, and showed consistent positive relationships with moisture (increasing SSA through the season as surface relative humidity and total columnar water vapor increased), in-situ surface-level SSA was nevertheless significantly lower by 0.12–0.17. This could be related to vertical heterogeneity and/or instrumental issues. DAK is at ~1,500 m above sea level in heterogeneous terrain, and the resulting strong diurnal variability in planetary boundary layer depth above the site leads to high temporal variability in both surface and column measurements, and acts as a controlling factor to the ratio between surface particulate matter (PM) levels and column AOD. In contrast, while some hygroscopic effects were observed relating to aerosol particle size and Ångström exponent, relative humidity variations appear to be less important for the PM:AOD ratio here.

Trace gases and aerosols (particularly biomass-burning aerosols) have important implications for air quality and climate studies in Southeast Asia (SEA). This paper describes the purpose, operation, and datasets collected from NASA Goddard Space Flight Center's (NASA/GSFC) Chemical, Optical, and Microphysical Measurements of In-situ Troposphere (COMMIT) laboratory, a mobile platform designed to measure trace gases and optical/microphysical properties of naturally occurring and anthropogenic aerosols. More importantly, the laboratory houses a specialized humidification system to characterize hygroscopic growth/enhancement, a behavior that affects aerosol properties and cloud-aerosol interactions and is generally underrepresented in the current literature. A summary of the trace gas and optical/microphysical measurements is provided, along with additional detail and analysis of data collected from the hygroscopic system during the 2015 Seven South-East Asian Studies (7-SEAS) field campaign. The results suggest that data from the platform are reliable and will complement future studies of aerosols and air quality in SEA and other regions of interest.

Spectral remote sensing reflectance, R rs ( λ ) (sr -1 ), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved R rs . Furthermore, since the associated algorithms require R rs at multiple spectral bands, the spectral (i.e., band-to-band) error covariance in R rs is needed to accurately estimate the uncertainty in those derived properties. This study establishes a derivative-based approach for propagating instrument random noise, instrument systematic uncertainty, and forward model uncertainty into R rs , as retrieved using NASA’s multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in R rs . The approach is applied to measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite and verified using Monte Carlo (MC) analysis. We also make use of this full spectral error covariance in R rs to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chl a , mg/m 3 ) and diffuse attenuation coefficient of downwelling irradiance at 490 nm ( K d (490), m -1 ). Accounting for the error covariance in R rs generally reduces the estimated relative uncertainty in chl a by ∼1-2% (absolute value) in waters with chl a < 0.25 mg/m 3 where the color index (CI) algorithm is used. The reduction is ∼5-10% in waters with chl a > 0.35 mg/m 3 where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For K d (490), the reduction by error covariance is generally ∼2%, but can be higher than 20% in some regions. The error covariance in R rs is further verified through forward-calculating chl a from MODIS-retrieved and in situ R rs and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chl a can be achieved over deep ocean waters (chl a ≤ 0.1 mg/m 3 ). While the derivative-based approach generates reasonable error covariance in R rs , some assumptions should be updated as our knowledge improves. These include the inter-band error correlation in top-of-atmosphere reflectance, and uncertainties in the calibration of MODIS 869 nm band, in ancillary data, and in the in situ data used for system vicarious calibration.

This study evaluates the height of biomass burning smoke aerosols retrieved from a combined use of Visible Infrared Imaging Radiometer Suite (VIIRS), Ozone Mapping and Profiler Suite (OMPS), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations. The retrieved heights are compared against spaceborne and ground-based lidar measurements during the peak biomass burning season (March and April) over Southeast Asia from 2013 to 2015. Based on the comparison against CALIOP, a quality assurance (QA) procedure is developed. It is found that 74% (81-84%) of the retrieved heights fall within 1 km of CALIOP observations for unfiltered (QA-filtered) data, with root-mean-square error (RMSE) of 1.1 km (0.8-1.0 km). Eliminating the requirement for CALIOP observations from the retrieval process significantly increases the temporal coverage with only a slight decrease in the retrieval accuracy; for best QA data, 64% of data fall within 1 km of CALIOP observations with RMSE of 1.1 km. When compared with Micro-Pulse Lidar Network (MPLNET) measurements deployed at Doi Ang Khang, Thailand, the retrieved heights show RMSE of 1.7 km (1.1 km) for unfiltered (QA-filtered) data for the complete algorithm, and 0.9 km (0.8 km) for the simplified algorithm.

NASA's Deep Blue aerosol project has developed global aerosol data records using consistent retrieval algorithms applied to various satellite sensors. The primary components of these data records are derived from the series of Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) and the National Oceanic and Atmospheric Administration or NOAA-20+ satellites as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), among others. These instruments provide over 23 years of measurements with similar radiometric characteristics for aerosol retrievals. The algorithms used for the initial Version 1 SNPP VIIRS data set were based on the MODIS Collection 6.1 Deep Blue algorithm over land and Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water. For VIIRS Version 2 data reprocessing, major updates have been made to the algorithm suite, including better accounting for effects of surface pressure, improved determination of surface reflectance, and the inclusion of fine-mode aerosol optical models to better represent anthropogenic aerosols over land. Cross-calibration gain factors are derived for the NOAA-20 VIIRS measurements to be consistent with the SNPP VIIRS, which allows the use of a unified algorithm package for both instruments. Comparisons against AERONET observations indicate that the Version 2 AOD data from SNPP VIIRS are significantly better than the Version 1 counterpart over land and slightly degraded over water in exchange for better spatial coverage. The AOD data from SNPP and NOAA-20 VIIRS are comparable, indicating that cross-calibration enables the creation of consistent aerosol data records using the series of VIIRS sensors. Aerosols are tiny particles suspended in the air that can come from natural sources like dust and sea salt, as well as human activities such as burning fossil fuels and industrial processes. Understanding these particles are important because they play a crucial role in the Earth's system through interactions with sunlight, clouds, and precipitation. These interactions are in turn closely related to global warming and cooling effects. Additionally, certain types of aerosols can harm human health when inhaled, leading to respiratory problems. Because of these important aspects, scientists have developed various tools to study aerosols. Satellite remote sensing is one such tool and have provided valuable data on global aerosol properties. This study describes updates made to a satellite remote sensing technique to enhance data quality. The new data sets are significantly improved compared to previous versions, indicating increased utility of the data sets. Introducing NASA's VIIRS Version 2 Deep Blue aerosol products Major updates in Deep Blue algorithm resulting in significant improvements in global aerosol optical depth retrievals Consistent data records between SNPP and NOAA-20+ VIIRS ensuring long-term data continuity

The objectives of 7-SEAS/BASELInE (Seven SouthEast Asian Studies/Biomass-burning Aerosols & Stratocumulus Environment: Lifecycles & Interactions Experiment) campaigns in spring 2013–2015 were to synergize measurements from uniquely distributed ground-based networks (e.g., AERONET, MPLNET) and sophisticated platforms (e.g., SMARTLabs, regional contributing instruments), along with satellite observations/retrievals and regional atmospheric transport/chemical models to establish a critically needed database, and to advance our understanding of biomass-burning aerosols and trace gases in Southeast Asia (SEA). We present a satellite-surface perspective of 7-SEAS/BASELInE and highlight scientific findings concerning: (1) regional meteorology of moisture fields conducive to the production and maintenance of low-level stratiform clouds over land, (2) atmospheric composition in a biomass-burning environment, particularly tracers/markers to serve as important indicators for assessing the state and evolution of atmospheric constituents, (3) applications of remote sensing to air quality and impact on radiative energetics, examining the effect of diurnal variability of boundary-layer height on aerosol loading, (4) aerosol hygroscopicity and ground-based cloud radar measurements in aerosol-cloud processes by advanced cloud ensemble models, and (5) implications of air quality, in terms of toxicity of nanoparticles and trace gases, to human health. This volume is the third 7-SEAS special issue (after Atmospheric Research, vol. 122, 2013; and Atmospheric Environment, vol. 78, 2013) and includes 27 papers published, with emphasis on air quality and aerosol-cloud effects on the environment. BASELInE observations of stratiform clouds over SEA are unique, such clouds are embedded in a heavy aerosol-laden environment and feature characteristically greater stability over land than over ocean, with minimal radar surface clutter at a high vertical spatial resolution. To facilitate an improved understanding of regional aerosol-cloud effects, we envision that future BASELInE-like measurement/modeling needs fall into two categories: (1) efficient yet critical in-situ profiling of the boundary layer for validating remote-sensing/retrievals and for initializing regional transport/chemical and cloud ensemble models, and (2) fully utilizing the high observing frequencies of geostationary satellites for resolving the diurnal cycle of the boundary-layer height as it affects the loading of biomass-burning aerosols, air quality and radiative energetics.

In Earth science, accurate retrieval of cloud properties including cloud masking, cloud phase classification, and cloud optical thickness (COT) prediction is essential in atmospheric and environmental studies. Conventional methods rely on distinct models for each sensor due to their unique spectral characteristics. Recently, machine/deep learning has been embraced to extract features from satellite datasets, yet existing approaches lack architectures capturing hierarchical relationships among tasks. Additionally, given the spectral diversity among sensors, developing models with robust generalization capabilities remains challenging for related research. There is also a notable absence of methods evaluated across different satellite sensors. In response, we propose MT-HCCAR, an end-to-end deep learning model employing multi-task learning. MT-HCCAR simultaneously handles cloud masking, cloud phase retrieval (classification tasks), and COT prediction (a regression task). It integrates a hierarchical classification network (HC) and a classification-assisted attention-based regression network (CAR), enhancing precision and robustness in cloud labeling and COT prediction. Experimental evaluations, including comparisons with baseline methods and ablation studies, demonstrate that MT-HCCAR achieves optimal performance across various evaluation metrics and satellite datasets.

Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and uncertainties during the period 1998-2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5-3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010-2019 by 1.6-2.6 μg/m3/year, with decreases beginning 2-3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellite-derived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regional-scale representation, with residual uncertainty inversely proportional to the sample size.

We report on observed trend anomalies in climate-relevant global ocean biogeochemical properties, as derived from satellite ocean color measurements, that show a substantial decline in phytoplankton carbon concentrations following eruptions of the submarine volcano Hunga Tonga-Hunga Ha'apai in January 2022. The anomalies are seen in remotely-sensed ocean color data sets from multiple satellite missions, but not in situ observations, thus suggesting that the observed anomalies are a result of ocean color retrieval errors rather than indicators of a major shift in phytoplankton carbon concentrations. The enhanced concentration of aerosols in the stratosphere following the eruptions results in a violation of some fundamental assumptions in the processing algorithms used to obtain marine biogeochemical properties from satellite radiometric observations, and it is demonstrated through radiative transfer simulations that this is the likely cause of the anomalous trends. We note that any future stratospheric aerosol disturbances, either natural or geoengineered, may lead to similar artifacts in satellite ocean color and other remote-sensing measurements of the marine environment, thus confounding our ability to track the impact of such events on ocean ecosystems.

Understanding the 3D structure of clouds is of crucial importance to modeling our changing climate. Both active and passive sensors are restricted to two dimensions: as a cross-section in the active case and an image in the passive case. However, multi-angle sensor configurations contain implicit information about 3D structure, due to parallax and atmospheric path differences. Extracting that implicit information requires computationally expensive radiative transfer techniques. Machine learning, as an alternative, may be able to capture some of the complexity of a full 3D radiative transfer solution with significantly less computational expense. In this work, we develop a machine-learning model that predicts radar-based vertical cloud profiles from multi-angle polarimetric imagery. Notably, these models are trained only on center swath labels but can predict cloud profiles over the entire passive imagery swath. We compare with strong baselines and leverage the information-theoretic nature of machine learning to draw conclusions about the relative utility of various sensor configurations, including spectral channels, viewing angles, and polarimetry. Our experiments show that multi-angle sensors can recover surprisingly accurate vertical cloud profiles, with the skill strongly related to the number of viewing angles and spectral channels, with more angles yielding high performance, and with the oxygen A band strongly influencing skill. A relatively simple convolutional neural network shows nearly identical performance to the much more complicated U-Net architecture. The model also demonstrates relatively lower skill for multilayer clouds, horizontally small clouds, and low-altitude clouds over land, while being surprisingly accurate for tall cloud systems. These findings have promising implications for the utility of multi-angle sensors on Earth-observing systems, such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and Atmosphere Observing System (AOS), and encourage future applications of computer vision to atmospheric remote sensing.

The 2013 7-SEAS/BASELInE campaign over northern Southeast Asia (SEA) provided, for the first time ever, comprehensive ground-based W-band radar measurements of the low-level stratocumulus (Sc) systems that often exist during the spring over northern Vietnam in the presence of biomass-burning aerosols. Although spatially limited, ground-based remote sensing observations are generally free of the surface contamination and signal attenuation effects that often hinder space-borne measurements of these low-level cloud systems. Such observations permit detailed measurements of structures and lifecycles of these clouds as part of a broader effort to study potential impacts of these coupled aerosol-cloud systems on local and regional weather and air quality. Introductory analyses of the W-band radar data show these Sc systems generally follow a diurnal cycle, with peak occurrences during the nighttime and early morning hours, often accompanied by light precipitation. Preliminary results from idealized simulations of Sc development over land based on the observations reveal the familiar response of increased numbers and smaller sizes of cloud droplets, along with suppressed drizzle formation, as aerosol concentrations increase. Slight reductions in simulated W-band reflectivity values also are seen with increasing aerosol concentrations and result primarily from decreased droplet sizes. As precipitation can play a large role in removing aerosol from the atmosphere, and thereby improving air quality locally, quantifying feedbacks between aerosols and cloud systems over this region are essential, particularly given the negative impacts of biomass burning on human health in SEA. Such an endeavor should involve improved modeling capabilities along with comprehensive measurements of time-dependent aerosol and cloud profiles.

Accurate long-term sensor calibration and periodic re-processing to ensure consistency and continuity of atmospheric, land and ocean geophysical retrievals from space within the mission period and across different missions is a major requirement of climate data records. In this work, we applied the Multi-Angle Implementation of Atmospheric Correction (MAIAC)-based vicarious calibration technique over Libya-4 desert site to perform calibration analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polarorbiting Partnership (SNPP) and NOAA-20 satellites. For both VIIRS sensors we characterized residual linear calibration trends and cross-calibrated both sensors to MODerate resolution Imaging Spectroradiometer (MODIS) Aqua regarded as a calibration standard. The relative spectral response (RSR) differences were accounted for using the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) hyperspectral surface reflectance data. Our results agree with independent vicarious calibration results of both the MODIS/VIIRS Characterization Support Team as well as the CERES Imager and Geostationary Calibration Group within estimated uncertainty of 1-2%. Analysis of MAIAC geophysical products with the new calibration shows a high level of agreement of MAIAC aerosol, surface reflectance and NDVI records between MODIS and VIIRS. Excluding high aerosol optical depth (AOD), all three sensors agree in AOD with mean difference (MD) less than 0.01 and residual mean squared difference rmsd - 0.04. Spectral geometrically normalized surface reflectance agrees within rmsd of 0.003-0.005 in the visible and 0.01-0.012 at longer wavelengths. The residual surface reflectance differences are fully explained by differences in spectral filter functions. Finally, difference in NDVI is characterized by rmsd 0.02 and MD less than 0.003 for NDVI based on VIIRS imagery bands I1/I2 and less than 0.01 for NDVI based on VIIRS radiometric bands M5/M7. In practical sense, these numbers indicate consistency and continuity in MAIAC records ensuring the smooth transition from MODIS to VIIRS.

Abstract. The UMBC Hyper-Angular Rainbow Polarimeter (HARP2) will be onboard NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in January 2024. In this study we systematically evaluate the retrievability and uncertainty of aerosol and ocean parameters from HARP2 multi-angle polarimeter (MAP) measurements. To reduce the computational demand of MAP-based retrievals and maximize data processing throughput, we developed improved neural network (NN) forward models for space-borne HARP2 measurements over a coupled atmosphere and ocean system within the FastMAPOL retrieval algorithm. A cascading retrieval scheme is further implemented in FastMAPOL, which leverages a series of NN models of varying size, speed, and accuracy to optimize performance. A full day of global synthetic HARP2 data was generated and used to test various retrieval parameters including aerosol microphysical and optical properties, aerosol layer height, ocean surface wind speed, and ocean chlorophyll-a concentration. To assess retrieval quality, pixel-wise retrieval uncertainties were derived from the Jacobians of the cost function and evaluated against the difference between the retrieval parameters and truth based on a Monte Carlo error propagation method. We found that the fine-mode aerosol properties can be retrieved well from the HARP2 data, though the coarse-mode aerosol properties are more uncertain. Larger uncertainties are also associated with a reduced number of available viewing angles, which typically occurs near the scan edge of the HARP2 instrument. Results of the performance assessment demonstrate that the algorithm is a viable approach for operational application to HARP2 data after PACE launch.

This paper provides the theoretical basis and simulated retrievals for the Cloud Height Retrieval from O2 Molecular Absorption (CHROMA) algorithm. Simulations are performed for the Ocean Color Instrument (OCI), which is the primary payload on the forthcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, and the Ocean Land Colour Instrument (OLCI) currently flying on the Sentinel 3 satellites. CHROMA is a Bayesian approach which simultaneously retrieves cloud optical thickness (COT), cloud-top pressure and height (CTP and CTH respectively), and (with a significant prior constraint) surface albedo. Simulated retrievals suggest that the sensor and algorithm should be able to meet the PACE mission goal for CTP error, which is ±60 mb for 65 % of opaque (COT ≥3) single-layer clouds on global average. CHROMA will provide pixel-level uncertainty estimates, which are demonstrated to have skill at telling low-error situations from high-error ones. CTP uncertainty estimates are well-calibrated in magnitude, although COT uncertainty is overestimated relative to observed errors. OLCI performance is found to be slightly better than OCI overall, demonstrating that it is a suitable proxy for the latter in advance of PACE's launch. CTP error is only weakly sensitive to correct cloud phase identification or assumed ice crystal habit/roughness. As with other similar algorithms, for simulated retrievals of multi-layer systems consisting of optically thin cirrus clouds above liquid clouds, retrieved height tends to be underestimated because the satellite signal is dominated by the optically thicker lower layer. Total (liquid plus ice) COT also becomes underestimated in these situations. However, retrieved CTP becomes closer to that of the upper ice layer for ice COT ≈3 or higher.

Atmospheric aerosols and their interaction with clouds are the largest uncertainty in the human forcing of the climate system. Anthropogenic emissions have increased aerosol concentrations, increasing the concentration of cloud droplets and leading to reductions in droplet size and increases in cloud reflectivity (a negative radiative forcing). Central to this climate impact is the susceptibility of cloud droplet number to aerosol. This susceptibility varies widely with the method and data used to estimate it and within global climate models, explaining much of the variation in estimates of the radiative forcing from aerosol-cloud interactions (RFaci). Better constraints on the susceptibility have been a key target for recent observation-based constraints on the aerosol forcing. Previous work has shown that the aerosol burden of the clean, pre-industrial atmosphere has been demonstrated as a key uncertainty for the aerosol forcing. Here we show that the behaviour of clouds under these clean conditions is of equal importance for understanding the spread in radiative forcing estimates between models and observations. This means that the uncertainty in the aerosol impact on clouds is, counterintuitively, driven by situations with little aerosol. Removing these clean conditions from observational estimates of the susceptibility produces a close agreement between different model and observational estimates of the cloud response to aerosol, but does not provide a strong constraint on the RFaci. If we are to produce tighter constraints on the radiative forcing from aerosol-cloud interactions, better constraints on the behaviour of cloud in keen conditions are vital.