Abstract This paper describes improvements to geophysical retrievals from NASA's Advanced Microwave Precipitation Radiometer (AMPR) during the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex). The retrieved products are validated using independent data sets, and example applications in addressing science questions about the maritime tropics are provided. Multi‐linear regression equations previously developed to retrieve cloud liquid water path (CLW), total precipitable water vapor (WV), and 10‐m wind speed (WS) from AMPR brightness temperatures in the midlatitudes were examined. Initial testing revealed that the CLW methods required modification for the maritime tropics, likely due to the stark environmental differences. Minor WS adjustments were also needed to account for the presence of a new AMPR radome. Compared with numerical simulations, the updated CLW equation performed nearly an order of magnitude better than its predecessor. Validating AMPR CLW with airborne polarimeter‐derived CLW throughout CAMP2Ex yielded a median absolute deviation that is less than the predecessor CLW equation's uncertainty and comparable to CLW precisions observed in past studies. In situ WV and WS validation using dropsondes was promising, with mean deviations that are less than their target uncertainties. Correlating AMPR CLW with polarimeter‐derived cloud‐top height (CTH) indicated an expected CLW ∝ CTH2 relation for CTH < 4 km, but reduced CLW for CTH > 4 km may have been associated with cloud droplet removal via accretion and/or mixed‐phase onset. These results demonstrate the power of airborne radiometer geophysical retrievals as standalone metrics and the insight they provide when used alongside other data sets.

As the international community moves towards the second Global Stocktake under the Paris Agreement, the demand for independent, transparent, and verifiable greenhouse gas (GHG) emission estimates has never been more critical. While satellite-based monitoring offers a powerful verification tool, the uncertainty of top-down flux estimates is currently dominated by substantial uncertainties in local wind speed input, which typically relies on coarse meteorological reanalysis models. This dependency introduces potential biases and correlated errors that undermine the scientific integrity required for high-stakes climate policy. Addressing this bottleneck, we present a comprehensive science study dedicated to developing an independent, data-driven in-plume wind retrieval framework designed specifically for potential future satellite missions equipped with multi-angle or multi-platform observations. By simulating the data products of such missions using high-resolution Large-Eddy Simulations (LES), we generated a robust dataset of realistic plume dynamics to develop and validate our algorithms. Exploring the temporal information embedded across consecutive plume images, we propose and evaluate two distinct, complementary methodologies for deriving wind velocity fields directly from plume imagery. First, we apply an Multi-Image Correlation Image Velocimetry (CIV) algorithm, optimized to dynamically correct temporal centering errors by averaging correlation surfaces across the observations sequence. Second, we introduce CVision-CIV, a novel deep learning approach based on the UnLiteFlowNet-PIV architecture, which utilizes Convolutional Neural Networks (CNNs) to extract morphological flow features directly from noisy imagery sets. Through an application on simulated CO2 emission plumes, we demonstrate that while physical CIV methods provide robust baselines, the CVision-CIV model exhibits superior stability in low signal-to-noise regimes, effectively suppressing sensor noise where traditional correlation breaks down. By validating these parallel pathways on LES-generated observations, this work establishes a comprehensive algorithmic foundation for defining observational requirements for future missions aiming to replace reanalysis proxies with precise, observation-based wind products for improved GHG monitoring. We will discuss the methods’ sensitivity to observational noise, number of images, time-difference between images and resolution.

This paper assesses the capability of liquid cloud droplet effective radius (CER) and cloud effective variance (CEV) retrieval from space-borne multi-angular hyperspectral measurements. The capability and sensitivity study is based on a neural network (NN) retrieval approach which is developed for the Spectropolarimeter for Planetary EXploration-one (SPEXone), a multi-angular hyperspectral polarimeter onboard Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite. The synthetic measurements used in NN training and the sensitivity experiments are generated by Remote sensing of Trace gas and Aerosol Products (RemoTAP) forward model, and include variations of cloud, surface and aerosol properties, as well as cloud fraction. On the basic validation set, the NN performs similar over ocean and land with a mean absolute error (MAE) around 2 mu m on CER and around 0.04 on CEV. The performance over different cloud fraction (CF) and cloud optical thickness (COT) is evaluated, and indicates that most accurate retrievals can be performed for cases where CF > 0.6 and COT between 2 and 12. The sensitivity to above-cloud aerosols (both fine-mode-dominated and dust-mode-dominated cases) suggests the retrieval is more sensitive to absorbing fine mode aerosols (CER MAE < 2.5 mu m up to AOT of 0.2 for fully cloudy scene), than to dust aerosols (CER MAE < 2.5 mu m up to AOT of 0.5). Moreover, the retrievals are virtually insensitive to above cloud cirrus over fully cloudy scene, but shows large sensitivity over partly cloudy scenes over land. Finally, synthetic measurements from partly cloudy scenes are generated based on the 3D MYSTIC radiative transfer model. The retrieval on these measurements suggests no significant 3D cloud radiative effect artifacts.

Proper proxies for CCN are vital to provide accurate observation-based estimates of Aerosol-Cloud Interactions (ACI), which are commonly used to constrain climate models. An effective proxy for CCN is the column number of aerosol particles that surpass a predetermined threshold radius (NCCN [m-2]). This CCN proxy has been estimated from PARASOL using level 2 aerosol microphysical and/or optical property retrievals. With the advanced multi-angle polarimeters (MAPs) such as the Spectro-Polarimeter for Planetary EXploration one (SPEXone) onboard the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite, further improvements in NCCN retrievals are expected. This paper presents a deep neural network (NN) MAP algorithm as an extension for the Remote sensing of Trace gas and Aerosol Products (RemoTAP)-NN algorithm to directly retrieve NCCN and NCCN within the 0-2 km atmosphere (a representation for the boundary layer (BL)), NCCNBL[m-2] from SPEXone measurements. The algorithm consists of two distinct NN models to perform the retrieval and to estimate the goodness-of-fit of the retrieval. The NN models are trained on synthetic SPEXone measurements based on 3 aerosol modes: fine mode, insoluble coarse/dust mode, and soluble coarse mode. The algorithm's potential has been validated independently against synthetic SPEXone measurements simulated based on the 7 aerosol modes from the ECHAM-HAM global aerosol-climate model. The relative RMSE of NCCN retrieved from the NN algorithm is 0.20 over the ocean and 0.29 over land. For NCCNBL, the relative RMSE increases to 0.46 over the ocean and 1.04 over land. For the same validation data, the relative RMSE of NCCN estimated from the RemoTAP physics-based/classical algorithm is 0.34 over the ocean and 0.54 over land. For NCCNBL, the relative RMSE is 0.93 over the ocean and 1.18 over land. Our study demonstrates that the NN algorithm can outperform the capabilities of classical algorithms by accurately retrieving CCN information.

Changes in aerosols since the preindustrial era have altered the top-of-the-atmosphere radiation balance by directly scattering solar radiation and indirectly interacting with clouds, known as aerosol effective radiative forcing (ERFaer). ERFaer persistently remains one of the most uncertain components in global climate model simulations, due to the imperfect representations of aerosol and cloud properties and processes. Perturbed parameter ensembles (PPEs) are increasingly used to quantify these sources of uncertainty and to constrain models with observations.Here, we first present a single-model PPE using the ICON-A-HAM2.3 model, designed to identify key sources of ERFaer uncertainty. This PPE comprises 383 simulations for both preindustrial and present-day conditions, in which 42 parameters related to aerosol emissions, aerosol properties and processes, cloud microphysics, convection, and turbulence are perturbed simultaneously. Gaussian process emulators are trained on model outputs to enable efficient sampling of this high-dimensional parameter space. Our analysis focuses on uncertainty quantification and attribution for aerosol and cloud properties as well as ERFaer, along with comparisons against satellite observations from SPEXone/PACE and MODIS. Our results show a global mean ERFaer of −1.10 W m⁻² (5–95 percentile: −1.54 to −0.68 W m⁻²), with the overall uncertainty dominated by aerosol-related processes, particularly aerosol emissions.Building on this single-model framework, we further propose a Multi-Model PPE (MMPPE) initiative within the AeroCom Phase IV experiments. This multi-model approach allows us to simultaneously address structural and parametric uncertainties across models, providing a coordinated pathway toward reducing ERFaer uncertainty in current climate models. An overview of the MMPPE design and objectives will be presented.

Abstract. Changes in aerosols since the preindustrial era have altered the top-of-the-atmosphere radiation balance by scattering and absorbing solar radiation (ARI) and indirectly interacting with clouds (ACI), known as aerosol effective radiative forcing (ERFaer). ERFaer persistently remains one of the most uncertain components in climate projections, due to imperfect representations of aerosol and cloud processes in climate models. Here, we construct a perturbed parameter ensemble (PPE) with the aerosol–climate model ICON2.6.4–A–HAM2.3 (hereafter ICON–HAM) to quantify key sources of ERFaer uncertainty. We perturb 42 aerosol and cloud parameters over 383 PPE members. Parametric uncertainties in aerosol and cloud processes yield an ERFaer of −1.04Wm−2, with a 90 % credible range of −1.42 to −0.65 Wm−2 for the period 2024–2025. The parameters related to emissions (anthropogenic sulfur dioxide, natural dimethyl sulfide, and emitted particle size) dominate ACI uncertainty and hence ERFaer uncertainty (80 %), while absorption-related parameters (anthropogenic black carbon emissions and aerosol refractive indices) drive ARI uncertainty (60 %). Cloud parameters account for 13 % of ERFaer uncertainty, mainly via convection and entrainment processes. The sensitivity analysis of model diagnostics to parameters reveals that many present-day aerosol and cloud observables share dominant causes of uncertainty with ACI and ARI forcing, highlighting the potential for constraining ERFaer using existing space- and ground-based measurements. Notably, model biases against SPEXone and MODIS observations coincide spatially with parametric uncertainties, suggesting that much of these biases may be mitigated through appropriate constraint with observations, while the remainder requires structural model developments in combination with improved observations.

Aerosols play an important role in governing the Earth's radiation budget. In addition to scattering and absorbing radiation themselves, they affect the formation and properties of clouds. In both of these processes, and especially in the latter, are affected by the uptake of water. Nevertheless, the particulars of the uptake of water by aerosols remain poorly understood. The efficiency of water uptake (i.e., hygroscopicity) for a given aerosol is highly sensitive to its composition, history, and mixing state, making it a difficult property to model or predict. This leads to stark disagreements between models using different aerosol prescriptions, which greatly contributes to the large uncertainties in the resulting radiative forcing estimates. A better understanding of aerosol water uptake is therefore crucial for accurate warming predictions.This understanding is currently held back by a lack of data. In most cases, in-situ measurements of aerosol properties are preceded by a drying step that removes any information about water content, so hygroscopicity data is only available for the small subset of studies where it is explicitly targeted. As such the spatial and temporal coverage of these data are very limited. To properly inform and constrain model choices, then, a satellite dataset would be incredibly valuable.While the aerosol water uptake is a difficult property to measure from space, the rich information content of multi-angle polarimeter (MAP) nstruments such as POLDER-PARASOL and SPEXone-PACE presents new opportunities. We aim to use these instruments to produce a satellite dataset of the aerosol water content, and use it to assemble a first-of-its-kind global climatology on aerosol hygroscopicity. To retrieve a volume water fraction we compare the retrieved real component of the refractive index to an average refractive index for dry material and the known refractive index of pure water, assuming a linear scaling with the volume fraction. Here, refractive indices are retrieved from MAP measurements using the RemoTAP algorithm. The resulting water content measurements can then be used in combination with ambient relative humidity data from reanalysis products to estimate the hygroscopicity.Recent years have seen efforts to validate these volume water fraction retrievals using both airborne (campaign) and ground-based in-situ measurements, with promising results. We now feel sufficiently confident to begin assembling the data into a global climatology, beginning with the POLDER era. We investigate regional and seasonal trends in the data, and compare them to the corresponding average relative humidities from ERA5 reanalysis to get an indication of hygroscopicity. Initial findings include a clear land/ocean divide, as well as a north-south contrast consistent with pollution patterns. Regions known for biomass burning are additionally investigated for seasonal patterns.We briefly review the results of the aforementioned validation process, and present a first look at a global climatology on water uptake for the years 2006 through 2009.

Aerosol-cloud interactions (ACI) are a major source of uncertainty in climate science, critically affecting our ability to project near-term climate evolution and assess societal risks. These interactions influence effective radiative forcing, cloud dynamics, and precipitation patterns, yet remain insufficiently constrained due to limitations in observations, modeling, and process understanding. This uncertainty hampers robust policy advice across multiple domains-from estimating remaining carbon budgets and climate sensitivity, to anticipating regional extreme events and evaluating climate interventions such as solar radiation modification. In many cases, the influence of ACI is either underappreciated or excluded from decision-making frameworks due to its complexity and lack of quantification. This perspective outlines a path forward to overcome these barriers by leveraging emerging opportunities in satellite remote sensing, ground-based and airborne observations, high-resolution climate modeling, and machine learning. We identify key areas where rapid progress is feasible, including improved retrievals of cloud microphysical properties, better representation of natural aerosols in a warming world, and enhanced integration of observational and modeling communities. Even as anthropogenic aerosol and its impacts on clouds is reducing owing to emissions controls, addressing ACI uncertainties remains essential for refining climate projections, supporting effective mitigation and adaptation strategies, and delivering actionable science to policymakers in a rapidly changing climate system.

Aerosol-cloud interactions (ACI) remain the largest uncertainty in anthropogenic climate forcings. Observation-based estimates of instantaneous radiative forcing from ACI (RFaci; the Twomey effect) rely on the choice of aerosol quantities as proxies for cloud condensation nuclei (CCN) concentrations, which differ in their ability to represent cloud-base CCN and data accuracy. Using diverse observations and aerosol-climate models, we evaluate the utility of different proxies with two independent approaches. Both approaches reveal that surface CCN exhibits the smallest bias in predicting RFaci (+5%), followed by aerosol index, surface sulfate and column CCN with similar biases of +25%, while aerosol optical depth and column sulfate show the largest biases (-60% and +92%). Constraining RFaci with the optimal proxy reduces uncertainty from 66 to 43%, yielding a less negative RFaci (-1.0 W m-2) than the unconstrained case (-1.2 W m-2). Our findings highlight the crucial role of proxy constraint in reconciling and improving RFaci estimates.

In Part III of the series, we evaluate the accuracy and applicability of the tomographic algorithm introduced in Part I and applied to real measurements by the research scanning polarimeter in Part II. We focus on the core part of the algorithm, producing a nested family of cloud shapes corresponding to a range of brightness thresholds. This family is then used to derive a 2D field of cloud extinction coefficient. We relate the resolution of the multi-angle measurements to the spatial accuracy of the cloud shape retrievals and determine constraints on the cloud aspect ratios required for the applicability of the algorithm. The expressions for overpass length and time derived in this study allow for estimating how much the cloud can move or change during the measurement process. We estimate biases in cloud size and position retrievals caused by the cloud’s advection during the measurements. Our accuracy estimation techniques are applied to previously published examples of clouds, both simulated and real.

The precise retrieval of aerosol properties from satellite data is pivotal for advancing our understanding of their impacts on climate and air quality. The RemoTAP (Remote Sensing of Trace Gas and Aerosol Products) algorithm represents a significant leap forward, leveraging data from multi-angle polarimeters (MAPs), such as the past PARASOL-POLDER instrument, the current PACE-SPEXone and the future Metop-SG-3MI and CO2M-MAP instruments. A unique ability of these instruments to measure both the intensity and polarization of sunlight across multiple wavelengths and viewing angles offers an unparalleled dataset for aerosol characterization, including number concentrations, size distributions, and refractive indices. We have substantially enhanced the RemoTAP results by integrating improved cloud fraction values derived from MAPs using a neural network approach, ensuring more accurate aerosol retrievals through better cloud filtering techniques. To further elevate data quality, advanced quality filters utilizing multiple key metrics were developed, effectively enhancing data integrity, resulting in a more refined aerosol dataset essential for precise atmospheric analysis. The validation of these enhancements involved comparisons with ground-based AERONET (Aerosol Robotic Network) observations over 284 sites, demonstrating the reliability of RemoTAP-derived aerosol properties. Furthermore, a pixel-level cross-comparison was carried out with GRASP-derived PARASOL-based aerosol data, as RemoTAP and GRASP are similar kind of algorithms for polarimetric measurements. The scientific implications of these advancements are profound, as the improved retrieval of aerosol size and composition using advanced polarimetric observations directly refines the estimation of cloud condensation nuclei (CCN) (proxy) concentrations and consequently the global CCN-Nd (cloud droplet number concentration) relationship. This refined relationship is crucial for understanding aerosol-cloud interactions, allowing for more accurate quantification of aerosol-induced cloud albedo changes, thereby reducing uncertainties in radiative forcing estimates due to aerosol-cloud interactions (RFaci). Such improvements contribute to a more precise representation of aerosol impacts in climate models, ultimately enhancing predictions of climate sensitivity and future warming scenarios. By advancing the RemoTAP algorithm, our findings underscore the transformative potential of these methodologies in delivering accurate and reliable aerosol climatology, driving forward the frontier of atmospheric science and climate research.

Proper proxies for CCN are vital to provide accurate constraints for Aerosol-Cloud Interactions (ACI) in climate models. An effective proxy for CCN is the column number of aerosol particles that surpasses a predetermined threshold radius (Nccn). This CCN proxy has been estimated from PARASOL using level 2 aerosol microphysical and/or optical property retrievals. With the launch of SPEXone on Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite, further improvements on the Nccn retrievals are expected. For example, retrieved refractive index can be used to estimate the volume fraction of aerosol-water, which can help deduce the dry aerosol size distibution and subsequently dry CCN. Further, the retrieved Aerosol-Layer Height (ALH) can be used to estimate the boundary layer (BL) contribution of Nccn (Nccn (BL)) which is better suited for quantifying ACI as it is more related to CCN at cloud base than the total column.The estimation of Nccn from physics based MAP algorithms can be challenging given its dependance on multiple retrieved aerosol parameters. We have implemented a deep neural network (NN) algorithm as an extension for the Remote sensing of Trace gas and Aerosol Products (RemoTAP)-NN algorithm to directly retrieve dry Nccn and Nccn (BL) from SPEXone measurements. The algorithm is trained on synthetic SPEXone measurements based on 3 aerosol modes which are fine mode, insoluble coarse/dust mode and soluble coarse mode. It has been validated using synthetic SPEXone measurements, simulated based on the 7 mode aerosol model from the ECHAM-HAM global aerosol-climate model. The performance of the NN algorithm was compared with RemoTAP classical algorithm.The NN algorithm retrieved dry Nccn has a relative RMSE of 0.197 over the ocean and 0.301 over the land whereas dry Nccn estimated by RemoTAP level-2 retrievals for the same synthetic measurements has a relative RMSE of 0.382 over ocean and 0.559 over land. Nccn (BL) retrieved from the NN algorithm has a relative RMSE of 0.349 and 0.825 over the ocean and the land respectivey. The relative RMSE of Nccn (BL) derived from the RemoTAP classical algorithm is 1.039 and 1.233 over the ocean and land respectively. Our study demonstrates that the NN algorithm can accurately retrieve Nccn, outperforming the capabilities in classical algorithms.

The NASA Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) conducted 162 joint flights with two aircraft over the northwest Atlantic to study aerosol-cloud interactions (ACIs), which represent the largest uncertainty in estimating total anthropogenic radiative forcing. The combination of a high-flying King Air and low-flying HU-25 Falcon, equipped with remote sensing and in situ instruments, characterized trace gases, aerosol particles, clouds, and meteorological variables with data collected nearly simultaneously below, within, and above marine boundary layer (MBL) clouds. Flights spanning warm and cold seasons across 3 years (2020-22) provided a broad range of conditions associated with aerosol particles, cloud properties (including particle size and phase), and meteorology, ideally suited for robust ACI calculations and assessing how well models simulate a wide range of MBL clouds from stratiform to cumulus. ACTIVATE data suggest that drivers of cloud droplet number concentration Nd, including aerosol particles and MBL dynamics, vary between winter and summer months with a stronger potential to convert aerosol particles into cloud droplets in winter. Models of varying complexity not only highlight some skills in simulating winter and summer cloud types but also identify challenges that still need to be addressed such as treatment of turbulence, wet scaveng-ing, and mesoscale organization. Remote sensing advances range from new retrieval methods for Nd, cloud phase classification, vertically resolved aerosol and cloud condensation nuclei number concentration, and ocean surface wind speed. This work describes these scientific and technologi-cal advances along with efforts in outreach and open data science. SIGNIFICANCE STATEMENT: Depending on the number and type of aerosol particles there are in the air, the properties of cloud droplets can vary in number concentration, size, and lifetime, and this leads to varying effects of clouds on climate and weather. We took an ambitious approach to investigate aerosol-cloud interactions, which represent the largest uncertainty in estimating human impacts on climate change. The NASA ACTIVATE mission conducted 162 joint airborne flights over the northwest Atlantic with two spatially coordinated planes making measurements relevant to understanding clouds spanning the continuum from stratiform to cumulus clouds. Along with newfound knowledge of how clouds evolve and interact with aerosol particles, extensive technological advancements were made assisted by the carefully designed sampling strategy.

In Part II of the series we present results of application of our recently developed tomographic technique to real measurements made by the Research Scanning Polarimeter (RSP). This instrument served as a prototype for the Aerosol Polarimetery Sensor launched on-board the NASA Glory satellite. The retrieval algorithms developed for the Research Scanning Polarimeter were adopted for analysis of the measurements by the space-borne polarimeters on the recently launched NASA's Plankton, Aerosol, Cloud Ocean Ecosystem (PACE) satellite. The RSP is an airborne along-track scanner with uniquely high angular resolution and high frequency of measurements. Besides characterization of liquid-water cloud droplet sizes the RSP observations also provide for derivation of 2D fields of extinction coefficient inside the cloud using a tomographic technique described in Part I of the series. This technique utilizes the family of cloud shapes derived using “cutout” method, which can be interpreted as level curves of an abstract “reflectance density”. The latter is then used for derivation of the directional cloud optical thickness (dCOT) tomogram, a collection of dCOTs is parameterized by the angles and offsets of the view rays (chords). After this, the inverse Radon Transform (the mathematical basis of the X-ray computed tomography) is applied to the dCOT tomogram yielding 2D spatial distribution of the extinction coefficient. The later can be converted into droplet number concentration using the droplet size profiles derived from the RSP’s polarized reflectance measurements. After successful tests on synthetic data, this technique was applied to real RSP measurements from NASA’s Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) conducted in the vicinity of the Philippines during the Southwest Monsoon (August–September 2019). We have investigated the interiors of a number of clouds observed during CAMP2Ex focusing on Cu and CuCg (Tcu) cases, two of which are presented in this paper. Our retrievals were compared with the correlative measurements by lidar (HSRL-2) and cloud radar (APR-3) that were deployed on the same airborne platform (NASA’s P-3B) during this field experiment.

Aerosols are emitted into the atmosphere by natural and anthropogenic events. They scatter and absorb the incoming solar radiation playing a major role in heating and cooling the atmosphere. To determine the heating and cooling rates, remote sensing and global climate modelers use the optical and physical properties of these aerosols. Table of Aerosol Optics (TAO) database is a platform for the scientists to acquire the optical properties of several aerosol species and types for wavelengths ranging from 0.25 µm to 40 µm (673 values) including phase matrices for 203 angles and 36 relative humidity values. TAO will provide the users with choices on the source, shape, size distribution, wavelength range, etc. to accommodate the needs from various atmospheric science groups. We have generated the optical calculations such as the extinction, absorption, single scattering albedo, asymmetry parameter, phase functions etc. using Mie theory for spherical aerosol species like Black Carbon (BC), Organic Carbon (OC) etc. along with an OCBC mixture. OC varies in composition depending on the source of emission. For instance, wildfire emits highly absorbing OC such as Tarballs (type of BrC) along with low viscous OC whereas, anthropogenic emissions generate slightly absorbing OC such as Toluene derived aerosols. The number fraction of Tarballs range from 10% to 95% depending on transport distance and atmospheric aging. Anthropogenic aromatic compounds such as Toluene and Xylene constitute about 70% of the total OC. Organic aerosol being complex in composition, we have classified them based on their optical parameters such as single scattering albedo (SSA) and mass absorbing coefficient (MAC). In TAO we classified organic aerosols into three groups based on their MAC values as: Brown Carbon, Slightly Absorbing Aerosol, and Scattering Aerosol. This classification will improve the representation of organic aerosols in climate and weather models. Hence the TAO database has a potential to replace the database that was developed decades ago and are being widely used among the modeling communities. Using the measurements from latest study will improve the climate and weather data.

Cloud optical thickness (COT) retrieved from airborne and satellite nadir-view measurements can be seriously underestimated if 3D radiative effects are not taken into account. This happens when retrievals for isolated or broken clouds are based on 1D radiative transfer computations (such as the widely used bispectral technique). In our previous work, we introduced and validated a linear correction technique for retrieved COT, which is based on the cloud's geometric aspect ratio. A heuristic theoretical framework was developed to show that this technique is consistent with the process of radiation escape from cloud sides in 3D geometry. However, the theory suggests that the correction factor is to be applied to reflectance, while in our technique, it is applied to COT. In this study, we resolve this inconsistency by introducing general renormalization theory based on a nonlinear renormalization function for reflectances, which must satisfy certain conditions. The link between the renormalization function and COT is established within this theory by using COT-dependent model reflectance functions. In these terms, the linear correction for COT corresponds to a specific form of the renormalization function for reflectance, which satisfies the required conditions. This proves the validity of the linear COT correction technique within the framework of general renormalization theory. We tested our conclusions on two specific model reflectance functions based on the two-stream diffuse approximation (power law) and backscattering approximation (that utilizes an exponential function).

There is a need to quickly convert aerosol microphysical properties into optical properties for global modeling, data assimilation, and remote sensing applications. This is generally accomplished through look-up tables (LUTs) of aerosol mass extinction coefficients (MEC), mass absorption coefficients (MAC), asymmetry parameters, normalized phase functions, etc. Unfortunately, many scientists are using outdated LUTs that are based upon measurements and computational techniques first published by Shettle and Fenn (1979) and later updated by Hess et al. (1998). Thus, the computations in common use are still largely based upon Mie theory and in situ information that has not been updated during this century.The Table of Aerosol Optics (TAO) is an open relational database (under construction) that expands upon existing LUTs by including recent measurements and new computational techniques for non-spherical particles (https://science.larc.nasa.gov/mira-wg/topics/tao/). The ‘open’ aspect of TAO is important, since the measurements and techniques of today will undoubtedly yield to different values in the future. This open architecture allows specialists to add new tables and gain exposure for their work and benefits modelers and remote sensing scientists by giving them easy access to computations that utilize the latest techniques. Quality is controlled by requiring methods to be peer-reviewed in the scientific literature.Thus far, we have computed mass extinction coefficients, mass absorption coefficients, lidar ratios, etc., at 73 wavelengths ranging from 0.25-40 µm for black carbon (BC), brown carbon (BrC), non-absorbing organic carbon, and mineral dust. For mineral dust, we use hexahedra shapes and mineral mixtures of montmorillonite, illite, hematite, and goethite. The illite volume fraction varies from 0 to 59% to capture the range of real refractive indices found in AERONET climatologies; the sum of the hematite and goethite mass fractions are ~2%. Additional mixtures will be added as appropriate.We have also computed optical properties for 22 size distributions of bare aggregated BC using the Multi-Sphere T-Matrix (MSTM) code (https://github.com/dmckwski/MSTM) at several remote sensing wavelengths. Our MSTM computations use aggregates of 20-nm spherules with particle-cluster growth. We obtained mass absorption coefficients (MACs) of 7.2-7.5 m2/g at a mid-visible wavelength (532 nm) when the BC fractal dimension was fixed at Df = 1.8 (i.e., fresh BC), consistent with values commonly recommended in literature reviews.We will present the TAO vision and example results for several aerosol types. TAO is part of the Models, In situ, and Remote sensing of Aerosols (MIRA) working group. MIRA seeks to build collaboration, consistency, and openness amongst the aerosol disciplines. We seek community feedback from aerosol scientists regarding the construction and content of TAO, especially in this early phase. Check out the MIRA webpage at https://science.larc.nasa.gov/mira-wg/ and subscribe to our mailing list at https://espo.nasa.gov/lists/listinfo/mira.Hess et al. (1998): Optical properties of aerosols and clouds: The software package OPAC, BAMS, 79, 831–844.Shettle and Fenn (1979): Tech. Rep. AFGL-TR-790214, Air Force Geophysics Laboratory, 1979.

The Earth’s atmosphere contains suspended particles and molecules with a wide range of characteristics. Their interaction with radiation (in both solar and thermal spectral regions) affects the transfer of energy as well as its spatial distribution in the atmosphere, affecting the weather at any moment and climate in the long term. Multi-Angular Polarimetric (MAP) observations have a great potential for quantifying the properties (e.g., size, concentration, etc.) of aerosol particles at a high accuracy. For this reason, a MAP is included on the Copernicus Carbon Dioxide Monitoring satellite mission (CO2M; intended launch date: 2026) to provide a correction of the light path to meet the mission’s stringent requirements for CO2 column retrievals. However, for both trace gas and aerosol retrievals it is also essential to filter out any cloud-contaminated measurements, because clouds strongly interact with radiation and cover between 60-70% of the Earth’s surface at any given time. This study presents an algorithm designed for detecting clouds based on the MAP instrument on CO2M. The algorithm is an adaptation of an approach that was newly developed at SRON Netherlands Institute for Space Research for the MAP instrument onboard the Polarisation and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) platform (i.e., POLarization and Directionality of Earth Reflectances; POLDER) and we are working towards making it applicable to other MAP instruments. This algorithm consists of an Artificial Neural Network model that is trained based on synthetic measurements with realistic geometry, aerosol, and cloud inputs. The synthetic measurements correspond to a wide range of atmospheric conditions and were produced for using the Remote Sensing of Trace Gases and Aerosol Products (RemoTAP) forward radiative transfer model developed at SRON Netherlands Institute for Space Research. This algorithm is designed to predict the cloud fraction based on the observed multi-angular polarization and radiance data, plus the instrument specifications and the corresponding viewing- and solar- geometry parameters. Here we focus on the efficacy of the approach for the CO2M mission. Furthermore, the sensitivity of the algorithm’s performance as a function of instrument characteristics (e.g, viewing angles, wavelengths, accuracy) will be discussed.

The Plankton, Aerosol, Cloud, Ocean Ecosystem Postlaunch Airborne eXperiment (PACE-PAX) is a multi-platform, multi-instrument field campaign designed to validate NASA’s PACE mission. Two research aircraft participated in this month-long campaign: the CIRPAS Twin Otter, conducting in situ observations of aerosols and clouds, and NASA’s high-altitude research aircraft ER-2, equipped with remote sensing instruments. Among these instruments is SPEX airborne, an airborne proxy for the Dutch SPEXone instrument onboard PACE. SPEX airborne, like SPEXone, is a multi-angle spectropolarimeter for wavelengths between 400 and 780 nm, designed to characterize aerosols in the Earth’s atmosphere. It has nine viewing angles (nadir, ±14°, ±28°, ±42°, and ±56°) and an across-track swath of about 2.1 km at nadir at nominal ER-2 flight altitudes. SPEX airborne radiance and polarization data are formatted identically to SPEXone data, enabling the use of the same RemoTAP algorithm to retrieve aerosol properties such as aerosol optical depth, size distributions, refractive index, layer height, and composition. During multiple flights, totaling over 80 flight hours, the ER-2 frequently flew under PACE and ESA’s EarthCARE satellite, as well as over the Twin Otter, calibration sites, and aerosol ground stations, facilitating extensive data comparisons. In this presentation, we present preliminary validation of publicly released SPEX airborne level-1 data and collocate these with SPEXone observations. Additionally, we present validation of SPEX airborne aerosol retrievals against AERONET stations and other instruments deployed during PACE-PAX. The RemoTAP aerosol retrievals from SPEX airborne data emphasize the key role of PACE-PAX in confirming aerosol properties derived from SPEXone.