Intensive field data collection efforts (i.e., field campaigns) can be complex undertakings. Field campaign teams must balance multifaceted and competing objectives, known and unknown constraints, and must be adaptable to changing conditions. We developed several tools to manage these challenges for a recent field campaign, the Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Postlaunch Airborne Experiment (PACE-PAX). PACE is a recently launched multidisciplinary satellite mission, and PACE-PAX was part of the data validation efforts for PACE. Two aircraft, two research ships, and various other surface-based measurements were coordinated to provide a validation dataset useful for many of PACE's data products. Two specific tools were utilized for this purpose. First, we developed a validation traceability matrix (VTM) which connects validation objectives to measurement design and implementation. Crucially, measurement objectives in the VTM are numerically weighted to express differences in importance. They are further assessed in terms of the quantity of measurements needed to satisfy that objective and the likelihood of successful observation. The VTM is coupled with a decision algorithm, which provides a scoring mechanism. This score can be used prior to the campaign to compare mission design options with trade studies. During the campaign, it is used to assess the value of completed observations and guide planning for future observations. In this paper, we demonstrate how our traceability matrix decision support tools were used successfully with the PACE-PAX field campaign and provide guidelines and implementation tools for their use in future field campaigns.

Global aerosol and ocean color properties are simultaneously retrieved from multi-angle polarimetric measurements acquired by the SPEXone instrument aboard NASA’s PACE mission. These coupled retrievals are enabled by the FastMAPOL algorithm, which combines optimal estimation with deep neural network forward models to achieve efficient and accurate operational processing. Ocean remote-sensing reflectance (Rrs) is derived through multi-angle atmospheric correction, introducing an explicit angular dimension for characterizing aquatic optical properties. Since early 2024, both Level-2 and Level-3 global products have been generated in near real time and are publicly distributed through the NASA Earthdata archive.Validation against the Aerosol Robotic Network (AERONET) and associated AERONET-Ocean Color (OC) observations demonstrates robust performance for both atmospheric and oceanic retrievals. Retrieved aerosol optical depth (AOD) exhibits overall uncertainties (RMSD) of approximately 0.06, with mean biases smaller than 0.01, while 63% of retrievals satisfy the uncertainty threshold of max(0.04,10%), across a wide range of aerosol loadings and water conditions. Retrieved Rrs exhibits uncertainties of approximately 0.001–0.002sr−1 and biases on the order of 0.001sr−1 across phytoplankton-dominated waters. Rrs retrieval uncertainties increase with aerosol loading, whereas AOD retrieval uncertainties increase with water complexities, highlighting the coupled nature of aerosol and ocean color retrievals. Moreover, global gridded analyses reveal realistic spatial patterns of aerosol type, absorption, and ocean color variability. The angular variability of multi-angle Rrs is further examined across latitudes and seasons, including a closure assessment based on a self-consistent bidirectional reflectance correction. This study provides a comprehensive validation of the FastMAPOL SPEXone V3 data products and establishes a baseline for the expected improvements in aerosol and Rrs retrievals with the upcoming V4 release, which incorporates updated radiometric calibration.PACE multi-angle polarimetry thus establishes a new capability for fully integrated global aerosol and ocean color retrievals, enhancing our ability to characterize aerosol–ocean interactions and advance understanding of the coupled Earth system.

Earth observation satellites transform our understanding of Earth's biological, atmospheric, and surface systems. The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, launched in 2024, represents NASA's latest investment in multidisciplinary Earth system science, building upon multi-decadal heritage while introducing revolutionary capabilities. Since launch, PACE has extended and improved upon NASA's 30+ years of global satellite observations of our living ocean, atmosphere, and land, and initiated an advanced set of climate- and applications-relevant data records. Mission goals include extending and improving upon systematic ocean color, aerosol, cloud, and terrestrial data records for Earth system studies, and addressing emerging science questions related to socioeconomic applications. PACE helps assess ocean health by determining the distribution of aquatic phytoplankton. In doing so, it 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. Many stakeholders rely on these key data to forecast weather, visibility, and air quality. Also, PACE provides novel and near-daily views of land surfaces, collectively extending heritage vegetation time series while introducing novel terrestrial measurements previously only achievable at local scales. PACE ultimately provides observations that benefit Earth system research and society as a whole in ways that other current satellites cannot. As it passes its two-year anniversary, the mission exemplifies how combining hyperspectral radiometry with multi-angle polarimetry enables transformative Earth system science, addressing critical uncertainties while establishing new paradigms for integrated observations of our changing planet. This review examines how PACE fills critical gaps across aquatic, terrestrial, and atmospheric science disciplines.

NASA Langley Research Center airborne second generation High Spectral Resolution Lidar (HSRL-2) participated in a multiyear (2020-2022) NASA field campaign over the North Atlantic Ocean that studied aerosol-cloud interactions using two coordinated aircraft. One aircraft deployed in situ instruments and flew above, within, and below shallow marine clouds while another aircraft followed the same ground tracks and deployed two remote sensing instruments that sampled near the cloud top. The remote sensing instruments were the HSRL-2 and the Goddard Institute for Space Studies Research Center's Scanning Polarimeter (RSP). The lidar provided profiles of cloud top extinction and average lidar ratios to within 2.5 optical depths into the cloud, and the polarimeter provided size distribution parameters derived from the cloud bow region of the scattering phase function. The measurements are then combined to derive the cloud top droplet number density, Nd. Here we present data products from both the lidar and polarimeter and compare Nd derived from the remote sensing and in situ measurements.

The contribution to effective radiative forcing (ERF) of climate due to interactions between clouds and atmospheric aerosols remains highly uncertain after decades of research. One key piece of information needed to reduce this uncertainty and better understand such aerosol-cloud interactions (ACI) is knowledge about the vertical distribution of cloud condensation nuclei (CCN), or the subset of aerosols that activate into cloud droplets and directly impact cloud microphysical properties. Recently, many studies have taken advantage of lidar observations to glean information about the vertical distribution of aerosols and CCN. Specifically, Redemann & Gao (2024) developed a machine learning (ML) technique that uses lidar observables to predict CCN concentration (NCCN) with mean relative errors of about 15% for the most complete sets of lidar observables.In this study, we take advantage of the high vertical resolution of this ML-derived NCCN dataset to investigate ACI over the Southeast Atlantic (SEA), where a seasonal biomass burning aerosol plume resides atop a semi-permanent deck of marine stratocumulus clouds. We assess the simultaneous impact of above- and below-cloud NCCN on cloud top microphysical properties via clear-sky, cloud-adjacent lidar profiles and collocated polarimetric retrievals of cloud properties. Through this method we observe a decrease in cloud droplet effective radius (Reff) and an increase in cloud droplet number concentration (Nd) associated with an increase in above-cloud NCCN concentration within 100 m of the cloud top, which aligns well with previous in situ-based results. We find that the relationship between below-cloud NCCN and cloud top microphysical properties is weaker than those with above-cloud NCCN. Additionally, we find that the magnitude of these ACI are strongly dependent on lower tropospheric stability (LTS), with ACIREFF = -∂ln(Reff)/∂ln(NCCN) and ACICDNC = dln(Nd)/dln(NCCN) both decreasing by approximately 74% as LTS increases from 10 to 22 K. These findings demonstrate the importance of vertically resolved NCCN in ACI studies and establish a remote sensing-based analysis method which future satellite-based studies can employ to investigate ACI.

Abstract. The PACE Postlaunch Airborne eXperiment (PACE-PAX) was a field campaign conducted in September 2024 in California to collect validation data for the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and JAXA/ESA Earth, Cloud, Aerosol and Radiation Explorer (EarthCARE) missions. PACE-PAX utilized coordinated observations from two research aircraft, several research vessels, and additional ocean and ground-based assets to collect comprehensive measurements of atmospheric aerosols, clouds, ocean color properties, and land surface characteristics. The NASA ER-2 high-altitude aircraft flew 13 research flights with 80.9 flight hours total, carrying remote sensing instruments serving as a proxy for those on PACE, plus a High Spectral Resolution Lidar. The Navy Postgraduate School CIRPAS Twin Otter flew 17 research flights totaling 60 flight hours at low altitude, equipped with in-situ aerosol and cloud microphysics instrumentation. The NOAA R/V Shearwater conducted 15 day trips collecting ocean color radiometry, bio-optical properties, and water samples in the Santa Barbara Channel. The R/V Blissfully, a 30-foot sailboat, conducted 9 sampling days in the San Pedro Channel collecting ocean color radiometry profiles with a HyperPro instrument and discrete water samples for phytoplankton pigment and absorption measurements. A Slocum glider occupied a 10 km transect in the Santa Barbara Channel for 24 days with in-situ CTD, ocean color radiometry, optical backscatter, and broadband acoustic measurements. Data were also collected as part of the PACE Validation Science Team (PVST) or other externally funded efforts. Operations were coordinated using a Validation Traceability Matrix (VTM) decision support algorithm to maximize validation opportunities while targeting diverse geophysical conditions. This included varying atmospheric, land and ocean properties throughout California and nearby regions, as well as observations of a major biomass burning event in the Los Angeles region and harmful algal blooms (HABs) in the Monterey Bay. PACE-PAX airborne data are archived at the NASA Atmospheric Science Data Center with standardized formatting at https://doi.org/10.5067/SUBORBITAL/PACE-PAX/DATA001 (Knobelspiesse et al., 2025a). Ocean data are archived at the SeaWiFS Bio-optical Archive and Storage System (SeaBASS), at https://doi.org/10.5067/SeaBASS/PACE-PAX/DATA001 (Knobelspiesse et al., 2025b). This comprehensive dataset provides critical early-mission validation data for novel PACE and EarthCARE remote sensing capabilities and is also a rich dataset for other purposes.

Abstract. The NASA airborne Arctic Radiation-Cloud-aerosol-Surface-Interaction Experiment (ARCSIX) collected a unique data set providing a near-simultaneous characterization of radiative fluxes, surface, cloud, and aerosol particle properties to address science questions on the surface radiation budget, the processes governing the cloud lifecycle, atmospheric composition, and the interactions between the surface and atmosphere. The overarching goal of ARCSIX was to quantify the contributions of surface, clouds, aerosol particles, and precipitation to summer sea ice melt. ARCSIX consisted of two deployments in 2024 (Spring: 2024-05-28 through 2024-06-13 and Summer: 2024-07-25 through 2024-08-15) to capture pre- and post-melt conditions. ARCSIX provided coordinated remote sensing and in situ sampling using three aircraft in a high-flyer/low-flyer configuration. The NASA G-III served as the high-flying remote sensing platform with two lower flying in situ and near-target remote sensor observing platforms, NASA P-3B and SPEC Inc. Learjet. ARCSIX data are well-suited to improve satellite remote sensing capabilities in the Arctic. ARCSIX included an array of sea ice mass balance buoys deployed in the Lincoln Sea that were regularly overflown during the campaign. ARCSIX research flights spanned the Baffin Bay, Lincoln Sea, west and north of the Canadian Archipelago, and the Greenland north and northeast coasts. During the spring deployment, 19 research flights took place covering 114 flight hours: 10 flights and 68 hours by the P-3B and nine flights and 46 hours by the G-III. During summer, 24 research flights covered 136 flight hours: nine flights and 75 hours by the P-3B, five flights and 26 hours by the G-III, and 10 flights and 35 hours by the Learjet. A total of 13 coordinated flights with 2+ aircraft were carried out. This paper describes the ARCSIX flight strategy, instrumentation, and data set access, and usage details. ARCSIX data are publicly available at https://doi.org/10.5067/SUBORBITAL/ARCSIX/DATA001.

Abstract The primary uncertainty in anthropogenic climate forcing arises from a limited understanding of aerosol effects on cloud albedo, which in combination with other effects, is termed the radiative forcing from aerosol‐cloud interactions (RFaci). Although climate models provide estimates of RFaci, observational constraints remain critical for reducing its uncertainty. Observationally based estimates of RFaci traditionally have been inferred from large‐scale satellite relationships between aerosol and cloud properties, but these approaches rely on substantial assumptions. Here, we develop a novel framework that investigates cloud responses to aerosol variability using Machine Learning (ML) derived cloud condensation nuclei (CCN) profiles from lidar, combined with polarimetric cloud retrievals. Our results demonstrate that the ML‐CCN product consistently improves estimates of CCN‐cloud relationships. By providing vertically resolved CCN information and avoiding complications from aerosol humidification and vertical heterogeneity, this approach yields tighter and more physically plausible constraints on aerosol‐cloud interactions than conventional methods based on aerosol optical properties.

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 rich history of observing system simulation experiments (OSSEs) does not yet include a well-established framework for using climate models. The need for a climate OSSE is triggered by the need to quantify the value of a particular measurement for reducing the uncertainty in climate predictions, which differ from numerical weather predictions in that they depend on future atmospheric composition rather than the current state of the weather. However, both weather and climate modeling communities share a need for motivating major observing system investments. Here we outline a new framework for climate OSSEs that leverages the use of machine-learning to calibrate climate model physics against existing satellite data. We demonstrate its application using NASA's GISS-E3 model to objectively quantify the value of potential future improvements in spaceborne measurements of Earth's planetary boundary layer. A mature climate OSSE framework should be able to quantitatively compare the ability of proposed observing system architectures to answer a climate-related question, thus offering added value throughout the mission design process, which is subject to increasingly rapid advances in instrument and satellite technology. Technical considerations include selection of observational benchmarks and climate projection metrics, approaches to pinpoint the sources of model physics uncertainty that dominate uncertainty in projections, and the use of instrument simulators. Community and policy-making considerations include the potential to interface with an established culture of model intercomparison projects and a growing need to economically assess the value-driven efficiency of social spending on Earth observations.

NASA’s Plankton, Aerosol, Clouds and ocean Ecosystems (PACE) Mission, scheduled to be launched in early 2024, will produce a variety of ocean color, aerosol, cloud and land surface data products from its three sensors. Some of these products will be created with established ‘heritage’ algorithms, and others are new, representing recent algorithm development and the unique measurement capability of the PACE sensors. A crucial part of the validation activities is the PACE Postlaunch Airborne eXperiment (PACE-PAX), that is planned to occur in September of 2024. This dedicated field campaign, due to its platform and instrumental setup, offers an opportunity to support not only PACE, but the EarthCARE mission as well, opening opportunities for validation, new collaborations, and development of new algorithms for both Earth science missions.

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.

Postfrontal clouds, often appearing as marine cold-air outbreaks (MCAOs) along eastern seaboards, undergo overcast-to-broken cloud regime transitions. Earth system models exhibit diverse radiative biases connected to postfrontal clouds, rendering these marine boundary layer (MBL) clouds a major source of uncertainty in projected global-mean temperature. The recent NASA multi-year campaign Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) therefore dedicated most of its resources to sampling postfrontal MCAOs, deploying 71 flights from 2020 through 2022. We provide an overview of (1) the synoptic context within the parent extratropical cyclone, (2) the meteorological conditions with respect to the season, (3) the suitability of case data and measurements for Lagrangian analysis and modeling studies, and (4) the encountered cloud properties. A proposed subset of flights deemed most suitable for Lagrangian modeling case studies is highlighted throughout. Such flights typically cover a greater fetch range, were better aligned with the MBL wind direction, and revisited sampled air masses when key instruments were operational. Like many other flights, these flights often probed cloud formation and some cloud regime transitions. Surveying cloud properties from remote sensing and in situ probes, we find a great range in cloud-top heights and a relatively large concentration of frozen hydrometeors, which suggest strong free tropospheric entrainment and secondary ice formation, respectively. Both processes are expected to leave marked signatures in cloud evolution, such as strongly ranging cloud droplet number concentrations. ACTIVATE data combined with satellite retrievals can establish observational constraints for future model improvement work.

We previously developed the Cloud Height Retrieval from O2 Molecular Absorption (CHROMA) algorithm for the Ocean Color Instrument (OCI) on the new NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Here, we apply CHROMA to observations from the Ocean Land Colour Instrument (OLCI) to guide expectations for PACE, as it will take some time to obtain large-scale validation data for OCI. We use cloud top height (CTH), phase, and (for liquid clouds) cloud optical thickness (COT) data from the ground-based Atmospheric Radiation Measurement (ARM) network to evaluate the OLCI retrievals. We found that OLCI and Moderate Resolution Imaging Spectroradiometer (MODIS) CTH compare similarly well to the ARM reference. OLCI has a tendency to underestimate CTH as CTH increases, and algorithm assumptions about cloud geometric thickness may contribute to this. ARM COT from multifilter shadowband radiometers (MFRSR) and Sun photometers are well-correlated with one another, albeit with a roughly 30 % offset on average; OLCI and MODIS COT agree more closely with the MFRSR data. OLCI retrieval uncertainty estimates show skill at telling low-uncertainty cases from high-uncertainty ones, although CTH uncertainties are underestimated. Additionally, we compare the OLCI data to satellite retrievals based on thermal infrared measurements from MODIS and Sea and Land Surface Temperature Radiometer (SLSTR) data. Differences are broadly consistent with physical expectations based on the A-band vs. thermal techniques, although one key challenge in such aggregated comparisons is different cloud masking sensitivities and algorithm failure rates meaning additional sampling differences are introduced. We conclude by discussing the transition to and possible enhancements for PACE OCI.

Satellite remote sensing retrievals of cloud effective radius (CER) are widely used for studies of aerosol–cloud interactions. Such retrievals, however, rely on forward radiative transfer (RT) calculations using simplified assumptions that can lead to retrieval errors when the real atmosphere deviates from the forward model. Here, coincident airborne remote sensing and in situ observations obtained during NASA's ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign are used to evaluate retrievals of CER for marine boundary layer stratocumulus clouds and to explore impacts of forward RT model assumptions and other confounding factors. Specifically, spectral CER retrievals from the Enhanced MODIS Airborne Simulator (eMAS) and the Research Scanning Polarimeter (RSP) are compared with polarimetric retrievals from RSP and with CER derived from droplet size distributions (DSDs) observed by the Phase Doppler Interferometer (PDI) and a combination of the Cloud and Aerosol Spectrometer (CAS) and the Two-Dimensional Stereo Probe (2D-S). The sensitivities of the eMAS and RSP spectral retrievals to assumptions about the DSD effective variance (CEV) and liquid water complex index of refraction are explored. CER and CEV inferred from eMAS spectral reflectance observations of the backscatter glory provide additional context for the spectral CER retrievals. The spectral and polarimetric CER retrieval agreement is case dependent, and updating the retrieval RT assumptions, including using RSP polarimetric CEV retrievals as a constraint, yields mixed results that are tied to differing sensitivities to vertical heterogeneity. Moreover, the in situ cloud probes, often used as the benchmark for remote sensing CER retrieval assessments, themselves do not agree, with PDI DSDs yielding CER values 1.3–1.6 µm larger than CAS and with CEV roughly 50 %–60 % smaller than CAS. Implications for the interpretation of spectral and polarimetric CER retrievals and their agreement are discussed.

Abstract We present the first Spectropolarimeter for Planetary EXploration ‐ one (SPEXone) aerosol retrieval results over land and ocean using the Remote sensing of Trace gas and Aerosol Products algorithm, covering the period 23 February–31 August 2024. We validate the retrieved Aerosol Optical Depth (AOD), Angstrom Exponent (AE), and Single Scattering Albedo (SSA) with AErosol RObotic NETwork (AERONET) data. The validation results show that SPEXone provides products of good quality, with comparable performance over land and ocean. For AOD, the Root‐Mean‐Square Error (RMSE) is 0.053 over land and 0.043 over ocean, while respectively 77% and 75% of the retrievals are within the requirement formulated by the Global Climate Observing System (GCOS). For AE, the RMSE is 0.25 over land and 0.26 over ocean. For SSA, the RMSE is 0.036 over land and 0.036 over ocean, with respectively 69% and 69.4% within the GCOS requirement. After a gap of more than 10 years, this is the first new global Multi‐Angle Polarimeter aerosol dataset.

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).