The Multi-Angle Imager for Aerosols (MAIA) investigation was selected by NASA's Earth Venture program in 2016 to examine the impacts of different types of aerosols on human health. In preparation for launch of the MAIA satellite instrument into Earth orbit, the Level 2 aerosol retrieval algorithm has been prototyped to derive aerosol optical depth (AOD) and other optical and microphysical properties from the instrument’s multi-angle, multispectral, and polarimetric measurements. In this study, the prototype MAIA aerosol retrieval algorithm was tested using proxy data generated from combined measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR) instruments aboard NASA’s Terra satellite. The proxy data are constructed using MISR observations at nine view angles together with MODIS single-angle measurements over a broad spectral range (from 0.4 to 2.1μm). A subset of five viewing-angle measurements, similar to MAIA’s projected viewing geometry, was selected for aerosol retrieval evaluation. To improve retrieval accuracy, the MAIA’s optimization-based algorithm was modified to incorporate two sources of a priori information: a) AOD obtained using a MISR-like lookup table approach; and b) a bidirectional surface reflectance factors from the MODIS product MCD19A3D generated with the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. With the application of these constraints, 68.0% AOD₅₅₈ retrievals have errors less than the maximum of (0.05, 20%) when assessed against ground-based observations. Under low-AOD conditions, this fraction increases to 73.1%.

The impacts of sulfate aerosol from volcanic eruptions on the climate have been well recognized and simulated in climate models like the Community Earth System Model (CESM) 2.1.0. However, these models often neglect insoluble volcanic ash despite its substantial emission during eruptions and potential impact on the Earth system through mechanisms such as regional cooling, air quality degradation, and phytoplankton fertilization from deposition. Here, we incorporate volcanic ash into the CESM 2.1.0 for the first time and evaluate the model’s performance against both in situ and remote sensing observations of the 2010 Eyjafjallajökull eruption in Iceland. We further assess the impacts of volcanic ash from this eruption by conducting a suite of perturbation experiments to vary key parameters, including ash plume height and size distribution. The preliminary results suggest that the volcanic ash had an average global direct radiative forcing of 0.21 W/m 2 and a regional average of 23.6 W/m 2 during the approximately month-long eruptive phase. The volcano also added 0.08 Tg of soluble iron and increased local iron deposition approximately 28 to 38 times. These findings highlight the necessity of incorporating ash into the model to better understand how volcanic ash interacts with the Earth system.

Observations from the thermal infrared HyTES instrument deployed on NASA's high-altitude ER-2 aircraft during the 2019 HySPIRI field campaign are used to characterize and quantify ammonia (NH3) enhancements in California's Imperial Valley. The objective is to demonstrate the value of high-altitude airborne HyTES measurements for resolving sub-kilometer spatial variability in NH3 associated with heterogeneous surface sources-variability that cannot be captured by current satellite products with spatial resolutions of 15 km or larger. Major NH3 sources in California include livestock operations, composting and fertilized soils, and industrial activities. Ammonia also plays a central role in secondary aerosol formation through reactions with sulfuric and nitric acids, producing ammonium sulfate and ammonium nitrate that contribute substantially to PM2.5. This is of particular relevance to the California Air Resources Board (CARB), as mitigation strategies targeting dairy methane (CH4) emissions may inadvertently increase NH3 emissions and exacerbate PM2.5air quality. This work presents an analysis of sensitivity and retrievability of NH3 from ER-2 HyTES observations (20 km swath with 34.73 m spatial resolution) collected over densely distributed multi-sources in the California Imperial Valley on September 12, 2019. The information content of HyTES observations from the ER-2 altitudes is discussed in context of earlier studies of NH3 HyTES retrievals from a low-flying Twin Otter aircraft. The collocated satellite NH3 product from Cross-track Infrared Sounder (CrIS) is shown to demonstrate that the spatially up-scaled HyTES ER-2 retrievals are mostly within the range of estimated retrieval uncertainties. By adjusting spatial resolutions of the retrieved NH3 maps, we assess the added value of high-resolution NH3 observations. Our analysis demonstrates that a spatial resolution of at least 1.6 km is necessary to preserved information on NH3 point sources in the California Imperial Valley.

Accurate modeling of anisotropic bidirectional surface reflectance is essential for multi-angle aerosol retrievals over land, where surface contributions often dominate the top-of-atmosphere signal. Two widely used semiempirical bidirectional reflectance factor (BRF) formulations are the Ross-Li model, adopted by the MODIS/ MAIAC land surface product, and the Rahman-Pinty-Verstraete (RPV) model, used operationally in MISR and the forthcoming MAIA aerosol retrieval algorithms. Differences in surface parameterization across retrieval systems complicate the transfer and reuse of surface reflectance constraints. To address this issue, we develop a computationally efficient algorithm that converts Ross-Li BRF parameters into their RPV counterparts. The method exploits the quasi-linearity of the RPV model in logarithmic space to enable initialization-free, linear least-squares estimation of spectral weight and angular-shape parameters, optionally imposing spectral invariance constraints. Additional steps convert the exponential scattering term of the modified RPV into the HenyeyGreenstein phase function of RPV and ensure physical consistency by conserving directional hemispherical reflectance through analytic hotspot adjustment. Using MAIAC BRF parameters as input, we evaluate the conversion for urban, desert, and vegetated surfaces. The resulting RPV BRFs generally reproduce the spectral magnitude and angular structure of the Ross-Li simulations, with relatively larger differences confined to the hotspot region and extreme viewing geometries. Based on this conversion, we assess the effect of the two surface models by performing retrievals using combined MISR and MODIS observations. Comparison against the AEROENT reference data shows that the two retrievals exhibit comparable overall performance. The RPV-based retrieval yields slightly improved agreement in aerosol optical depth, whereas the Ross-Li-based retrieval provides more consistent estimates of aerosol single-scattering albedo and particle size.

Mineral dust aerosols affect Earth's energy balance in multiple ways, including interactions with solar radiation, but this effect remains poorly quantified. A central limitation has been the lack of reliable global information on dust mineral composition, particularly light-absorbing iron oxides. An imaging spectrometer, placed aboard the International Space Station by the Earth Surface Mineral Dust Source Investigation mission, provides high-resolution, near-global retrievals of surface mineralogy. Here we incorporate these retrievals into four Earth system models to constrain the dust shortwave direct radiative effect. Analysis shows that the retrievals reduce the radiative effect uncertainty by more than a factor of six in both present-day (2007-2011) and late twenty-first-century (2090-2094) climates. This improvement is enabled by tighter constraints on iron oxide content, which reduce its uncertainty contribution from 0.62 W m-2 to 0.10 W m-2. Greatest improvement occurs over the Sahara, where high dust abundance and surface reflectance amplify the influence of iron oxides. Orbital spectroscopy thus shifts the primary uncertainty from mineral composition to processes controlling the spatial distribution of dust. These findings mark a transition towards confident aerosol composition modelling and provide an improved basis for assessing how dust alters Earth's energy balance today and in a warming future.

Accurate monitoring of Chlorophyll-a (Chla) is critical for assessing aquatic ecosystem health, yet ecological complexity often leads to ambiguous spectral signatures in satellite data. Traditional deterministic models assume a one-to-one mapping between spectra and pigments, often failing to capture these high-dimensional analytical challenges. In this study, we propose a novel deep learning architecture, the Channel Attention-Mixture Density Network (CA-MDN), to retrieve Chla from the National Aeronautics and Space Administration (NASA) Earth Surface Mineral Dust Source Investigation (EMIT) hyperspectral mission. The CA-MDN integrates an attention mechanism to dynamically select ecologically relevant spectral bands and employs a probabilistic output layer to quantify retrieval uncertainty. Trained and tested against the global GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) in situ dataset, the model achieved a prediction error of 40.52%, significantly outperforming conventional machine learning baselines. Case studies in California waters demonstrate the model's ecological utility, showing how probabilistic modeling can resolve fine-scale variability and flag high-uncertainty regions. This study presents a robust computational framework for leveraging spaceborne imaging spectroscopy in complex coastal and inland environments.

A Markov chain approach is developed to model polarized mid-to long-wave infrared radiative transfer in an optically anisotropic medium. Such a medium contains oriented non-spherical particles with azimuthal randomness. Our model considers variations of temperature, pressure, gas concentration, and particle size distribution in the vertical dimension of the medium and resolves the total and polarized radiation in twodimensional angular space. It also accounts for emission, scattering, and absorption of the medium, as well as directional and polarized surface emission and reflection. Illustrative simulations are performed using MODIS infrared bands centered at 9.73, 11.03, and 12.02 mu m and several standard model atmospheres containing oriented dust or ice spheroids. The results are compared to those obtained for spherical droplets. Our preliminary numerical results demonstrate that while infrared brightness temperature contains information about particle amount, size, and layer height, adding multi-angle and spectro-polarimetry provides further remote sensing sensitivity to type, orientation, and morphology of the non-spherical particles. When reliable a priori information on the atmospheric physical temperature and absorbing gas profiles is available, infrared multi-angle polarimetry is a promising tool to fill the gap of shortwave and microwave remote sensing by resolving micro-meter scale particle properties to which shortwave and microwave frequencies have less sensitivity. Our simulation also reveals that solar radiation exerts a pronounced influence on the top-of-atmosphere brightness temperature when the wavelength approaches the short-wavelength end of the mid-wave infrared (approximate to 3-4 mu m).

Satellite-based measurements have been widely used for estimating fire-emitted pollutants based on the parameters of either burned area or fire radiative power (FRP). Fire-related remote sensing additionally requires information on active fire areas and fire temperature at a subpixel scale, as well as the combustion phases (i.e., smoldering and flaming) to infer the plume injection height and to understand the mechanics of resulting atmospheric processes like pyro-convection. The FRP is as a key indicator of fire intensity that is frequently retrieved using infrared signals. The fire properties at a subpixel level, including the effective fire temperature and fire area, can be retrieved by the bi-spectral method. However, these approaches normally neglect the heat transport phenomena and subsequently fail to characterize the fire area that could be composed of different combustion phases (e.g. smoldering, flaming, or a combination of the two). Neglecting the phenomena of heat transfer leads to mis-estimation of the actual fire area and its associated emission profile for combustion products that are a function of the combustion phase. To address this challenge, this work presents a new approach to resolve the effective temperature variation inside each fire pixel using a semi-empirical heat-transfer algorithm. This algorithm utilizes radiance observations from geostationary satellites as inputs. With the aid of fine-spatial-resolution spaceborne and airborne observations, we evaluated and validated the fire retrieval performance on western US wildfires corresponding to the 2019 season. Our results show that FRP obtained through this heat-transfer method exhibits a stronger linear correlation with those retrieved from airborne measurements. Moreover, by analyzing the temperature variation curve obtained using this method, it is possible to further retrieve the fire area under different combustion conditions within the fire pixels.

Decentralized solar is an emerging strategy for advancing modern energy access among rural populations globally. However, both natural and anthropogenic aerosols can significantly worsen solar panel performance. Although the effect of aerosols is typically assessed using satellite or reanalysis data during system sizing, these datasets often underestimate extreme aerosol conditions in West Africa. This study evaluates the impact of aerosols on photovoltaic (PV) output by developing irradiance and generation models that accept as input three reanalysis and satellite-derived datasets with varying spatial and temporal resolution. The accuracy of each dataset is evaluated through comparisons to ground-based AERONET measurements. We find none of the aerosol datasets capture the highest aerosol loadings well, underestimating the 99th percentile aerosol optical depth (AOD) values between 18-49 %, which can lead to undersizing PV systems by up to 11 % for high-reliability designs. To capture total regional dust impacts, we combine dust aerosol with dust soiling loss modeling. Modeled irradiance shows that daily energy losses during the annual Harmattan dry season can reach 50 %, and seasonal energy losses caused by dust can be between 19-40 %. In locations within the Sahel, soiling dominates dust-associated losses (62-66 % of total losses), while for coastal locations near the Gulf of Guinea, dust aerosols drive losses (56 %). These findings highlight the need for location-specific mitigation strategies to effectively address PV dust losses. The modeling framework developed in this study can be used to improve the siting, sizing and maintenance strategies for PV systems in dry regions worldwide.

Mineral dust impacts climate through complex interactions with radiation, which remain poorly quantified due to uncertainties in the amount of light-absorbing iron oxides within dust particles. NASA’s EMIT imaging spectrometer, now delivering high-resolution soil mineralogy from the International Space Station, provides the first observational basis to address this gap at a global scale. Using the EMIT data within Earth system model ensembles, we show that surface composition retrievals, especially of iron oxides, reduce uncertainty in the dust shortwave direct radiative effect by over 50% for both present-day and late-21st-century climates. The greatest improvements occur over the Sahara, where the regional dust concentration is high and dust radiative impacts are simulated with improved fidelity. While uncertainties remain, EMIT shifts the primary uncertainty source from mineralogical composition to our imprecise knowledge of the processes controlling the mass concentration of dust particles, especially those related to emission. These findings represent a pivotal step toward mineral-resolved dust aerosol modeling, offering improved insight into how dust alters Earth’s energy balance today and in a warming future.

Fuels are a large source of uncertainty in fire emissions estimates due to variability in the physical and chemical properties of fuels and how they are represented. These uncertainties can be addressed using imaging spectroscopy and lidar data, that provide observations of the chemical and physical traits and spatial distribution of vegetation. Combined with ground fuel measurements, these data provide information on fuel distribution and quantity important for mapping and modeling fire effects. In this study, we present a methodology to develop models and continuous maps of pre-fire fuel characteristics for use in fire emissions modeling. We first addressed any spatial gaps over fire areas for Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) chemical trait data using Random Forests regression and for derived fractional cover. We used the AVIRIS fractional cover and chemical traits or AVIRIS estimates alongside lidar, multispectral, and topographic variables to build fuel characteristic models informed by ground measurements with partial least squares regression. We derived maps of predictive uncertainty alongside a suite of uncertainty statistics for each fuel characteristic that inform the use of fuels data within fire effects models. We used two study sites: the Williams Flats wildfire in eastern Washington state, USA and three prescribed crown fires in Utah, USA. The results show similar error between calibration and validation sets and NRMSE of around 20 % or lower for a majority of the fuel models. We present fuel characteristic and uncertainty maps for all fires. This study shows that the use of imaging spectroscopy and lidar data have the potential to represent fuel heterogeneity and continuously map fuel characteristics for fire effects modeling.

Aerosol pollution events pose serious threats to humans and ecosystems. Natural aerosols are much less studied than fossil fuel-sourced aerosols for their air quality impacts, even though ‘natural’ aerosols such as dust and wildfires are likely to have substantially changed because of anthropogenic activities. Using the Community Earth System Model version 2 and the Community Atmospheric Model version 6 atmospheric model, we simulated dust and open-fire PM _2.5 concentrations over Africa for preindustrial (PI), present-day, and future scenarios. Health impacts were assessed using the integrated exposure–response model applied to gridded population and disease-specific mortality data. Currently, we estimate that desert dust causes 30 000 (95% confidence interval (CI) 29 700–303 000) excess deaths annually in Africa (36% of the total excess premature mortality), while open fires result in 20 000 (95% CI: 19 500–22 500) excess deaths (24% of the mortality). All mortality estimates reported in this study represent annual excess deaths, calculated based on annual average PM _2.5 concentrations. Concentrations of dust dominate aerosol concentration at the continental scale, and paleo records suggest dust loading has increased by 55% since PI times. Because of the lower population in dust-dominated regions, these do not affect health as much as combustion but are still important. In PI times, we estimate deaths due to dust as 6400 (95% CI: 6050–6750) (or 7000 if we keep the population at current levels), showing a large growth to the present day (over 400% increase). Excess deaths due to open fires have increased from 6900 (95% CI: 5800–7400) in the PI to 20 000 today (approximately 190% increase). For future scenarios in 2100, there is significant uncertainty; therefore, we present a high and low scenario, indicating that in the future, between 53 000 (95% CI: 49 000–55 000) and 67 000 (95% CI: 63 000–70 000) deaths will result from dust. Similarly, for future open fire excess deaths, we estimate a range of 19 000–40 000 (95% CI: 33 000–44 000) deaths, including population changes (−1% to +100% changes relative to today). In the current climate, as well as in the past and future, the amount and proportion of deaths attributed to ‘natural’ aerosols exceeds 50% of the total PM _2.5 attributable mortality in Africa, showing that addressing possible changes in ‘natural’ aerosols is extremely important for improving air quality.

Airborne longwave-infrared (LWIR) hyperspectral imagery acquisitions were coordinated with stationary and mobile ground-based in situ measurements of atmospheric ammonia in regions surrounding California’s Salton Sea, an area of commingled intensive animal husbandry and agriculture operations that is encumbered by exceptionally high levels of persistent ammonia and PM2.5 pollution. The goal of this study was to validate remotely sensed ammonia retrievals against ground truth measurements as part of a broader effort to elucidate the behavior of the atmospheric ammonia burden in this area of abundant diffuse and point sources. The nominal 2 m pixel size of the airborne data revealed variability in ammonia concentrations at a diversity of scales within the study area. At this pixel resolution, ammonia plumes emitted by individual facilities could be clearly discriminated and their dispersion characteristics inferred. Several factors, including thermal contrast and atmospheric boundary layer depth, contributed to the overall uncertainty of the intercomparison between airborne ammonia quantitative retrievals and the corresponding in situ measurements, for which agreement was in the 16–37% range under the most favorable conditions. Hence, while the findings attest to the viability of airborne LWIR spectral imaging for quantifying atmospheric ammonia concentrations, the accuracy of ground-level estimations depends significantly on precise knowledge of these atmospheric factors.

Global warming in Central Asia has primarily led to glacier melting and the degradation of snow cover in mountainous regions. Research conducted by the Central Asian Institute of Applied Geosciences using Landsat data (2013–2016) revealed that glacier areas in the Tien Shan river basins have decreased by 10–47% compared to data from the USSR Glacier Catalog (1940–1970). Over approximately 70 years, Kyrgyzstan's glacier area has decreased by 16%, with large glaciers shrinking by 17%, while the area of small glaciers has increased by 2.5 times.Field studies conducted on nine representative glaciers in Kyrgyzstan between 2011 and 2023 indicate a negative mass balance for glaciers in the mountain regions, with the exception of certain years for the Golubin Glacier (Ala-Archa River basin) and the Abramov Glacier (southern border of the Fergana Valley).In addition to Landsat data, the dynamics of snow cover have been analyzed using MODIS data processed through the MODSNOW-Tool program. This tool provides valuable insights into snow cover dynamics and accumulation in the Tien Shan Mountains. Seasonal snow reserves and glacier runoff are the primary sources of water for mountain rivers in the Tien Shan. At altitudes above 2.5–3 km, melting in river basins lasts 5–6 months, contributing 80–90% of the annual runoff.Snow cover data has also been utilized to forecast river flow in Kyrgyzstan. This forecasting methodology was developed under the Central Asia Water (CAWa) project and transferred to Kyrgyzhydromet for implementation in 2015. Over the past decade, it has been actively employed to predict water inflows into reservoirs. An evaluation of the methodology for the Naryn, Karadarya, Chu, and Talas rivers (2021–2023) demonstrated its high efficiency in operational hydrological forecasting.

Remote sensing of wildland fires is required for many cross-disciplinary science investigations including wildland fire impacts on ecology. For decades this research has been hindered by insufficient spatial resolution and detector saturation at short and mid-infrared wavelengths where the spectral radiance from high temperature (> 800 K) surfaces is most significant. To address this, we're developing a compact high dynamic range (HDR) multispectral imager. The Compact Fire Infrared Radiance Spectral Tracker (cFIRST), which leverages digital focal plane array (DFPA). The DFPA is hybridized from a state-of-the-art high operating temperature barrier infrared detector (HOT-BIRD) and a digital readout integrated circuit (DROIC), which features an in-pixel digital counter to prevent current saturation, and thereby provides dynamic range (> 100 dB). The DFPA will thus enable unsaturated, high-resolution imaging and quantitative retrievals of targets with a large variation in temperatures, ranging from 300 K to > 1600 K (flaming fires). With the resolution to resolve 50 m-scale thermal features on the Earth's surface from a nominal orbital altitude of 500 km, the full temperature and area of wildland fires and the cool background are captured in a single observation, increasing science content per returned byte. The use of a non-saturating FPA is novel, overcomes previous problems where high radiance values saturate FPA pixels (which diminishes the science content), and demonstrates a breakthrough capability in remote sensing. Thus, c- FIRST is suitable for quantifying emissions from wildland fires, which is critical for establishing their impact on ecosystems at global scales. The FPA for the c-FIRST was fabricated using InAs/InAsSb HOT-BIRD epitaxial material into 20 m pixel pitch, 1280x480 format detector arrays and hybridized to analog DROIC. The 50% cutoff of the DFPA is at lambda similar to 4.5.m and the measured external QE similar to 50% across the full QE spectrum at 140K operating temperature. We fix the integration time at 6 ms in order to obtain good sensitivity in the MWIR bands when observing normal 300K background scene at 150 Hz frame rate. For a standard analog ROIC, the detector pixels are easily saturated at target temperatures similar to 700 K. With the D- ROIC operating in the 16-bit mode, we can increase the saturation temperatures significantly to similar to 1100 K. With the D-ROIC operating in the ultraHDR 32-bit mode (28 trillion e- well depth), the detectors do not come near to saturation even for 1600 K targets. A critical metric for remote sensing of fires is the minimum detectable target size. The c-FIRST would provide an order of magnitude improvement in the minimum size of a detectable fire, primarily due to the spatial resolution of the non-saturating detector than the current servicing instruments such as Advance Baseline Imager on GOES, etc. with reduced power, size, and weight. c-FIRST was flown on NASA B-200 aircraft six days after the Los Angeles fires on January 13, 2025. It has detected many lingering hot-spots which can flare up when favorable wind conditions returns back.

An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observations, and connecting them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions. Since 2021, NASA's Advanced Information Systems Technology (AIST) program has invested in two ESDT efforts to tackle the impacts of our changing climate. The establishment of ESDT for flood and air quality enabled our teams to formalize the software framework. The open-source framework is called the Integrated Digital Earth Analysis System (IDEAS). By working with the Apache Science Data Analytics Platform (SDAP) community, IDEAS is now a subproject of SDAP. The paper presents the ongoing development of IDEAS and its current applications.

This work helps address recent calls for systematic water quality assessment in Central Asia and considers how nutrient and salinity sources, and transport, affect water quality along the continuum from the cryosphere to the lowland plains. Spatial and, for the first time, temporal variations in stream water pH, temperature, electrical conductivity, and nitrate and phosphate concentrations are presented for four catchments (485 -13,500 km 2 ), all with glaciers and major urban areas. The catchments studied were: Kaskelen (Kazakhstan), Ala-Archa (Kyrgyzstan), Chirchik (Uzbekistan) and the Kofarnihon (Tajikistan). Measurements were made in cryosphere, stream water, groundwater, reservoir and lake samples over a 22 -month period at fortnightly intervals from 35 sites. The results highlight that glacier, permafrost and rock glacier outflows were primary and secondary nitrate sources ( >1 mg N L -1 ) to the headwaters, and there were major increases in salinity and nitrate concentrations where rivers receive inputs from agriculture and settlements. Overall, the water quality complied with national and World Health Organization standards, however there were pollution hot -spots with shallow urban groundwaters contaminated with nitrate ( >11 mg N L -1 ) and stream electrical conductivity above 800 mu S cm -1 in some agricultural areas indicative of high salinity. Phosphate concentrations were generally low ( <0.06 mg P L -1 ) throughout the catchments, though elevated ( >0.2 mg P L -1 ) in urban areas due to effluent contamination. A melt water dilution effect along the main river channels was discernible, in the electrical conductivity and nitrate concentration seasonal dynamics, 100 s of km from the headwaters. Thus, the input of relatively clean water from the cryosphere is an important regulator of main channel water quality in the urban and farmed lowland plains adjacent to the Tien Shan and Pamir. Improved sewage treatment is needed in urban areas.

Traditionally, aerosol retrieval algorithms are customized to specific instruments because of the diverse nature of remote sensing hardware architectures and data formats. This diversity can hinder the utilization of lower-level data products. Furthermore, a generalized aerosol retrieval approach has the potential to allow simultaneous use of observations from multiple platforms within a single retrieval framework. In this work, a comprehensive set of solutions for integrating open-source aerosol retrieval software and publicly available multiangle spectropolarimetric data products is presented as a complementary software program (CSP). This CSP is adaptable to observations obtained from various instruments, including the Airborne Multiangle Spectropolarimetric Imager (AirMSPI) and the upcoming Multiangle Imager for Aerosols (MAIA) (Diner et al., 2013); (Maia 2022). Established methods for reconciling coordinate systems, curating data, and structuring data for input to the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) open-source software are addressed by the CSP (Grasp-open, 0000). The CSP provides the functionality to compute multiple GRASP aerosol retrievals from the same polarimetric observations by varying the user-defined coordinate system selection. Each coordinate system is defined by a unique reference plane, which rotates the reported polarization orientation but does not alter the information content. Therefore retrieving aerosol values from different coordinate systems is suggested as a tool for: (1) verifying the coordinate systems of the instrument and algorithm have been properly reconciled and (2) quantifying numerical variations in the retrieval’s optimization algorithm. The CSP is demonstrated using AirMSPI data from the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign (Diner et al., 2013); (Warneke, 2019). Aerosol retrievals performed with the meridian versus the scattering reference plane differed up to 0.068 in aerosol optical depth and 0.015 in single scattering albedo. These variations in smoke properties from multiangle spectropolarimetric observations quantify uncertainty which is applicable for cross-validation instrument comparisons and studies of different retrieval algorithms.

Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions (ACIs), contributing to the largest uncertainties in the understandings of global climate change. We present a novel remote-sensing-based algorithm that quantifies the vertically resolved CCN number concentrations (NCCN) using aerosol optical properties measured by a multiwavelength lidar. The algorithm considers five distinct aerosol subtypes with bimodal size distributions. The inversion used the lookup tables developed in this study, based on the observations from the Aerosol Robotic Network, to efficiently retrieve optimal particle size distributions from lidar measurements. The method derives dry aerosol optical properties by implementing hygroscopic enhancement factors in lidar measurements. The retrieved optically equivalent particle size distributions and aerosol-type-dependent particle composition are utilized to calculate critical diameters using κ-Köhler theory and NCCN at six supersaturations ranging from 0.07 % to 1.0 %. Sensitivity analyses indicate that uncertainties in extinction coefficients and relative humidity greatly influence the retrieval error in NCCN. The potential of this algorithm is further evaluated by retrieving NCCN using airborne lidar from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign and is validated against simultaneous measurements from the CCN counter. The independent validation with robust correlation demonstrates promising results. Furthermore, the NCCN has been retrieved for the first time using a proposed algorithm from spaceborne lidar – Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) – measurements. The application of this new capability demonstrates the potential for constructing a 3D CCN climatology at a global scale, which helps to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing.