Piyushkumar N. Patel

The accurate retrieval of aerosol properties from space is a cornerstone of our ability to quantify climate forcing and monitor global air quality. Multi-angle polarimeters (MAPs), such as PARASOL-POLDER and the recently launched PACE-SPEXone and Metop-SG-3MI, offer unprecedented information content, disentangling complex aerosol microphysics from surface scattering. However, the efficacy of retrieval algorithms, such as RemoTAP, is often constrained by post-processing quality controls. Traditional methods rely on static goodness-of-fit thresholds or "one-size-fits-all" post-processing filters (e.g., Χ2 < 5), which enforce a rigid trade-off between data coverage and accuracy. Our recent analysis reveals that such static thresholds often fail to account for systematic biases over complex surfaces, leading to unnecessary data loss or effectively allowing high-error retrievals to silently contaminate climate records. In this work, we present a paradigm shift in quality assessment: a Predictive Dynamic Quality Filter powered by a Physics-Aware Deep Learning Framework. Unlike generic "black-box" approaches, our architecture is designed to explicitly decouple the competing influence of atmospheric state variables from complex surface reflectance signatures. By processing these distinct physical signals alongside a rich set of spectral multi-directional total and polarized reflection signatures of the surface, the model dynamically constructs a pixel-level error profile that adapts to the underlying scene, robustly handling diverse conditions ranging from bright surfaces to the intricate directional reflectance of heterogenous vegetation. This Surface-Aware framework effectively learns to identify the "trustworthiness" of a retrieval based on its physical context, rather than a fixed goodness-of-fit cost. Here we present results applying this framework to POLDER RemoTAP retrievals. To ensure robust generalization and address potential overfitting, we employed a rigorous validation strategy using a comprehensive dataset from 477 global AERONET sites spanning four years (2006-2009). The model was trained on a strategically stratified subset of these observations while its performance was evaluated against a strictly independent, hold-out validations group. Unlike static filtering, our dynamics framework adapts to local conditions, substantially increasing the volume of valid observations data while simultaneously driving a significant reduction in error. By optimizing the selection of high-quality retrievals without discarding valuable data, this method significantly refines the inputs available for climate models. The primary outcome of this framework is the ability to predict pixel-level compliance with Global Climate Observing System (GCOS) standards, offering a metric directly applicable to climate studies. This "Predictive Dynamic Quality Filter" transforms aerosol retrieval quality filtering from a passive estimation task into an active, self-assessing framework. By unlocking the full statistical potential of the RemoTAP algorithm, we provide a robust pathway for generating climate-quality datasets from historical POLDER archives, current instruments as SPEXone and 3MI and future missions like and CO2M, significantly refining our constraints on aerosol-cloud interactions and radiative forcing.

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.

Dust loading in West and South Asia has been a major environmental issue due to its negative effects on air quality, food security, energy supply and public health, as well as on regional and global weather and climate. Yet a robust understanding of its recent changes and future projection remains unclear. On the basis of several high-quality remote sensing products, we detect a consistently decreasing trend of dust loading in West and South Asia over the last two decades. In contrast to previous studies emphasizing the role of local land use changes, here, we attribute the regional dust decline to the continuous intensification of Arctic amplification driven by anthropogenic global warming. Arctic amplification results in anomalous mid-latitude atmospheric circulation, particularly a deepened trough stretching from West Siberia to Northeast India, which inhibits both dust emissions and their downstream transports. Large ensemble climate model simulations further support the dominant role of greenhouse gases induced Arctic amplification in modulating dust loading over West and South Asia. Future projections under different emission scenarios imply potential adverse effects of carbon neutrality in leading to higher regional dust loading and thus highlight the importance of stronger anti-desertification counter-actions such as reforestation and irrigation management.

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 (N_CCN) 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 N_CCN 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 N_CCN. The potential of this algorithm is further evaluated by retrieving N_CCN 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 N_CCN 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.

Extreme smog in India widely impacts air quality in late autumn and winter months. While the links between emissions, air quality and health impacts are well‐recognized, the association of smog and its intensification with climatic trends in the lower troposphere, where aerosol pollution and its radiative effects manifest, are not understood well. Here we use long‐term satellite data to show a significant increase in aerosol exceedances over northern India, resulting in sustained atmospheric warming and surface cooling trends over the last two decades. We find several lines of evidence suggesting these aerosol radiative effects have induced a multidecadal (1980–2019) strengthening of lower tropospheric stability and increase in relative humidity, leading to over fivefold increase in poor visibility days. Given this crucial aerosol‐radiation‐meteorological feedback driving the smog intensification, results from this study would help inform mitigation strategies supporting stronger region‐wide measures, which are critical for solving the smog challenge in India.

Over the past two decades, smoke aerosols from crop residue burning have increasingly degraded postmonsoon (October‐November) air quality in north India. We use satellite data and atmospheric modeling to investigate whether cascading delays in monsoon rice growth and postmonsoon fires over 2003–2019 have exacerbated the already poor urban air quality downwind of the fires. Beginning in 2008, a government effort to combat groundwater depletion in Punjab mandated rice sowing until closer to the arrival of monsoon rains. We find evidence of district‐level delays in the timing of both monsoon rice growth and postmonsoon fires, which vary from 1 to 4 weeks with largely an east‐west gradient. These delays are correlated spatially (r = 0.51–0.77), with northern and western districts in Punjab, which rely less on groundwater for irrigation, tending to have the greatest delays. Had the delays in fire activity not occurred, we estimate that cities downwind and near the fire source would have consistently seen less smoke‐related fine particulate matter (PM_2.5), on average ranging from 11% to 21% for New Delhi, Bathinda, and Jind during 2008–2019. This net benefit of earlier postmonsoon burning could have been even larger given that (a) a longer rice‐to‐wheat transition could incentivize farmers to find alternatives to burning crop residues; and (b) background PM_2.5 is less abundant earlier in the season, decreasing the likelihood of extreme pollution episodes. Strategies aiming to mitigate air pollution while conserving groundwater may be more effective by promoting an earlier monsoon growing season in districts with less groundwater depletion.

Cloud condensation nuclei (CCN) are mediators of aerosol-cloud interactions (ACI), 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 (N_CCN) using aerosol optical properties measured by a multiwavelength lidar. The algorithm considers five distinct aerosol subtypes with bimodal size distributions. The inversion used the look-up 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 to lidar measurements. The retrieved optically equivalent particle size distributions and aerosol type dependent particle composition are utilized to calculate critical diameter using the κ-Köhler theory and N_CCN 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 N_CCN. The potential of this algorithm is further evaluated by retrieving N_CCN using airborne lidar from the NASA ORACLES campaign and validated against simultaneous measurements from the CCN counter. The independent validation with robust correlation demonstrates promising results. Furthermore, the N_CCN 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 help to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing.

The wildfires over the central Indian Himalayan region have attracted the significant attention of environmental scientists. Despite their major and disastrous effects on the environment and air quality, studies on the forest fires’ impacts from a renewable energy point of view are lacking for this region. Therefore, for the first time, we examine the impact of massive forest fires on the reduction in solar energy production over the Indian subcontinent via remote sensing techniques. For this purpose, we used data from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO), the Satellite Application Facility on support to Nowcasting/Very Short-Range Forecasting Meteosat Second Generation (SAFNWC/MSG) in conjunction with radiative transfer model (RTM) simulation, in addition to 1-day aerosol forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). The energy production during the first quarter of 2021 was found to reach 650 kWh/m² and the revenue generated was about INR (Indian rupee) 79.5 million. During the study period, the total attenuation due to aerosols and clouds was estimated to be 116 and 63 kWh/m² for global and beam horizontal irradiance (GHI and BHI), respectively. The financial loss due to the presence of aerosols was found to be INR 8 million, with the corresponding loss due to clouds reaching INR 14 million for the total Indian solar plant’s capacity potential (40 GW). This analysis of daily energy and financial losses can help the grid operators in planning and scheduling power generation and supply during the period of fires. The findings of the present study will drastically increase the awareness among the decision makers in India about the indirect effects of forest fires on renewable energy production, and help promote the reduction in carbon emissions and greenhouse gases in the air, along with the increase in mitigation processes and policies.