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The Atmospheric Correction Inter-comparison eXercise (ACIX) is an international initiative with the aim to analyse the Surface Reflectance (SR) products of various state-of-the-art atmospheric correction (AC) processors. The Aerosol Optical Thickness (AOT) and Water Vapour (WV) are also examined in ACIX as additional outputs of AC processing. In this paper, the general ACIX framework is discussed; special mention is made of the motivation to initiate the experiment, the inter-comparison protocol, and the principal results. ACIX is free and open and every developer was welcome to participate. Eventually, 12 participants applied their approaches to various Landsat-8 and Sentinel-2 image datasets acquired over sites around the world. The current results diverge depending on the sensors, products, and sites, indicating their strengths and weaknesses. Indeed, this first implementation of processor inter-comparison was proven to be a good lesson for the developers to learn the advantages and limitations of their approaches. Various algorithm improvements are expected, if not already implemented, and the enhanced performances are yet to be assessed in future ACIX experiments.

Accurately understanding the evolution and development of cloud physical properties (CPP) in advance is crucial for extreme weather forecasting and early warning. This study utilized the Fourier neural operator (FNO) method to develop a short‐term forecasting model of Cloud (Cloud‐FNO). Using the multi‐task learning framework and autoregression strategy, the model achieves accurate 6‐hr forecasting of cloud phase (CLP), cloud top height (CTH), cloud effective radius (CER), and cloud optical thickness (COT). Evaluation results on the independent testing data set show that the Cloud‐FNO model achieves an average CLP identification accuracy exceeding 74%, and the average root mean square errors for CTH, CER, and COT forecasts are 2.28 km, 6.52 μm, and 9.01, respectively. Importantly, the Cloud‐FNO model demonstrates strong forecasting capability and promising application potential for the CPP evolution under severe weather.

To quantitatively estimate and analyze the contribution of different aerosol types to radiative forcing, we thoroughly investigated their optical and radiative properties using the Aerosol Robotic Network (AERONET) data (2007-2018) over an urban-industrial (Lahore) and coastal (Karachi) cities located in Pakistan. The contribution of inferred aerosol types following the threshold applied for FMF500 versus SSA440 and EANG440−870 versus AANG440−870 were found the highest for pure dust (PUD, 31.90%) followed by polluted continental (POC, 24.77%) types of aerosols, with moderate contribution was recorded for polluted dust (POD, 20.92%), organic carbon dominating (OCD, 11.85%), black carbon dominating (BCD, 8.77%) and the lowest for the non-absorbing (NOA, 1.79%) aerosol type. Seasonally, the mean (±SD) aerosol optical thickness at 440 nm (AOT440) was found maximum (0.73 ± 0.36) for PUD type in summer and minimum for BCD (0.25 ± 0.04) during spring at Karachi. However, the mean (±SD) AOT440 varied from 0.85 ± 0.25 during summer to 0.57 ± 0.30 in winter at Lahore, with the highest contributions for POC (29.91%) and BCD (22.58%) and the lowest for NOA (5.85%) type of aerosols. Further, the intensive optical properties showed significant temporal and spectral changes and the complexity of inferred aerosol types over the study sites. The results are well substantiated with the air mass analysis obtained from the concentration weighted trajectory (CWT) model for different aerosol types. The Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model revealed the strong presence of BCD aerosol type led to a surface (BOA) and top of atmosphere (TOA) forcing of −70.12, −99.78 Wm−2 and −9.60, −19.74 Wm−2, with an annual heating rate of 2.10 and 2.54 Kday−1, respectively, at Karachi and Lahore sites.

Cirrus clouds provide a significant radiative forcing on the Earth's climate system. This paper looks at the connection in space and time between monthly mean lightning activity observed from the Lightning Imaging Sensor on board the International Space Station, and the global monthly mean cirrus cloud cover obtained from the MERRA‐2 reanalysis product. The correlation coefficient between the global monthly mean cloud optical thickness of the cirrus clouds (clouds at altitudes above the 400 hPa pressure levels) with the monthly mean lightning flash counts is 0.84, implying that monthly mean lightning can explain 70% of monthly variability of the global high cloud optical thickness. In addition, lightning amount explains nearly 60% of the monthly mean global area coverage of cirrus clouds. Given these statistically significant connections between lightning and cirrus clouds, we propose using global lightning data as an additional tool for monitoring monthly variability of cirrus clouds. Plain Language Summary Cirrus clouds are one of the most important components maintaining the Earth's radiation budget. They reflect shortwave radiation from the Sun while absorbing the longwave radiation from the Earth. The net cloud radiative forcing for cirrus clouds results a warming of the climate. More/less cirrus clouds result in more/less warming of the planet. The moisture for the formation of cirrus clouds in the upper atmosphere is transported there in large part via deep convective storms, many associated with lightning activity and hence defined as thunderstorms. An increasing in cirrus clouds in a warmer atmosphere will amplify the initial warming. We explored in this study the relationship between global cirrus cloud coverage and global lightning activity. The results of the research show that 60%–70% of the monthly variability in global cirrus clouds can be explained by mean global lightning activity. Key Points Cirrus clouds are one of the essential components in the atmosphere, with many important feedbacks on the radiation balance of Earth Monthly mean lightning can explain 70% of the monthly variability of the global high cloud optical thickness Lightning explains nearly 60% of the monthly mean global area coverage of cirrus clouds

Cloud optical thickness (COT) stands as a critical parameter governing the radiative properties of clouds. This study develops a convolutional neural network (CNN) model to retrieve the COT of single-layer non-precipitating clouds during nighttime using FY-4B satellite data. The model integrates multi-channel brightness, temperature, and geographic and temporal features, without relying on auxiliary meteorological data, using the multi-point averaged 532 nm COT from CALIPSO as ground truth for training. Performance evaluation demonstrates robust retrieval accuracy, achieving coefficients of determination (R2) of 0.88 and 0.73 for satellite zenith angles (SAZAs) < 70° and >70°, respectively. Key advancements include the incorporation of temporal features, the Squeeze-and-Excitation (SE) module, and a multi-point averaging technique, each validated through ablation experiments to reduce bias and enhance stability. Meanwhile, a model error analysis experiment was conducted that further clarified the performance boundaries of the model. These findings underscore the model’s capability to retrieve the COT of single-layer non-precipitating clouds during nighttime with high precision.

An extinction of incoming solar radiation is taking place by absorption and scattering by dust, water droplets, and gaseous molecules. Such phenomena are responsible for altering meteorological variables. In the present study, temporal analysis of the aerosol optical thickness (AOT) and black carbon (BC) surface mass concentration was undertaken using an ozone monitoring instrument (OMI) and modern-era retrospective analysis for research and applications, version 2 (MERRA-2) satellite from the year 2018 to 2022. The study was mainly focused on the western states of India which are Rajasthan, Gujarat, and Maharashtra. The correlation of AOT and BC surface mass concentration with near-surface temperature (2m above ground level) was analyzed. BC and temperature shows strong negative correlation as BC is known for its absorption of radiation. It accumulates in the atmosphere and contributes to atmospheric warming while simultaneously bringing down the near-surface air temperature due to the reduced sunlight reaching the ground. Also, seasonal analysis was conducted for winter, summer, monsoon, and post-monsoon, which shows the higher values of AOT in monsoon; however, seasonal average BC surface mass concentration was found high in winter in each year for all three states. AERONET data from Jaipur, Rajasthan, and Pune, Maharashtra for the year 2021 was used to further evaluate the AOT generated from OMI. The results demonstrated a significant connection, with R 2 values of 0.62 and 0.69, respectively. The temperature retrieved from MERRA-2 was also validated with ground truth data of the Continuous Ambient Air Quality Monitoring Station (CAAQMS) at both stations showing high agreement with R 2  > 0.70.

The relative dispersion of the cloud droplet spectrum (ε) is a key parameter that characterizes the spectral shape. Uncertainties in its parameterization can introduce significant biases in the simulation of cloud and fog microphysical and optical properties, making its accurate diagnosis in models critically important. In this study, we conduct sensitivity experiments using the large‐eddy simulation model to evaluate the performance of a newly developed nonlinear ε parameterization based on volume‐mean diameter of cloud droplets (Dv). Four typical surface types associated with cloud and fog processes are tested. Compared to conventional linear parameterizations based on droplet number concentration or Dv, the new parameterization significantly improves the simulation of droplet spectral shape parameters and effective radius, with average improvements of 90.59% and 78.49%, respectively. Moreover, the new parameterization captures the complex relationship between ε and liquid water content observed across different surface types and exhibits distinct impacts on cloud‐to‐rain autoconversion rate from those of default parameterization with a net mean change of +72.21%, thereby affecting the formation and intensity of precipitation. The new parameterization also leads to reduced cloud and fog optical thickness, which weakens cloud radiative cooling. These findings highlight the importance of improved ε parameterizations for the studied regions in China and underscore the potential for enhancing model performance in representing microphysics, radiation, and precipitation processes in low clouds and fog. Assessing the generality of the conclusions across different geographic regions and climatic regimes would be a valuable focus for future work. Plain Language Summary Clouds and fog are made up of many tiny water droplets. The way these droplet sizes are spread out, known as droplet spectrum dispersion, plays an important role in how clouds reflect radiation and produce rain. However, many weather and climate models use simplified ways to represent this dispersion, which can cause errors in simulating cloud properties and precipitation. In this study, we introduce a new method for describing droplet dispersion based on the volume‐mean diameter and test it in a high‐resolution weather model. Compared to traditional methods, the new approach more accurately captures cloud droplet characteristics and improves the simulation of cloud microphysical and optical properties. These improvements can help make weather and climate predictions more reliable, especially for low clouds and fog. Key Points Novel parameterization improves the simulations of cloud droplet spectral shape and effective diameter It exerts distinct impacts on cloud‐to‐rain autoconversion process over different surface types It mitigates cloud radiative cooling strength in model simulations

Surface solar irradiance (SSI) is a critical factor influencing the power generation capacity of photovoltaic (PV) power plants. Dynamic changes in cloud cover pose significant challenges to the accurate nowcasting of SSI, which in turn directly affects the reliability and stability of renewable energy systems. However, existing research often simplifies or overlooks changes in the optical and morphological characteristics of clouds, leading to considerable errors in SSI nowcasting. To address this limitation and improve the accuracy of ultra-short-term SSI forecasting, this study first forecasts changes in cloud optical thickness (COT) within the next 3 h based on a spatiotemporal long short-term memory model, since COT is the primary factor determining cloud shading effects, and then integrates the zenith and regional averages of COT, along with factors influencing direct solar radiation and scattered radiation, to achieve precise SSI nowcasting. To validate the proposed method, we apply it to the Albuquerque, New Mexico, United States (ABQ) site, where it yielded promising performance, with correlations between predicted and actual surface solar irradiance for the next 1 h, 2 h, and 3 h reaching 0.94, 0.92, and 0.92, respectively. The proposed method effectively captures the temporal trends and spatial patterns of cloud changes, avoiding simplifications of cloud movement trends or interference from non-cloud factors, thus providing a basis for power adjustments in solar power plants.

Clouds are key to understanding the atmosphere and climate, and a long series of satellite observations provide invaluable information to study their properties. EUMETSAT has published Release 1 of the Optimal Cloud Analysis (OCA) Climate Data Record (CDR), which provides a homogeneous time series of cloud properties of up to two overlapping layers, together with uncertainties. The OCA product is derived using the 15 min Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements onboard Meteosat Second Generation (MSG) in geostationary orbit and covers the period from 19 January 2004 until 31 August 2019. This paper presents the validation of the OCA cloud-top pressure (CTP) against independent lidar-based estimates and the quality assessment of the cloud optical thickness (COT) and cloud particle effective radius (CRE) against a combination of products from satellite-based active and passive instruments. The OCA CTP is in good agreement with the CTP sensed by lidar for low thick liquid clouds and substantially below in the case of high ice clouds, in agreement with previous studies. The retrievals of COT and CRE are more reliable when constrained by solar channels and are consistent with other retrievals from passive imagers. The resulting cloud properties are stable and homogeneous over the whole period when compared against similar CDRs from passive instruments. For CTP, the OCA CDR and the near-real-time OCA products are consistent, allowing for the use of OCA near-real time products to extend the CDR beyond August 2019.

Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are focused on three latitude belts where DCCs appear more frequently in the climatology: the northern middle latitude (NML), tropical latitude (TRL), and southern middle latitude (SML). It was found that the aerosol effect on marine DCCs may be detected only in NML from long-term averaged satellite aerosol and cloud observations. Specifically, cloud particle size is more susceptible to the aerosol effect compared to other cloud micro-physical variables (e.g., cloud optical depth). The signature of the aerosol effect on DCCs can be easily obscured by meteorological covariances for cloud macro-physical variables, such as cloud cover and cloud top temperature (CTT). From a machine learning analysis, we found that the primary aerosol effect (i.e., the aerosol effect without meteorological feedbacks and covariances) can partially explain the aerosol convective invigoration in CTT and that meteorological feedbacks and covariances need to be included to accurately capture the aerosol convective invigoration. From our singular value decomposition (SVD) analysis, we found the aerosol effects in the three leading principal components (PCs) may explain about one third of the variance of satellite-observed cloud variables and significant positive or negative trends are only observed in the lead PC1 of cloud and aerosol variables. The lead PC1 component is an effective mode for detecting the aerosol effect on DCCs. Our results are valuable for the evaluation and improvement of aerosol-cloud interactions in the long-term climate simulations of global climate models.