Search Results (155)

Sulfur dioxide is a trace constituent of the upper atmosphere of Venus but plays a dominant role in the photochemistry above the cloud tops. Because SO₂ undergoes indirect dissociation to a relatively long-lived excited state, it has a line-type absorption spectrum in the dissociation region (~190–220 nm). This leads to strong isotopic fractionation under optically thick conditions, a process referred to as self-shielding. Here, I use SO₂ cross-sections, shielding functions, and a simple steady-state photochemical model to estimate sulfur isotope ratios in SO₂. The results indicate that large isotope depletion relative to SO₂ in the deep atmosphere is expected in SO₂ below 70 km altitude, with δ³⁴S ~ −100 to −200 permil. This is readily detectable by the VTLS tunable laser spectrometer planned for the NASA DAVINCI mission. Full article

Wildfires represent a significant global environmental challenge, necessitating advanced monitoring and assessment techniques. This study explores the integration of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data within a Google Earth Engine (GEE) framework to enhance wildfire detection, burned area estimation, and severity assessment. By leveraging SAR’s capability to penetrate atmospheric obstructions and optical data’s spectral sensitivity to vegetation changes, the proposed methodology addresses limitations of single-sensor approaches. The results demonstrate strong correlations between SAR-based indices, such as the Radar Vegetation Index (RVI) and Dual-Polarized SAR Vegetation Index (DPSVI), and traditional optical indices, including the Normalized Burn Ratio (NBR) and differenced NBR (ΔNBR). Despite challenges related to terrain influence, sensor resolution differences, and computational demands, the integration of multi-sensor data in a cloud-based environment offers a scalable and efficient solution for wildfire monitoring. During the peak of the fire events, significant atmospheric obstruction was technically verified using Sentinel-2 metadata and the QA60 cloud mask band, which confirmed persistent cloud cover and thick smoke plumes over the study areas. This interference limited the reliability of purely optical monitoring, further justifying the integration of SAR data. Future research should focus on refining data fusion techniques, incorporating additional datasets such as thermal infrared imagery and meteorological variables, and enhancing automation through artificial intelligence (AI). This study underscores the potential of remote sensing advancements in improving fire management strategies and global wildfire mitigation efforts. Full article

Ice clouds play a significant role in the Earth’s radiation balance due to their unique microphysical and radiative properties, which vary with formation mechanisms and regions and influence the local energy budget. In this study, six years of Ka-band Zenith Radar (KAZR) observations from the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) and the Southern Great Plains (SGP) sites, combined with the Fu–Liou radiative transfer model, were used to examine the macrophysical and microphysical properties of ice clouds, their radiative effects, and contributions to the surface energy budget. The results show that the frequency of ice cloud occurrence at SACOL is 40%, significantly higher than the 27% observed at SGP. At both sites, ice cloud altitudes exhibit an increasing trend in the context of recent warming, with a more pronounced increase at SGP. Seasonal variations are evident, with spring characterized by relatively thick and widespread ice clouds, while summer is dominated by high-altitude, optically thin clouds. Ice cloud occurrence peaks at night and decreases during the day at both sites; however, cloud diurnal variations in summer are much greater at SGP than at SACOL. Radiative analysis indicates that longwave radiation-induced warming dominates ice cloud radiative forcing. Net radiative forcing at the top of the atmosphere is 6.08 W/m² at SACOL and 3.06 W/m² at SGP, contributing to atmospheric heating within and beneath cloud layers. At the surface, sensible heat dominates the energy budget at SACOL (over 63%) due to its arid climate, whereas latent heat dominates at SGP (about 67%) because of abundant moisture; and ice clouds have the greatest impact in winter, reducing surface net radiation by 29% at SACOL and 26% at SGP, producing a cooling effect. Full article

The vertical structure and optical–microphysical properties of ice clouds determine their radiative effects. With an average altitude above 3000 m above mean sea level (AMSL) and unique thermal circulation, the Tibetan Plateau forms ice clouds with seasonally varying microphysical characteristics. In this study, satellite lidar observations from CALIPSO and ERA5 reanalysis from 2006 to 2023 reveal significant seasonal variation in ice clouds over the Tibetan Plateau and adjacent regions. In winter, maximums of the backscatter coefficient (β₅₃₂) and ice water content (IWC) were found south of the Qinling-Huaihe Line, as well as in the Sichuan Basin and the Yangtze Plain. In summer, these maximums move onto the Plateau, and the cloud height rises by about 1 km. The altitude of the β₅₃₂ maximum rises from about 4 km in winter to nearly 6 km in summer. Among four cloud categories defined by joint geometric and optical thickness thresholds, clouds with small geometric thickness and large optical thickness (thin and dense clouds) are the most radiatively important. While these clouds are seldom observed over the Tibetan Plateau in winter, they contribute to over thirty percent of local ice cloud occurrences during summer. Their preferred altitude rises from 3–4 km to 6–7 km, occurring under comparatively warmer environmental temperatures. Although limited in geometric depth, the thin and dense clouds exhibit the highest β₅₃₂ and IWC, the lowest multiple scattering coefficient (η₅₃₂), and the highest depolarization ratio (δ₅₃₂). They contribute about thirty percent of the total extinction and backscatter, despite representing only ten to twenty percent of all cases. Full article

With the upgrading of geostationary meteorological satellites, their capabilities in Convective Initiation (CI) identification have been enhanced. To improve the applicability of the ARGI-based CI algorithm in central and eastern China, this study uses Fengyun-4A data, integrates radar and precipitation data to construct a True_CI dataset, and defines False_CI events (satellite-identified events without radar or precipitation signals) for comparative analysis. The results show that True_CI events tend to have longer durations, larger cloud cluster areas, and lower central cloud-top brightness temperature (BT) during development. They exhibit distinct features such as reduced differences between water vapor and infrared channels, increased cloud optical thickness, and ice-phase transformation 30 min before CI occurrence—features absent in most False_CI events. Based on these comparative findings, a new satellite-based CI definition is proposed with a set of reference thresholds, which should be adjusted for different latitudes and seasons. The evaluation of the Defined_CI events (defined using the CI definition) via True_CI events indicates that the CI definition on satellite cloud imagery proposed in this study is reliable, and suggests that further research on the pre-CI environmental conditions of weak convection is needed. Supported by hyperspectral data or numerical model products, such research will help clarify which cloud clusters are prone to developing into convective weather. Full article

Cloud contamination, especially thick cloud cover, severely limits the usability of optical remote sensing imagery by obscuring surface information. Due to the strong penetrability of microwave signals, Synthetic Aperture Radar (SAR) has emerged as an effective source for thick cloud removal. While SAR-assisted deep learning methods, such as CNNs and GANs, have made notable progress, the quality of generated imagery still requires improvement. Diffusion models, which offer strong potential for enhancing generation fidelity, could address this limitation but suffer from slow sampling speeds that constrain practical use and underscore the need for greater efficiency. To simultaneously enhance both reconstruction quality and sampling efficiency, this paper proposes a fast-sampling SAR-conditioned consistency model based on consistency distillation, named CM-CR, which adopts a teacher–student architecture to divide the reconstruction process into a rapid coarse prediction stage and a detailed refinement stage, significantly reducing per-scene processing time while maintaining high reconstruction fidelity. Specifically, a SAR-Conditioned Score-Based Diffusion Model (SCSBD) is first developed as the teacher network for learning a SAR-conditioned optical image generation model. Consistency distillation is then used to derive the student network SAR-conditioned consistency model (SCCM), which enables a rapid coarse prediction through single-step sampling. Finally, a Progressive Denoising via Multistep Resampling (PDMSR) strategy is introduced to iteratively refine the single-step output, producing fine-grained reconstructions. Comparative experiments conducted on the widely used cloud removal benchmark dataset SEN12MS-CR demonstrate that the proposed CM-CR method achieves state-of-the-art (SOTA) performance across all image quality metrics. Notably, although its design uses approximately 80 times more parameters compared with a standard Denoising Diffusion Probabilistic Model (DDPM), it delivers up to a 40-fold acceleration at inference. Full article

Aerosol–cloud interaction remains one of the largest sources of uncertainty in weather and climate modeling. This study investigates the impacts of aerosols on the macro- and microphysical properties of different cloud types over East Asia, based on nine years of joint satellite observations from CloudSat, CALIPSO, and MODIS, combined with ERA5 reanalysis data. Results reveal pronounced cloud-type dependence in aerosol effects on cloud fraction, cloud top height, and cloud thickness. Aerosols enhance the development of convective clouds while suppressing the vertical extent of stable stratiform clouds. For ice-phase structures, ice cloud fraction and ice water path significantly increase with aerosol optical depth (AOD) in deep convective and high-level clouds, whereas mid- to low-level clouds exhibit reduced ice crystal effective radius and ice water content, indicating an “ice crystal suppression effect.” Even after controlling for 14 meteorological variables, partial correlations between AOD and cloud properties remain significant, suggesting a degree of aerosol influence independent of meteorological conditions. Humidity and wind speed at different altitudes are identified as key modulating factors. These findings highlight the importance of accounting for cloud-type differences, moisture conditions, and dynamic processes when assessing aerosol–cloud–climate interactions and provide observational insights to improve the parameterization of aerosol indirect effects in climate models. Full article

Cloud seeding is a targeted weather modification strategy aimed at enhancing precipitation, particularly in regions facing water scarcity. This study evaluates the impacts of wintertime cloud seeding events in the western United States, focusing on three regions: the Lake Tahoe area, the Santa Rosa Range, and the Ruby Mountains, using an integrated remote sensing approach. Ground-based AgI generators were deployed to initiate seeding, and the atmospheric responses were assessed using multispectral observations from the Advanced Baseline Imager (ABI) aboard the GOES-R series satellites and regional radar reflectivity mosaics derived from NEXRAD data. Satellite-derived cloud microphysical properties, including cloud top brightness temperatures, optical thickness, and phase indicators, were analyzed in conjunction with radar reflectivity to evaluate microphysical changes associated with seeding. The analysis revealed significant regional variability: Tahoe events consistently exhibited strong seeding signatures, such as droplet-to-ice phase transitions, cloud top cooling, and thickened cloud structures, often followed by increased radar reflectivity. These outcomes were linked to favorable atmospheric conditions, including colder temperatures, elevated mid-to-upper tropospheric moisture, and sufficient supercooled liquid water. In contrast, events in the Santa Rosa Range generally showed weaker responses due to warmer, drier conditions and limited cloud development, while the Ruby Mountains presented mixed outcomes. This study improves the detection of seeding impacts by characterizing microphysical changes and precipitation development, capturing the progression from initial cloud phase transitions to hydrometeor development. The results highlight the importance of aligning seeding strategies with local atmospheric conditions and demonstrate the practical value of satellite-based tools for evaluating seeding effectiveness, particularly in data-sparse regions. Overall, this work contributes to advancing both the scientific insight and operational practices of weather modification through remote sensing. Full article

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. Full article

This study utilized Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite level-2 data with high-confidence cloud–aerosol discrimination (|CAD| > 70) to investigate the optical properties, vertical distributions, seasonal variations, and aerosol interactions of near-surface cloud layers (cloud base height < 2.5 km) over Australia from 2006 to 2021. This definition encompasses both traditional low clouds and part of mid-level clouds that extend into the lower troposphere, enabling a comprehensive view of cloud systems that interact most directly with boundary-layer aerosols. The results showed that the optical depth of low clouds (CODL) exhibited significant spatial heterogeneity, with higher values in central and eastern regions (often exceeding 6.0) and lower values in western plateau regions (typically 4.0–5.0). CODL values demonstrated clear seasonal patterns with spring peaks across all regions, contrasting with traditional summer-maximum expectations. Pronounced diurnal variations were observed, with nighttime CODL showing systematic enhancement effects (up to 19.29 maximum values compared to daytime 11.43), primarily attributed to surface radiative cooling processes. Cloud base heights (CBL) exhibited counterintuitive nighttime increases (41% on average), reflecting fundamental differences in cloud formation mechanisms between day and night. The geometric thickness of low clouds (CTL) showed significant diurnal contrasts, decreasing by nearly 50% at night due to enhanced atmospheric stability. Cloud layer number (CN) displayed systematic nighttime reductions (18% decrease), indicating dominance of single stratiform cloud systems during nighttime. Regional analysis revealed that the central plains consistently exhibited higher CODL values, while eastern mountains showed elevated cloud heights due to orographic effects. Correlation analysis between cloud and aerosol layer properties revealed moderate but statistically significant relationships (|R| = 0.4–0.6), with the strongest correlations appearing between cloud layer heights and aerosol layer heights. However, these correlations represent only partial influences among multiple factors controlling cloud development, suggesting measurable but modest aerosol effects on cloud properties. This study provides comprehensive observational evidence for cloud optical property variations and aerosol–cloud interactions over Australia, contributing to an improved understanding of Southern Hemisphere cloud systems and their climatic implications. Full article

Solar radiation is a first-order essential climate variable like temperature and precipitation. Its significant spatiotemporal variability, mainly due to atmospheric conditions, makes modelling particularly challenging, especially in regions with complex atmospheric dynamics and sparse meteorological stations. This study evaluates 6 solar radiation models (SARAH, PVGIS, Constant Atmospheric Conditions, Physical Solar Model, CAMS Worldwide, and InsolMets) using monthly measurements from 141 ground-based stations across the Iberian Peninsula from 2004–2020. Although all models consistently captured intra-annual variability, discrepancies in absolute values arise due to factors such as the differences in their functional designs and input parameters. InsolMets, which integrates cloud optical thickness, cloud fractional cover, the diffuse radiation component, and enhanced solar illumination geometry, was the most robust model, showing relevant improvements (61.5% in January, 59.7% in November, and 52.0% in December) compared to the worst-performing model (constant atmospheric conditions). Using as a threshold three times the root-mean-square error (RMSE) proposed by the Global Climate Observing System, InsolMets achieved the highest number of months (10) under this limit, also achieving the best overall result, with only 1 month showing non-significant correlations over the same time span. Nevertheless, SARAH and PVGIS matched InsolMets’ performance during March, November, and December. The results provide insights for selecting and improving solar radiation estimations, highlighting the need to incorporate remote sensing atmospheric data to minimize uncertainties. While all models that account for atmospheric effects enhance accuracy, InsolMets stands out as the most accurate model for estimating solar radiation across the Iberian Peninsula throughout the year, achieving the lowest RMSE and normalized RMSE values. Full article

The multi-band atmospheric correction algorithms, now referred to as remote sensing reflectance (R_rs) algorithms, have been implemented on a NASA computing facility for global remote sensing of ocean color and atmospheric aerosol parameters from data acquired with several satellite instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi spacecraft platform. These algorithms are based on the 2-band version of the SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) algorithm. The bands centered near 0.75 and 0.865 μm are used for atmospheric corrections. In order to obtain high-quality R_rs values over Case 1 waters (deep clear ocean waters), strict masking criteria are implemented inside these algorithms to mask out thin clouds and very turbid water pixels. As a result, R_rs values are often not retrieved over bright Case 2 waters. Through our analysis of VIIRS data, we have found that spatial features of bright Case 2 waters are observed in VIIRS visible band images contaminated by thin cirrus clouds. In this article, we describe methods of combining cirrus and aerosol corrections to improve spatial coverage in R_rs retrievals over Case 2 waters. One method is to remove cirrus cloud effects using our previously developed operational VIIRS cirrus reflectance algorithm and then to perform atmospheric corrections with our updated version of the spectrum-matching algorithm, which uses shortwave IR (SWIR) bands above 1 μm for retrieving atmospheric aerosol parameters and extrapolates the aerosol parameters to the visible region to retrieve water-leaving reflectances of VIIRS visible bands. Another method is to remove the cirrus effect first and then make empirical atmospheric and sun glint corrections for water-leaving reflectance retrievals. The two methods produce comparable retrieved results, but the second method is about 20 times faster than the spectrum-matching method. We compare our retrieved results with those obtained from the NASA VIIRS R_rs algorithm. We will show that the assumption of zero water-leaving reflectance for the VIIRS band centered at 0.75 μm (M6) over Case 2 waters with the NASA R_rs algorithm can sometimes result in slight underestimates of water-leaving reflectances of visible bands over Case 2 waters, where the M6 band water-leaving reflectances are actually not equal to zero. We will also show conclusively that the assumption of thin cirrus clouds as ‘white’ aerosols during atmospheric correction processes results in overestimates of aerosol optical thicknesses and underestimates of aerosol Ångström coefficients. Full article

The thick cloud layer enshrouding Venus influences its thermal balance and climate evolution. However, our knowledge of total optical depth, spatial and temporal variations in the clouds is limited. We present the first complete study of the SPICAV IR spectrometer observations in the 1.28- and 1.31-µm atmospheric transparency windows during the Venus Express mission in 2006–2014. The nadir spectra were analyzed with one-dimensional multiple scattering radiative transfer model to obtain the variability of total cloud opacity on the Venus night side. The optical depth recomputed to 1 µm averages 36.7 with a standard deviation of 6.1. Cloud opacity depends on latitude, with a minimum at 50–55° N. In the Southern Hemisphere, this latitude dependence is less pronounced due to the reduced spatial resolution of the experiment, determined by the eccentricity of the spacecraft’s orbit. Cloud opacity exhibits strong variability at short time scales, mostly in the range of 25–50. The variability is more pronounced in the equatorial region. The lack of imaging capability limits the quantitative characterization of the periodicity. No persistent longitude or local time trends were detected. Full article

A novel data assimilation technique is developed to assimilate MODIS (Moderate Resolution Imaging Spectroradiometer) level two (L2) cloud products, including cloud optical thickness (COT), cloud particle effective radius (Re), cloud water path (CWP), and cloud top pressure (CTP), into the Weather Research and Forecast (WRF) model. Its impact on the analysis and forecast of Typhoon Talim in 2023 at its initial developing stage is demonstrated. First, the conditional generative adversarial networks–bidirectional ensemble binned probability fusion (CGAN-BEBPF) model ) is applied to retrieve three-dimensional (3D) CloudSat CPR (cloud profiling radar) equivalent W-band (94 Ghz) radar reflectivity factor for the typhoons Talim and Chaba using the MODIS L2 data. Next, a W-band to S-band radar reflectivity factor mapping algorithm (W2S) is developed based on the collocated measurements of the retrieved W-band radar and ground-based S-band (4 Ghz) radar data for Typhoon Chaba at its landfall time. Then, W2S is utilized to project the MODIS-retrieved 3D W-band radar reflectivity factor of Typhoon Talim to equivalent ground-based S-band reflectivity factors. Finally, data assimilation and forecast experiments are conducted by using the WRF Hydrometeor and Latent Heat Nudging (HLHN) radar data assimilation technique. Verification of the simulation results shows that assimilating the MODIS L2 cloud products dramatically improves the initialization and forecast of the cloud and precipitation fields of Typhoon Talim. In comparison to the experiment without assimilation of the MODIS data, the Threat Score (TS) for general cloud areas and major precipitation areas is increased by 0.17 (from 0.46 to 0.63) and 0.28 (from 0.14 to 0.42), respectively. The fraction skill score (FSS) for the 5 mm precipitation threshold is increased by 0.43. This study provides an unprecedented data assimilation method to initialize 3D cloud and precipitation hydrometeor fields with the MODIS imagery payloads for numerical weather prediction models. Full article

Flood disasters are one of the major natural hazards threatening agricultural production. To reduce agricultural disaster losses, accurately identifying agricultural flood-affected areas is crucial. Taking Shengzhou City as a case study, we proposed a refined method for identifying agricultural flood-affected areas by integrating microwave and optical remote sensing data with deep learning techniques, GIS, and the pixel-based direct differencing method. Complementary advantages of microwave and optical remote sensing data can effectively solve the problem of difficulty in accurately detecting floods due to thick clouds before and after flood disasters. Deep learning technology can effectively identify farmland areas, and the pixel direct difference method can accurately analyze agricultural flood disasters. Analyzing three typical rainfall events along with the topographical and geomorphological characteristics of Shengzhou City, the results indicate that agricultural flood disaster areas exhibit significant spatial heterogeneity. The primary influencing factors include rainfall intensity, topography, and drainage infrastructure. The northern, eastern, and southwestern regions of Shengzhou City, particularly the peripheral areas adjacent to mountainous and hilly terrains, contain most of the flood-affected farmland. These areas, characterized by low-lying topography, are highly susceptible to flood disasters. Therefore, optimizing the drainage systems of farmland in low-lying areas near mountainous and hilly regions of Shengzhou City is essential to enhance flood resilience. Full article