Cloud turbulence can affect the spatial and temporal variability of cloud properties, but its relationship with cloud optical characteristics can vary with cloud phase and atmospheric conditions. Observational evidence on this relationship, particularly for natural ice and supercooled-liquid clouds, remains limited. In addition, the response of cloud optical properties to short-term variations in turbulence has not been sufficiently examined using ground-based remote sensing. This study therefore investigates the association between cloud optical properties and a lidar-derived refractive-index structure parameter (Cn2) proxy under different cloud conditions. Five cloud cases observed over Palampur and New Delhi during November 2024 - April 2025 were analysed using Raman lidar measurements of backscatter coefficient (BC), linear volume depolarization ratio (LVDR), cloud optical depth (COD), and the lidar-derived Cn2 (turbulence proxy). Radiosonde observations were used to characterize the thermodynamic conditions within the observed cloud layers. Four cases were predominantly ice clouds, while one was predominantly supercooled liquid. Vertical profiles revealed increase in LVDR coincided with enhanced lidar-derived Cn2, suggesting possible changes in ice-crystal orientation. Pearson and Spearman correlations and cross-correlation analysis were used to examine the temporal relationships between the lidar-derived Cn2 and cloud properties. The results showed a consistent vertical separation between the maximum lidar-derived Cn2 and the strongest optical signals, with higher lidar-derived Cn2 generally occurring in the lower cloud region. Three ice-cloud cases showed positive Cn2-COD associations, with both near-simultaneous and short delayed correspondence. One ice-cloud case showed an inverse association, while the supercooled-liquid case showed no meaningful overall COD dependence despite having the highest average Cn2. These contrasting associations indicate that the relationship between the lidar-derived Cn2 and cloud optical properties was case dependent and varied under different cloud and atmospheric conditions. The findings demonstrate the usefulness of ground-based Raman lidar for resolving short-term and vertical variability in cloud properties, while direct turbulence and microphysical measurements are needed to identify the processes underlying these observed associations.
A widespread mesoscale convective system (MCS), which sustained for over two days over the rain shadow region of the peninsular Indian region observed during CAIPEEX-IV, is illustrated in this study. The study used collocated state-of-the-art C-band polarimetric radar and microwave radiometer, along with numerical simulations utilising bin microphysics. Unlike previous CAIPEEX studies, this work provides a detailed characterisation of MCS rainfall and microphysical evolution using dual-polarisation radar fingerprints combined with high-resolution spectral-bin microphysics (SBM) simulation. The analysis allows for the explicit quantification of the impact of warm- and ice-phase processes on precipitation in convective and stratiform regions. The study also identifies a systematic graupel‑related bias in WRF‑SBM simulations, advancing understanding of model precipitation errors over the Indian monsoon region. Fingerprinting analysis of warm-rain microphysics reveals that the collision-coalescence process dominates during convection, while in stratiform regions, breakup weakens toward the surface. In contrast, during cold-rain processes, aggregation becomes dominant with riming following vapour deposition, particularly in convective regions. The study offers detailed insights into the microphysical processes shaping precipitation during the MCS, highlighting the importance of accurate representation for reliable precipitation predictions. Observations indicate that 89.9
Radiative transfer models such as SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer model) are essential for simulating atmospheric spectral transmittance and reflectance, but their computational cost becomes prohibitive for large-scale analyses under turbid and cloudy conditions. This study develops machine-learning emulators of SBDART over the 0.25–2.50 µm spectral range using a reference database of 1,006,648 physically valid atmospheric scenarios spanning aerosol loading proxy(AOD-proxy), surface albedo, cloud-base altitude, cloud optical thickness, and solar zenith angle. Three supervised algorithms were compared: XGBoost, Random Forest, and multilayer perceptron (MLP). Tree-based models clearly outperform the neural-network baseline, though no single model dominates both targets. For spectral transmittance, XGBoost achieved the best global performance (RMSE = 0.003914, MAE = 0.001830, MBE = −0.000003, R2 = 0.999625), while Random Forest yielded the strongest results for spectral reflectance (RMSE = 0.012911, MAE = 0.003464, MBE = 0.000017, R2 = 0.997885), both computed over 27,044,215 concatenated test predictions. Wavelength-wise statistics confirm the strong spectral fidelity of both tree-based models, whereas the MLP showed lower and less stable performance. Beyond predictive accuracy, the surrogate models preserve the wavelength-dependent structure of SBDART across the full shortwave domain. SHAP-based interpretability analysis further shows that the learned dependencies among cloud optical thickness, aerosol loading, solar geometry, and surface albedo are consistent with radiative-transfer theory; this consistency is corroborated by an independent finite-difference sensitivity analysis performed directly on SBDART, which agrees with the emulator on the dominant predictor in 94
Annual precipitation can change differently across low, medium, and high annual totals, yet this non-monotonic behavior has not been mapped across Pernambuco’s three climatic regions. We analyzed annual precipitation from 820 BR-DWGD grid cells for 1961–2022 using Innovative Trend Analysis (ITA), the Mann-Kendall (MK) test, and Sen’s slope. For regional mean series, ITA slopes were − 2.34, − 0.07, and − 6.28 mm yr⁻¹ for Sertão, Agreste, and Zona da Mata, respectively; ITA detected significant negative trends in Sertão and Zona da Mata. The corresponding Sen’s slopes were − 1.48, − 0.09, and − 3.82 mm yr⁻¹, with a significant MK trend only in Zona da Mata. At the grid-cell scale, ITA identified significant negative and positive trends in 76
The instability in the atmosphere causes thunderstorms. Sudden changes in temperature, wind velocity, atmospheric pressure, precipitation and hail are caused by thunderstorms. This study assesses thunderclouds by examining their structure, physical characteristics, and related atmospheric stability indicators, and it attempts to classify these clouds statistically based on their frequencies in Basra. The thermal, humidity and dynamic features of the atmosphere were analyzed from the European Centre for Medium-Range Weather Forecasts reanalysis data and data from the MERRA-2 reanalysis from 1990 to 2019 in the upper atmosphere linked to jet streams in the region of Basra Airport. The analysis shows that 1999 had the most thunderstorms in the entire year (22 days). The investigation revealed that spring has the highest frequency (56.44
