Cloud Cover Research Articles

A Methodology for the Multitemporal Analysis of Land Cover Changes and Urban Expansion Using Synthetic Aperture Radar (SAR) Imagery: A Case Study of the Aburrá Valley in Colombia

The Aburrá Valley, located in the northwestern region of Colombia, has undergone significant land cover changes and urban expansion in recent decades, driven by rapid population growth and infrastructure development. This region, known for its steep topography and dense urbanization, faces considerable environmental challenges. Monitoring these transformations is essential for informed territorial planning and sustainable development. This study leverages Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission, covering 2017–2024, to propose a methodology for the multitemporal analysis of land cover dynamics and urban expansion in the valley. The novel proposed methodology comprises several steps: first, monthly SAR images were acquired for every year under study from 2017 to 2024, ensuring the capture of surface changes. These images were properly calibrated, rescaled, and co-registered. Then, various multitemporal fusions using statistics operations were proposed to detect and find different phenomena related to land cover and urban expansion. The methodology also involved statistical fusion techniques—median, mean, and standard deviation—to capture urbanization dynamics. The kurtosis calculations highlighted areas where infrequent but significant changes occurred, such as large-scale construction projects or sudden shifts in land use, providing a statistical measure of surface variability throughout the study period. An advanced clustering technique segmented images into distinctive classes, utilizing fuzzy logic and a kernel-based method, enhancing the analysis of changes. Additionally, Pearson correlation coefficients were calculated to explore the relationships between identified land cover change classes and their spatial distribution across nine distinct geographic zones in the Aburrá Valley. The results highlight a marked increase in urbanization, particularly along the valley’s periphery, where previously vegetated areas have been replaced by built environments. Additionally, the visual inspection analysis revealed areas of high variability near river courses and industrial zones, indicating ongoing infrastructure and construction projects. These findings emphasize the rapid and often unplanned nature of urban growth in the region, posing challenges to both natural resource management and environmental conservation efforts. The study underscores the need for the continuous monitoring of land cover changes using advanced remote sensing techniques like SAR, which can overcome the limitations posed by cloud cover and rugged terrain. The conclusions drawn suggest that SAR-based multitemporal analysis is a robust tool for detecting and understanding urbanization’s spatial and temporal dynamics in regions like the Aburrá Valley, providing vital data for policymakers and planners to promote sustainable urban development and mitigate environmental degradation.

Enhanced cloud removal via temporal U-Net and cloud cover evolution simulation

Enhanced cloud removal via temporal U-Net and cloud cover evolution simulation

Clouds Are Crucial to Capture Antarctic Sea Ice Variability

AbstractModels from the Coupled Model Intercomparison Project phase 6 (CMIP6) typically struggle to reproduce observed Antarctic sea ice trends, a bias that is substantially alleviated when constraining winds. We use wind‐nudged simulations from two CMIP models to investigate the influence of clouds on sea ice area (SIA). We find that nudging model winds in coupled simulations toward reanalysis, in addition to improving SIA variability, is crucial to reproduce realistic anomalies in cloud radiative effect (CRE) and cloud cover. Biases in the variability of cloud properties at sea ice edge—characterized by CRE anomalies—help explain the remaining discrepancies between simulated and observed SIA; a bias of 1 in the CRE anomaly corresponds to a negative bias of 0.43 in SIA anomaly. Finally, we find that most CMIP6 models show positive trends in CRE anomaly biases, which should contribute to enhanced SIA decline, a long‐standing bias in CMIP models.

A Novel Agricultural Remote Sensing Drought Index (ARSDI) for high-resolution drought assessment in Africa using Sentinel and Landsat data.

Drought poses a significant threat to agricultural productivity in Africa due to climate variability and water scarcity. To improve drought monitoring using high-resolution remote sensing data, this paper proposes a novel Agricultural Remote Sensing Drought Index (ARSDI). Imagery from Sentinel-1(S-1), Sentinel-2 (S-2), and Landsat 8 was utilized to develop a drought monitoring system for 2017-2023, during which Egypt and Kenya experienced significant drought events. In Kenya, particularly from 2021 to 2023, recurrent droughts severely affected agricultural output, necessitating the evaluation of ARSDI's efficiency in capturing drought severity. Similarly, in Egypt, drought-like conditions linked to reduced Nile River flow and rainfall variability posed significant challenges. ARSDI exhibited strong correlations with traditional drought indicators, such as the Soil Moisture Condition Index (SMCI), ranging from 0.64 to 0.88 in Egypt and 0.74 to 0.85 in Kenya. It also correlated well with the Vegetation Health Index (VHI), with values (0.96 to 0.98) in Egypt and (0.53 to 0.98) in Kenya. Furthermore, ARSDI aligned with Standardized Precipitation Index (SPI), ranging (0.44 to 0.71) in Egypt and (0.47 to 0.60) in Kenya. By integrating Sentinel-1 radar data, ARSDI mitigated the limitations of cloud cover, providing more reliable vegetation monitoring. Compared to other indices, such as the Normalized Difference Vegetation Index (NDVI), ARSDI exhibited superior performance, particularly during the drought events 2019 in Egypt and 2020 in Kenya, demonstrating its robustness in assessing soil moisture and vegetation health.

Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels

Abstract. The cloud radiative kernel method is a popular approach to quantify cloud feedbacks and rapid cloud adjustments to increased CO2 concentrations and to partition contributions from changes in cloud amount, altitude, and optical depth. However, because this method relies on cloud property histograms derived from passive satellite sensors or produced by passive satellite simulators in models, changes in obscuration of lower-level clouds by upper-level clouds can cause apparent low-cloud feedbacks and adjustments, even in the absence of changes in lower-level cloud properties. Here, we provide a methodology for properly diagnosing the impact of changing obscuration on cloud feedbacks and adjustments and quantify these effects across climate models. Averaged globally and across global climate models, properly accounting for obscuration leads to weaker positive feedbacks from lower-level clouds and stronger positive feedbacks from upper-level clouds while simultaneously removing a mostly artificial anti-correlation between them. Given that the methodology for diagnosing cloud feedbacks and adjustments using cloud radiative kernels has evolved over several papers, and obscuration effects have only occasionally been considered in recent papers, this paper serves to establish recommended best practices and to provide a corresponding code base for community use.

Factors Influencing Emergence Timing Patterns of Long-Tailed Bats in Exotic and Native Forest in New Zealand.

Understanding temporal behavioural patterns in animals can be crucial to their conservation management. Emergence timing in bats, that is, the decision on when to depart day-roosts for foraging, is one such example and is well studied in Northern Hemisphere bats. The emergence timing of New Zealand long-tailed bats (Chalinolobus tuberculatus) is not yet fully understood, including when and where they may be vulnerable to threats. We investigated factors influencing long-tailed bat emergence timing in the Kinleith Forest (exotic plantation, 38° S) and the Eglinton Valley (native beech forest, 45° S). We recorded emergence times during late pregnancy through post-lactation (October to March), to determine whether the month, temperature at sunset, tree density, cloud cover, presence of rain and the number of bats within a roost influenced emergence timing. Most long-tailed bats emerged after sunset in the Kinleith Forest, whilst 80% of first emerging bats departed before sunset in the Eglinton Valley where nights are much shorter in summer, reducing foraging time. Month, temperature at sunset, and roost population size were the most important predictors of emergence timing at both sites. Long-tailed bats in the Kinleith Forest also emerged earlier as tree density increased, a pattern potentially associated with predator defence. The factors influencing long-tailed bat emergence timing are likely context dependent, namely latitude and habitat structure, which has implications for roost protection protocols, timing of bat surveys and interpretation of bat acoustic monitoring data.

Assessing the Spatio-Temporal Variability in the Frequency and Magnitude of Flash Floods and their Driving Mechanisms: Evidence from Haor Region of Bangladesh

There are a limited number of studies addressing the spatiotemporal variability of pre-monsoon flash floods and their driving forces in Bangladesh. This study examines long-term trends in temperature, rainfall, and the frequency and magnitude of flash floods in the five most vulnerable haor districts of northeastern Bangladesh. Temperature, rainfall, and surface water level datasets, up to 2018, were collected from the Bangladesh Meteorological Department (BMD) and the Bangladesh Water Development Board (BWDB). Based on the normal distribution of these datasets observed in quantile-quantile (Q-Q) plots, regression models were used to analyze the long-term temperature trends. The models predict a gradual rise in maximum temperature, ranging from 1.06% to 1.94% over decadal periods. Additionally, an average annual rainfall increase of 4.1% at Sreemangal and 2.28% at Sylhet stations are forecasted. Analysis of historical data from the past sixty years shows a relatively lower peak river stage in tidal rivers compared to non-tidal river stations during pre-monsoon months. The frequency of peak surface water levels at six non-tidal and ten tidal river monitoring stations was estimated using Gumbel's probability estimation method. Frequency analysis suggests a high probability of flash floods across most floodplain areas, with a return period of five to ten years, based on flood danger levels established by government agencies. Furthermore, MODIS satellite imagery (with cloud cover <10%) from the peak flood months (March to May) between 2004 and 2017 was analyzed to assess the extent of flash floods in the study area. Geospatial analysis revealed temporal variations in peak flood extents across different locations. While no clear trends were observed in the frequency of flash floods, their magnitude has significantly increased in recent decades, potentially leading to greater losses in agriculture and property. The increased vulnerability to flash floods in the region can be attributed to several factors, including a rise in pre-monsoon heavy rainfall in the upstream hilly regions of Assam and Meghalaya, high sediment loads in Transboundary Rivers, drainage congestion, poorly designed and maintained flood control structures, and the absence of a reliable flash flood warning system. J. Asiat. Soc. Bangladesh, Sci. 50(1-2): 33-50, June-December 2024

Long‐Term Trend and Seasonal Cycles of Gap‐Free Downscaled Diurnal/Nocturnal LST and the Interaction to Functional Plant Trait Under Tropical Monsoon Climate

AbstractLand surface temperature (LST) monitoring via Earth observation constellation will become optimized and consistent with spatiotemporal‐explicit characteristics. Besides, scientific evidence for the interaction between LST and vegetation biophysical variables remains limited through spatial large‐scale assessment and seamless long‐term tracking. This study addresses this gap by utilizing gap‐filled fine spatial resolution LST products in understanding the dynamic over the period 2000–2023 and the spatiotemporal relationship with leaf area index (LAI). Firstly, Moderate Resolution Imaging Spectroradiometer (MODIS) LST 1,000 m of both daytime and nighttime were downscaled to a finer resolution of 250 m using the Random Forest algorithm. The Whittaker algorithm was then applied to obtain gap‐free LST products due to the typical cloud cover under tropical monsoon climate. Time series decomposition of gap‐filled fine resolution LST revealed slight warming trends in daytime (0.005°C year−1), nighttime (0.036°C year−1), and mean of all‐day time (0.02°C year−1) over recent 24 years, while seasonal amplitude in daytime (−3.7°C–4.8°C) is more fluctuated than in nighttime (−2.5°C–1.9°C). Spatial correlations of monthly LSTs and LAI indicated a consistent negative correlation (R ranging from −0.717 to −0.45). These findings shed light on the quantitative relationship between vegetation LAI and LST, contributing to a more unified theoretical framework for understanding functional vegetation responses under diverse climatic conditions.

Symmetry in Mesoscale Circulations Explains Weak Impact of Trade Cumulus Self‐Organization on the Radiation Budget in Large‐Eddy Simulations

AbstractWe investigate if mesoscale self‐organisation of trade cumuli in 150 km‐domain large‐eddy simulations modifies the top‐of‐atmosphere radiation budget relative to 10 km‐domain simulations, across 77 characteristic, idealized environments. In large domains, self‐generated mesoscale circulations produce fewer, larger and deeper clouds, raising the cloud albedo. Yet they also precipitate more than small‐domain cumuli, drying and warming the cloud layer, and reducing cloud cover. Consequently, large domains cool slightly less through the shortwave cloud‐radiative effect, and slightly more through clear‐sky outgoing longwave radiation, for a net cooling (−0.5 W ). This cooling is generally smaller than the large‐domain radiation's sensitivity to large‐scale meteorological variability, which is similar in small‐domain simulations and observations. Hence, mesoscale self‐organisation would not alter weak trade‐cumulus feedback estimates previously derived from small‐domain simulations. We explain this with a symmetry hypothesis: ascending and descending branches of mesoscale circulations symmetrically increase and reduce cloudiness, weakly modifying the mean radiation budget.

EXPLORING GEOTHERMAL ZONES IN NORTHERN NIGERIA USING LAND SURFACE TEMPERATURE DATA FROM REMOTE SENSING

Nigeria is still unable to meet even the most basic of its energy needs, this lack of power is most evident in houses located in the North-Central and North-East areas. This paper focused on evaluating geothermal potential through remote sensing techniques in parts of Northern Nigeria. Four digital elevation model (DEM) scenes, three Landsat-9(OLI-2/TIR-2) with minimum zero or minimum cloud cover (<6%), and Terra Moderate Resolution Imaging Spectroradiometer satellite images for the research region were processed using ArcMap 10.7.1, Google Earth Pro, and QGIS 3.36.3. The linear correlation analysis performed between Landsat LST and MODIS LST images showed a high correlation coefficient (R² = 0.907). Anomalously high lineament density correlates with high land surface temperature, dominantly in the basement complex of the study area; it's possible that the fracturing will increase the permeability, enabling warm or hot springs to rise to the surface. Fault lines that permit the movement of hot/warm water to the Earth's surface can be linked to active geothermal zones. The stream/rivers in or around the targeted high LST are probably thermal springs, as they were overlaid on the LST, and high-temperature spots(>280) were identified. The regions around Jibam, Langtang, Aikri, Adikpo, Shemdam, and Ashinge prove to be areas where warm or hot springs can be located.

Regional modeling of surface solar radiation, aerosol, and cloud cover spatial variability and projections over northern France and Benelux

Abstract. Investigating the current and future evolution of surface solar radiation (SSR) is essential in the context of climate change and associated environmental issues. We focus on the influence of atmospheric aerosols, along with cloud cover and water vapor content, on northern France and Benelux in spring and summer. Our analysis relies on the National Centre for Meteorological Research–Limited Area Adaptation Dynamic International Development v6.4 (CNRM-ALADIN64) regional climate model at 12.5 km resolution, which includes an interactive aerosol scheme. A regional evaluation of 2010–2020 ALADIN hindcast simulations of clear-sky and all-sky SSR, clear-sky frequency, and aerosols, through comparison to coincident multi-site ground-based measurements, shows reasonable agreement. In addition, these hindcast simulations emphasize how elevated aerosol loads over Benelux and high cloud cover over southwestern England reduce the SSR. Additional ALADIN climate simulations for 2050 and 2100 under Coupled Model Intercomparison Project phase 6 (CMIP6) Shared Socioeconomic Pathway (SSP) 1-1.9 predict a significant reduction in aerosol loads compared to 2005–2014, especially over Benelux, associated with future increases in clear-sky SSR but geographically limited all-sky SSR evolution. In contrast, under SSP3-7.0, clear-sky and all-sky SSR is projected to decline significantly over the domain. This decline is greatest in spring over Benelux due to combined increases in cloud cover and nitrate aerosols projected from 2050 onwards. In summer, projected decreases in cloud cover largely attenuate the reduction in SSR due to aerosols in 2050, while by 2100 rising water vapor contents counteract this attenuation. Thus, our results highlight seasonally and spatially variable impacts of future anthropogenic aerosol emissions on SSR evolution due to cloud cover and water vapor modifications that will likely largely contribute to the modulation of forthcoming aerosol influences.

Preliminary Assessment of the Impact of the Copernicus Imaging Microwave Radiometer (CIMR) on the Copernicus Mediterranean Sea Surface Temperature L4 Analyses

This study evaluates the potential impact of the Copernicus Imaging Microwave Radiometer (CIMR) mission on the sea surface temperature (SST) products of the Mediterranean Sea. Currently, infrared (IR) radiometers provide accurate, high-resolution SST measurements, but they are limited by their inability to see through clouds. Passive microwave (PMW) radiometers, on the other hand, offer monitoring capabilities in almost all weather conditions but typically at lower spatial resolutions. The CIMR mission represents a notable advance in microwave remote sensing of SSTs, as it will ensure a ≤15 km spatial resolution in the recovered SST field. Using an observing system simulation experiment (OSSE), this study evaluates the effect of inserting synthetic CIMR observations into the Copernicus Mediterranean SST analysis system, which is based on an optimal interpolation (OI) algorithm. The OSSE was conducted using data for the year 2017, including daily SST and salinity outputs from a Mediterranean Sea model, hourly precipitation rates from the IMERG, and wind and cloud cover data from ERA5. The results suggest that the improved spatial resolution and accuracy of the CIMR could potentially improve SST retrievals in the Mediterranean Sea, offering better insights for climate and environmental monitoring in semi-closed basins. Including CIMR data in the OI algorithm reduced the mean error and root mean square error (RMSE) of the SST analysis, especially under conditions of low IR coverage. The greatest improvements were found to occur in July, corresponding to coastal upwelling and Atlantic inflow into the Alboran Sea. Improvements ranged from 16% to 29%, with an overall improvement of 26% for the full year of 2017. In conclusion, this preliminary study indicates that Copernicus Mediterranean Sea HR SST products could benefit from the inclusion of the CIMR in the current IR sensor constellation.

Phase-resolved Hubble Space Telescope WFC3 Spectroscopy of the Weakly Irradiated Brown Dwarf GD 1400 and Energy Redistribution–Irradiation Trends in Six White Dwarf–Brown Dwarf Binaries

Abstract Irradiated brown dwarfs offer a unique opportunity to bridge the gap between stellar and planetary atmospheres. We present high-quality Hubble Space Telescope/Wide Field Camera 3/G141 phase-resolved spectra of the white dwarf–brown dwarf binary GD 1400, covering more than one full rotation of the brown dwarf. Accounting for brightness variations caused by ZZ Ceti pulsations, we revealed weak (∼1%) phase-curve amplitude modulations originating from the brown dwarf. Subband light-curve exploration in various bands showed no significant wavelength dependence on amplitude or phase shift. Extracted day- and nightside spectra indicated chemically similar hemispheres, with slightly higher dayside temperatures, suggesting efficient heat redistribution or the dominance of radiative escape over atmospheric circulation. A simple radiative and energy redistribution model reproduced the observed temperatures well. Cloud-inclusive models fit the day and night spectra better than cloudless models, indicating global cloud coverage. We also begin qualitatively exploring atmospheric trends across six irradiated brown dwarfs, from the now complete “Dancing with the Dwarfs” white dwarf–brown dwarf sample. The trend we find in the dayside/nightside temperature and irradiation levels is consistent with efficient heat redistribution for irradiation levels less than ∼109 erg s−1 cm−2 and decreasing efficiency above that level.

Decadal Trend Assessment and Future Projections of Artificial Light Pollution in Ahmedabad, Surat, and Vadodara City of Gujarat, India

The study focuses on a detailed assessment of artificial light pollution in three major cities of Gujarat: Ahmedabad, Surat, and Vadodara for year 2014 to 2023. Night light pollution, resulting from the excessive directed use of artificial lighting, is a pressing environmental issue with far-reaching implications. The findings of this study are expected to offer valuable insights into the extent and nature of light pollution in these cities. The research includes the calculation of average night light radiation for urban areas. By employing the Mann Kendall and Sen’s Slope, this research also aims to identify trends and patterns in night light pollution over the study period. Moreover, by employing the ARIMA and SARIMA model, this research also aims to identify trends and patterns as well as forecasting in night light pollution for 2024. Vadodara, Ahmedabad, and Surat exhibit distinct seasonal patterns in night light radiance, characterized by peaks during winter and troughs during the monsoon season, influenced by local environmental and climatic factors. From 2014 to 2023, all three cities show a consistent upward trend in radiance, with significant seasonal fluctuations observed from July to September due to monsoonal cloud cover. Surat, in particular, maintains relatively stable radiance levels but experiences reduced trends in July and September. These findings underscore the interplay between seasonal variability and long-term growth, highlighting the severity of light pollution in urban areas and emphasizing the need for data-driven interventions to inform urban planning and resource management strategies.

An Arctic sea ice concentration data record on a 6.25 km polar stereographic grid from 3 years of Landsat-8 imagery

Abstract. The decline in Arctic sea ice in the global warming era has received much attention as a contributing factor to the changes in the weather and climate in the Arctic and beyond. The coverage of Arctic sea ice (i.e. sea ice concentration (SIC)) has been monitored since 1972 using satellite passive microwave (PMW) measurements because of their extensive coverage and all-weather capability. However, the fundamental basis of algorithms for estimating SIC has not improved much since the early days due to the lack of reference SIC data, leading to discrepancies between existing PMW SIC algorithms. To overcome this issue, this study aims to construct data records of reference SIC over Arctic sea ice using 30 m resolution imagery from the Operational Land Imager (OLI) on board Landsat-8. In order to collect relatively bright and clear scenes, thresholds of solar elevation >15° and cloud cover <10 % were applied in this study. Clouds in each Landsat-8 scene were masked using the cloud-masking array provided in Landsat data. Due to the poor accuracy of the cloud-masking array over ice-covered surface types, an additional step of visually inspecting the state of the cloud mask using the true-colour image was designated in this study. Each Landsat-8 scene was sorted into four categories depending on the state of the cloud mask. The normalized difference snow index and OLI band-5 reflectivity were used to differentiate between ice and open water within each selected Landsat-8 pixel. The classified data were projected onto a 6.25 km polar stereographic grid, and SIC for each grid cell was obtained by counting ice-classified pixels within the grid. SIC was only computed for grid cells where more than 99 % of their area was covered with Landsat-8 pixels to limit the uncertainty in SIC arising from grids that are not fully concentrated with Landsat-8 pixels. Uncertainty in the produced SIC was 1 %–4 %, inferred using the Gaussian error propagation method. Out of 15 286 collected Landsat-8 images, 14 297 images were translated into SIC maps, and a total of 2 934 399 Landsat-8 SIC grid cells were generated. Evaluation of Landsat-8 SIC with SIC from ice charts revealed a good linear relationship (correlation coefficient of 0.96) between the two products, as well as a mean negative bias which fell within the uncertainty range of Landsat-8 SIC. SIC based on Landsat-8 can be used as reference SIC to evaluate existing SIC products, and, thus, one can improve SIC products, as well as use the improved SIC for other applications such as data assimilation and retrieval studies. The vast amount of Landsat-8 SIC generated in this study may also be used to train deep-learning models for the estimation of Arctic SIC coverage. The Landsat-8 SIC dataset can be publicly accessed at https://doi.org/10.5281/zenodo.10973297 (Jung et al., 2024), and the Python codes for the production and evaluation of the Landsat-8 SIC dataset are accessible at https://doi.org/10.5281/zenodo.12754602 (Jung, 2024).

The very-high-resolution configuration of the EC-Earth global model for HighResMIP

Abstract. We present the very-high-resolution (VHR) version of the EC-Earth global climate model, EC-Earth3P-VHR, developed for HighResMIP. The model features an atmospheric resolution of ∼16 km and an oceanic resolution of 1/12° (∼8 km), which makes it one of the finest combined resolutions ever used to complete historical and scenario-like CMIP6 simulations. To evaluate the influence of numerical resolution on the simulated climate, EC-Earth3P-VHR is compared with two configurations of the same model at lower resolution: the ∼100 km grid EC-Earth3P-LR (LR) and the ∼25 km grid EC-Earth3P-HR (HR). Of the three configurations, VHR shows the smallest drift in the global mean ocean temperature and salinity at the end of a 100-year 1950s control simulation, which points to a faster equilibrating phase than in LR and HR. In terms of model biases, we compare the historical simulations against observations over the period 1980–2014. In contrast to LR and HR, VHR shows a reduced equatorial Pacific cold tongue bias, an improved Gulf Stream representation with a reduced coastal warm bias and a reduced subpolar North Atlantic cold bias, and more realistic orographic precipitation over mountain ranges. By contrast, VHR shows a larger warm bias and overly low sea ice extent over the Southern Ocean. Such biases in surface temperature have an impact on the atmospheric circulation aloft, connected with a more realistic storm track over the North Atlantic yet a less realistic storm track over the Southern Ocean compared to the lower-resolution model versions. Other biases persist or worsen with increased resolution from LR to VHR, such as the warm bias over the tropical upwelling region and the associated cloud cover underestimation, a precipitation excess over the tropical South Atlantic and North Pacific, and overly thick sea ice and an excess in oceanic mixing in the Arctic. VHR shows improved air–sea coupling over the tropical region, although it tends to overestimate the oceanic influence on the atmospheric variability at midlatitudes compared to observations and LR and HR. Together, these results highlight the potential for improved simulated climate in key regions, such as the Gulf Stream and the Equator, when the atmospheric and oceanic resolutions are finer than 25 km in both the ocean and atmosphere. Thanks to its unprecedented resolution, EC-Earth3P-VHR offers a new opportunity to study climate variability and change of such areas on regional and local spatial scales, in line with regional climate models.

Iterative inversion method for retrieving the optical properties of aerosol under cloud by ground-based scanning lidar

Low and dense cloud cover is often encountered in daily observing experiments with lidar. In such scenarios, the laser cannot penetrate the clouds and it is not possible to use the clean atmosphere to initially calibrate the calibration heights required for the actual inversion, which can pose a significant challenge to the inversion of aerosols under clouds. This paper proposes an iterative proximal calibration algorithm based on slope method and Fernald's backward integral equation, combined with the inherent observational mode characteristics of the micro infrared lidar (mIRLidar). By conditioning the iterative process, the relative error between the obtained extinction coefficient value and the true value becomes smaller and smaller as the number of iterations increases. According to the inversion results of the actual lidar observation data, it can be tentatively concluded that the use of the proximal calibration iterative algorithm eliminates the limitations of the traditional Fernald height calibration method and improves the accuracy of the under-cloud aerosol inversion.

Short-Medium-Term Solar Irradiance Forecasting with a CEEMDAN-CNN-ATT-LSTM Hybrid Model Using Meteorological Data

In recent years, the adverse effects of climate change have increased rapidly worldwide, driving countries to transition to clean energy sources such as solar and wind. However, these energies face challenges such as cloud cover, precipitation, wind speed, and temperature, which introduce variability and intermittency in power generation, making integration into the interconnected grid difficult. To achieve this, we present a novel hybrid deep learning model, CEEMDAN-CNN-ATT-LSTM, for short- and medium-term solar irradiance prediction. The model utilizes complete empirical ensemble modal decomposition with adaptive noise (CEEMDAN) to extract intrinsic seasonal patterns in solar irradiance. In addition, it employs a hybrid encoder-decoder framework that combines convolutional neural networks (CNN) to capture spatial relationships between variables, an attention mechanism (ATT) to identify long-term patterns, and a long short-term memory (LSTM) network to capture short-term dependencies in time series data. This model has been validated using meteorological data in a more than 2400 masl region characterized by complex climatic conditions south of Ecuador. It was able to predict irradiance at 1, 6, and 12 h horizons, with a mean absolute error (MAE) of 99.89 W/m2 in winter and 110.13 W/m2 in summer, outperforming the reference methods of this study. These results demonstrate that our model represents progress in contributing to the scientific community in the field of solar energy in environments with high climatic variability and its applicability in real scenarios.

Understanding the atmospheric dynamics over high-altitude glaciated regions in the central Himalaya using high-resolution numerical simulations

Abstract The atmospheric dynamics over the higher Himalaya play a crucial role in controlling glacier variability and ultimately regulating downstream water availability. However, the paucity of station observations makes exploring these atmospheric dynamics challenging. To address these issues, we employed a high-resolution configuration of the Weather Research and Forecasting (WRF) model with a 2-km convection-permitting grid scale to simulate atmospheric variables over the central Himalaya. We analyze the atmospheric variables over three high-altitude glaciated regions, namely the Langtang, Rolwaling, and Everest regions, in the central Himalaya. The model reproduces precipitation and temperature seasonality well, with the model precipitation displaying better agreement with the station data and outperforming the satellite observations. Furthermore, we investigate the spatial variability of precipitation during the monsoon and winter seasons and explore the associated dynamics by computing the vertically integrated moisture transport (VIMT) and vertically integrated moisture flux divergence (VIMFD). The VIMT and VIMFD analysis reveal that the valleys that meridionally dissect the Himalaya transport the moisture from the Indo-Gangetic plains to the higher Himalaya, and the sharp rise in elevation results in moisture convergence and precipitation, with the results that the ridges and windward slopes accumulate more precipitation than the leeward slopes and valleys. The orographic effect on moisture convergence, cloud formation, and precipitation consequently controls the glacier energy balance. During the monsoon season, the enhancement of net radiation by thick monsoon cloud cover, coupled with added latent heat and reduced albedo from the dominant liquid precipitation below 5,000 m amsl, increases the melt energy, promoting glacier melt.

Identifying narco-trafficking landing zones using satellite imagery and geospatial indicators in Costa Rica

Transnational cocaine trafficking, or ‘narco-trafficking’, networks often move large shipments of harmful and illegal drugs by sea through the use of unregulated boats in remote maritime spaces. This study presents a framework for identifying narco-trafficking drop-off zones by detecting and analyzing Unreported and Unregulated Boats (UUBs) potentially linked to narco-trafficking in Costa Rica’s Guanacaste and Puntarenas regions, utilizing a combination of high-resolution satellite imagery, machine learning, and spatial analysis. By training a YoloV5 convolutional neural network model, we detected boat wakes in PlanetScope satellite images, which were then cross-referenced with legal vessel traffic data (Automatic Identification System and Vessel Monitoring System) to isolate UUBs. A spatial autocorrelation analysis revealed a positive association between UUB locations and narco-trafficking activity indicators, such as drug-related media reports, court arrest records, and cocaine seizure data. High-high clusters of UUBs and trafficking indicators suggested that particular coastal districts may serve as primary landing zones for illicit shipments, a finding consistent with secondary data on cocaine trafficking in the region. By integrating geospatial intelligence with contextual data sources, this study advances methodology for identifying narco-trafficking drop-off zones and contributes a spatially explicit perspective to the broader understanding of cocaine and other illicit supply chains. Despite the limitations of cloud cover and restricted nighttime visibility, this framework offers a proof-of-concept approach for identifying UUB concentrations and cocaine drop-off shipment zones. Future work should consider expanding temporal coverage and multiple imagery to further enhance the identification of narco-trafficking zones.