Cloud Cover Research Articles

Analyzing Temporal Characteristics of Winter Catch Crops Using Sentinel-1 Time Series

Catch crops are intermediate crops sown between two main crop cycles. Their adoption into the cropping system has increased considerably in the last years due to its numerous benefits, in particular its potential in carbon fixation and preventing nitrogen leaching during winter. The growth period of catch crops in Germany is often marked by dense cloud cover, which limits land surface monitoring through optical remote sensing. In such conditions, synthetic aperture radar (SAR) emerges as a viable option. Despite the known advantages of SAR, the understanding of temporal behavior of radar parameters in relation to catch crops remains largely unexplored. Hence, in this study, we exploited the dense time series of Sentinel-1 data within the Copernicus Space Component to study the temporal characteristics of catch crops over a test site in the center of Germany. Radar parameters such as VV, VH, VH/VV backscatter, dpRVI (dual-pol Radar Vegetation Index) and VV coherence were extracted, and temporal profiles were interpreted for catch crops and preceding main crops along with in situ, temperature, and precipitation data. Additionally, we examined the temporal profiles of winter main crops (winter oilseed rape and winter cereals), that are grown parallel to the catch crop growing cycle. Based on the analyzed temporal patterns, we defined 22 descriptive features from VV, VH, VH/VV and dpRVI, which are specific to catch crop identification. Then, we conducted a Kruskal–Wallis test on the extracted parameters, both crop-wise and group-wise, to assess the significance of statistical differences among different catch crop groups. Our results reveal that there exists a unique temporal pattern for catch crops compared to main crops, and each of these extracted parameters possess a different sensitivity to catch crops. Parameters VV and VH are sensitive to phenological stages and crop structure. On the other hand, VH/VV and dpRVI were found to be highly sensitive to crop biomass. Coherence can be used to detect the sowing and harvest events. The preceding main crop analysis reveals that winter wheat and winter barley are the two dominant main crops grown before catch crops. Moreover, winter main crops (winter oilseed rape, winter cereals) cultivated during the catch crop cycle can be distinguished by exploiting the observed sowing window differences. The extracted descriptive features provide information about sowing, harvest, vigor, biomass, and early/late die-off nature specific to catch crop types. In the Kruskal–Wallis test, the observed high H-statistic and low p-value in several predictors indicates significant variability at 0.001 level. Furthermore, Dunn’s post hoc test among catch crop group pairs highlights the substantial differences between cold-sensitive and legume groups (p < 0.001).

NDWI as a method for studying natural and technogenic aufeis along the Lena Highway (Southern Yakutia)

The research conducted has yielded, for the first time, a quantitative evaluation of the NDWI (Normalized Difference Water Index) for the purpose of identifying areas of natural and technogenic aufeis within Southern Yakutia. In contrast to the widely used NDSI (Normalized Difference Snow Index) in aufeis investigations, the NDWI offers the advantage of delineating aufeis contours without being influenced by the presence or absence of snow cover. Calculations pertaining to the aufeis areas in the “Gorbyllakh” and “Durai” sections of the Federal Highway “Lena” during the winters of 2020–2021 and 2021–2022 indicate a notable increase in these areas, attributed to heightened precipitation levels anticipated in the subsequent summer period. However, limitations of this approach include the interference of cloud cover in satellite imagery, which can obscure the study area, as well as the potential for ambiguous interpretations of shaded regions within the aufeis meadow, where the NDWI may suggest the presence of surface water.

ANALYSIS OF GRANITE ROCK WEATHERING IN THE PARMONANGAN AREA USING LANDSAT 8 SATELLITE IMAGERY

A research has been conducted to analyze the weathering of granite rocks in the Parmonangan area using Lansat 8 satellite imagery which aims to determine the distribution of rocks, weathering processes, and types of rock forming elements in Parmonangan District, North Tapanuli Regency. The method used is using shp geologic map data and Lansat-8 image data processed using Arcgis Map 10.6.1 to determine the distribution of rocks and land cover. XRF is used to identify compounds and rock-forming elements. The results obtained by the rock distribution of the parmonangan area are in the form of the Kluet Formation, Sibolga Complex and Toba Tuff. Land cover in the parmonagan area has differences from 2016 to 2024. Open land in 2024 has increased and the cloud cover obtained has also increased. The rock elements found are Al2O3 (25.31), SiO2 (24.97), K2O (1.56), TiO2 (0.81), Mn2O (30.66), Fe2O3 (4.66), ZrO2, (0.21), Sb2O3 (0.16), and BaO (0.17).

Generation of the Quasi‐Biweekly Oscillation in the Surface Potential Vorticity Over the Tibetan Plateau During Boreal Summer: A Case Study of 2014

AbstractThe atmospheric circulation around the Tibetan Plateau (TP) exhibits a substantial 10–20‐day quasi–biweekly oscillation (QBWO), profoundly impacting weather and climate locally and remotely. Understanding the factors influencing the generation of QBWO over the TP (QBWOTP) and its physical mechanism is crucial. This study has investigated the influence of multi–timescale and land–atmosphere interactions on the generation of the QBWOTP in surface potential vorticity (SPV), a valuable tool for characterizing the mechanical and thermal variabilities in mountain forcing, based on a 2014 case study. Results indicate that in the free atmosphere, the summer monsoon onset over the Bay of Bengal induces a northward shift in the westerly jet toward the TP, manifested as an increase in low‐frequency zonal winds. This shift facilitates the propagation of wave trains, leading to atmospheric quasi–biweekly potential temperature anomalies (θa) over the TP through a multi‐timescale interaction. Additionally, the TP's surface thermal forcing and arrival of wave trains trigger anomalous upward motion and increase cloud cover. The resultant decrease in net short‐wave radiations and increase in net long‐wave radiations contribute to variations in surface potential temperature (θs) over the TP. As θa and θs evolve, the difference between them enlarges, resulting in the generation of the SPV QBWOTP. Given the relationship between the QBWOTP and downstream rainfall, this study could provide novel insights into understanding and predicting downstream rainfall QBWO.

Remote sensing detection of plastic-mulched farmland using a temporal approach in machine learning: case study in tomato crops.

The increasing use of plastics in rural environments has led to concerns about agricultural plastic waste (APW). However, the plasticulture information gap hinders waste management planning and may lead to plastic residue leakage into the environment with consequent microplastic formation. The location and estimated quantity of the APW are crucial for territorial planning and public policies regarding land use and waste management. Agri-plastic remote detection has attracted increased attention but requires a consensus approach, particularly for mapping plastic-mulched farmlands (PMFs) scattered across vast areas. This article tests whether a streamlined time-series approach minimizes PMF confusion with the background using less processing. Based on the literature, we performed a vast assessment of machine learning techniques and investigated the importance of features in mapping tomato PMF. We evaluated pixel-based and object-based classifications in harmonized Sentinel-2 level-2A images, added plastic indices, and compared six classifiers. The best result showed an overall accuracy of 99.7% through pixel-based using the multilayer perceptron (MLP) classifier. The 3-time series with a 30-day composite exhibited increased accuracy, a decrease in background confusion, and was a viable alternative for overcoming the impact of cloud cover on images at certain times of the year in our study area, which leads to a potentially reliable methodology for APW mapping for future studies. To our knowledge, the presented PMF map is the first for Latin America. This represents a first step toward promoting the circularity of all agricultural plastic in the region, minimizing the impacts of degradation on the environment.

Benchmark of estimated solar irradiance data at high latitude locations

Estimated solar irradiances from CAMS, PVGIS SARAH-2, Solargis, Meteonorm, PVGIS ERA5, and NASA POWER are benchmarked against measurements conducted at 34 ground stations in Norway at latitudes between 58 and 76°N. We find that the data products that mainly rely on high-resolution, geostationary satellite images, i.e., CAMS, PVGIS SARAH-2, and Solargis, have higher accuracy with lower relative Mean Absolute Error (rMAE) and relative Mean Bias Error. By dividing the stations in distinct categories, such as above 65°N, snow-affected and horizon-shaded, challenges with irradiance estimation that are common in Norway and at high latitudes in general are highlighted and discussed. The accuracy of the data products is dependent on latitude, and by excluding stations above 65°N, the median rMAE of the different data products improves 3.2 – 9.4 %abs compared to the median rMAE when including all stations, depending on data product. Similarly, by excluding snow-affected stations, the median rMAE improves 1.9 – 8.1 %abs, depending on data product. The improvement in rMAE by excluding snow-affected stations is partially related to the difficulty of separating snow on the ground from cloud cover in satellite images. This difficulty is illustrated by concrete examples of irradiance time series from clear sky days when the ground is covered in snow. Although the performance of the data products is dependent on the categorization of stations, i.e., latitude, snow conditions, and local topography, the relative performance between the products is maintained regardless of sub-division.

Performance of advanced-deep learning algorithm for modeling and prediction of rain height over some selected locations in the tropics and sub-tropics zone for radio propagation applications

As demand for high-frequency broadband communication services keeps rising, rain-induced attenuation remains the predominant threat to radiowave propagation. Accurate prediction of attenuation requires continuous measurement and monitoring of rain-induced meteorological parameters, specifically rain rate and rain height, due to their spatio-temporal variations. Rain height is an upper dataset mostly computed from Zero- degree Isotherm Heights (ZDIH) measured by radar. This research proposes a novel approach for predicting rain height from earth surface data such as surface temperature, pressure, total cloud cover, dew point temperature, surface solar radiation, water vapor amount in the air, and humidity. This research investigates the relationship between meteorological surface data and rain height. Subsequently, six machine learning models were employed for predicting rain height using the ten years surface data as input variables. The models were applied to six sub-tropical (Polokwane, Pretoria. and Cape Town) and tropical (Sokoto, Akure, and Port Harcourt (PH)) stations in South Africa and Nigeria, respectively. Analysis of the results shows that the Gradient Boosting Algorithm (GBA) performed best with determination coefficients greater than 0.80 and RMSE less than 350 in all three stations in South Africa. However, all the models failed to produce good result for the Nigeria stations. The Random Forest model has the fairest performance metrics with r2 of 0.40, 0.46 and 0.46 in Sokoto, Akure and PH. respectively. GBA is recommended for predicting rain height in South Africa. The research outcome would assist radio engineers in improving the prediction of rain-induced attenuation and determining appropriate fade mitigation techniques.

Machine learning-based modeling of surface water temperature dynamics in arctic lakes.

Lake surface-water temperature (LSWT) regulates physical and biochemical processes in lakes. Therefore, understanding the LSWT dynamics is important, especially in Arctic zone since the region is experiencing a warming rate that is greater than the Earth's average. However, regular measurements of LSWT in the remote Arctic lakes always face difficulties or cannot be done by satellites accurately due to the cloud cover and their limited spatiotemporal resolution. Here, we used a historically rich data (1960-2023) to develop four machine learning-based algorithms for the daily LSWT modeling in Lake Inari, situated in Arctic zone, using the air-temperature data. Our results showed that both air-temperature (0.030 °C/yr) and LSWT (0.023m °C/yr) were warming with a rate faster than those in the globe. The long-short-term memory model, with the coefficients of determination varied from 0.96 to 0.98, outperformed other algorithms in modeling of the daily LSWT dynamics in Lake Inari, followed by both support vector regression and neural network tools, and random forest model. As the air-temperature data are widely accessible through synoptic stations and remote sensing techniques, our suggested models can be simply adopted for other Arctic lakes, where the local water-temperature data are often lacking or contain large windows of missing data due to harsh atmospheric conditions and equipment failure.

Characterizing Wet Season Precipitation in the Central Amazon Using a Mesoscale Convective System Tracking Algorithm

AbstractTo comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting model at a convection‐permitting resolution. This study spans a 2‐month period during the wet seasons of 2014 and 2015. We observe a strong correlation between the MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewer MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥10 mm hr−1). These biases in simulated MCS properties could vary with the constraints on the convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A case study for 1 April 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in both stratiform and convective precipitation features.

Comparison and Optimization of Light Use Efficiency-Based Gross Primary Productivity Models in an Agroforestry Orchard

The light-use efficiency-based gross primary productivity (LUE-GPP) model is widely utilized for simulating terrestrial ecosystem carbon exchanges owing to its perceived simplicity and reliability. Variations in cloud cover and aerosol concentrations can affect ecosystem LUE, thereby influencing the performance of the LUE-GPP model, particularly in humid regions. In this study, the performance of six big-leaf LUE-GPP models and one two-leaf LUE-GPP model were evaluated in a humid agroforestry ecosystem from 2018–2020. All big-leaf LUE-GPP models yielded GPP values consistent with that derived from the eddy covariance system (GPPEC), with R2 ranging from 0.66–0.73 and RMSE ranging from 1.81–3.04 g C m−2 d−1. Differences in model performance were attributed to the differences in the quantification of temperature (Ts) and moisture constraints (Ws) and their combination forms in the models. The Ts and Ws algorithms in the eddy covariance-light-use efficiency (EF-LUE) model well characterized the environmental constraints on LUE. Simulation accuracy under the common limitation of Ts and Ws (Ts × Ws) was higher than the maximum limitation of Ts or Ws (Min (Ts, Ws)), and the combination of the Ts algorithm in the Carnegie–Ames–Stanford Approach (CASA) and the Ws algorithm in the EF-LUE model was optimized in combination forms, thereby constraining LUE for GPP estimates (GPPBLO, R2 = 0.76). Various big-leaf LUE-GPP models overestimated or underestimated GPP on sunny or cloudy days, respectively, while the two-leaf LUE-GPP model, which considered the transmission of diffuse radiation and the difference in photosynthetic capacity of canopy leaves, performed well (R2 = 0.72, p < 0.01). Nevertheless, the underestimation/overestimation for shaded/sunlit leaves remained under different weather conditions. Then, the clearness index (Kt) was introduced to calculate the dynamic LUE in the big-leaf and two-leaf LUE-GPP models in the form of exponential or power functions, resulting in consistent performance even in different weather conditions and an overall higher simulation accuracy. This study confirmed the potential applicability of different LUE-GPP models and emphasized the importance of dynamic LUE on model performance.

Comparing Sentinel-2 and Landsat 9 for land use and land cover mapping assessment in the north of Congo Republic: a case study in Sangha region

ABSTRACT A study of land use and land cover (LULC) mapping using satellite data was conducted in the Congo Basin rainforest in the Sangha area of the northern Congo Republic, which is crucial for sustainable land management. However, the heterogeneous nature of LULC features makes data classification difficult in this area because persistent cloud cover is challenging for observing the land surface and tropical humid conditions characterizing the study area. This study compares and evaluates the potential of two of the most widely used and popular satellite image sources, the recently launched Landsat 9 and Sentinel-2, to generate land-use land cover (LULC) maps in the Sangha area using pixel-based supervised machine learning algorithms, Random Forest (RF), for classification. All procedures were performed using ArcGIS Pro. The potential to improve Landsat 9 performance was tested using the nearest resampling technique, and different bands were resampled from 30 × 30 to 10 × 10 m. Owing to the heterogeneous characteristics of the Sangha study area, six classes were defined: dense forest, open forest, wetland forest, water bodies, urban areas, and bare soils. To classify and compare Sentinel-2 and Landsat 9, the shortwave infrared (SWIR), Near Infrared (NIR), and Red, Green, Blue (RGB) bands were used. These two satellite images were compared to test the quality of their results, particularly for assessing the accuracy of the LULC classifications and identifying which dataset had the highest accuracy. Results show that overall accuracy was 93.80% for Sentinel-2 while it was 91.60% for Landsat 9, similarly, the Kappa coefficient was calculated 0.89 and 0.85 for Sentinel-2 and Landsat 9, respectively. Therefore, Sentinel-2 exhibits significant classification ability compared to the nearest resample technique Landsat 9, despite the improved resolution of the latter, which offers a scientific basis for choosing the appropriate satellite imagery to create accurate LULC maps.

Annotated Dataset for Training Cloud Segmentation Neural Networks Using High-Resolution Satellite Remote Sensing Imagery

The integration of satellite data with deep learning has revolutionized various tasks in remote sensing, including classification, object detection, and semantic segmentation. Cloud segmentation in high-resolution satellite imagery is a critical application within this domain, yet progress in developing advanced algorithms for this task has been hindered by the scarcity of specialized datasets and annotation tools. This study addresses this challenge by introducing CloudLabel, a semi-automatic annotation technique leveraging region growing and morphological algorithms including flood fill, connected components, and guided filter. CloudLabel v1.0 streamlines the annotation process for high-resolution satellite images, thereby addressing the limitations of existing annotation platforms which are not specifically adapted to cloud segmentation, and enabling the efficient creation of high-quality cloud segmentation datasets. Notably, we have curated the Annotated Dataset for Training Cloud Segmentation (ADTCS) comprising 32,065 images (512 × 512) for cloud segmentation based on CloudLabel. The ADTCS dataset facilitates algorithmic advancement in cloud segmentation, characterized by uniform cloud coverage distribution and high image entropy (mainly 5–7). These features enable deep learning models to capture comprehensive cloud characteristics, enhancing recognition accuracy and reducing ground object misclassification. This contribution significantly advances remote sensing applications and cloud segmentation algorithms.

Atmospheric variability drives anomalies in the Bering Sea air-sea heat exchange

Abstract High-latitudes, including the Bering Sea, are experiencing unprecedented rates of change. Long-term Bering Sea warming trends have been identified, and marine heatwaves (MHWs), event-scale elevated sea surface temperature (SST) extremes, have also increased in frequency and longevity in recent years. Recent work has shown that variability in air-sea coupling plays a dominant role in driving Bering Sea upper ocean thermal variability, and that surface forcing has driven an increase in the occurrence of positive ocean temperature anomalies since 2010. In this work, we characterize the drivers of the anomalous surface air-sea heat fluxes in the Bering Sea over the period 2010-2022 using ERA5 fields. We show that the surface turbulent heat flux dominates the net surface heat flux variability from September-April, and is primarily a result of near-surface air temperature and specific humidity anomalies. The air-mass anomalies that account for the majority of the turbulent heat flux variability are a function of wind direction, with southerly (northerly) wind advecting anomalously warm (cool), moist (dry) air over the Bering Sea, resulting in positive (negative) surface turbulent flux anomalies. During the remaining months of the year, anomalies in the surface radiative fluxes account for the majority of the net surface heat flux variability, and are a result of anomalous cloud coverage, anomalous lower tropospheric virtual temperature, and sea ice coverage variability. Our results indicate that atmospheric variability drives much of the Bering Sea upper ocean temperature variability through mediation of the surface heat fluxes during the analysis period.

Satellite retrieval of oceanic particulate organic carbon: Towards an accurate and seamless dataset for the global ocean

Particulate organic carbon (POC) plays crucial roles in the global ocean carbon cycle and the oceanic biological pump. Satellite remote sensing has been demonstrated to be an effective technique for the retrieval of surface oceanic POC concentration. However, the complex spatiotemporal variations of the relationships between POC and oceanic optical properties across different waters posed challenges for accurate retrieval of POC concentration from satellite observations. Additionally, interference factors, such as cloud cover and sun glint, resulted in severe data missing problems and impeding daily coverage of the global ocean. With an attempt to generate accurate, seamless and readily available POC products for the global ocean, this study aimed to develop accurate satellite POC retrieval models for the Moderate Resolution Imaging Spectroradiometer (MODIS) data from both Terra and Aqua satellites, and to explore the possibility of using the empirical orthogonal function interpolation technique (DINEOF) to reconstruct satellite-retrieved POC data to generate gap-free global oceanic POC products. Results showed that the eXtreme Gradient Boosting (XGBoost) method could accurately retrieve POC with R2 approximately 0.80 and RMSE about 0.20 in log10 scale, obviously outperforming the operational blue-to-green band ratio algorithm and the hybrid polynomial algorithm based on two multi-band indices; and the DINEOF method, which could reconstruct approximately 88 % missing pixels for the global ocean, contributed to better revealing the global oceanic POC variations at a daily scale than the satellite-retrieved POC products. Based on the developed models, a suit of long time-series accurate and seamless POC products of the global surface ocean were generated, which is readily available for other applications and should be helpful to investigate the spatiotemporal variations of POC concentrations over global ocean and its roles in the global carbon cycle. The generated seamless products are openly accessible via the DOIs listed in the data availability section.

Study of parabolic trough collector with crucial analysis on controller and estimator with variable mirror efficiency

Solar Thermal Power (STP) plants are a crucial technology for converting solar energy into electricity. Among the STP, the Parabolic Trough Collector (PTC) is one of the integral parts of STP systems for electrical power generation. Maximizing PTC performance is a challenge due to dynamic and unpredictable solar radiation and environmental disturbances, such as cloud cover and dust accumulation. The optical efficiency parameter of the PTC is critical for calculating the desired heat gain, which is difficult to measure in real-time, necessitating sophisticated control and estimation strategies for optimal heat regulation. This study introduces two control mechanisms for the PTC system and comparing their efficacy. A classical PI controller supplemented by Static Feed-Forward (SFF) control demonstrates improved performance with an optimal transfer function model. In contrast, Nonlinear Model Predictive Control (NMPC) excels in disturbance rejection and setpoint tracking, considering operational constraints. The NMPC controller notably outperforms the PI controller with SFF in performance metrics, with the Integral Time Squared Error (ITSE) decreasing by approximately 99% across case studies. Also, in the case of heat gain, NMPC controller exhibits percentage increases when compared to the PI controller. Furthermore, since the measurement of optical efficiency is essential, but due to difficulty in the measurement for various reasons, the estimator is used as a virtual sensor to estimate those optical efficiency. The unmeasured states and parameters, including optical efficiencies of the PTC, are estimated using Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) techniques, with UKF exhibiting superior accuracy. However, considering the average computation time, EKF is preferred for real-time applications.

EMD-based ultraviolet radiation prediction for sport events recommendation with environmental constraint

With the rising awareness of health and wellness, accurate ultraviolet (UV) radiation forecasts have become crucial for planning and conducting outdoor activities safely, particularly in the context of global sporting events arrangement and recommendation with definite constraint on environmental conditions. The dynamic nature of UV exposure, influenced by factors such as solar zenith angles, cloud cover, and atmospheric conditions, makes accurate UV radiation data forecasting difficult and challenging. To cope with these challenges, we present an innovative approach for predicting the UV radiation levels of a certain region during a certain time period using Empirical Mode Decomposition (EMD), a robust method for analyzing non-linear and non-stationary data. Our model is specifically designed for urban areas, where outdoor events are common, and integrates meteorological data with historical UV radiation records from specific geographic regions and time periods. The EMD-based model offers precise predictions of UV levels, essential for event organizers and city planners to make informed decisions regarding the scheduling, relocation and recommendation of events to minimize health risks associated with UV exposure. At last, the effectiveness of this model is validated through various experiments across different spatial and temporal contexts based on the Urban-Air dataset (recording 2,891,393 Air Quality Index data that cover four major Chinese cities), demonstrating its adaptability and accuracy under diverse environmental conditions.

Machine Learning approach in the prediction of Fog: An Early Warning System.

The aviation sector is extremely vulnerable to fog. Thus, accurate fog predictions are essential foraviation sector efficiency, particularly airport management and flight scheduling. Even with numerical weather predictionmodels and guiding systems, fog prediction is challenging. The difficulty of fog prediction is due to the inability to graspthe micro-scale factors that cause fog to form, intensify, augment and dissipate in the boundary layer. This study looks athow well machine learning (ML) tools can predict fog (Visibility <1000 m) and dense fog (Visibility <200 m) at India'sJay Prakash Narayan International Airport (ICAO Index-VEPT), a representative station of the Indo-Gangetic Plains(IGP). The proposed ensemble ML-based model was trained using hourly synoptic data from 2014 to 2020 and testedusing data from 2021 to 2022 (December to February). Once the features are chosen and the forecasters' local knowledgeis taken into account, the dry bulb temperature (°C), dew point temperature (°C), relative humidity (%), cloud amount(octa), wind direction (degrees from true north) and wind speed (knots) are used to build the proposed ML models. MLalgorithms were trained on meteorological data from 1500 to 2200 UTC to predict fog (Visibility <1000 m) and densefog (Visibility <200 m) for the next day at 0000 UTC, with a two-hour lead time. For fog forecasting at 0000 UTC with a 4-hour lead time, ML models were trained with data from 1300 to 2000 UTC and so on. This study evaluates parameter tuning in six level 0 ML models: distributed random forest (DFR), deep learning (DL), gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized tree (XRT), XG Boost and stacked ensemble at level 1. The performance metrics and statistical skill scores indicate that DRF and DL models perform well for lead times of 2 and 4 hours at level 0 for fog (visibility <1000 m) and dense fog (visibility <200 m). But the proposed ensemble models outperform all the base models at level 0 and are recognized as the best instrument for predicting fog at Patna Airport.

Detectability Simulations of a Near-infrared Surface Biosignature on Proxima Centauri b with Future Space Observatories

Telescope missions are currently being designed that will make direct imaging of habitable exoplanets possible in the near future, and studies are needed to quantify the detectability of biosignature features in the planet’s reflectance spectrum. We simulated the detectability of a near-infrared-absorbing surface biosignature feature with simulated observations of the nearby exoplanet Proxima Centauri b. We modeled a biosignature spectral feature with a reflectance spectrum based on an anoxygenic photosynthetic bacterial species that has strong absorption at 1 μm, which could make it well suited for life on an M-dwarf-hosted planet. We modeled the distribution of this organism across the planet’s surface based on climate states from a 3D general circulation model (GCM) that were Archean- and Proterozoic-like exo-Earth analogs. We included the GCM states' prognostically simulated water clouds and added organic haze into the Archean-like atmospheres. We simulated observations of these Proxima Centauri b scenarios with the LUVOIR-A and B telescope concepts, with LUVOIR-B serving as a proxy to the planned Habitable Worlds Observatory. We calculated the integration times necessary to detect the biosignature and found that it would be detectable on Proxima Centauri b if the organism is moderately abundant (greater than a 1%–4% global surface area coverage), as long as the atmosphere is transmitting in the wavelength range under consideration. Small amounts of methane, clouds, and haze do not greatly impede detectability. We found preliminary evidence that such a biosignature would be detectable on exoplanets within 15 pc, but further investigations are needed to corroborate this.

AI-Driven Forecasting for Morning Fog Expansion (Sea of Clouds)

Abstract This study attempted to forecast the morning fog expansion (MFE), commonly referred to as the “sea of clouds,” utilizing an artificial intelligence (AI) algorithm. The radiation fog phenomenon that contributes to the sea of clouds is caused by various weather conditions. Hence, the MFE was predicted using datasets from public meteorological observations and a mesoscale numerical model (MSM). In this study, a machine learning technique, the gradient-boosting method, was adopted as the AI algorithm. The Miyoshi basin in Japan, renowned for its MFE, was selected as the experimental region. Training models were developed using datasets from October to December 2018–21. Subsequently, these models were applied to forecast MFE in 2022. The model employing the upper-atmospheric prediction data from the MSM demonstrated the highest robustness and accuracy among the proposed models. For untrained data in the fog season during 2022, the model was confirmed to be sufficiently reliable for forecasting MFE, with a high hit rate of 0.935, a low Brier score of 0.119, and a high area under the curve (AUC) of 0.944. Furthermore, the analysis of the importance of the features elucidated that the meteorological factors, such as synoptic-scale weak wind, temperatures close to the dewpoint temperature, and the absence of middle-level cloud cover at midnight, strongly contribute to the MFE. Therefore, the incorporation of upper-level meteorological elements improves the forecast accuracy for MFE. Significance Statement An AI-driven forecasting model for predicting morning fog expansion (MFE), sea of clouds, which often affects local livelihoods, was constructed. Fog forecasting machine learning techniques were utilized in the Japanese region famous for the morning fog. This study revealed that more accurate forecasting models incorporate numerically predicted weather elements sourced from the public routine system rather than real-time observed weather elements. Notably, the upper-level wind speed reflecting synoptic-scale dynamics, surface dewpoint depression, and middle-level cloud cover play significant roles in governing MFE. Therefore, incorporating upper-level meteorological elements into the features to machine learning is crucial for improving the forecasting accuracy of MFE.

Influence of Mini Warm Pool Extent on Phytoplankton Productivity and Export in the Arabian Sea

The Arabian Sea is a highly productive tropical ecosystem of the Indian Ocean that supports high fluxes of particulate organic carbon to the mesopelagic zone from two distinct periods of elevated biological productivity associated with the semiannual reversals of the monsoonal wind system. There are now strong indications that the Arabian Sea’s monsoonal wind patterns and hydrographic conditions are being impacted by long-term temperature increases, but the consequences of these changes on primary production and carbon export to the mesopelagic zone are unknown. This is especially true for the summer monsoon period when cloud cover obscures much of the Arabian Sea basin and therefore precludes remotely sensed ocean color measurements for estimating phytoplankton biomass and productivity. Here we overcome this limitation by using a database of bio-optical profiles from Biogeochemical Argo floats collected over the last decade to evaluate the impact of interannual temperature increases on Arabian Sea primary production and carbon export. We classify individual years of float observations based on the spatial extent of the Arabian Sea Mini Warm Pool that appears in the southeast Arabian Sea before the onset of the summer monsoon. This mini warm pool, which begins to build in winter and collapses with the onset of the summer monsoon in late spring, has gained considerable interest on account of its influence on the timing of the onset of the summer monsoon. We observed a 35 percent decrease in primary production during the summer monsoon phytoplankton bloom in strong warm pool years, and a 13 percent decrease in particle stocks in the upper mesopelagic zone following the peak of the bloom. Decreases in production and export were additionally accompanied by a decrease in average particle size, indicating a shift from larger cells like diatoms that appear from fertilization of the oligotrophic waters to smaller phytoplankton size classes in response to a deepening of the thermocline and increased stratification of the water column. These results suggest changes in phytoplankton community structure and further decreases in primary production and carbon export in the Arabian Sea in response to future warming.