Depth Data Research Articles

Short-Term Wind Power Prediction Based on Encoder-Decoder Network and Multi-Point Focused Linear Attention Mechanism.

Wind energy is a clean energy source that is characterised by significant uncertainty. The electricity generated from wind power also exhibits strong unpredictability, which when integrated can have a substantial impact on the security of the power grid. In the context of integrating wind power into the grid, accurate prediction of wind power generation is crucial in order to minimise damage to the grid system. This paper proposes a novel composite model (MLL-MPFLA) that combines a multilayer perceptron (MLP) and an LSTM-based encoder-decoder network for short-term prediction of wind power generation. In this model, the MLP first extracts multidimensional features from wind power data. Subsequently, an LSTM-based encoder-decoder network explores the temporal characteristics of the data in depth, combining multidimensional features and temporal features for effective prediction. During decoding, an improved focused linear attention mechanism called multi-point focused linear attention is employed. This mechanism enhances prediction accuracy by weighting predictions from different subspaces. A comparative analysis against the MLP, LSTM, LSTM-Attention-LSTM, LSTM-Self_Attention-LSTM, and CNN-LSTM-Attention models demonstrates that the proposed MLL-MPFLA model outperforms the others in terms of MAE, RMSE, MAPE, and R2, thereby validating its predictive performance.

First telemetry insights into the movements and vertical habitat use of megamouth shark (Megachasma pelagios) in the northwest Pacific

The megamouth shark (Megachasma pelagios) is one of the ocean's largest and most enigmatic elasmobranchs, with only a few hundred individuals ever recorded. Most of what is known about the species comes from rare fishery bycatch, stranding, or sighting events, precluding an in-depth understanding of its movement ecology. Here, we report the results from three megamouth sharks outfitted with pop-up satellite archival transmitting tags to assess the species' horizontal and vertical movement patterns. Deployments of 12, 58, and 244 d in duration provided the first direct evidence of multi-month fidelity to the waters east of Taiwan and seasonal movement out of the region. Depth and temperature data revealed a pattern of normal diel vertical migration, with the majority of the day spent in the mesopelagic zone and night in the epipelagic. Vertical habitat use suggests potential behavioral thermoregulation and was consistent with tracking of migrating mesopelagic prey across diel periods. We discuss the specialized analytical methods needed to reconstruct the spatial habitat use of deep-diving megamouth shark from tag sensor measurements of the magnetic field, as well as avenues for future research on this understudied megaplanktivore.

Fuzzy c-mean (FCM) integration of geophysical data from an iron-oxide copper gold (IOCG) deposit under thick cover

Geophysical methods depend on a range of physical and chemical mechanisms, and each method has different sensitivities and resolution of Earth parameters, and over different scale lengths. Additionally, such geophysical methods will also have their own statistical characteristics. Thus, it is a significant challenge to combine different methods through a single inversion framework. Rather than attempting to find an optimal and single model for different geophysical responses, an alternative approach is to use FCM clustering to identify clusters of parameters from two or more different geophysical data sets or models that have similar statistical properties. In this paper, we apply the FCM approach to integrate data and model sets for an array of 100 broadband magnetotelluric (MT) and 100 passive seismic receivers spaced 1 km apart on a 10 by 10 grid above the Vulcan IOCG prospect in the Olympic Cu–Au Province, southern Australia. The challenge for exploration of the Vulcan prospect is that it lies beneath 750 m of regolith sedimentary cover, no single geophysical method provides a unique characterization of the deposit geometry, and drilling is very expensive. Fuzzy c-mean cluster analyses are undertaken in 2D for gravity data and shear-wave velocity model data for basement depths beneath cover and in 3D for shear-wave velocity and electrical resistivity model data. Fuzzy c-mean clustering is shown to provide a simple and efficient method of integrating different geophysical measurements to produce a geological framework that can be verified with drilling information.

3D surface change detection of slope using 3D point cloud based on construction of double surface

Aiming at the complex geological conditions of slopes and the difficulty of manual investigation, this paper proposes a method of constructing double surface based on 3D point cloud data to estimate the depth of slope deformation and disaster analysis. Terrestrial Laser Scanner is utilized to collect the slope point cloud data. Through the construction of double surface to simulate the original slope, the deformation depth is estimated and the depth data are fused into the point cloud to realize the automatic identification of slope deformation disaster and the calculation of deformation depth, and provide the deformation depth map. The shortcomings of the traditional Multiscale Model and Model Point Cloud Comparison (M3C2) algorithm, which needs to capture multiple time periods of point cloud data for calculation can be overcome. And there is no need to recognize the segmentation of the slope point cloud. The study results show that the maximum relative error of slope deformation is 10.82% in slope deformation. The proposed method is suitable for detecting slope hazards in various scenarios, providing a basis for further analysis and the development of effective erosion control measures. It introduces a new technology for daily monitoring of slope deformations and can be extended to drone-based LiDAR applications.

Evaluation of WRF-Chem air quality forecasts during the AEROMMA and STAQS 2023 field campaigns

ABSTRACT A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system’s 1-, 2-, and 3-day ozone (O3) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O3 pollution. Comparisons to vertical O3 profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O3 concentrations on high O3 days and overestimate on low O3 days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O3 levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM2.5) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O3 levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system. Implications: Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system.

Accuracy of Bathymetric Depth Change Maps Using Multi-Temporal Images and Machine Learning

Most work to date on satellite-derived bathymetry (SDB) depth change estimates water depth at individual times t1 and t2 using two separate models and then differences the model estimates. An alternative approach is explored in this study: a multi-temporal Sentinel-2 image is created by “stacking” the bands of the times t1 and t2 images, geographically coincident reference data for times t1 and t2 allow for “true” depth change to be calculated for the pixels of the multi-temporal image, and this information is used to fit a single model that estimates depth change directly rather than indirectly as in the model-differencing approach. The multi-temporal image approach reduced the depth change RMSE by about 30%. The machine learning modelling method (categorical boosting) outperformed linear regression. Overfitting of models was limited even for the CatBoost models having the maximum number of variables examined. The visible Sentinel-2 spectral bands contributed most to the model predictions. Though the multi-temporal stacked image approach produced clearly superior depth change estimates compared to the conventional approach, it is limited only to those areas for which geographically coincident multi-temporal reference/“true” depth data exist.

High-Throughput and Accurate 3D Scanning of Cattle Using Time-of-Flight Sensors and Deep Learning.

We introduce a high-throughput 3D scanning system designed to accurately measure cattle phenotypes. This scanner employs an array of depth sensors, i.e., time-of-flight (ToF) sensors, each controlled by dedicated embedded devices. The sensors generate high-fidelity 3D point clouds, which are automatically stitched using a point could segmentation approach through deep learning. The deep learner combines raw RGB and depth data to identify correspondences between the multiple 3D point clouds, thus creating a single and accurate mesh that reconstructs the cattle geometry on the fly. In order to evaluate the performance of our system, we implemented a two-fold validation process. Initially, we quantitatively tested the scanner for its ability to determine accurate volume and surface area measurements in a controlled environment featuring known objects. Next, we explored the impact and need for multi-device synchronization when scanning moving targets (cattle). Finally, we performed qualitative and quantitative measurements on cattle. The experimental results demonstrate that the proposed system is capable of producing high-quality meshes of untamed cattle with accurate volume and surface area measurements for livestock studies.

Shallow Water Depth Estimation of Inland Wetlands Using Landsat 8 Satellite Images

Water depth affects many aspects of wetland ecology, hydrology, and biogeochemistry. However, acquiring water depth data is often difficult due to inadequate monitoring or insufficient funds. Satellite-derived bathymetry (SBD) data provides cost-effective and rapid estimates of the water depth across large areas. However, the applicability and performance of these techniques for inland wetlands have not been thoroughly evaluated. Here, a time series of bathymetry data for inland wetlands in West Kentucky and Tennessee were derived from Landsat 8 images using two widely used empirical models, Stumpf and a modified Lyzenga model and three machine learning models, Random Forest, Support Vector regression, and k-Nearest Neighbor. We processed satellite images using Google Earth Engine and compared the performance of water depth estimation among the different models. The performance assessment at validation sites resulted in an RMSE in the range of 0.18–0.47 m and R2 in the range of 0.71–0.83 across all models for depths < 3.5 m, while in depths > 3.5 m, an RMSE = 1.43–1.78 m and R2 = 0.57–0.65 was obtained. Overall, the empirical models marginally outperformed the machine learning models, although statistical tests indicated the results from all the models were not significantly different. Testing of the models beyond the domain of the training and validation data suggested the potential for model transferability to other regions with similar hydrologic and environmental characteristics.

Intensification mechanisms and moisture dynamics of super cyclonic storm ‘Amphan’ over the Bay of Bengal: Implications for aerosol re-distribution

The present research investigates the dynamics and underlying causes contributing to the exceptional intensity of Super Cyclonic Storm (SuCS) Amphan (16th to 21st May 2020) over the Bay of Bengal (BoB), as well as its impact on aerosol redistribution along the four cities of eastern coast and north-eastern India. Notably, the SuCS was formed during the first phase of the COVID-19 lockdown in India, giving it a unique aspect of study and analysis. Our analysis based on 30 years of climatology data from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis reveals ‘positive’ monthly anomalous winds (0.8 to 1.6 m/s) prevailed over the central BoB for May 2020. The present study further found the evolution of ‘barrier layer thickness’(BLT) leading up to landfall, noting a thickening trend from 8 to 3 days before landfall, contributing to maintaining warmer sea surface temperatures near the coast. Additionally, utilizing European Centre for Medium-Range Weather Forecasts (ECMWF), reanalysis version-5 (ERA-5) data, a mean positive sea surface temperature (SST) anomaly of 0.8 to 1 °C was observed ‘before’ cyclone period (10–15 May 2020) near the cyclogenesis point. A detailed examination of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical cross-section plots during the cyclone's intensification stage reveals the presence of high-altitude clouds composed primarily of ice crystals. Further, analysis also indicates that the cyclone transported Sea-salt PM2.5 aerosols from the ocean, dispersing them in the landfall region.The aerosol optical Depth (AOD) data obtained from the National Aeronautics and Space Administration's (NASA) ‘Clouds and the Earth's Radiant Energy System (CERES)’ mission and MERRA-2 were also analysed, revealing that the cyclone redistributed aerosols over the Bengal basin region (mainly over ‘Kolkata') and three other nearby cities along the track of the cyclone (i.e., Bhubaneswar (Odisha) Agartala (Tripura) and Shillong (Meghalaya) respectively).

Temporal and spatial distribution of PM2.5 in the Xinjiang region based on AOD fusion data and the GTWR model

ABSTRACT By integrating MODIS aerosol optical depth data and combining it with ground-based PM2.5 measurements and meteorological data, a geographically and temporally weighted regression (GTWR) model was used to estimate the spatiotemporal distribution of PM2.5 in Xinjiang in 2022. Findings include the following: (1) The GTWR model effectively represents PM2.5 distributions, with a correlation coefficient of 0.92, surpassing the geographically weighted regression model by 5.7% and the land use regression model by 9.5%. (2) Monthly PM2.5 levels in Xinjiang exhibit a pattern of initial decline followed by an increase. Elevated coal-fired heating in February and high air humidity in July contribute to higher atmospheric particulate concentrations. The peak monthly average concentration reaches 79.05 µg m−3 in February, and the lowest concentration drops to 33.86 µg m−3 in July. (3) Influenced by dust-prone conditions and atmospheric diffusion in the Taklimakan Desert region, PM2.5 levels in Xinjiang show a notable southwest-to-northeast gradient.

High resolution mapping of nitrogen dioxide and particulate matter in Great Britain (2003–2021) with multi-stage data reconstruction and ensemble machine learning methods

In this contribution, we applied a multi-stage machine learning (ML) framework to map daily values of nitrogen dioxide (NO2) and particulate matter (PM10 and PM2.5) at a 1 km2 resolution over Great Britain for the period 2003–2021. The process combined ground monitoring observations, satellite-derived products, climate reanalyses and chemical transport model datasets, and traffic and land-use data. Each feature was harmonized to 1 km resolution and extracted at monitoring sites. Models used single and ensemble-based algorithms featuring random forests (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), as well as lasso and ridge regression. The various stages focused on augmenting PM2.5 using co-occurring PM10 values, gap-filling aerosol optical depth and columnar NO2 data obtained from satellite instruments, and finally the training of an ensemble model and the prediction of daily values across the whole geographical domain (2003–2021). Results show a good ensemble model performance, calculated through a ten-fold monitor-based cross-validation procedure, with an average R2 of 0.690 (range 0.611–0.792) for NO2, 0.704 (0.609–0.786) for PM10, and 0.802 (0.746–0.888) for PM2.5. Reconstructed pollution levels decreased markedly within the study period, with a stronger reduction in the latter eight years. The pollutants exhibited different spatial patterns, while NO2 rose in close proximity to high-traffic areas, PM demonstrated variation at a larger scale. The resulting 1 km2 spatially resolved daily datasets allow for linkage with health data across Great Britain over nearly two decades, thus contributing to extensive, extended, and detailed research on the long-and short-term health effects of air pollution.

Mitigating RGB-D camera errors for robust ultrasonic inspections using a force-torque sensor

ABSTRACT Robot-based phased array ultrasonic testing is widely used for precise defect detection, particularly in complex geometries and various materials. Compact robots with miniature arms can inspect constrained areas, but payload limitations restrict sensor choice. RGB-D cameras, due to their small size and light weight, capture RGB colour and depth data, creating colourised 3D point clouds for scene representation. These point clouds help estimate surface normals to align the ultrasound transducer on complex surfaces. However, sole reliance on RGB-D cameras can lead to inaccuracies, affecting ultrasonic beam direction and test results. This paper investigates the impact of transducer pose and RGB-D camera limitations on ultrasonic inspections and proposes a novel method using force-torque sensors to mitigate errors from inaccurately estimated normals from the camera. The force-torque sensor, integrated into the robot end effector, provides tactile feedback to the controller, enabling joint angle adjustments to correct errors in the estimated normal. Experimental results show the successful application of ultrasound transducers using this method, even with significant misalignment. Adjustments took approximately 4 seconds to correct deviations from 12.55°, with an additional 4 seconds to ensure the probe was parallel to the surface, enhancing ultrasonic inspection accuracy in complex, constrained environments.

TOWARDS HARMONY IN THE CAPITAL OF THE ARCHIPELAGO: VISION OF FUTURE SUSTAINABLE CITY DEVELOPMENT FUTURISTIC-ORIENTED

This research aims to explore and formulate a sustainable and futuristic-oriented development strategy for the capital of the archipelago that can integrate the principles of environmental, economic, and social sustainability in addition to utilizing sustainable advanced technology. This study employs an exploratory qualitative research methodology. and descriptive study design. This approach is carried out by collecting and analyzing data in depth through a document study. This study uses data analysis techniques by making a SWOT analysis to assess the advantages, disadvantages, and opportunities faced during the development of the Indonesian capital. Thus, it can be said that the overall plan for development for the Indonesian capital is sustainable and futuristic using an integrated approach by applying ESG sustainability principles and the use of environmentally friendly technology. Urban development close to the 20th century was accompanied by the growth of urbanization processes without planning so cities experienced gradual inefficiency, which resulted in the erosion of old buildings that were unable to fulfill the requirements of society. Through the above policies, it’s hoped that Indonesian capital will develop into a futuristic-oriented sustainable city by integrating the principles of ESG (environmental, social, and economic) sustainability using advanced, environmentally friendly technology. Recommendation formulation regarding the sustainable and futuristic-oriented development strategy for the Indonesian capital consists of Environmental Policy, Economic Policy, Social Policy, Planning and Supervision Policy, Technology Policy, and Collaboration and Participation for Sustainable City Futuristic.

A branched Convolutional Neural Network for RGB-D image classification of ceramic pieces

From smart sensors on assembly lines to robots performing complex tasks, the fourth industrial revolution is rapidly transforming manufacturing. The growing prominence of 3D cameras in the industry has led the computer vision community to explore innovative ways of integrating depth and color data to achieve higher precision, essential for ensuring product quality in manufacturing. In this study, we introduce an innovative branched convolutional neural network designed to produce high-speed classification of multimodal images, such as RGB-Depth (RGB-D) images. The fundamental concept underlying the branched approach is the specialization of each branch as a dedicated feature extractor for a single modality, followed by their merge (intermediate fusion) to enable effective classification. Feeding our model is our novel multimodal dataset, named CeramicNet, composed of 8 classes that include RGB, depth, and RGB-D variations to enable extensive experimentation and evaluation of the models which, to the best of our knowledge, has not been previously introduced in the computer vision community. We conducted a series of experiments on the CeramicNet dataset. These experiments aimed at fine-tuning the model, assessing the influence of various depth technologies, exploring individual modalities, examining their collective impact, and performing comprehensive data analysis. Comparing our solution against seven widely used models, we achieved remarkable results, securing the top position with a precision of 99.89, with a lead of over 1% against the nearest competitor. What is more, the proposed solution yields an inference time of 127.6 ms — being nearly three times faster than the second-best performer.

Feasibility of Biology Teaching Materials for Class X SMA

Teaching materials in general are things that contain information and knowledge that can be learned by their users. In learning activities, teaching materials act as a medium that mediates the process of conveying knowledge and skills from the resource person to the person learning. This type of research is both descriptive and qualitative. The qualitative descriptive approach method is a data processing method that analyzes factors related to the research object by presenting data in more depth regarding the research object. The data obtained from interviews with informants was described thoroughly. From the results of the interviews that have been conducted, it can be concluded that the teaching materials used by teachers are quite good, but there is still a lack of image references.

Research on multi-beam survey line system based on dynamic programming algorithm

This paper establishes a calculation model for the coverage width and overlap rate of a multi-beam survey line system [1], and optimizes the multi-beam bathymetric survey plan for marine areas. Through the dynamic programming algorithm, a reasonable survey line plan is designed based on the actual marine area depth. The research content includes: Model Establishment: Construct a calculation model for coverage width and overlap rate, determining the coverage width and overlap rate at each position. Three-Dimensional Space Model: Using the survey vessel as the origin and combining marine area information, solve for the coverage width at different angles. Shortest Survey Line Plan: Using the dynamic programming algorithm, determine the shortest survey line plan, with the dynamic transfer equation determined by the overlap rate index. The total length of the survey line in this plan is calculated to be 114,824 meters. Specific Survey Line Plan Design: Using sea depth data and cubic spline interpolation to complete the discrete point plane, construct approximate width and slope. Under the condition of relaxing the overlap rate to 5%-30%, the shortest survey line length is obtained as 398,059 meters, with a missing survey area ratio of 0%, and the total length of parts with an overlap rate exceeding 20% is 25,759 meters, accounting for 6.4%. This study provides theoretical basis and calculation methods for optimizing multi-beam bathymetric survey plans.

Large-scale flood modeling and forecasting with FloodCast

Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost. This limits their ability to accurately forecast flood crests and issue time-critical hazard warnings. In this work, we build a fast, stable, accurate, resolution-invariant, and geometry-adaptive flood modeling and forecasting framework that can perform at large scales, namely FloodCast. The framework comprises two main modules: multi-satellite observation and hydrodynamic modeling. In the multi-satellite observation module, a real-time unsupervised change detection method and a rainfall processing and analysis tool are proposed to harness the full potential of multi-satellite observations in large-scale flood prediction. In the hydrodynamic modeling module, a geometry-adaptive physics-informed neural solver (GeoPINS) is introduced, benefiting from the absence of a requirement for training data in physics-informed neural networks (PINNs) and featuring a fast, accurate, and resolution-invariant architecture with Fourier neural operators. To adapt to complex river geometries, we reformulate PINNs in a geometry-adaptive space. GeoPINS demonstrates impressive performance on popular partial differential equations across regular and irregular domains. Building upon GeoPINS, we propose a sequence-to-sequence GeoPINS model to handle long-term temporal series and extensive spatial domains in large-scale flood modeling. This model employs sequence-to-sequence learning and hard-encoding of boundary conditions. Next, we establish a benchmark dataset in the 2022 Pakistan flood using a widely accepted finite difference numerical solution to assess various flood simulation methods. Finally, we validate the model in three dimensions - flood inundation range, depth, and transferability of spatiotemporal downscaling - utilizing SAR-based flood data, traditional hydrodynamic benchmarks, and concurrent optical remote sensing images. Traditional hydrodynamics and sequence-to-sequence GeoPINS exhibit exceptional agreement during high water levels, while comparative assessments with SAR-based flood depth data show that sequence-to-sequence GeoPINS outperforms traditional hydrodynamics, with smaller simulation errors. The experimental results for the 2022 Pakistan flood demonstrate that the proposed method enables high-precision, large-scale flood modeling with an average MAPE of 14.93 % and an average Mean Absolute Error (MAE) of 0.0610 m for 14-day water depth simulations while facilitating real-time flood hazard forecasting using reliable precipitation data.

Long-Term Evaluation of Aerosol Optical Properties in the Levantine Region: A Comparative Analysis of AERONET and Aqua/MODIS

The focus on aerosol analysis in the Levantine Region is driven by climate-change impacts, the region’s increasing urban development and industrial activities, and its geographical proximity to major dust-source areas. This study conducts a comparative analysis of aerosol optical depth data from Aqua/MODIS and AERONET during different periods between 2003 and 2023 at four stations: IMS-METU-ERDEMLI (Mersin/Türkiye) (2004–2019), CUT-TEPAK (Limassol/Cyprus) (2010–2023), Cairo_EMA_2 (Cairo/Egypt) (2010–2023), and SEDE_BOKER (Sede Boker/Israel) (2003–2023). The objective is to evaluate the variability and reliability of AOD measurements between satellite and ground-based observations and to determine how well they represent regional climatology. The highest percentage of measurements within the expected error envelope was observed at the IMS-METU-ERDEMLI station, indicating the best agreement between MODIS and AERONET data at this location. The Seasonal-Trend Decomposition using Loess (STL) method revealed consistent spring and summer peaks influenced by dust transport from the Sahara and the Middle East, with lower values in winter. The study also considers the influence of cloud fraction on MODIS measurements and includes aerosol classification. A statistically significant slight positive trend in AOD values was identified at the IMS-METU-ERDEMLI station. Conversely, no significant trends were detected at the other stations. The results of this study agree with those of previous research on the impact of long-range dust transport on regional aerosol loadings, emphasizing the importance of integrating satellite and ground-based observations.

Aerosol optical depth data fusion with Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2) instruments GEMS, AMI, and GOCI-II: statistical and deep neural network methods

Aerosol optical depth data fusion with Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2) instruments GEMS, AMI, and GOCI-II: statistical and deep neural network methods

The Effects of Summer Snowfall on Arctic Sea Ice Radiative Forcing

AbstractSnow is the most reflective natural surface on Earth. Since fresh snow on bare sea ice increases the surface albedo, the impact of summer snow accumulation can have a negative radiative forcing effect, which would inhibit sea ice surface melt and potentially slow sea‐ice loss. However, it is not well known how often, where, and when summer snowfall events occur on Arctic sea ice. In this study, we used in situ and model snow depth data paired with surface albedo and atmospheric conditions from satellite retrievals to characterize summer snow accumulation on Arctic sea ice from 2003 to 2017. We found that, across the Arctic, ∼2 snow accumulation events occurred on initially snow‐free conditions each year. The average snow depth and albedo increases were ∼2 cm and 0.08, respectively. 16.5% of the snow accumulation events were optically thick (>3 cm deep) and lasted 2.9 days longer than the average snow accumulation event (3.4 days). Based on a simple, multiple scattering radiative transfer model, we estimated a −0.086 ± 0.020 W m−2 change in the annual average top‐of‐the‐atmosphere radiative forcing for summer snowfall events in 2003–2017. The following work provides new information on the frequency, distribution, and duration of observed snow accumulation events over Arctic sea ice in summer. Such results may be particularly useful in understanding the impacts of ephemeral summer weather on surface albedo and their propagating effects on the radiative forcing over Arctic sea ice, as well as assessing climate model simulations of summer atmosphere‐ice processes.