Clear Sky Research Articles

Cloud detection method using ground-based sky images based on clear sky library and superpixel local threshold

In the intra-hour time scale, shielding of solar radiation by clouds is the main reason for the fluctuation of photovoltaic, so cloud parameters are important for intra-hour solar irradiance and photovoltaic power forecasting. Extracting cloud regions from cloud images provides a basis for quantifying cloud size and shape. It is difficult to detect clouds in these three cases, such as clouds in circumsolar region, clouds in haze weather and thin clouds at cloud edge. Aiming at such problems, this study firstly establishes a clear sky library based on pixel-level sun positions and haze conditions to deal with the complex changes of sky brightness. Then this study proposes a cloud detection method for ground-based images, which performs two segmentations on the cloud image. The initial segmentation process is a combination method that uses clear sky library method when the sun is visible and uses adaptive threshold method when the sun is occluded. The secondary segmentation process is based on superpixels and local threshold method, which restores some thin clouds that are easily ignored. Finally, for the influence of ghosts, this study summarizes the position and color features of ghosts, and uses different color channel information to deal with them.

Radiative Effect of Two Contrail Cirrus Outbreaks Over Western Europe Estimated Using Geostationary Satellite Observations and Radiative Transfer Calculations

AbstractEstimation of the perturbation to the Earth's energy budget by contrail outbreaks is required for estimating the climate impact of aviation and verifying the climate benefits of proposed contrail avoidance strategies such as aircraft rerouting. Here we identified two successive large‐scale contrail outbreaks developing in clear‐sky conditions in geostationary and polar‐orbiting satellite infrared images of Western Europe lasting from 22–23 June 2020. Their hourly cloud radiative effect, obtained using geostationary satellite cloud retrievals and radiative transfer calculations, is negative or weakly positive during daytime and positive during nighttime. The cumulative energy forcing of the two outbreaks is 7 PJ and −8.5 PJ, with uncertainties of 3 PJ, stemming each from approximately 15–20 flights over periods of 19 and 7 hr, respectively. This study suggests that an automated quantification of contrail outbreak radiative effect is possible, at least for contrails forming in clear sky conditions.

Efficient simulation of bitter gourd drying in active solar dryer: A state-of-the-art model

Greenhouse drying system is used to dry agricultural products or low-temperature thermal drying using a greenhouse structure. In this paper, an experimental and simulation study is carried out to evaluate the drying performances of an active solar greenhouse dryer using COMSOL Multiphysics. The experimental setup includes a compact thermal storage-based greenhouse dryer (1.5 m × 1.0 m x 0.5 m) equipped with a black gravel-covered Aluminium jacket and 35 kg of paraffin wax for heat retention which is located on the floor of the dryer. The experiments were conducted in Ranchi, India (23.34 °N, 85.30 °E) under clear sky conditions in the month of May. The amount of solar radiation (global solar radiation) varied from (630 W/m2 to 1052 W/m2) with an average of 936 W/m2, ambient air temperature (30.2 °C–38.2 °C), air relative humidity (31.6 %–34.6 %), and wind speed (0.9–1.0 m/s). The inside dryer temperature, humidity, wind speed, and floor temperature were also measured every hour. The average values of these parameters were 59.1 °C, 30.2 %, 0.91 m/s, and 69.1 °C, respectively. The FE (finite element) modelling finds out the maximum temperature in drying products, floor and dryer's outlet is 55.3 °C,72.4 °C and 67.4 °C, respectively at 13:00 h. The proposed dryer costs 19633.50 INR with an embodied energy of 1358.01 kW h. The proposed dryer has a break-even period of 1.87 years, and its lifespan is 35 years. During this time, the net CO2 emission was found to be 21.45 tonnes, and the earned carbon credit varies from 1505.01 to 30030.00 INR. The result shows that the drying efficiency is 42.52 %, reducing the initial moisture content from 88.64 % to 2.28 % within five consecutive hours. The energy and exergy efficiencies of the dryer are found to be 61.84 % and 56 % respectively. The system is a viable, sustainable choice for the large-scale production of bitter gourd flakes.

Dynamic Monitoring and Analysis of Dual-Axis movement of SPV Power Plant Parameters under various Atmospheric Conditions

This research paper presents comprehensive insight into the testing of a 1 KW sun tracking photovoltaic (PV) performance. The study uses a state-of-the-art real-time string monitoring system to allow this analysis while covering an extensive variety of atmospheric conditions. The design of the 1 KW solar tracker system incorporates a tracking sensor circuit, motor driver circuit, string monitoring system, and solar tracker control circuit. Solar tracking systems boost energy generation by adjusting the angle of PV panels to optimum sunlight exposure. The effectiveness of such devices can, however, be severely impacted by atmospheric changes. In a photovoltaic (PV) facility, we have successfully developed and placed into use a real-time system for monitoring certain strings. The system collected data on key parameters such as current, voltage, and temperature, providing insights into the health and efficiency of each string. Our study encompassed various atmospheric conditions, including clear skies, cloudy days, and fluctuating solar radiation. A DAQ system developed communicate with observations such as the voltage and current of photovoltaic arrays and the voltage and current of DC motors. When comparing the dual-axis tracking system to the static PV system, a maximum of 48% more electricity can be extracted. Both days with clear skies and days with clouds provide satisfactory results. The supply for the tracker control circuit comes from PV cells. Therefore, there is no need for any auxiliary supply, such as a battery.

A novel machine learning-based approach for improving global correction of AIRS-derived water vapor satellite product

Precise precipitable water vapor (PWV) observations play a crucially important role in weather and climate research. The Atmospheric Infrared Sounder (AIRS) on-board the Aqua spacecraft is a hyperspectral instrument that offers operational PWV products using infrared (IR) channels. However, the observational accuracy of AIRS-sensed cloudy-sky PWV products is much poorer than that of PWV retrievals under clear sky conditions. We present a new machine learning-based calibration model to improve the observational performance of operational AIRS-derived IR PWV satellite products under all sky conditions, which considers using several dependence elements correlated with remotely sensed IR PWV observations. The first-guess PWV estimates, obtained from the ERA5 reanalysis, are also utilized in the newly developed calibration approach. The ground-based GNSS-sensed PWV observations, collected in 2017 across the world, are used as the expected PWV output for the training of the newly proposed calibration model. The newly calibrated PWV data records considerably outperform operational AIRS-sensed PWV products, compared with independent PWV estimates from radiosonde observations during 2017–2020. The root-mean-square error of operational PWV satellite products decreases 19.05 % from 2.31 mm to 1.87 mm under cloud fraction (CF) = 0 condition (clear sky), 30.71 % from 3.68 mm to 2.55 mm under CF = (0,1] condition (cloudy sky), and 30.14 % from 3.55 mm to 2.48 mm under CF = [0,1] condition (all sky). The calibration method exhibits much higher RMSE reductions than previous approaches that do not utilize the first-guess PWV estimates, illustrating the effectiveness of our calibration approach. This research provides insights into calibrating satellite-sensed PWV estimates based on machine learning by jointly using ground-based and reanalysis-based PWV observations. The newly proposed calibration method has significant potential to calibrate operational PWV products from other satellite-borne sensors, in addition to the AIRS instrument.

Improving parameterization of an evapotranspiration estimation model with eddy covariance measurements for a regional irrigation scheduling program

Actual evapotranspiration (ETa) is an essential variable in linking energy cycles, carbon, and water, yet challenging to measure. Inputs uncertainty and deficiencies in the key elements of hydrologic models are fundamental challenges for optimizing model performance. Furthermore, the performance of land surface model-based ETa, reanalysis, and remote sensing products varies with spatiotemporal scales. Here, we evaluate sources of bias in the regional Wisconsin Irrigation and Scheduling Program (WISP) model and develop a correction using eddy covariance (EC) observations. ETa, observations were made for five years (2018–2022) using EC systems in agricultural fields in Wisconsin. WISP model ETa bias was linked to underestimation of net longwave radiation (LWnet) that was traced to incorrect specification of effective clear sky atmospheric emissivity (εa,clr). Applying a correction to the εa,clr led to reduced WISP model percent bias (pbias) and error for both LWnet and ETa. The calibrated model more accurately represented observed ETa. The results indicate that explicit treatment of the LWnet balance decreases the uncertainty of model parameters and improves the WISP model performance at independent sites. Applying this improved model parameterization reduced the bias of LWnet radiation from 62.8% to -6.2%, which improved the Nash-Sutcliffe Efficiency (NSE) from -0.08 to 0.52 for ETa on training sites. Additionally, overall pbias was significantly reduced (p = 0.035) for validation sites after WISP correction. Hence, WISP performance improved for different crop types when optimal regional parameters were used, confirming the physical parameters' reliability. Our results highlight that model development should focus on energy balance parameterizations to improve ET simulation and the accuracy of hydrologic and climatic simulations for understanding critical processes underlying hydrologic and climatic variability and change over land.

A novel approach combining satellite and in situ observations to estimate the daytime variation of land surface temperatures for all sky conditions

Land surface temperature (LST) and its diurnal variability are key to understanding the land-atmosphere interactions, hydrological processes and climate change. However, at any given point in time approximately half of the Earth's surface is covered by clouds. This restricts the availability of LST through satellite remote sensing, which works best under clear skies. However, in situ observations continue to monitor atmospheric conditions beneath the clouds that could complement satellite measurements during cloudy conditions. The present study explores a novel approach to estimate hourly LST during the daylight hours using remotely sensed surface solar absorption and in situ observations of daily LST extremes (maximum and minimum) together with an adaptive non-linear fitting approach. A learning algorithm trained against in-situ measurements of LST extrema and diurnal cycle of surface solar absorption together with the associated linear correlation between the two parameters, is used to estimate an optimized set of parameters to approximate hourly LST for each day during the daylight hours between sunrise and sunset. Results show that the method captures the intra-day variability of LST very well under most sky conditions with rms errors below 1.5 K.

Study on enhancing groundwater quality in a coastal area of Saudi Arabia using a curved glass solar still

Due to the complex geological factors and their causes, it is essential to realize the saltiness and pollution of the groundwater in the arid shoreline aquifer. The intrusion of seawater into coastline aquifers is an increasingly significant issue due to urbanization, the exploitation of groundwater, and the global rise in temperature. The increasing salt concentration of groundwater in shoreline aquifers has emerged as a significant social and economic problem in Saudi Arabia. Consequently, groundwater alkalinity levels must be accurately determined to ensure the effective protection and preservation of water resources. A compilation of eight water samples was collected along the south-western coast of Saudi Arabia, and a comprehensive evaluation of the groundwater quality was conducted using a combination of indicators. The findings indicated that the intrusion of saltwater caused a significant increase in the salt concentration and damage to the groundwater quality near the coastline. In this work, a curved glass solar still (CGSS) has been deployed to process the groundwater, and the performance of the CGSS in improving the groundwater quality was assessed under clear sky conditions. The findings indicate that the desalination process executed using the CGSS resulted in a more substantial improvement in groundwater quality, with an average daily yield of 3513 ml/day achieved with the CGSS.

Experimental Evaluation of a Solar-Powered Air Conditioner

Air conditioning is vital in maintaining indoor comfort and improving air quality, particularly in regions with high temperatures and humidity. However, the increasing demand for air conditioning has significant implications for energy consumption and the environment. Solar air conditioning can play a vital role in mitigating such impacts. This study presents an experimental setup that utilizes a solar photovoltaic system to power an air conditioning unit. The system is installed in a 36 m2-research lab at The University of Jordan, equipped with PV panels with a capacity of 2.67 kWp, a battery pack, a charge controller, an inverter charger, and an air conditioning unit. Two testing scenarios are conducted: an on-grid system connected to the electrical grid and an off-grid system operating independently. A clear sky day and a partially cloudy day were selected for each scenario in testing. The results were evaluated based on the solar fraction, energy consumption, and operational time fraction (OTF). The solar fraction was found to be higher during periods of higher demand and ambient temperature, and the average OTF was 70 % for all trials. In off-grid trials, the system had maintained operation solely on PV power during the studied load profile in the clear sky day. In contrast, limitations in charging power caused system shutdown in the partially cloudy trial. An economic analysis for the local payback period (PBP) was conducted based on the Hourly Analysis Program and PVsyst software simulation, which shows an attractive simple local PBP for on-grid and off-grid systems of 6.91 and 6.4 years, respectively.

Verification of parameterizations for clear sky downwelling longwave irradiance in the Arctic

Abstract. Ground-based high resolution observations of downward longwave irradiance (DLI), surface air temperature, water vapor surface partial pressure and column amount, zenith sky infrared (IR) radiance in the atmospheric window, and all-sky camera images are regularly obtained at the Thule High Arctic Atmospheric Observatory (THAAO, 76.5° N, 68.8° W), northwestern Greenland. The datasets for the years 2017 and 2018 have been used to assess the performance of different empirical formulas used to infer clear sky DLI. An algorithm to identify clear sky observations has been developed, based on value, variability, and persistence of zenith sky IR radiance. Seventeen different formulas to estimate DLI have been tested against the THAAO dataset, using the originally determined coefficients. The formulas that combine information on total column water vapor and surface air temperature appear to perform better than others, with a mean bias with respect to the measured DLI smaller than 1 W m−2 and a root mean squared error (RMSE) around 6 W m−2. Unexpectedly, some formulas specifically developed for the Arctic are found to produce poor statistical results. This is attributed partly to limitations in the originally used dataset, which does not cover a whole year or is relative to very specific condition (i.e., the presence of an ice sheet). As expected, the bias displays a significant improvement when the coefficients of the different formulas are calculated using the THAAO dataset. The presence of 2 full years of data allows the determination and the applicability of the coefficients for singular years and the evaluation of results. The smallest values of the bias and RMSE reach 0.1 and 5 W m−2, respectively. Overall, the best results are found for formulas that use both surface parameters and total water vapor column content, and have been developed from global datasets. Conversely, formulas that express the atmospheric emissivity as a linear function of the logarithm of the column integrated water vapor appear to reproduce poorly the observations at THAAO.

Reconstructing hyper-spectral downwelling irradiance from multi-spectral measurements

Checking the radiometric calibration of satellite hyper-spectral sensors such as the PACE Ocean Color Instrument (OCI) while they operate in orbit and evaluating remote sensing reflectance, the basic variable from which a variety of optical and biogeochemical ocean properties can be derived, requires measuring upwelling radiance just above the surface (Lw) and downwelling planar irradiance reaching the surface (Es). For this, the current HyperNav systems measure Lw at about 2 nm spectral resolution in the ultraviolet to near infrared, but Es in only four 10 nm wide spectral bands centered on 412, 489, 555, and 705 nm. In this study, the Es data acquired in these spectral bands in clear sky conditions are used to reconstruct via a multi-linear regression model the hyper-spectral Es signal at 0.5 nm resolution from 315 to 900 nm, the OCI spectral range, allowing an estimate of Es at the HyperNav, OCI, and other sensors’ resolutions. After correction of gaseous absorption and normalization by the top-of-atmosphere incident solar flux, the atmospheric diffuse transmittance is expressed as a linear combination of Es measured in those 4 spectral bands. Based on simulations for Sun zenith angles from 0 to 75° and a wide range of (i.e., expected) atmospheric, surface, and water conditions, the Es spectrum is reconstructed with a bias of less than 0.4% in magnitude and an RMS error (RMSE) ranging from 0% to 2.5%, depending on wavelength. The largest errors occur in spectral regions with strong gaseous absorption. In the presence of typical noise on Es measurements and uncertainties on the ancillary variables, the bias and RMSE become −2.5% and 7.0%, respectively. Using a General Additive Model with coefficients depending on Sun zenith angle and aerosol optical thickness at 550 nm improves statistical performance in the absence of noise, especially in the ultraviolet, but provides similar performance on noisy data, indicating more sensitivity to noise. Adding spectral bands in the ultraviolet, e.g., centered on 325, 340, and 380 nm, yields marginally more accurate results in the ultraviolet, due to uncertainties in the gaseous transmittance. Comparisons between the measured and reconstructed Es spectra acquired by the MOBY spectroradiometer show agreement within predicted uncertainties, i.e., biases less than 2% in magnitude and RMS differences less than 5%. Reconstruction can also be achieved accurately with other sets of spectral bands and extended to cloudy conditions since cloud optical properties, like aerosol properties, tend to vary regularly with wavelength. These results indicate that it is sufficient, for many scientific applications involving hyper-spectral Es, to measure Es in a few coarse spectral bands in the ultraviolet to near infrared and reconstruct the hyperspectral signal using the proposed multivariate linear modeling.

Assessing radiometric calibration methods for multispectral UAV imagery and the influence of illumination, flight altitude and flight time on reflectance, vegetation index and inversion of winter wheat AGB and LAI

Radiometric correction is essential to unmanned aerial vehicle (UAV) precision agriculture applications, especially for crop multi-period analysis and quantitative inversion of phenotypic and physiological parameters. In this study, we investigated the performance of four radiometric correction methods: “Camera only” (A), “Camera and sun irradiance” (B) and “Camera, sun irradiance and sun angle” (C) methods provided by Pix4d Mapper and an irradiance sensor-based method (D) implemented with the R software. UAV campaigns were conducted at flight altitudes of 30 m, 60 m and 90 m under clear and cloudy skies at different times over an experimental field of winter wheat. The influences of illumination, flight altitude and flight time on reflectance and vegetation indexes derived from different radiometric calibration methods were analyzed. Moreover, we further assessed the impact of reflectance and vegetation index variations on the above-ground biomass (AGB) and leaf area index (LAI) estimations of winter wheat. Results showed that method D is a promising approach for radiometric calibration, which produced consistent reflectance with method A at noon (e.g., 10:44:41 AM March 17 and 11:39:37 AM April 7) and mid-afternoon (e.g., 15:30:02 PM May 1) and more reliable reflectance in the morning (e.g., 7:15:44 AM March 17). Methods B and C seriously underestimated the reflectance at 10:44:41 AM March 17 and 11:39:37 AM April 7. This was caused by the incorrect attitude angles of the irradiance sensor and further miscalculation of the solar irradiance by Pix4d Mapper. Interestingly, these errors were eliminated in the calculation of the ratio type of vegetation indexes. Methods B and C yielded vegetation indexes that were almost the same as those of methods A and D. Under cloudy sky, when direct sunlight is not blocked by clouds, method D was effective; with cloud occlusion, results on April 24 indicated that the method failed to estimate the total sunlight and direct sunlight ratio. Reflectance and vegetation indexes obtained from different radiometric correction methods were all obviously affected by flight altitude. Furthermore, the variations of NDVI with flight altitude increasing were different with reflectance. Changes in reflectance and vegetation indexes with flight altitude had little effect on the correlation coefficients yielded with AGB, but showed a significant influence on those of LAI. Through analyzing the variation of observed solar radiation at different time intervals, caution is recommended when using the assumption that solar radiation is stable during the UAV flight. The longer the flight, the greater the variation in solar radiation, especially at sunrise and sunset. Even at noon, when the flight duration is 30 min, the change in solar radiation may exceed 10 % (e.g., 16.5 % on March 20).

Validation of a 3D Local-Scale Adaptive Solar Radiation Model by Using Pyranometer Measurements and a High-Resolution Digital Elevation Model.

The result of the multidisciplinary collaboration of researchers from different areas of knowledge to validate a solar radiation model is presented. The MAPsol is a 3D local-scale adaptive solar radiation model that allows us to estimate direct, diffuse, and reflected irradiance for clear sky conditions. The model includes the adaptation of the mesh to complex orography and albedo, and considers the shadows cast by the terrain and buildings. The surface mesh generation is based on surface refinement, smoothing and parameterization techniques and allows the generation of high-quality adapted meshes with a reasonable number of elements. Another key aspect of the paper is the generation of a high-resolution digital elevation model (DEM). This high-resolution DEM is constructed from LiDAR data, and its resolution is two times more accurate than the publicly available DEMs. The validation process uses direct and global solar irradiance data obtained from pyranometers at the University of Salamanca located in an urban area affected by systematic shading from nearby buildings. This work provides an efficient protocol for studying solar resources, with particular emphasis on areas of complex orography and dense buildings where shadows can potentially make solar energy production facilities less efficient.

Spectral analysis of solar-irradiance fluctuations

Solar-irradiance fluctuations possess a power-law spectrum with two different slopes in the intermediate (1/day <f<1/h) and high [1/h <f<1/(2 min)] frequency (f) regimes. This spectrum is a combination of a deterministic (latitude-dependent) variation of daylight duration and a stochastic component arising from various environmental attenuation factors. We propose a principled approach to decompose the solar-irradiance time series into the deterministic location-dependent clear sky and the stochastic environmental attenuation signals. Such decomposition permits one to systematically analyze the solar photovoltaic power-fluctuation spectra after accounting for the strong geographic dependence, failing which no baseline standard exists with which to compare spectra between disparate geographic locations. Our analysis for two different locations shows that the stochastic environmental factors determine the spectral power-law slope in the high-frequency range, while the deterministic clear-sky signal plays a role in the intermediate frequency range. We close by discussing its implications for solar photovoltaic power production, in particular, for the geographic smoothing of fluctuations. Published by the American Physical Society 2024

‘Me and the Sky’: smashing the glass ceiling in Come from Away, neoliberal feminism, and climate change

ABSTRACT This article considers neoliberal feminism and environmental concerns in Irene Sankoff and David Hein’s musical Come from Away. While the musical is not directly concerned with anthropogenic climate change, many types of skies appear as thematic and metaphorical preoccupations in the piece: feminist blue skies, stormy Newfoundland skies, the clear skies of the American Dream, skies of terror, and skies of war. I argue that Come from Away hides the tragedy of climate change in plain sight, in the sky. Undertaking a close analysis of the showstopping number ‘Me and the Sky,’ sung by the airline pilot Beverly Bass – based on the real-life woman of the same name – I observe the rift between neoliberal feminist success, acts of terror, and the slow-moving tempo of a changing climate. Come from Away’s celebration of neoliberal feminism is cut short in the song, but it foreshadows the possibilities of a more successful recognition of the conjunction between feminist and environmental awareness.

Improving the Estimation of Rice Crop Damage from Flooding Events Using Open-Source Satellite Data and UAV Image Data

Having an additional tool for swiftly determining the extent of flood damage to crops with confidence is beneficial. This study focuses on estimating rice crop damage caused by flooding in Candaba, Pampanga, using open-source satellite data. By analyzing the correlation between Normalized Difference Vegetation Index (NDVI) measurements from unmanned aerial vehicles (UAVs) and Sentinel-2 (S2) satellite data, a cost-effective and time-efficient alternative for agricultural monitoring is explored. This study comprises two stages: establishing a correlation between clear sky observations and NDVI measurements, and employing a combination of S2 NDVI and Synthetic Aperture Radar (SAR) NDVI to estimate crop damage. The integration of SAR and optical satellite data overcomes cloud cover challenges during typhoon events. The accuracy of standing crop estimation reached up to 99.2%, while crop damage estimation reached up to 99.7%. UAVs equipped with multispectral cameras prove effective for small-scale monitoring, while satellite imagery offers a valuable alternative for larger areas. The strong correlation between UAV and satellite-derived NDVI measurements highlights the significance of open-source satellite data in accurately estimating rice crop damage, providing a swift and reliable tool for assessing flood damage in agricultural monitoring.

Evaluation of the WRF-solar model for 72-hour ahead forecasts of global horizontal irradiance in West Africa: A case study for Ghana

Accurate global horizontal irradiance (GHI) forecasting is critical for integrating solar energy into the power grid and operating solar power plants. The Weather Research and Forecasting model with its solar radiation extension (WRF-Solar) has been used to forecast solar irradiance in different regions around the world. However, the application of the WRF-Solar model to the prediction of GHI in West Africa, particularly Ghana, has not yet been investigated. The aim of this study is to evaluate the performance of the WRF-Solar model for predicting GHI in Ghana, focusing on three automatic weather stations (Akwatia, Kumasi and Kologo) for the year 2021. We used two one-way nested domains (D1 = 15 km and D2 = 3 km) to investigate the ability of the fully coupled WRF-Solar model to forecast GHI up to 72-hour ahead under different atmospheric conditions. The initial and lateral boundary conditions were taken from the ECMWF high-resolution operational forecasts. Our findings reveal that the WRF-Solar model performs better under clear skies than cloudy skies. Under clear skies, Kologo performed best in predicting 72-hour GHI, with a first day nRMSE of 9.62 %. However, forecasting GHI under cloudy skies at all three sites had significant uncertainties. Additionally, WRF-Solar model is able to reproduce the observed GHI diurnal cycle under high AOD conditions in most of the selected days. This study enhances the understanding of the WRF-Solar model’s capabilities and limitations for GHI forecasting in West Africa, particularly in Ghana. The findings provide valuable information for stakeholders involved in solar energy generation and grid integration towards optimized management in the region.

The Role of Rapid Changes in Weather on Phytoplankton Spring Bloom Dynamics From Mid‐Norway Using Multiple Observational Platforms

AbstractThe spring phytoplankton bloom plays a major role in pelagic ecosystems; however, its dynamics are not well understood due to insufficient, highly resolved observational data. Here we investigate the start, peak, and decline of a 2‐week phytoplankton spring bloom in Frohavet located in a biological hotspot of the coast of mid‐Norway. We used observations from an uncrewed surface vehicle (USV) combined with buoy measurements, satellite images, discrete water sampling and modeling approaches. The spring bloom (March–June 2022) consisted of multiple peaks (up to 5 mg Chla m−3), with a long peak in April dominated by the diatom Skeletonema, coincident with the period when the USV captured the temporal and spatial dynamics of the bloom. Short‐term episode of calm weather in spring, such as clear skies and consistent low wind speed (<7 m s−1) shoaled the mixed layer depth (<15 m) and promoted the rapid development of the spring bloom ‐ from 1 to 5 mg Chla m−3 in 5 days. Likewise, the collapse of the bloom was rather quick, 1–2 days and coincides with low nitrate values and rapid increase in wind speed (>10 m s−1), suggesting strong influence of the environment on phytoplankton dynamics. Understanding the dynamics of the spring bloom is crucial for predicting its impact on marine trophic web and biogeochemical cycles. Integration of distinct observational platforms has the potential to unveil the environmental factors underlying phytoplankton bloom dynamics.

Global horizontal irradiance prediction for renewable energy system in Najran and Riyadh

Producing and supplying energy efficiently are important for many countries. Using models to predict energy production can help reduce costs, improve efficiency, and make energy systems work better. This research predicts solar electricity production in the Najran and Riyadh regions of Saudi Arabia by analyzing 14 weather factors. The weather factors that were considered in the study include date, time, Global Horizontal Irradiance (GHI), clear sky, top of atmosphere, code, temperature, relative humidity, pressure, wind speed, wind direction, rainfall, snowfall, and snow depth. GHI is the most important factor because it determines how much solar energy a system can produce. Therefore, it is important to be able to predict GHI accurately. This study used a variety of data-driven models to predict GHI, including the elastic net regression, linear regression, random forest, k-nearest neighbor, gradient boosting regressor, light gradient boosting regressor, extreme gradient boosting regressor, and decision tree regressor. The models were evaluated using a set of metrics, including the mean absolute error, mean squared error, root mean square error, coefficient of determination (R2), and adjusted coefficient of determination. This study found that the decision tree regression, Random Forest (RF), and Extreme Gradient Boosting (XGB) models performed better in the Riyadh region than in the Najran region. The R2 values for the Riyadh region were 99%, 99%, and 98%, while the R2 values for the Najran region were 89%, 94%, and 94%. This suggests that the Riyadh region is a more suitable location for solar energy conversion systems. These findings are important for policymakers and investors who are considering the development of solar energy projects in Saudi Arabia.

Cross-Modality Interaction-Based Traffic Accident Classification

Traffic accidents on the road lead to serious personal and material damage. Furthermore, preventing secondary accidents caused by traffic accidents is crucial. As various technologies for detecting traffic accidents in videos using deep learning are being researched, this paper proposes a method to classify accident videos based on a video highlight detection network. To utilize video highlight detection for traffic accident classification, we generate information using the existing traffic accident videos. Moreover, we introduce the Car Crash Highlights Dataset (CCHD). This dataset contains a variety of weather conditions, such as snow, rain, and clear skies, as well as multiple types of traffic accidents. We compare and analyze the performance of various video highlight detection networks in traffic accident detection, thereby presenting an efficient video feature extraction method according to the accident and the optimal video highlight detection network. For the first time, we have applied video highlight detection networks to the task of traffic accident classification. In the task, the most superior video highlight detection network achieves a classification performance of up to 79.26% when using video, audio, and text as inputs, compared to using video and text alone. Moreover, we elaborated the analysis of our approach in the aspects of cross-modality interaction, self-attention and cross-attention, feature extraction, and negative loss.