Radiation Data Research Articles

Recognition and Prediction of Precursory Feature Signals of Coal Mine Rock Burst Based on Random Forest and MK Trend Test

This paper aims to identify and predict the precursory characteristic signals of coal mine rock burst by employing the Random Forest and MK (Mann-Kendall) trend test methods. Initially, the study conducts data preprocessing and analysis on indicators such as electromagnetic radiation intensity, acoustic wave intensity, and type. It uses clustering methods to distinguish various data types and analyzes the data after visual representation. The visualization of data illustrates the distribution and trends, which aids in understanding the characteristics of the data, such as the upward trend observed in precursory feature data. During the data preprocessing phase, diagrams of various classes of data for Acoustic Emission (AE) and Electromagnetic Radiation (EMR) intensity are presented, providing an intuitive reference for subsequent analysis. The research utilizes the Random Forest algorithm and MK trend test to recognize precursor signals and predict their occurrence intervals. The Random Forest model is chosen for its efficiency and accuracy in handling classification and regression issues, while the MK trend test provides a statistical basis for identifying precursory signals by analyzing monotonic trends within the dataset. This study not only enhances the accuracy of precursory signal identification for coal mine rock bursts but also offers scientific early warning and control measures for coal mine safety production, which is of significant practical value.

Role of assimilation of microwave humidity sounder (MHS) satellite radiance in forecast of structure and intensity of VSCS Vardah 2016

The present study evaluated the performance of the Weather Research and Forecasting (WRF) model in the forecast of a very severe cyclonic storm (VSCS) Vardah that developed over the Bay of Bengal (BoB) and make landfall over the Tamil Nadu coast on 12 December 2016. The study examined the impact of microwave humidity sounder (MHS) satellite radiance data assimilation to forecast of structure, intensity and rainfall of Vardah cyclone near to the coast using two different land surface model (LSM; Noah and Noah-MP). The mean track error is about 33 and 38 km with data assimilation using Noah and Noah-MP LSM respectively and this error is about 41 km in without data assimilation using Noah and Noah-MP LSM. The predicted intensity of the storm in terms of the maximum surface wind is also slightly better predicted in data assimilation experiment with Noah LSM, this is due to the improved initial condition. Accumulated rainfall and the maximum reflectivity in terms of spatial distribution and magnitude of the VSCS Vardah is well simulated in all the experiments but slightly better in data assimilation experiments. Hovmoller diagram is also presented to see the shifting pattern of accumulated rainfall across the region during simulation period.

Meta-Learning Guided Weight Optimization for Enhanced Solar Radiation Forecasting and Sustainable Energy Management with VotingRegressor

Solar radiation prediction plays a crucial role in renewable energy management, impacting various decision-making processes aimed at optimizing the utilization of solar resources and promoting sustainability. Ensemble regression methods, notably VotingRegressor, have emerged as promising tools for accurate solar radiation forecasting. By integrating predictions from multiple base estimators, ensemble methods have the potential to capture intricate patterns inherent in solar radiation data. However, achieving optimal predictive performance with ensemble methods heavily relies on the careful weighting assigned to each base estimator, presenting a significant challenge. In this study, a novel approach is presented to enhance solar radiation prediction by utilizing meta-learning techniques to optimize the weighting mechanism in the VotingRegressor ensemble. Meta-learning, a subfield of machine learning focusing on learning algorithms across different tasks, provides a systematic framework for learning to learn. This enables models to adapt and generalize more effectively to new datasets and tasks. Our proposed methodology demonstrated significant improvements, with the VotingRegressor with meta-learning techniques achieving an RMSE of 8.7343, an MAE of 5.42145, and an R² of 0.991913. These results mitigate the need for manual weight tuning and improve the adaptability of the VotingRegressor to varying solar radiation conditions, ultimately contributing to the sustainability of renewable energy systems. The methodology involves a comprehensive exploration of meta-learning techniques, encompassing gradient-based optimization, reinforcement learning, and Bayesian optimization.

Understory plants evade shading in a temperate deciduous forest amid climate variability by shifting phenology in synchrony with canopy trees.

Global warming is leading understory and canopy plant communities of temperate deciduous forests to grow leaves earlier in spring and drop them later in autumn. If understory species extend their leafy seasons less than canopy trees, they will intercept less light. We look for mismatched phenological shifts between canopy and understory in 28 years (1995-2022) of weekly data from Trelease Woods, Urbana, IL, USA. The observations cover 31 herb species of contrasting seasonality (for 1995-2017), three sapling species, and the 15 most dominant canopy tree species for all years, combined with solar radiation, temperature and canopy light transmittance data. We estimate how understory phenology, cold temperatures, canopy phenology, and solar radiation have individually limited understory plants' potential light interception over >2 decades. Understory and canopy phenology were the two factors most limiting to understory light availability, but which was more limiting varied greatly among species and among/within seasonality groups; solar radiation ranked third and cold fourth. Understory and canopy phenology shifts usually occurred in the same direction; either both strata were early or both were late, offsetting each other's effects. The four light-limiting factors combined showed significant temporal trends for six understory species, five toward less light interception. Warmer springs were significantly associated with shifts toward more light interception in three sapling species and 19 herb species. Canopy phenology became more limiting in warmer years for all three saplings species and 31 herb species. However, in aggregate, these variables mostly offset one another; only one sapling and seven herb species showed overall significant (and negative) relationships between light interception and spring temperature. The few understory species mismatched with canopy phenology due to changing climate are likely to intercept less light in future warmer years. The few species with data for carbon assimilation show broadly similar patterns to light interception.

The photosynthetic uptake of inorganic carbon from Pyropia seaweed aquaculture beds: Scaling up population-level estimations

Seaweed aquaculture beds (SABs) contribute positively to CO2 removal (CDR) worldwide. Among cultivated seaweed species, Pyropia represents approximately 8% of the global seaweed production and has the capacity to sequester a significant amount of carbon from the surface layer of the coastal ocean. In this study, we evaluated the carbon uptake efficiency of Pyropia SABs by measuring their photosynthetic rates. Pyropia individuals were collected from Pyropia SABs on the south and west coasts of South of Korea from December to March (cultivation period) in 2016 to 2019, and the photosynthetic light response curves (P-E curves) were measured. Oxygen-based photosyntheses were converted into carbon-based photosynthetic rates using theoretical photosynthetic quotients. Pyropia thallus consumed an average 37 mg C g−1ww d−1, with a high ratio of gross primary production to respiration (P/R ratio; 5–14). To quantify the carbon uptake potential in the coastal areas of South Korea during the cultivation period, we extrapolated the carbon uptake rates using the estimated biomass, total area of Pyropia SABs, and meteorological irradiance data. The highest carbon uptake rate (2143 kt C month−1) was observed in the Southwestern Sea of South Korea in December. Considering all productivity data from the entire cultivation period, approximately 6789 kt C was taken up by the Pyropia SABs. Therefore, our study indicates a significant potential to mitigate climate change by reducing greenhouse gas emissions using Pyropia SABs.

A novel control strategy to neutralize internal heat source within solid oxide electrolysis cell (SOEC) under variable solar power conditions

The integration of solid oxide electrolysis cells (SOECs) with a photovoltaic (PV) system presents a viable method of storing variable solar energy through the production of green hydrogen. To ensure the safety and longevity of SOEC amidst dramatic fluctuations in solar power, control strategies are needed to limit temperature gradients and rates of temperature change within the cell. Recognizing that the supply of the reactant influences the current, a novel control strategy is developed to modulate internal heat source in the SOEC by adjusting the steam flow rate. The effectiveness of this strategy is assessed through numerical simulations conducted on a coupled PV-SOEC system using actual solar irradiance data. The irradiance data are recorded at two-second intervals to account for rapid changes in solar exposure. The results indicate that conventional control strategies, which increase airflow rates, are inadequate in effectively suppressing the rate of temperature variation in scenarios of drastic changes in solar power. In contrast, our proposed strategy demonstrates precise management of SOEC internal heat generation, thus reducing the temperature gradient and variation within the cell to less than 5 K cm−1 and 1 K min−1, respectively, and maintaining a high electricity-to-hydrogen conversion efficiency of 94.9%.

Surface primary producer phenology in Dease Strait, NU, Canada, examined using submersed oceanographic sensors and satellite remote sensing

Thinning sea ice cover and earlier melt in the Arctic impact primary producer (PP) phenology, causing earlier ice algal bloom termination and phytoplankton bloom commencement. However, logistic constraints limit capturing the complete seasonal evolution of PPs and their physical drivers. Here, we combine spectral irradiance data from subsurface oceanographic moorings with synthetic aperture radar backscatter and meteorological variables to study light in Dease Strait, investigating its relation to timing and magnitude of surface PPs for 2017 and 2019. Ice algal blooms in 2017 and 2019 lasted 66 and 84 days, respectively, peaking within 2 days of snow melt onset. In 2019, lower temperatures and a deeper snowpack before snow melt extended the ice algal bloom. Melt pond formation increased light transmission, enabling a short, 6–7-day under-ice phytoplankton bloom in both years that was likely nutrient-limited. The 2019 phytoplankton bloom was less productive, possibly due to the longer ice algal bloom depleting surface nutrients. After ice break-up in 2019, a 31-day late-summer bloom occurred via wind-driven mixing. Our findings suggest that the combined remote sensing technique has novel applicability in other settings, providing insights into the changing state of PP phenology, and the need for long-term Arctic observations to discern regional climate change effects.

Enhancing solar radiation predictions through COA optimized neural networks and PCA dimensionality reduction

The inherent variability and uncertainty of solar radiation, influenced by factors such as seasons, weather, and cloud cover, pose significant challenges in accurately forecasting energy output from solar power systems. Robust forecasting models that can capture the dynamic behavior of solar radiation are crucial for optimizing energy generation, distribution, and storage processes. This study proposes an integrated approach that combines neural networks, metaheuristic optimization algorithms, and principal component analysis (PCA) for precise solar radiation forecasting. The methodology uses one year of solar radiation data from November 2022 to October 2023 from Vellore district, India. PCA is employed for dimensionality reduction, mitigating the curse of dimensionality. The reduced dataset is segregated into winter, summer, and monsoon subsets to capture distinct seasonal patterns. Neural networks are trained on these seasonal datasets, with their weights optimized by metaheuristic algorithms, including particle swarm optimization (PSO), gray wolf optimization (GWO), whale optimization algorithm (WOA), and coati optimization algorithm (COA). A comprehensive comparative study evaluated the performance of ANN-PSO, ANFIS-GWO, ANN-WOA, ANN-COA, and traditional ANN models. The ANN-COA model, integrating ANN with COA for weight optimization and PCA for dimensionality reduction, achieved the most accurate forecasts, outperforming other approaches. It attained optimal RMSE of 12.06 for winter, 6.72 for summer, and 9.49 for monsoon season, MAE of 6.66, 5.50, and 6.25, MAPE of 3.58 %, 1.68 %, and 2.31 %, and R2 of 0.973, 0.982, and 0.985 for winter, summer, and monsoon seasons respectively, demonstrating its robustness and suitability for time-series solar radiation forecasting.

A multi-generation system with integrated solar energy, combining energy storage, cooling, heat, and hydrogen production functionalities: Mathematical model and thermo-economic analysis

Increasing the proportion of renewable energy is of paramount importance for all countries in the world. In this work, a novel multi-generation system is designed to fully utilize solar energy, which includes a photovoltaic/thermal subsystem (PV/T), an absorption refrigeration cycle (ARC), a proton-exchange membrane (PEM) electrolysis, and a promising pumped thermal electricity storage (PTES) energy storage subsystem, which can simultaneously achieve the purposes of refrigeration, hydrogen production, heat production, and energy storage. Meanwhile, the solar radiation data of Wuwei City, Gansu Province, China for the year 2022 are selected for the study. According to different seasons, the thermodynamic and thermo-economic performances of the combined power, heat and hydrogen production (CPHH) mode operated in spring, autumn, winter, and the combined power, cooling and hydrogen production (CPCH) mode operated in summer are investigated, and multi-objective optimization is conducted. The results indicate that the direct normal irradiance (DNI) of solar, the area of PV/T, the thermal storage temperature of the PTES subsystem, and the pinch point temperature difference significantly affect the total energy efficiency, total exergy efficiency, and total product unit cost of the system. The multi-objective optimization results show that the optimal solutions of total energy efficiency and total product unit cost of the system are 85.90 %, 24.25 $·GJ−1 and 86.97 %, 24.25 $·GJ−1 for the CPHH mode in spring and winter, respectively, and 88.26 % and 22.21 $·GJ−1 for the CPCH mode in summer. This work can provide a valuable reference for the research of multi-generation systems with integrated renewable energy sources.

Energy cost optimization of Linear Fresnel Reflector (LFR) systems for different regions of installation

The Linear Fresnel Reflector (LFR) is a promising Concentrating Solar Thermal Power (CSP) technology due to its simplicity and cost-effectiveness. This paper presents a novel method for optimizing the Levelised Cost of Electricity (LCOE) for site-specific LFR systems. The method integrates ray tracing and a thermal model with a genetic algorithm to simulate and optimize LFR designs for three different annual solar irradiance profiles. For an LFR with a standard evacuated tube receiver with a 70 mm diameter, the optimal design variables are determined to be within the ranges of 8.9–9.2 m for receiver height, 18–22 for the number of mirrors, 0.63–0.81 m for mirror width and 0.10–0.13 m for mirror spacing. The results show that the optimized LFR design variables remain relatively consistent across different locations, allowing for the efficient use of one design in multiple locations. A sensitivity analysis shows that the LCOE is predominantly affected by receiver height. Comparing the optimized designs with recent commercial LFR installations reveals that, when 6 hours of thermal energy storage is used, LCOE savings of up to 20% are still achievable. Overall, the study demonstrates that there are still substantial LFR design improvements that can be made to reduce the cost of electricity from an LFR system and make it a cost-competitive solution for clean power generation.

Empirical models and artificial intelligence for estimating hourly diffuse solar radiation in the state of Alagoas, Northeastern Brazil

Diffuse solar irradiation (HD) data are essential for the design and management of photovoltaic solar systems, biosphere-atmosphere modeling, and other applications. However, HD observations are scarce in several locations, especially in tropical regions. Employing hourly diffuse solar irradiation (HDh) and global solar irradiation (HGh) data collected between 2002─2003 and 2007─2008 in Alagoas State, Northeast Brazil, this study assesses various modeling techniques. Empirical models, including third-degree polynomial, logistic, sigmoidal, and rational functions, were compared with AI methods such as artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Additionally, it explores how solarimetric and meteorological variables impact the performance of these models. The empirical models showed similar performance in estimating KDh(=HDh/HGh) (r2 > 0.726, modified Willmott – dm > 0.704, and RMSD < 0.103), with the third-degree polynomial model standing out. The empirical models had difficulty estimating KDh for hourly atmospheric transmittance (KTh) > 0.80, which indicated that they are not able to adequately simulate clear sky conditions, mostly due to surface reflections and clouds at the end of the day. ANN (r2 > 0.718, dm > 0.702, and RMSD < 0.105) showed better precision and accuracy of estimates for a greater number of schemes in relation to SVM and ANFIS (r2 > 0.704, dm > 0.699, RMSD < 0.108) and to empirical models. AI methods were able to represent the complexity of these conditions, with overall performance in estimating KDh superior or equivalent to empirical models. This study underscores the significance of exploring diverse methods for HD estimation, demonstrating promising potential for accurate and reliable estimation of hourly diffuse solar irradiation.

Solar irradiance variability around Asia Pacific: Spatial and temporal perspective for active use of solar energy

This study discusses solar irradiance variability in the spatial and temporal domains for the active use of solar energy. The spatial domain was calculated using the difference between one location and another in a focused small tile area. The principal component analysis (PCA) was used for this analysis in the spatial and temporal domain. The Umbrella Effect Index (UI) was calculated to determine the probability of an umbrella effect event within a specific time range. This study discusses solar irradiance variability using solar irradiance data analyzed every 10 min from high-temporal observations of geostationary satellites in the Asia-Pacific region. This study shows that solar heterogeneity around the Asia-Pacific ranges from 0–135 W/m2 with a high tendency in the area around the equator and highlands. In addition, this study also shows that the Asia Pacific region has umbrella effect events of approximately 50–2224 h/year with an index ranging approximately 0–0.34. This study presents the annual analysis and seasonal trends of solar irradiance variability over the entire study area. Determining seasonal trends is important because electricity demand fluctuates significantly as seasons progress. An integrated analysis of heterogeneity and UI revealed trends in spatiotemporal variations in solar irradiance. This approach provides useful information for optimizing and managing the locations for installing solar power plants. Moreover, an evaluation of existing solar power plants is presented. Evaluations based on spatiotemporal data reveal impossible characteristics using traditional approaches that use long-term simple averages or typical solar irradiance data. This research shows the distributed photovoltaic (PV) system in wide area can increase the stability of solar energy supply to the grid.

Integrated satellite observations unravel the relationship between urbanization and anthropogenic non-methane volatile organic compound emissions globally

As urban areas expand globally, human activities are leading to a sustained increase in non-methane volatile organic compound (NMVOC) emissions, escalating both environmental and health-related concerns. Given their diverse origins, estimating anthropogenic NMVOC emissions levels from global urban areas remains challenging. Here, we integrate TROPOspheric Monitoring Instrument (TROPOMI) formaldehyde (HCHO) column data, Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) radiance data, and the Emission Database for Global Atmospheric Research (EDGAR) to develop a method for estimating global anthropogenic NMVOC emissions. Furthermore, we construct a linear model to analyze the relationship between urbanization and anthropogenic NMVOC emissions. Our research reveals that meticulously filtered TROPOMI HCHO columns have a high Pearson correlation coefficient (r = 0.91) with anthropogenic NMVOC emissions, indicating its reliability as an indicator reflecting the levels of anthropogenic NMVOC emissions. We establish linear models at various scales, including global, continental, and national, linking HCHO columns (as indicators of anthropogenic NMVOC emissions) and NTL radiance (as an indicator of urbanization). The global-scale linear model exhibits an r of 0.81, with a slope of 0.42 × 1015 molec. cm−2 nanoWatts−1 cm2 sr and an intercept of 9.26 × 1015 molec. cm−2. This linear model reflects a positive correlation between urbanization and anthropogenic NMVOC emissions, also serving as a tool for estimating the levels of anthropogenic NMVOC emissions in urban areas. This study offers valuable insights for real-time monitoring of extensive anthropogenic NMVOC emissions.

Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms

In this study, a fuzzy multi-objective framework is performed for optimization of a hybrid microgrid (HMG) including photovoltaic (PV) and wind energy sources linked with battery energy storage (PV/WT/BES) in a 33-bus distribution network to minimize the cost of energy losses, minimizing the voltage oscillations as well as power purchased minimization from the HMG incorporated forecasted data. The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler’s laws of planetary motion, piecewise linear chaotic map and using the FDMT. In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. The optimization problem is implemented in three optimization scenarios based on real and forecasted data as well as the investigation of the battery's depth of discharge in the HMG optimization in the distribution network and its effects on the different objectives. The results including energy losses, voltage deviations, and purchased power from the HMG have been presented. Also, the MOIKOA superior capability is validated in comparison with the multi-objective conventional Kepler optimization algorithm, multi-objective particle swarm optimization, and multi-objective genetic algorithm in problem-solving. The findings are cleared that microgrid multi-objective optimization in the distribution network considering forecasted data based on the MLP-ANN causes an increase of 3.50%, 2.33%, and 1.98%, respectively, in annual energy losses, voltage deviation, and the purchased power cost from the HMG compared to the real data-based optimization. Also, the outcomes proved that increasing the battery depth of discharge causes the BES to have more participation in the HMG effectiveness on the distribution network objectives and affects the network energy losses and voltage deviation reduction.

Interval forecasting strategy of photovoltaic generation considering multi-factor self-fluctuation

Nowadays, interval forecasting is crucial for the power dispatching department because it can provide reliable boundaries of photovoltaic (PV) generation. However, few countermeasures are proposed to avoid the multi-factor self-fluctuation involved in the forecasting. To enhance the credibility of PV interval forecasting, this paper mainly conducts the following research around two factors “temperature” and “solar irradiance”: 1. Considering the periodicity of temperature data, an interval forecasting strategy based on auto-regressive integrated moving average model (ARIMA) and Bootstrap algorithms is designed to obtain its fluctuation range. 2. For solar irradiance data, considering it has temporal and non-temporal features, an interval forecasting strategy based on interval optimization and multi-extreme learning machine (ELM) is designed to obtain its fluctuation range; 3. Based on the obtained fluctuation boundaries of temperature and solar irradiance, the ELM is combined with the empirical mode decomposition (EMD) to complete error fitting and apply it to complete the interval forecasting of PV generation. Related case studies are presented in this paper. Finally, on the basis of ensuring the interval coverage requirement, we find the proposed method improves the forecast performance by 7.5 % compared to the existing methods at least, which will provide more reliable data for decision-making and management.

Radiation exposure in pediatric videourodynamics: An evaluation of safety in comparison to voiding cystourethrogram

Children with spina bifida (SB) undergo a videourodynamic study (VUDS) or urodynamic study and voiding cystourethrogram (VCUG). A standardized protocol for imaging during a pediatric VUDS has not been established. Our aim is to quantify radiation exposure and establish a baseline for children with spina bifida (SB) undergoing VUDS in current practice at our institution. This is a retrospective study from 2013 to 2020 of consecutive pediatric SB patients undergoing VUDS by a single provider. Patients were categorized into three groups based on age; group 1 (0-2YR), group 2 (2-10YR), group 3 (>10YR). Radiation data was reported as mean air kerma (AK), dose area product (DAP) and exposure time (seconds). Effective dose (ED) was calculated based on radiation quantity (Air Kerma, AK) and organ sensitivity. The lifetime attributable risk (LAR) was calculated based on AK and a risk coefficient. Data points calculated for patients undergoing VUDS were then compared to age matched institutional VCUG data in the same age groups. 398 patients undergoing VUDS met inclusion criteria and 262 independent patients underwent VCUG. ED increased with age in both VUDS and VCUG. All VCUG groups were found to have a higher ED than VUDS. The LAR for VUDS groups 1-3 was 0.001, 0.002, and 0.006, respectively. Reported in percentages, there is a 0.1%, 0.2%, and 0.6% chance, respectively, of age groups 1, 2 and 3 developing cancer as a result of the radiation exposure from a VUDS. Our study found that ED was low across all age groups for VUDS, comparing favorably to the VCUG groups. VCUG was selected as a benchmark comparison for its diagnostic similarities and, at times, overlapping indications. Few studies have described ED with respect to VUDS or extrapolate the ED of VUDS into LAR in the pediatric population. We recognize that we have not determined the true ED of the gonads and bladder, rather we have overestimated, as the data is based on an international reference point proximal to the exposed individual. However, LAR was calculated for each age group and revealed that patients are at a negligible increased risk of developing malignancy secondary to exposure compared to the general population. Our current practice for pediatric VUDS has exhibited consistently low radiation exposure amongst all age groups. Moving forward, we have the foundation and flexibility to create an imaging protocol for pediatric VUDS, while taking more calculated steps toward incorporating ALARA, as low as reasonably achievable, principles. A protocol adhering to the ALARA principle could provide consistency across institutions and aid in multi-institutional studies.

Lagged Response of MJO Convection and Precipitation to Solar Ultraviolet Variations on Intraseasonal Time Scales

AbstractComposite analyses of NOAA satellite‐based outgoing longwave radiation data and ERA5 reanalysis data for nearly six solar maximum periods support the existence of a response of tropical convection and precipitation to short‐term (∼27‐day) solar ultraviolet variations. Following solar UV peaks, the response consists of an increase in average convection and precipitation in the equatorial Indian Ocean and a decrease in the western and central tropical Pacific, with maximum amplitude at a lag of 4 to 8 days. The opposite occurs following short‐term solar UV minima. The observed responses are most detectable when the Madden‐Julian oscillation (MJO) is active and appear to be related to a reduced ability of the MJO to propagate across the Maritime Continent barrier following solar UV peaks relative to UV minima. A similar behavior has previously been found when the stratospheric quasi‐biennial oscillation is in its westerly phase relative to its easterly phase.

A Comparison of Channel Selection for the Assimilation of CrIS Radiances for NWP

Abstract With hyperspectral instruments measuring radiation emitted by Earth and its atmosphere in the thermal infrared range in multiple channels, several studies were made to select a subset of channels in order to reduce the number of channels to be used in a data assimilation system. An optimal selection of channels based on the information content depends on several factors related to observation and background error statistics and the assimilation system itself. An optimal channel selection for the Cross-track Infrared Sounder (CrIS) was obtained and then compared to selections made for different NWP systems. For instance, the channel selection of Carminati has 224 channels also present in our optimal selection, which includes 455 channels. However, in terms of analysis error variance, the difference between the two selections is small. Integrated over the whole profile, the relative difference is equal to 15.3% and 4.5% for temperature and humidity, respectively. Also, different observation error covariance matrices were considered to evaluate the impact of this matrix on channel selection. Even though the channels selected optimally were different in terms of which channels were selected for the various matrices, the results in terms of analysis error are similar. Significance Statement Satellites measure radiation from Earth and its atmosphere in the thermal infrared. Those radiance data contain thousands of measurements, called channels, and thus, a selection needs to be done retaining most of the information content since the large number of individual pieces of information is not usable for numerical weather prediction systems. The goal of this paper is to find an optimal selection for the instrument CrIS and to compare this selection with selections made for different numerical weather prediction systems. It was found that even though the channels selected optimally were different in terms of which channels were selected compared to other selections, the results in terms of precision of the analysis are similar and the results in terms of analysis error are similar due to the nature of hyperspectral instruments, which have multiple Jacobians overlapping.

The added value of Himawari-8 satellite radiance data assimilation for very heavy rainfall prediction in the Indonesian Maritime Continent (Case study: East Kalimantan)

Abstract This study evaluates the impact of assimilating Advanced Himawari Imager (AHI) radiance data on the 8-10 water vapor channel using the three-dimensional variational (3DVAR) technique for the very heavy rainfall events in East Kalimantan on June 2-4, 2019. We utilized the Weather Research and Forecasting (WRF) and WRF Data Assimilation (WRFDA) system to run assimilation and prediction model simulations. To compare the assimilated model with AHI data and without assimilation, we devised two experimental schemes: 3DVAR and NODA. We assessed the enhancement in model prediction by comparing simulation results based on convection evolution, vertically integrated moisture flux convergence (VIMFC) increment, and rain prediction skill metrics, including Threat Score (TS), Equitable Threat Score (ETS), and Fractions Skill Score (FSS). Our findings suggest that the incorporation of AHI data results in a substantial improvement of the WRF model’s prediction of the very heavy rainfall events in East Kalimantan.

Optimization of tilt angle for PV in China with long-term hourly surface solar radiation

The optimal tilt angle for photovoltaic (PV) systems is crucial for maximizing solar energy capture. China's diverse climate and geography pose challenges for tilt angle optimization. This study addresses the challenges by using a data-driven approach to determine grid-specific optimal tilt angles across China. Long-term ERA5 hourly solar radiation data and an optimization procedure are used to calculate the annual and monthly tilt angle that maximizes the total solar radiation received over different time periods (up to ten years) for every grid in China. Sensitivity analyses indicate that the optimized tilt angle varies by up to 10° depending on the time period considered, highlighting the importance of long-term radiation data. It is shown that four latitude-dependent schemes in China cannot accurately match the optimized tilt angles, emphasizing the need for optimization through the use of local solar radiation. The difference between our optimized tilt angles and ones via a best-performing latitude scheme makes for an estimated PV power loss of approximately 1.11 TWh/year based on China's PV installations in 2018, equivalent to the PV power generation of Philippines or Portugal in the same year. The presented data-driven framework contributes to China's PV industry by determination of optimal fixed tilt angles.