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With the expansion of the scale of application of photovoltaic (PV) power generation, PV maximum power point tracking (MPPT) technology has been transformed from uniform environmental conditions (UEC) to partial shading conditions (PSC), but there are certain limitations in the application of existing technologies under PSC. At present, a large number of several different PV MPPT algorithms have been proposed. In the research on MPPT technology under UEC, the MPPT technology based on the output characteristics of PV cells is used and the MPPT tracking speed has reached 20 times, in another improvement, the MPPT technology based on PV cell model is used, and the MPPT tracking accuracy has reached 99.4428%. In the process of MPPT technology transformation, there is a lack of sufficient comparative analysis and systematic review of these methods. This paper focuses on the tracking problem of the Voltage–Power (U–P) single-peak curve under the UEC to the U–P​ multi-peak curve under the partial shading conditions (PSC), and discuss four key issues in the development of PV MPPT research: (1) Under UEC, the performance of MPPT algorithms based on PV output characteristics and PV cell model; (2) The failure phenomenon when traditional MPPT technology is applied under PSC; (3) Under the PSC, the application process of the traditional methods and the swarm intelligence optimization algorithm (SIOA); (4) The bottlenecks faced by SIOA applications under PSC and the analysis of solutions. Through the analysis and comparison presented in this study, researchers can comprehensively understand the development and the important changes brought by PSC to MPPT technology. This could lead to exploration and research into the applications of SIOA in MPPT technology under PSC. The relevant conclusions will provide important directions for future research in the field of MPPT under PSC.

The global food crisis is becoming increasingly severe, and frequent grain bins fires can also lead to significant food losses at the same time. Accordingly, this paper proposes a model-compressed technique for promptly detecting small and thin smoke at the early stages of fire in grain bins. The proposed technique involves three key stages: (1) conducting smoke experiments in a back-up bin to acquire a dataset; (2) proposing a real-time detection model based on YOLO v5s with sparse training, channel pruning and model fine-tuning, and (3) the proposed model is subsequently deployed on different current edge devices. The experimental results indicate the proposed model can detect the smoke in grain bins effectively, with mAP and detection speed are 94.90% and 109.89 FPS respectively, and model size reduced by 5.11 MB. Furthermore, the proposed model is deployed on the edge device and achieved the detection speed of 49.26 FPS, thus allowing for real-time detection.

The accurate and reliable relative humidity (RH) prediction holds immense significance in effectively controlling the breeding cycle health and optimizing egg production performance in intensive poultry farming environments. However, current RH prediction research mainly focuses on short-term point predictions, which cannot meet the demand for accurate RH control in poultry houses in intensive farming. To compensate for this deficiency, a hybrid medium and long-term RH prediction model capable of precise point and interval prediction is proposed in this study. Firstly, the complexity of RH is reduced using a data denoising method that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and permutation entropy. Secondly, important environmental factors are selected from feature correlation and change trends. Thirdly, based on the results of data denoising and feature selection, a BiGRU-Attention model incorporating an attention mechanism is established for medium and long-term RH point prediction. Finally, the Gaussian kernel density estimation (KDE-Gaussian) method is used to fit the point prediction error, and the RH prediction interval at different confidence levels is estimated. This method was applied to analyze the actual collection of waterfowl (Magang geese) environmental datasets from October 2022 to March 2023. The results indicate that the CEEMDAN-FS-BiGRU-Attention model proposed in this study has excellent medium and long-term point prediction performance. In comparison to LSTM, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are reduced by 57.7%, 48.2%, and 56.6%, respectively. Furthermore, at different confidence levels, the prediction interval formed by the KDE-Gaussian method is reliable and stable, which meets the need for accurate RH control in intensive farming environments.

The egg production rate is a crucial metric in animal breeding, subject to biological and environmental influences and exhibits characteristics of small sample sizes and non-linearity. Currently, egg production rate prediction research predominantly focuses on single-step point prediction, lacking multistep and interval prediction exploration. To bridge these gaps, this study proposes a recursive, multistep interval prediction method for egg production rates, integrating environmental variables and attention mechanisms. Initially, this study employed three gradient boosting tree models (XGBoost, LightGBM, CatBoost) and the recursive feature elimination (RFE) method to select critical environmental variables and reduce data dimensionality. Subsequently, by scaling the time scale of important environmental variables and utilizing the variational modal decomposition improved by the grey wolf optimization (GWO-VMD) method for time-series decomposition, the volume of important environmental variable data is augmented and its complexity is reduced. Applying the long short-term memory (LSTM) neural network to obtain direct multistep predictions on IMFs, the predicted outcomes are averaged daily to yield the environmental variables for the upcoming two days. Finally, a multistep interval prediction model based on Seq2seq-Attention and Gaussian distribution is proposed in this study, and parameter optimization is carried out using the multi-objective grey wolf optimization algorithm (MOGWO). By inputting the historical egg production rate data and environmental variables into the proposed model, it is possible to achieve multistep point and interval prediction of egg production rates. This method was applied to analyze a dataset of egg production rates of waterfowl. The study demonstrated the feasibility of the recursive multistep prediction approach combined with environmental variables and guides egg production estimation and environmental regulation in animal husbandry.

Weather radar plays an important role in accurate weather monitoring and modern weather forecasting, as it can provide timely and refined weather forecasts for the public and for decision makers. Deep learning has been applied in radar nowcasting tasks and has exhibited a better performance than traditional radar echo extrapolation methods. However, current deep learning-based radar nowcasting models are found to suffer from a spatial “blurry” effect that can be attributed to a deficiency in spatial variability representation. This study proposes a Spatial Variability Representation Enhancement (SVRE) loss function and an effective nowcasting model, named the Attentional Generative Adversarial Network (AGAN), to alleviate this blurry effect by enhancing the spatial variability representation of radar nowcasting. An ablation experiment and a comparison experiment were implemented to assess the effect of the generative adversarial (GA) training strategy and the SVRE loss, as well as to compare the performance of the AGAN and SVRE loss function with the current advanced radar nowcasting models. The performances of the models were validated on the whole test set and inspected in two storm cases. The results showed that both the GA strategy and SVRE loss function could alleviate the blurry effect by enhancing the spatial variability representation, which helps the AGAN to achieve better nowcasting performance than the other competitor models. Our study provides a feasible solution for high-precision radar nowcasting applications.

Background: A large number of robots have put forward the new requirements for humanrobot interaction. One of the problems in human-swarm robot interaction is how to naturally achieve an efficient and accurate interaction between humans and swarm robot systems. To address this, this paper proposes a new type of human-swarm natural interaction system. Methods: Through the cooperation between three-dimensional (3D) gesture interaction channel and natural language instruction channel, a natural and efficient interaction between a human and swarm robots is achieved. Results: First, A 3D lasso technology realizes a batch-picking interaction of swarm robots through oriented bounding boxes. Second, control instruction labels for swarm-oriented robots are defined. The instruction label is integrated with the 3D gesture and natural language through instruction label filling. Finally, the understanding of natural language instructions is realized through a text classifier based on the maximum entropy model. A head-mounted augmented reality display device is used as a visual feedback channel. Conclusions: The experiments on selecting robots verify the feasibility and availability of the system.

This paper presents a new machine learning-based nowcasting model for hourly summer precipitation over the Eastern Alps. An artificial neural network (ANN) using the multi-layer perceptron algorithm was applied and evaluated against the Integrated Nowcasting through Comprehensive Analysis (INCA) nowcasting system and a multiple linear regression (MLR) model. Results show that the ANN model has a better nowcasting skill than the INCA model and the MLR model. The MLR model performs, too, also better than the INCA model. The improvement of precipitation intensity accuracy is substantial for both the morning to late evening period and for large rainfall thresholds. This study suggested that the machine learning approach is a promising methodology for precipitation forecasting.

The local convection initiation (CI) mechanisms of a convective case that occurred on 5 August 2017 in Cangzhou, northern China, were studied using Doppler radar and automatic weather station observational analysis, along with Variational Doppler Radar Analysis System assimilation analysis. During the convective process, a gust front appeared ahead of two existing convective systems, respectively. In the warm and moist environment ahead of the gust fronts in the south, there was a mesoscale air mass boundary. With the process of a gust front moving southward, approaching the mesoscale air mass boundary, the convergence intensified in the area between the gust front and the mesoscale air mass boundary. Finally, the strong convergent updraft exceeded the level of free convection and triggered the new convection.

Smoke detection based on video surveillance is important for early fire warning. Because the smoke is often small and thin in the early stage of a fire, using the collected smoke images for the identification and early warning of fires is very difficult. Therefore, an improved lightweight network that combines the attention mechanism and the improved upsampling algorithm has been proposed to solve the problem of small and thin smoke in the early fire stage. Firstly, the dataset consists of self-created small and thin smoke pictures and public smoke pictures. Secondly, an attention mechanism module combined with channel and spatial attention, which are attributes of pictures, is proposed to solve the small and thin smoke detection problem. Thirdly, to increase the receptive field of the smoke feature map in the feature fusion network and to solve the problem caused by the different smoke scenes, the original upsampling has been replaced with an improved upsampling algorithm. Finally, extensive comparative experiments on the dataset show that improved detection model has demonstrated an excellent effect.

Weather radar provides regional rainfall information with a very high spatial and temporal resolution. Because the radar data suffer from errors from various sources, an accurate quantitative precipitation estimation (QPE) from a weather radar system is crucial for meteorological forecasts and hydrological applications. In the South China region, multiple weather radar networks are widely used, but the accuracy of radar QPE products remains to be analyzed and improved. Based on hourly radar QPE and rain gauge observation data, this study first analyzed the QPE error in South China and then applied the Quantile Matching (Q-matching) method to improve the radar QPE accuracy. The results show that the rainfall intensity of the radar QPE is generally larger than that determined from rain gauge observations but that it usually underestimates the intensity of the observed heavy rainfall. After the Q-matching method was applied to correct the QPE, the accuracy improved by a significant amount and was in good agreement with the rain gauge observations. Specifically, the Q-matching method was able to reduce the QPE error from 39–44%, demonstrating performance that is much better than that of the traditional climatological scaling method, which was shown to be able to reduce the QPE error from 3–15% in South China. Moreover, after the Q-matching correction, the QPE values were closer to the rainfall values that were observed from the automatic weather stations in terms of having a smaller mean absolute error and a higher correlation coefficient. Therefore, the Q-matching method can improve the QPE accuracy as well as estimate the surface precipitation better. This method provides a promising prospect for radar QPE in the study region.

The echo of weather radar is seriously disturbed by clear-air turbulence echo (CAT) which needs identifying and eliminating to improve the data quality of weather radar. Using the data observed with the five X-band dual polarimetric radars in Changping, Fangshan, Miyun, Shunyi, and Tongzhou, Beijing in 2018, the probability density distribution (PDD) of the horizontal texture of four radar moments reflectively factor (ZH), differential reflectivity (ZDR), correlation coefficient (ρHV), differential propagation phase shift (ΦDP), and then the CAT is identified and removed using Bayesian method. The results show that the radar data can be effectively improved after the CAT has been eliminated, which include: (1) the removal rate of CAT is more than 98.2% in the analyzed cases. (2) In the area with high-frequency distribution of CAT, the CAT can be effectively suppressed; in the area with low-frequency distribution, some weather echo in the edge with SNR < 15 dB may be mistakenly identified as CAT, but the proportion of meteorological echoes to the total echoes is more than 85%, which indicate that the error rate is very low and does not affect the radar operation.

Disdrometer observations obtained by an OTT Parsivel2 during the 2017 Great Hunan Flood from 1:00 a.m. LST 23 June 2017 to 4:00 a.m. LST 2 July 2017 in Changsha, Hunan Province, southern China, are analyzed to diagnose characteristics of raindrop size distribution (DSD). This event was characterized by a large number of small- to medium-sized raindrops (diameters smaller than 1.5 mm) and the mean median volume diameter (D0) is about 1.04 mm. The median values of rain rate R (1.57 mm h−1), liquid water content W (0.10 g m−3), and radar reflectivity Z (25.7 dBZ) are smaller than that of the 2013 Great Colorado Flood. This event was composed of two intense rainfall periods and a stratiform period, and notable distinctions of rainfall microphysics among the three rainfall episodes are observed. Two intense rainfall periods were characterized by widespread and intense convection rains with a surface reflectivity of 48.8~56.7 dBZ. A maximum diameter of raindrops up to 7.5 mm was observed, as well as high concentrations of small and midsize drops, resulting in large rainfall amounts during the two intense rainfall episodes. The mean radar reflectivity of 22.6 dBZ, total rainfall of 17.85 mm and the maximum raindrop of approximately 4.25 mm were observed during the stratiform rainfall episode. The composite DSD for each rainfall episode peaked at 0.56 mm but higher concentrations of raindrops appeared in the two intense rainfall episodes. The Z-R relationships derived from the disdrometer measurements reflect the unusual characteristics of DSD during the flood. As a result, the standard NEXRAD Z-R relationship (Z = 300R1.4) strongly underestimated hourly rainfall by up to 27.5%. In addition, the empirical relations between rainfall kinetic energy (KE) versus rainfall intensity (R) and mean mass diameter (Dm) are also derived using DSDs to further investigate the impacts of raindrop properties on the rainfall erosivity.

The seasonal variations of raindrop size distribution (DSD) and rainfall are investigated using three-year (2016–2018) observations from a two-dimensional video disdrometer (2DVD) located at a suburban station (40.13° N, 116.62° E, ~30 m AMSL) in Beijing, China. The annual distribution of rainfall presents a unimodal distribution with a peak in summer with total rainfall of 966.6 mm, followed by fall. Rain rate (R), mass-weighted mean diameter (Dm), and raindrop concentration (Nt) are stratified into six regimes to study their seasonal variation and relative rainfall contribution to the total seasonal rainfall. Heavy drizzle/light rain (R2: 0.2~2.5 mm h−1) has the maximum occurrence frequency throughout the year, while the total rainfall in summer is primarily from heavy rain (R4: 10~50 mm h−1). The rainfall for all seasons is contributed primarily from small raindrops (Dm2: 1.0~2.0 mm). The distribution of occurrence frequency of Nt and the relative rainfall contribution exhibit similar behavior during four seasons with Nt of 10~1000 m−3 registering the maximum occurrence and rainfall contributions. Rainfall in Beijing is dominated by stratiform rain (SR) throughout the year. There is no convective rainfall (CR) in winter, i.e., it occurs most often during summer. DSD of SR has minor seasonal differences, but varies significantly in CR. The mean values of log10Nw (Nw: mm−1m−3, the generalized intercept parameter) and Dm of CR indicate that the CR during spring and fall in Beijing is neither continental nor maritime, at the same time, the CR in summer is close to the maritime-like cluster. The radar reflectivity (Z) and rain rate (?) relationship (Z = ?R?) showed seasonal differences, but were close to the standard NEXRAD Z-R relationship in summer. The shape of raindrops observed from 2DVD was more spherical than the shape obtained from previous experiments, and the effect of different axis ratio relations on polarimetric radar measurements was investigated through T-matrix-based scattering simulations.

The Beijing Broadband Lightning Network (BLNET) was successfully set up in North China and had yielded a considerable detection capability of total lightning (intracloud and cloud to ground) over the regions with complex underlying (plains, mountains, and oceans). This study set up a basic framework for the operational application of assimilating total lightning activities from BLNET and assesses the potential benefits in cloud-scale, very short-term forecast (nowcasting) by modulating the vertical velocity using the 4DVar technique. Nowcast statistics aggregated over 11 cycles show that the nowcasting performances with the assimilation of BLNET lightning datasets outperform RAD and the assimilation of GLD360 (Global Lightning) datasets. The assimilation of BLNET data improves the model's dynamical states in the analysis by enhancing the convergence and updraft in and near the convective system. To better implement of assimilating real-time lightning data, this study also conducts sensitivity experiments to investigate the impact of the horizontal length scale of a distance-weighted interpolation, binning time intervals, and different vertical profile or distance weights prior to the DA. The results indicate that the best forecast performance for assimilating BLNET lightning datasets is obtained in a 4DVar cycle when the lightning accumulation interval is 3 min, the radius of horizontal interpolation is 5 × 5, and the statistically vertical velocity profile and the distance weights obtained from cumulus cloud.

In wind tunnel tests, cantilever stings are often used as model-mount in order to reduce flow interference on experimental data. In this case, however, large-amplitude vibration and low-frequency vibration are easily produced on the system, which indicates the potential hazards of gaining inaccurate data and even damaging the structure. This paper details three algorithms, respectively, Classical PD Algorithm, Artificial Neural Network PID (NNPID), and Linear Quadratic Regulator (LQR) Optimal Control Algorithm, which can realize active vibration control of sting used in wind tunnel. The hardware platform of the first-order vibration damping system based on piezoelectric structure is set up and the real-time control software is designed to verify the feasibility and practicability of the algorithms. While the PD algorithm is the most common method in engineering, the results show that all the algorithms can achieve the purpose of over 80% reduction, and the last two algorithms perform even better. Besides, self-tuning is realized in NNPID, and with the help of the Observer/Kalman Filter Identification (OKID), LQR optimal control algorithm can make the control effort as small as possible. The paper proves the superiority of NNPID and LQR algorithms and can be an available reference for vibration control of wind tunnel system.

DC microgrid systems that integrate energy distribution, energy storage, and load units can be viewed as examples of reliable and efficient power systems. However, the isolated operation of DC microgrids, in the case of a power-grid failure, is a key factor limiting their development. In this paper, we analyze the six typical operation modes of an off-grid DC microgrid based on a photovoltaic energy storage system (PV-ESS), as well as the operational characteristics of the different units that comprise the microgrid, from the perspective of power balance. We also analyze the key distributed control techniques for mode transformation, based on the demands of the different modes of operation. Possible reasons for the failure of PV systems under the control of a voltage stabilizer are also explored, according to the characteristics of the PV output. Based on this information, we propose a novel control scheme for the seamless transition of the PV generation units between the maximum PV power tracking and steady voltage control processes, to avoid power and voltage oscillations. Adaptive drooping and stabilization control of the state of charge of the energy storage units are also considered, for the protection of the ESS and for reducing the possibilities of overcharging and/or over-discharging. Finally, various operation conditions are simulated using MATLAB/Simulink, to validate the performance of the proposed control strategy.

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods.

This paper analyses the transient characteristics and breaking performance of direct-current (DC) forced-interruption vacuum interrupters in 270 V power-supply systems. Three stages are identified in forced interruption: the DC-arcing stage, current-commutation stage, and voltage-recovery stage. During the current-commutation stage, the reverse peak-current coefficient k, which is a key design factor, is used to calculate the rate of current at zero-crossing (di/dt). MATLAB/Simulink simulation models are established to obtain the transient characteristics influenced by the forced-commutation branch parameters and the coefficient k. To study the breaking performance of spiral-type contacts, experiments are conducted for different contact materials and arcing times for currents less than 3.5 kA. During the DC-arcing stage, a locally intensive burning arc is observed in the CuW80 contact; however, it is not observed in the CuCr50 contact. On examining the re-ignition interruption results of the CuW80 contact, the intensive burning arc is found to be positioned within a possible re-ignition region. When the arcing time is longer than 1 ms, the intensive burning arc occurs and affects the breaking performance of the spiral-type contacts. If the DC-arcing stage is prolonged, the total arcing energy increases, which leads to a lower breaking capacity.

In this paper, we present our research into the interruption performance of vacuum circuit breakers in aviation intermediate-frequency (360 Hz to 800 Hz) power supply systems. Intermediate-frequency vacuum arc experiments were carried out in interrupters with a diameter of 41 mm and CuCr50 alloy contact material. The results show that, as the frequency and peak value of the current increase, both the peak value and rise rate of the intermediate-frequency vacuum arc voltage also increase, and the interruption ability decreases. However, compared to the power frequency current at the same value, the erosion of the contacts is weaker over a shorter arc period. When the vacuum arc reignites, metal droplets are emitted from the contacts. The drive force is from the center of the contact to the edge. If the density of the plasmas and metal vapors and the number of the metal droplets reaches a certain level, the arc may break down, which will cause the interruption to fail.

The output–voltage–power curves of photovoltaic (PV) arrays exhibit complex multi-peak shapes when local shading occurs. The existing maximum power point tracking (MPPT) algorithms to solve this multi-peak problem do not consider the possibility of tracking failures due to the time of the irradiance change. In this study, first, the reason for the failure of the global MPPT (GMPPT) algorithm is analyzed based on the PV array mathematical model and its output characteristics under partial shading conditions; then, in order to estimate the MPP voltage, an artificial neural network (ANN) is trained using environmental information such as irradiance. A hybrid MPPT method using an augmented state feedback precise linearization (AFL) controller combined with an ANN is proposed to solve problems such as the shift of the static operating point of the DC/DC boost converter. Finally, numerical simulations are conducted to validate the proposed method and eliminate the possibility of MPPT failure. The proposed hybrid MPPT method is compared with the conventional perturb and observe (P & O) method and the improved P & O method through simulations. Using the proposed neural network and nonlinear control strategy, the MPP can be tracked rapidly, accurately, and statically, proving that the method is feasible and effective.

Photovoltaic (PV) systems have non-linear characteristics that generate maximum power at one particular operating point. Environmental factors such as irradiance and temperature variations greatly affect the maximum power point (MPP). Diverse offline and online techniques have been introduced for tracking the MPP. Here, to track the MPP, an augmented-state feedback linearized (AFL) non-linear controller combined with an artificial neural network (ANN) is proposed. This approach linearizes the non-linear characteristics in PV systems and DC/DC converters, for tracking and optimizing the PV system operation. It also reduces the dependency of the designed controller on linearized models, to provide global stability. A complete model of the PV system is simulated. The existing maximum power-point tracking (MPPT) and DC/DC boost-converter controller techniques are compared with the proposed ANN method. Two case studies, which simulate realistic circumstances, are presented to demonstrate the effectiveness and superiority of the proposed method. The AFL with ANN controller can provide good dynamic operation, faster convergence speed, and fewer operating-point oscillations around the MPP. It also tracks the global maxima under different conditions, especially irradiance-mutating situations, more effectively than the conventional methods. Detailed mathematical models and a control approach for a three-phase grid-connected intelligent hybrid system are proposed using MATLAB/Simulink.

Hydrogen production by wind power is a full-cycle, zero-carbon emission hydrogen production method. However, the random and intermittent nature of wind energy leads to instability in the grid-connected power of wind power. Hence, this paper proposes an intraday energy management strategy for wind-hydrogen coupled systems to achieve flexible operation. Firstly, the AHP and EWM were used to optimise the capacity ratios of the hybrid electrolyzer to improve the objectivity of the hybrid electrolyser selection. Then, a wind-hydrogen coupled system model including a wind power system, hybrid electrolyser hydrogen generation system, and hydrogen fuel cell power system is constructed. Energy management strategies for six operating conditions are proposed to combine the operating characteristics of each system, which solves the problem of poor wind power consumption due to the fluctuation of wind power effectively. Finally, the effectiveness and feasibility of the proposed control strategy are verified based on actual wind farm data. [ABSTRACT FROM AUTHOR]

Polyurethane foam as an important sound absorption material is limited by the poor flame resistances in building field, but the modifications of flame resistances tend to have adverse effects on the sound absorption performance. Herein, dibutyltin dilaurate and triethylenediamine are used simultaneously as catalysts, and four new types of flame retardant polyether polyols (FPMPO) are synthesized and combined with modified expanded graphite (PEG) to prepare the flame resistance flexible polyurethane foam (FFPUF) by one-step method. The results show that the combination of the two catalysts can control the cell structure of FFPUF availably for sound absorption. The FPMPO have little negative influence on the cell morphology and the sound absorption performance of FFPUF, but the increase of flame resistances is finite due to the limited amount of FPMPO. In order to improve the flame retardant properties further, the FPMPO and the PEG are combined in the modification. Benefitting by the effective control structure and the modification with composite flame retardant, the FFPUF shows excellent sound absorption and flame retardant properties. The LOI value of FFPUF is 33.4 and the vertical burning level reaches V-0, and the average sound absorption coefficient maintains 0.68 in the 800-6300 Hz range. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of grid spacing and station network on surface analyses and forecasts including temperature, humidity, and winds in Beijing Winter Olympic complex terrain. The high-resolution analyses are generated by a rapid-refresh integrated system that includes a topographic downscaling procedure. Results show that surface analyses are more accurate with a higher targeted grid spacing. In particular, the average analysis errors of surface temperature, humidity, and winds are all significantly reduced when the grid size is increased. This improvement is mainly attributed to a more realistic simulation of the topographic effects in the integrated system because the topographic downscaling at higher grid spacing can add more details in a complex mountain region. From 1 km to 100 m, 1-12-h forecasts of temperature and humidity are also largely improved, while the wind only shows a slight improvement for 1-6-h forecasts. The influence of station network on the surface analyses is further examined. Results show that the spatial distributions of temperature and humidity at a 100-m space scale are more realistic and accurate when adding an intensive automatic weather station network, as more observational information can be absorbed. The adding of a station network can also reduce forecast errors, which can last for about 6 h. However, although surface winds display better analysis skill when more stations are added, the wind at the mountaintop region sometimes encounters a marginally worse effect for both analysis and forecast. The results are helpful to improve the analysis and forecast products in complex terrain and have some implications for downscaling from a coarse grid size to a finer grid. [ABSTRACT FROM AUTHOR]