The accurate estimation of atmospheric turbulence dissipation rate (ε) is crucial for better characterizing the atmospheric physical processes and understanding the boundary layer structure. Nevertheless, the ε derived from the traditional spectral width method (SWM) tends to be overestimated using the radar wind profiler (RWP) measurements, due to the non-turbulent broadening effects. To tackle this challenge, here we propose a novel intelligent algorithm that combines SWM and Random Forest (SWM-RF) to estimate ε. The experimental data were synchronized observations from RWP and meteorological tower at the Boulder Atmospheric Observatory site from March to April 2015. Firstly, a systematic analysis was conducted on the overestimation of the SWM under different turbulence intensity, time, and height scenarios. The SWM-RF model is then constructed by introducing the difference function as a physical constraint and combining multiple observation parameters of RWP. The determination coefficient of the ε between sonic anemometers and SWM-RF reached 0.59, and the mean absolute error is 0.007 m² s⁻³. The comparison of results with sonic anemometers under different times and height indicates that SWM-RF model is superior to the SWM and is not affected by variations in height and time. Results shows that the overestimation of SWM is reduced under strong turbulence conditions, while the correction effect is poor in weak turbulence scenarios. Finally, the semi-diurnal cycles of ε for March and April in 2015 are analyzed. The accurate intelligent detection of ε profiles will lay a solid foundation for advancing the understanding of atmospheric physical processes and improving turbulence parameterization.

Reinforcement learning (RL)-based methods have achieved significant success in managing grid-interactive efficient buildings (GEBs). However, RL does not carry intrinsic guarantees of constraint satisfaction, which may lead to severe safety consequences. Besides, in GEB control applications, most existing safe RL approaches rely only on the regularisation parameters in neural networks or penalty of rewards, which often encounter challenges with parameter tuning and lead to catastrophic constraint violations. To provide enforced safety guarantees in controlling GEBs, this paper designs a physics-inspired safe RL method whose decision-making is enhanced through safe interaction with the environment. Different energy resources in GEBs are optimally managed to minimize energy costs and maximize customer comfort. The proposed approach can achieve strict constraint guarantees based on prior knowledge of a set of developed hard steady-state rules. Simulations on the optimal management of GEBs, including heating, ventilation, and air conditioning (HVAC), solar photovoltaics, and energy storage systems, demonstrate the effectiveness of the proposed approach.

This paper explores the applications of Fusion Graph Neural Network (FuGNN) on power distribution systems. FuGNN effectively models dynamic networks with evolving topology and features. Applied to power system network reconfiguration, FuGNN demonstrates its feasibility in optimizing switch configurations to minimize unserved loads and operational costs during extreme events. Additionally, FuGNN supports various downstream tasks, such as node feature prediction, further enhancing its versatility and applicability in power system resilience.

The boundary layer height (BLH) is a vital parameter that affects the vertical distribution of matter within the atmospheric boundary layer (ABL). However, the traditional algorithms determine the BLH based on changes in gradient within the signal-to-noise ratio (SNR) profile. It often leads to significant uncertainty under complex atmospheric conditions. Here, a convolutional neural network (CNN) algorithm considering multiple atmospheric parameter profiles is proposed for determining the BLH from radar wind profiler (RWP) data. The CNN algorithm is applied to the RWP dataset of atmospheric radiation measurement (ARM) site at Southern Great Plains (SGP) from August 2019 to July 2023. The sensitivity analysis shows that the CNN algorithm overcomes the shortcomings of the traditional algorithms that are susceptible to multiple local peaks. Moreover, the CNN algorithm performs well under complex conditions. It exhibits strong consistency with the BLH estimated by radiosonde (RS), with correlation coefficients, mean absolute error (MAE), and root-mean-square error (RMSE) of 0.81, 0.24, and 0.34 km, respectively. The CNN algorithm is then compared with the covariance wavelet transform (CWT) algorithm and the peak detection algorithm (PDA) using the BLH estimated by RS as a reference. The results indicate that the accuracy of BLH estimated by the CNN algorithm is higher than that of the two traditional algorithms. The MAE and RMSE of the CNN algorithm reduce from$0.53~\pm ~0.56$km ($0.57~\pm ~0.60$km) and 0.77 km (0.83 km) of CWT (PDA) to$0.24~\pm ~0.25$and 0.34 km, respectively. Finally, the diurnal and seasonal variation patterns of BLH are explored. The BLH shows a high correlation with solar radiation, rising from sunrise and then decreasing after sunset. Regarding seasonal variation, BLH peaks in summer and troughs in winter. Overall, the CNN algorithm proposed here can improve the accuracy and stability of BLH estimation. This study verifies the great potential of deep learning algorithms in the BLH estimation.

The planetary boundary layer height (PBLH) has a significant impact on the energy and material exchange in the atmosphere. The traditional gradient method (GM) determines the PBLH based on the gradient change of the aerosol profile. It is susceptible to the effect of complex atmospheric conditions, which leads to uncertainties in the estimation of PBLH. Here, a random forest (RF) algorithm that considers the vertical distribution of aerosols is proposed to find the PBLH under complex atmospheric conditions. The height of the three minimum local peaks of the range correction signal profile and 7 other variables, such as aerosol layer number (ALN), relative humidity, solar radiation, and other meteorological parameters, from January 2017 to December 2021 is used as RF model input. The radiosonde estimated PBLH (PBLHRS) is used as reference value. The sensitivity analysis indicates that the relative error of RF-estimated PBLH (PBLHRF) is smaller than that of GM-estimated PBLH (PBLHGM), and it decreases with an increase in aerosol optical depth. Moreover, RF achieves good performance under different atmospheric conditions. It can effectively overcome the effects of complex atmospheric conditions in PBLH estimation. Based on the correlation analysis, it is found that the estimation accuracy of the RF algorithm is greatly improved compared with the GM. The correlation coefficient between the PBLHRFand the PBLHRSreaches 0.8, which is much larger than that of the PBLHGM(0.47). Finally, long-term PBLHRFanalysis show that there are obvious diurnal and seasonal variations of PBLH. It increases and then decreases from early morning to late evening. It is highest in summer and lowest in winter. Overall, RF can effectively overcome the shortcomings of traditional GM and has high accuracy and robustness for various atmospheric conditions. The findings obtained here have great potential for lidar application in obtaining reliable PBLH estimations.

Vertical distributions of temperature and humidity are two essential factors for understanding the atmospheric structure, extreme weather events, and regional and global climate. The ground-based microwave radiometer (MWR), which acts as a passive sensor and operates continuously under all weather conditions, has an irreplaceable role in measuring the vertical information of the temperature and water content in the atmosphere. In this letter, we proposed a four-layer back-propagation neural network (BPNN) method to retrieve temperature and relative humidity (RH) profiles from the bright temperature measured by the MWR. In contrast to the traditional BPNN, this method has greater advantages in dealing with the problems of overfitting, gradient disappearance, and gradient explosion in vertical atmospheric retrieval. By adding dropout layers, it can also help to describe the nonlinear relationships for RH profiles. Results showed that the performance of the four-layer BPNN method was better than the quadratic regression (QR, provided by MWR manufacturer) method under both cloud and cloud-free conditions. Compared with measurements of radiosonde data, root-mean-square error of temperature and RH, BPNN achieves 1.88 K and 19.30% under cloud conditions and 2.03 K and 15.10% under cloud-free conditions, respectively, whereas the corresponding values by using the QR method were only 3.07 K and 24.28% under cloud conditions and 4.14 K and 18.96% under cloud-free conditions, respectively. Temperature and RH profiles retrieval with high precision have increased the efficiency of the MWR observations and provided a data foundation for further atmospheric climate research.

Particulate pollution threatens the ecological environment, air quality, and public health. Therefore, it has become an increasing concern for the public and governments in recent decades. In this study, a full coverage PM2.5(aerodynamic diameter of less than 2.5 microns) estimation strategy is proposed based on spatiotemporal machine learning approaches including the Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and Random Forest (RF). The RF estimates PM2.5by considering the features of a single pixel, while the introduction of the CNN-LSTM (size of 7 × 7 × 4) assists in exploiting the spatiotemporal correlation of surrounding pixel features. Compared with linear models and empirical spatiotemporal weight methods, our CNN-LSTM+RF avoids the uncertainty and complexity owing to actual measurements of the surrounding sites. In addition, full coverage is achieved using both satellite data and reanalysis data. Results showed that, the Root Mean Squared Error (RMSE) and coefficient of determination (R2) of the CNN-LSTM+RF were 12.790 μg/m3and 0.910, respectively, in sample-based Cross-Validation (CV). From the perspective of the season, the best performance of the CNN-LSTM+RF was found in autumn (R2of 0.915) and the lowest was in summer (R2of 0.848). In the meantime, for the different regions of China, the CNN-LSTM+RF also showed stable performance. The proposed method can generate high-precision continuous PM2.5distribution maps that provide beneficial support for improving environmental and public health, and provide a reference for using deeper networks.

Satellite-derived aerosol optical depth (AOD) is an important parameter for studies related to atmospheric environment, climate change, and biogeochemical cycle. Unfortunately, the relatively high data missing ratio of satellite-derived AOD limits the atmosphere-related research and applications to a certain extent. Accordingly, numerous AOD fusion algorithms have been proposed in recent years. However, most of these algorithms focused on merging AOD products from multiple passive sensors, which cannot complementarily recover the AOD missing values due to cloud obscuration and the misidentification between optically thin cloud and aerosols. In order to address these issues, a spatiotemporal AOD fusion framework combining active and passive remote sensing based on Bayesian maximum entropy methodology (AP-BME) is developed to provide satellite-derived AOD data sets with high spatial coverage and good accuracy in large scale. The results demonstrate that AP-BME fusion significantly improves the spatial coverage of AOD, from an averaged spatial completeness of 27.9%–92.8% in the study areas, in which the spatial coverage improves from 91.1% to 92.8% when introducing Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD data sets into the fusion process. Meanwhile, the accuracy of recovered AOD nearly maintains that of the original satellite AOD products, based on evaluation against ground-based Aerosol Robotic Network (AERONET) AOD. Moreover, the efficacy of the active sensor in AOD fusion is discussed through overall accuracy comparison and two case analyses, which shows that the provision of key aerosol information by the active sensor on haze condition or under thin cloud is important for not only restoring the real haze situations but also avoiding AOD overestimation caused by cloud optical depth (COD) contamination in AOD fusion results.

The vertical distribution of fine particles with a diameter$ < 2.5~\mu \text{m}$(PM2.5) plays an important role in understanding the transport of air pollution and in making decisions regarding the prevention and control of regional air pollution. However, the studies of the vertical distribution of PM2.5were limited by the lack of monitoring data obtained with vertical sampling strategies. The lidar system can obtain the aerosol profile, which provides the possibility to measure PM2.5profile. Here, the vertical distributions of PM2.5concentrations were investigated on the basis of lidar data from January 2014 to October 2015. Linear regression, improved linear regression, and random forest (RF) models were used to retrieve the PM2.5concentration profile from lidar data. The models were built based on the relationship among extinction coefficient (EC), temperature ($T$), relative humidity (RH), and surface PM2.5mass concentration. Comparison of the estimated and observed PM2.5showed that the RF model exhibited the best inversion effect. The correlation coefficient reached 0.75, and the root mean absolute error (RMAE) and root mean square error (RMSE) were 3.94 and 21.1$\mu \text{g}/\text{m}^{3}$, respectively. Error analysis indicated that the estimated PM2.5retrieved using the linear and improved linear models (ILMs) was smaller than the observed PM2.5when EC was less than 0.7 km−1, whereas PM2.5was evidently overestimated during winter pollution days. The reason might be that the effects of$T$and RH were inaccurately considered. Finally, the seasonal variation of the PM2.5profiles was investigated. Results indicated that the mass concentration of PM2.5was relatively large within 0.5–1.5 km, with a maximum of 60$\mu \text{g}/\text{m}^{3}$. The findings obtained here provide guidance for PM2.5vertical observation and regional pollutant transport.

Satellite observation is an effective way of obtaining global aerosol information. The study focuses on developing a new scheme to apply the traditional dark target (DT) method to the advanced Medium Resolution Spectral Imager (MERSI II), which is a part of the Chinese Fengyun-3-D satellite. Compared with the Moderate Resolution Imaging Spectroradiometer (MODIS), MERSI II shows higher ratios between red (0.65$\mu \text{m}$) and near-infrared ($2.13~\mu \text{m}$) bands in surface reflectance estimation and the green band ($0.55~\mu \text{m}$) that is more sensitive to cloud screening. Aerosol optical depth (AOD) is retrieved from earlier MERSI II observations by following the adapted DT method over land in Asia in 2018. Overall, AOD from MERSI II has a good performance compared with ground-based measurements with an expected error (EE%) of 66.38% and$R^{2}$of 0.834, which is close to the MODIS EE% of 70.59% and$R^{2}$of 0.829. Both sensors slightly overestimate the AOD over heavy aerosol loading regions, but MERSI-II has larger retrieval area covering a wider swath than MODIS in heavy hazy areas. On a spatial scale, the MERSI II effectively reflects the AOD distribution pattern but tends to overestimate and underestimate AOD at low and high latitudes, respectively, when compared with MODIS. The MERSI II sensor shows good aerosol detection potential, and the DT algorithm can be applied. MERSI II will provide important observation data on climate change and atmospheric pollution for the investigations in the future.

The vertical structure of wind is a key factor in modulating air quality, from which the determination of boundary layer height (BLH) remains a major challenge. In this paper, we developed an improved threshold method to determine the BLH from radar wind profiler (RWP) measurements. The normalized signal-to-noise ratio (SNR) profiles were used instead of the original SNR profiles to avoid instrumental inconsistencies. Additionally, a peak filter was designed to indicate the BLH based on the maximum SNR by taking into account the multiple peaks in the SNR profile. This algorithm was then applied to the RWP measurements taken in the summer (June-July-August) of 2018 in Beijing to obtain the BLHs. Validation analyses suggested that the BLH retrievals from RWP exhibited high consistency with those from radiosondes, with an average correlation coefficient of 0.69 (0.66) and a root mean squared error of 0.39 (0.41) in the daytime (nighttime). Additionally, the major features of summertime BLHs in Beijing were examined. In particular, a distinct diurnal variation in BLH was observed with a peak (1630 ± 510 m) occurring at 0600 universal time coordinated (UTC) and a minimum (587 ± 343 m) at 2300 UTC. Therefore, the algorithm presented here has great potential to be applied to other regions to obtain reliable BLHs. The findings obtained here highlight the importance of vertical wind structure in air quality studies.

Comprehensive research was conducted to analyze the characteristics of haze pollution during winter in Wuhan based on data for winter 2014-2015. The results demonstrated that haze pollution could be divided into two types. Type-1 lasted for 1-2 days and peak values of PM2.5exceeded 200 ug.m-3, Type-2 displayed a long duration of 5-6 days, and the hourly concentrations of PM2.5 ranged from 100 to 200 ug·m-3. Meanwhile, our results showed that type-1 haze pollution was mainly due to photochemical pollution process caused by high relative humidity (RH). Type-2 haze pollution was mainly caused by the accumulation of anthropogenic pollutants near the surface. Both haze pollution in winter was mainly fine-mode particles, and sometimes coarse-mode particles appeared. The characteristics of haze pollution revealed in this study can be used in regional climate modeling and can provide guidance to the government regarding prevention of haze pollution over central China.