Soot particles positively influence radiative forcing due to their strong absorption. Because of their chain-like structure, aggregated soot particles become more compact with the aging process, and the monomers or particles are always covered by water coatings. In this study, the optical parameters of two typical soot-water mixtures (i. e. an aggregate with core-shell monomers and a soot aggregate inside a water droplet) at 550 nm were investigated using the superposition T-matrix method, with a focus on the impact of the morphology and water coating of soot aggregates. For the soot aggregate with core-shell monomers, a relationship among the fractal dimension, relative humidity (RH) and monomer number was established and used to calculate optical parameters. The intensity of forward scattering declined with the increasing RH. The Cext, Csca, Cabs and SSA are much more insensitive to RH under higher RH conditions (RH>90%) than at a lower RH levels. In addition, hygroscopic shrinkage and the thickness of water coating have a stronger effect on the optical properties of larger aggregated soot at higher RH than lower RH. For another mixing state, the soot aggregate inside a water droplet, the morphology of the soot core plays an important role in the optical properties when the thickness of the water shell is small. When D_ratio changes from 1.2 to 2.8, Cext difference increases from 0.23μm2 to 2.36μm2 for particles with N=100 and 500, whereas the SSA difference decreases from 0.12 to 0.01. If the agglomerated structure of the soot core is not considered, the Cext, Csca and SSA will be underestimated for a relatively small D_ratio of 1.2. Ignoring the soot core in the water droplet could introduce large errors into the calculation of the optical parameters, and ignoring the structure of the aggregated soot core could enlarge the errors.

When employing the conventional beamforming (CBF) for the estimation of the direction of arrival of the Direct rays, one can observe a corresponding relationship between the arrival angle and the source distance, which can be used for range estimation. In the actual deep ocean environment, the arrival angle matched location method performs effectively in solving range estimation problems, although its performance is susceptible to the signal-to-noise ratio (SNR). To enhance the environmental adaptability and expand the application range of the source ranging method using the arrival structures in the beam do-main received by a vertical line array (VLA), we introduce a deep transfer learning (DTL) based source ranging method. Initially, a pre-trained model is established using simulation data generated under various SNRs through an ocean ambient noise model. Then high SNR experimental data is employed for DTL of the pre-trained model to fine tune the parameters. Finally, the experimental datasets are used to test the performance of the proposed method, and results suggest that the performance of the deep transferred model is much better than those of the traditional arrival angle matched location method and the model trained on noise-free data.

Aerosols and clouds greatly affect the Earth’s radiation budget and global climate. Light detection and ranging (lidar) has been recognized as a promising active remote sensing technique for the vertical observations of aerosols and clouds. China launched its first space-borne aerosol-cloud high-spectral-resolution lidar (ACHSRL) on April 16, 2022, which is capable for high accuracy profiling of aerosols and clouds around the globe. This study presents a retrieval algorithm for aerosol and cloud optical properties from ACHSRL which were compared with the end-to-end Monte-Carlo simulations and validated with the data from an airborne flight with the ACHSRL prototype (A2P) instrument. Using imaging denoising, threshold discrimination, and iterative reconstruction methods, this algorithm was developed for calibration, feature detection, and extinction coefficient (EC) retrievals. The simulation results show that 95.4% of the backscatter coefficient (BSC) have an error less than 12% while 95.4% of EC have an error less than 24%. Cirrus and marine and urban aerosols were identified based on the airborne measurements over different surface types. Then, comparisons were made with U.S. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiles, Moderate-resolution Imaging Spectroradiometer (MODIS), and the ground-based sun photometers. High correlations ( R > 0.79) were found between BSC (EC) profiles of A2P and CALIOP over forest and town cover, while the correlation coefficients are 0.57 for BSC and 0.58 for EC over ocean cover; the aerosol optical depth retrievals have correlation coefficient of 0.71 with MODIS data and show spatial variations consistent with those from the sun photometers. The algorithm developed for ACHSRL in this study can be directly employed for future space-borne high-spectral-resolution lidar (HSRL) and its data products will also supplement CALIOP data coverage for global observations of aerosol and cloud properties.

In the deep ocean, a vector sensor is deployed near the seabed to receive direct waves from the reliable acoustic path (RAP). In the space domain, the vector sensor can estimate the vertical arrival azimuth of the signal, which is sensitive to distance. In the frequency domain, the periodic interference pattern formed by the direct and surface-reflected wave is decided by both the source depth and the vertical arrival azimuth. In this work, a multi-step method for passive broadband source localisation using a single-vector sensor (SVS) is proposed. At first, the depth is estimated by the interference pattern in the frequency domain. Then the ranging problem is solved based on the depth estimated result. The impact of the surface-generated noise is considered. Compared with the scalar vertical line array (VLA) in simulation, the localisation performance of SVS has higher requirements for the signal-to-noise ratio (SNR) but is much smaller in size. In the experiment, Gaussian white noise from 310 to 430 Hz was emitted to simulate the underwater target. Compared with traditional match-field processing (MFP), the multi-step method is much more stable, accurate, and efficient. The computing time is drastically shortened.

Non-uniform distribution of sound-speed profile (SSP) significantly impacts deep-sea sound propagation. A simple and efficient method is proposed for estimating spatial non-uniform SSP using empirical orthogonal function and towed temperature-depth sensors (TDs). Processing results of a deep-sea experiment show that this method requires only 2 towed TDs, and the root-mean-square error (RMSE) of reconstructed SSP is 1.0377 m/s. Results of the sound-field simulation using different SSPs show the necessity of considering non-uniform distribution of SSP, and the RMSE of transmission loss calculated using the reconstructed SSP is less than 2 dB when source depth is 50–350 m.

针对浅海环境下沉积层声学参数估计问题,本文提出一种基于单波束声呐的海底沉积层参数序贯估计方法.将浅海地声参数的声学反演问题建模为序贯估计问题,利用一种时域高频海底反向散射模型对海底散射回波包络进行预测,结合无迹卡尔曼滤波器,根据误差反馈动态校正模型参数,实现沉积层参数自适应估计.将该模型应用于海试数据,获得测线上海底底质类型平均粒径分布,并与全局优化模拟退火算法的预测精度和预测效率进行对比.结果表明:序贯估计预测的海底散射回波包络与实测结果符合较好,海底底质平均粒径的预测值与海底底质分类分布相比具有较高的可信度;序贯估计法在保证和全局优化法精度相似的情况下大幅提升算法效率,参数估计消耗的平均时间显著缩短.

A deep transfer learning (DTL) method is proposed for the direction of arrival (DOA) estimation using a single-vector sensor. The method involves training of a convolutional neural network (CNN) with synthetic data in source domain and then adapting the source domain to target domain with available at-sea data. The CNN is fed with the cross-spectrum of acoustical pressure and particle velocity during the training process to learn DOAs of a moving surface ship. For domain adaptation, first convolutional layers of the pre-trained CNN are copied to a target CNN, and the remaining layers of the target CNN are randomly initialized and trained on at-sea data. Numerical tests and real data results suggest that the DTL yields more reliable DOA estimates than a conventional CNN, especially with interfering sources.

A method to tighten the cloud screening thresholds based on local conditions is used to provide more stringent schemes for Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms. Cloud screening strategies are essential to remove scenes with significant cloud and/or aerosol contamination from OCO-2 observations, which helps to save on the data processing cost and ensure high quality retrievals of the column-averaged CO2 dry air mole fraction (XCO2). Based on the radiance measurements in the 0.76 μm O2A band, 1.61 μm (weak), and 2.06 μm (strong) CO2 bands, the current combination of the A-Band Preprocessor (ABP) algorithm and Iterative Maximum A Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) algorithm passes around 20%–25% of all soundings, which means that some contaminated scenes also pass the screening process. In this work, three independent pairs of threshold parameters used in the ABP and IDP algorithms are sufficiently tuned until the overall pass rate is close to the monthly clear-sky fraction from the MODIS cloud mask. The tightened thresholds are applied to observations over land surfaces in Europe and Japan in 2016. The results show improvement of agreement and positive predictive value compared to the collocated MODIS cloud mask, especially in summer and fall. In addition, analysis indicates that XCO2 retrievals with more stringent thresholds are in closer agreement with measurements from collocated Total Carbon Column Observing Network (TCCON) sites.

Multiple approaches for depth estimation in deep-ocean environments are discussed. First, a multispectral transformation for depth estimation (MSTDE) method based on the low-spatial-frequency interference in a constant sound speed is derived to estimate the source depth directly. To overcome the limitation of real sound-speed profiles and source bandwidths on the accuracy of MSTDE, a method based on a convolution neural network (CNN) and conventional beamforming (CBF) preprocessing is proposed. Further, transfer learning is adapted to tackle the effect of noise on the estimation result. At-sea data are used to test the performance of these methods, and results suggest that (1) the MSTDE can estimate the depth; however, the error increases with distance; (2) MSTDE error can be moderately compensated through a calculated factor; (3) the performance of deep-learning approach using CBF preprocessing is much better than those of MSTDE and traditional CNN.

A cloud structure construction algorithm adapted for the nighttime condition is proposed and evaluated. The algorithm expands the vertical information inferred from spaceborne radar and lidar via matching of infrared (IR) radiances and other properties at off-nadir locations with their counterparts that are collocated with active footprints. This nighttime spectral radiance matching (NSRM) method is tested using measurements from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS). Cloud layer heights are estimated up to 400 km on both sides of the ground track and reconstructed with the dead zone setting for an approximate evaluation of the reliability. By mimicking off-nadir pixels with a dead zone around pixels along the ground track, reconstruction of nadir profiles shows that, at 200 km from the ground track, the cloud top height (CTH) and the cloud base height (CBH) reconstructed by the NSRM method are within 1.49 km and 1.81 km of the original measurements, respectively. The constructed cloud structure is utilized for cloud classification in the nighttime. The same method is applied to the daytime measurements for comparison with collocated MODIS classification based on the International Satellite Cloud Climatology Project (ISCCP) standard. The comparison of eight cloud types over the expanded distance shows good agreement in general.

介绍了大温差温湿度独立控制动态冰蓄冷系统的系统原理和运行状态,并以广东省东莞市某超高层建筑为例,对空调系统设备选型、设备配电和运行节能经济性进行了分析,指出动态冰蓄冷系统的应用为新风深度除湿和大温差供冷创造了条件,送风含湿量低于6 g/kg,且年转移电量占比达39.40％;指出大温差低温供冷和高温供冷的应用为超高层建筑带来显著的节能、节费效果,年节省运行电量120.19万kWh,在东莞市优惠峰谷电价差下运行2.09年即可回收增加的投资.

受海面波浪起伏、降雨和内波等海洋动力学过程的影响,浅水声速剖面的时间演化具有高度非线性,针对该问题提出使用改进的粒子滤波方法进行声速剖面序贯反演.该方法通过建立声速剖面的经验正交模型(EOF)以及描述声速剖面时间演化特征的状态空间模型,将声速剖面反演问题建模为状态跟踪问题,利用不敏粒子滤波(UPF:Uncented Particle Filter)算法进行声速剖面序贯反演.仿真试验通过实测声速剖面数据和先验地声参数信息产生接收声场数据,再利用模拟声场数据估计声速剖面的时间变化.结果 表明,相比于集合卡尔曼滤波(EnKF:Ensemble Kalman Filter),在计算效率等同的情形下,该方法可以在状态参数的时间跳变点保持良好的跟踪性能,一定程度上克服了现有反演算法在跳变点发散的问题,可以有效提高声速剖面反演精度,尤其在声速剖面时变性较强时具有显著优势.

LIDAR plays significant roles in monitoring the vertical distribution characteristics of clouds and aerosols and studying their impacts on the global climate change. For the space-born LIDAR, discrimination between clouds and aerosol is the first step of cloud/aerosol vertically optical property retrieve, and to a great extent, the retrieval precision depends on the accuracy of cloud and aerosol classification algorithm. Based on the optical and geographic characteristics of aerosols and clouds observed by LIDAR, in this study, the CALIOP aerosol and cloud products over China in the year of 2016 were trained as the sample sets. An effective cloud/aerosol classification algorithm was developed by combining the support vector machines (SVM) and decision tree methods, Our algorithm includes 3 parts: cloud and aerosol discrimination, ice-water cloud classification and aerosol subtype classification. (1) The cloud and aerosol were discriminated by the classification confidence functionsof 5-D probability density function (PDF) with parameters of gamma(532), chi, delta, Z and lat. (2) Randomly oriented ice (ROI) and water cloud were classified based onthe SVM. And by constructing the PDFs with gamma(532) , chi, delta, Z and Tau, feature layers misclassified by SVM were corrected, and a small portion of the horizontally oriented ice (HOI) clouds were removed from the water clouds. (3) Based on the optical and geographic characteristics of aerosol subtypes, decision tree classification was used for the determination of aerosol subtypes. Our retrieval results showed a good agreement with the CALIOP VFM products. For the cloud and aerosol discrimination results, the consistency ratios between our retrieves and VFM products for aerosol and cloud are up to 98. 51% and 88. 43%, respectively. And the consistency ratios in the day are higher than those at night. For the cloud phase retrieval results, water clouds can be well separated, and the consistency ratio of water cloud between our retrieves and VFM products is as high as 93. 44%. The consistency ratio of HOI is low due largely to the confusion between HOI and ROI. For the aerosol subtype classification, most aerosol subtypes could be well recognized by our algorithm. However, the consistency ratios of the mixed subtypes (e. g. polluted continental and polluted dust) between retrieval results and VFM products are relatively lower. Moreover, the cloud/aerosol, cloud phase and aerosol subtype classifications were also compared with the VFM products under three typical air conditions, i. e. haze, dust and clean. Under the haze condition, our results for most of the smoke aerosols agree quite well with the corresponding results from VFM. Under the duststorm condition, our algorithm can effectively discriminate the most of dust and polluted dust aerosols. For the clear day, our results for the few existing cloud and aerosol layers are quite consistent with the VFM results. This paper is an important improvement of the cloud and aerosol classification algorithms, which can simplify the processing and improve efficiency with satis factory accuracy. In the future work, we will build day/night and seasonal training sample sets, and consider more ice cloud phases and aerosol properties in the cloud/aerosol classification retrieval algorithm.

针对数据中心机柜级冷却系统存在的缺点,提出行级备份式容灾方案,与传统房间级备份式容灾方案进行对比分析,以单机柜功率、故障空调位置和备份空调位置为研究对象,利用计算流体力学(CFD)模拟软件对不同方案下机柜内设备冷却效果进行模拟.结果表明:采用行级备份式容灾方案的设备冷却效果显著优于房间级精密空调的容灾方案,且能够保证设备工作在正常的温度范围内;行级备份式方案能够保证故障空调附近机柜的进出风温度与周围机柜相同,但房间备份式会出现故障空调附近局部高温,存在热点.

The GOCART–Thompson microphysics scheme coupling the GOCART aerosol model and the aerosol-aware Thompson–Eidhammer microphysics scheme has been implemented in the WRF-Chem to quantify and evaluate the effect of dust on the ice nucleation process in the atmosphere by serving as ice nuclei (IN). The performance of the GOCART–Thompson microphysics scheme in simulating the effect of dust in atmospheric ice nucleation is then evaluated over East Asia during spring, a typical dust-intensive season, in 2012. Based upon the dust emission reasonably reproduced by WRF-Chem, the effect of dust on atmospheric cloud ice water content is well reproduced. With abundant dust particles serving as IN, the simulated ice water mixing ratio and ice crystal number concentration increases by 15 and 7 % on average over the dust source region and downwind areas during the investigated period. The comparison with the ice water path from satellite observations demonstrated that the simulation of the cloud ice profile is substantially improved by considering the indirect effect of dust particles in the simulations. Additional sensitivity experiments are carried out to optimize the parameters in the ice nucleation parameterization in the GOCART–Thompson microphysics scheme. Results suggest that lowering the threshold relative humidity with respect to ice to 100 % for the ice nucleation parameterization leads to further improvement in cloud ice simulation.

针对淮南某矿井,在相同热负荷下对比分析了冰浆潜热输送矿井空调与常规冷水空调的能耗.研究了矿井空调运行策略,依据矿井负荷匹配最优原则,计算分析了矿井空调在冷水直供模式、冷水蓄冷模式、冰浆直供模式、冰浆蓄冷模式等4种模式下的系统能耗和运行费用.结果表明:相比于常规冷水空调,冰浆潜热输送矿井空调节电率在8％以上,运行费用节省40％以上.

The rapid development of atmospheric satellite instruments since 1990s provides unprecedented large amount of observational datasets concerning global atmospheric pollutants. The continuous and long-term large-scale satellite products such as aerosol optical depth, tropospheric NO2 and SO2 enable effective and objective evaluation of air quality. Satellite columnar aerosol optical parameters can be used to indicate particle pollution near surface after correction. By contrast, satellite results of trace gas pollutants such as NO2 and SO2 from fossil fuel combustion with short lifetime around half one day are used to estimate anthropogenic emissions. It is shown that the overall anthropogenic emissions in China have largely declined since strict emission reduction policy implemented since 2013. However, coarse pixel resolution of the trace gases, limited information and retrieval bias of aerosol properties tend to hinder further application of satellite in air quality research. Recently launched satellite missions with advanced detection abilities will greatly enhance global atmospheric observations with much more datasets available.

The high energy consumption of data center presents a major barrier to the development of information industry, while the utilization of natural cold sources is very important to realize energy-saving and consumption-reduction. This paper mainly studies the utilization of natural cold sources in data center. The recommended values of the critical wet bulb temperature of natural cold sources under evaporative cooling conditions are determined. The utilizing potential of natural cold source in major climate regions of China is analyzed in detail. Moreover, the concept of natural cold source sensitivity is introduced. In addition, a typical data center in hot summer and warm winter zone is taken as an example to analyze the energy-saving effect of natural sold sources. The result indicates that natural cold source utilizing ratio in data centers exceeds 50％ in most regions at a critical wet bulb temperature of 16℃. However, the temperate region had the highest sensitivity concerning the natural cold source. Annual comprehensive COΡvalue of data centers increases by 23.7％, and the energy saving rate is about 19.2％ when using natural cold sources in hot summer and warm winter zone.

One kind of multi-source composite heat pump water heater system by solar,waste heat and air-source is developed and its operation modes under various conditions are clarified.A novel finned-tube evaporator with two path channels is designed for solar or waste heat assisted defrosting.The experimental study on the operation performance of air-source heat pump system with and without waste heat assistance is conducted on the condition of no light.The results show that when the air-source heat pump system produces a certain amount of heat water (500 L) independently under the nominal condition,the COP of the system is 4.39;when the waste heat assisted air-source heat pump system produces heat water of 200 L under the minimum operation condition compared to the system without waste heat assistance,the rate of power saving is 17.2％,and the COP increases from 1.98 to 2.39.

The horizontal and vertical distributions of aerosol optical properties over China in 2013–2015 were investigated using RAMS (Regional Atmospheric Modeling System)-CMAQ (Models-3 Community Multiscale Air Quality) simulations and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations. To better understand the performance of the RAMS-CMAQ model over China, comparisons with the ground-based Sun photometers AERONET (Aerosol Robotic Network), MODIS (Moderate Resolution Imaging Spectroradiometers) data and the on-board Lidar CALIOP were used for comprehensive evaluations, which could characterize the abilities of the model to simulate the spatial and vertical distributions of the AOD (Aerosol Optical Depth) as well as the optical properties for four seasons. Several high value areas (e.g., the Sichuan Basin, Taklamakan Desert, North China Plain, and Yangtze River Delta) were found over China during the study period, with the maximum mean AOD (CALIOP: ~0.7; RAMS-CMAQ: >1) in the Sichuan district. Compared with AODs of AERONET, both the CALIOP and RAMS-CMAQ AODs were underestimated, but the RAMS-CMAQ data show a better correlation with AERONET (AERONET vs. RAMS-CMAQ R: 0.69, AERONET vs. CALIOP R: 0.5). The correlation coefficients between RAMS-CMAQ and CALIOP are approximately 0.6 for all four seasons. The AEC (Aerosol Extinction Coefficient) vertical profiles over major cities and their cross sections exhibit two typical features: (1) most of the AEC peaks occurred in the lowest ~0.5 km, decreasing with increasing altitude; and (2) the RAMS-CMAQ AEC underestimated the region with high AODs in the northwest of China and overestimated the region with high AODs in the east–central plain and the central basin regions. The major difference in the AEC values of RAMS-CMAQ and CALIOP is mainly caused by the level of relative humidity and the hygroscopic growth effects of water-soluble aerosols, especially, in the Sichuan district. In general, both the column and vertical RAMS-CMAQ aerosol optical properties could be supplemented efficiently when satellite observations are not available or invalid over China in the applications of climate change and air pollution.