MODIS data and SEBS model were used to estimate the surface energy flux in Hefei City from March to December 2021. Verified by comparison with the EC measured values, and the sensitivity analysis of each parameter in the model was carried out. The results show that the net radiation flux(Rn) is the highest in September and the lowest in December. The Rn of water body is the highest, and urban area is the lowest. There was little correlation between soil heat flux(G) and seasonal variation. The main urban area and water body were higher, while the G was lower in the area with high vegetation coverage. The sensible heat flux(H) is obviously affected by the seasons, and the average H in December is the lowest, and even negative. Compared with the measured value, the average absolute error is 9W/m2, and the average relative error is 7%. The latent heat flux(LE) average absolute error between the inversion value and the measured value is 97W/m2, and the average relative error is 25.9%. The LE is relatively small in the main urban area, and relatively large in the area with high vegetation coverage. Sensitivity analysis of the model parameters was shows that the Rn is negatively correlated with the surface reflectance and surface temperature, and the expansion and contraction of air pressure, NDVI and wind speed have no effect on the Rn, G was negatively correlated with NDVI. Surface temperature and air temperature have the greatest influence on H and LE.
In order to study the optical properties of aerosols in Qingdao, the temperature, humidity, wind speed and direction, and visibility were measured in Shinan District of Qingdao from 2019 to August 2020, and the seasonal variation characteristics of the optical thickness as well as Angstrom exponent in the area were analyzed using MODIS data. The analysis results found that particulate matter (PM) and relative humidity were the main factors affecting visibility. particulate matter concentration and visibility showed a negative exponential relationship. In the initial stage of PM governance, the improvement in visibility is not significant despite the reduction of particulate matter. However, once the PM concentration reaches a certain level, the improvement in visibility becomes remarkably evident. Analyzing the optical characteristics of Qingdao provides valuable insights into the local pollution control.
A comprehensive site testing campaign was carried on in the northwestern area of China from July to November 2022. We conduct the study focusing on the daytime optical turbulence and precipitable water vapor long-term variation in this area, which are essential for time-domain astronomy and site scheduling. A relatively quiet and dry atmosphere situation that benefits observation can be more easily found in September and October. The so-called ’conversion time’, an excellent condition for observation at dawn and dusk, behaved differently in different months. Meanwhile, better observation conditions can be found at dawn in July, August and September but at dusk in October and November in the daytime.
KEYWORDS: Visibility, Machine learning, Education and training, Atmospheric modeling, Performance modeling, Meteorology, Data modeling, Solids, Random forests, Linear regression
Since there are many possible influencing factors of visibility, lightweight data requirements in practical applications of machine learning in visibility prediction can reduce the corresponding data observation cost and collection difficulty. By using the long-term measured data in Qingdao, this research comprehensively compares the performance of five common machine learning methods under different training parameter schemes, including XGBoost, LightGBM, Random Forest (RF), Support Vector Machine (SVM) and Multiple Linear Regression (MLR). The lightweight visibility prediction schemes based on pollutant parameter optimization are established. The seasonal training data of five machine learning models is preprocessed, and then performance evaluations of predictions are carried out. The analysis results show that in terms of models, ensemble learning models, including XGBoost, LightGBM, and RF, have significantly better seasonal visibility prediction effects than SVM and MLR models; XGBoost and LightGBM also have slightly better prediction effects than RF models. In terms of pollutant parameters, solid pollutants have a greater impact on visibility prediction than gaseous pollutants; PM2.5 is more influential than PM10 in visibility prediction. The visibility prediction scheme with six parameters using meteorological parameters and PM2.5 based on XGBoost or LightGBM model is preferably established in this research. This scheme can achieve the same prediction performance as the 11 parameter prediction scheme. The Correlation Coefficient (CC) of the results is around 0.85. The results of this study can not only be used to provide a machine learning scheme reference for practical visibility prediction applications, but also help to deepen the understanding of the factors affecting visibility.
Mie-scattering lidar is an active remote sensing tool for inverting atmospheric properties by detecting the interaction between lasers and various molecules and aerosol particles in the atmosphere. It has become a powerful detection tool for atmospheric aerosols. However, whether it is a coaxial or parallel-axis laser radar, the accuracy of measurement and inversion in the blind zone and transition zone needs to be improved. This paper studies and establishes a new method of the Mie scattering lidar extinction profile correction based on the UAV-borne aerosol radiosonde. In this method, the UAV (unmanned aerial vehicle) is equipped with an optical aerosol radiosonde (Portable Optical Particle Profiler, POPS), and measures particle spectrum information and related meteorological parameters in the same detection path as lidar. Therefore, by using the Mie scattering theory simulation, the aerosol extinction profile in the lidar short-range blind zone and transition zone can be derived from the UAV-borne aerosol radiosonde data. The horizontal measurement verification test shows that the near-ground extinction coefficient by the new UAV method is in good agreement with that obtained by the lidar Collis slope method.
The refractive index structure parameter C2n is a physical quantity used to characterize atmospheric optical turbulence, which is of great significance to the study of light wave transmission in turbulent atmosphere. In this paper, the C2n of coastal area from November 2019 to early January 2020 were measured by shipborne ultrasonic anemometers and microthermometer. Turbulence characteristics are statistically analyzed and the differences between results derived from the two measurement methods are discussed. The results show that the C2n measured by two instruments is generally consistent but have deviation. It is preliminarily inferred that the ultrasonic anemometer will oscillate due to the influence of the experimental environment on the wind field and the land-sea breeze, resulting high frequency noise that leads to higher measurement results than the micro-thermometer.
As one of the current scientific research hotspots, atmospheric aerosol not only affects human health and environmental quality, but also has an important impact on atmospheric radiation transmission, laser engineering and other fields. In this paper, we used multi-rotors UAV as a platform, equipped with Portable Optical Particle Profiler (POPS) and meteorological parameter instrument to measure near-surface aerosol properties in Hefei, China, and analyzed the characteristics of aerosol particle number concentration, effective radius and vertical spectrum distribution. The results show that in Hefei, the concentration of aerosol particles in the near-surface layer varies significantly from day to day. The size distribution of aerosol particle shows "double peaks", which can be described by the superposition of two modes. The peak centers are at 0.18 μm and about 0.5 μm. In this study, the physical parameters of aerosols obtained by UAVs can be used to calculate the optical properties of aerosols. It also provides technical support for the subsequent research of aerosols in the modeling of atmospheric aerosols in the region.
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