Atmospheric aerosol affects electromagnetic radiation transmission through scattering and absorption, which has great influence on optical satellite remote sensing, environmental monitoring, climate forcing and aerosol-cloud interaction. In2021, based on the data collected in the Yellow Sea and the South China Sea near the coast, we developed the coast aerosol model (CAM) to predict the aerosols size distribution under coastal environments. This work makes a comprehensive model evaluation for the CAM with the atmospheric aerosol observation results at the South China Sea coastal station (Maoming) in November 2023. The comparison results show that the CAM can effectively describe the characteristics of aerosol (number concentration, particle size distribution and extinction coefficient) in this area. During the observation period, the average error of prediction results of aerosol concentration is around 20.6%, indicating that the CAM is promising in prediction coastal aerosol microphysical and optical properties.
We verify a simple alternative method to estimate the Fried parameter over a horizontal propagation path using the refractive index measured by a pair of micro-thermometers. The results show a relatively reliable estimate, especially when the optical turbulence in the path is relatively strong. Moreover, we also discuss the relationship between the Fried parameter value with the overall intensity of optical turbulence and the length of the propagation path theoretically. The influence of these two factors shows a prominent exponential characteristic, which also can be speculated from the formula.
Using the continuous observation data of wind profiler radar, the temporal and spatial variation characteristics of wind direction and wind speed in the summer Boundary layer atmosphere wind field in the southern foot of Tianshan Mountains in Xinjiang are analyzed. The preliminary exploration of the impact of low-level jet streams on near surface atmospheric turbulence activity is of great significance for studying laser atmospheric transport, triggering convective weather, and atmospheric pollution and diffusion. Experimental area is located in a valley, and the circulation characteristics dominated by Mountain breeze and valley breeze are the basic characteristics of the daily variation of the wind field in the region, and also the main reason for the occurrence of low-level jet. Through statistics and analysis, it was found that the summer turbulent activity near the surface in this area is strong, which is caused by thermal stress on the underlying surface. During the day, the surface temperature is high, and there is a strong exchange of air flow near the surface with the upper layer, resulting in strong turbulent activity. In the evening, it may be the low-level jet that brings the horizontal wind shear near the ground, which intensifies the turbulence vortex activity and leads to the occurrence of turbulence.
This research focuses on atmospheric aerosols in China's maritime regions, utilizing aerosol data from the next-generation geostationary meteorological satellite Himawari-8 (H8) and the spaceborne lidar CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations). It examines the distribution and variation of typical aerosols across different time and space scales and explores the optical properties of aerosols in the eastern maritime regions. The findings indicate: 1. Aerosol optical properties in coastal regions are predominantly influenced by anthropogenic aerosols from land, signifying a dominance of fine-mode aerosols in these areas, with the central and eastern parts primarily characterized by larger sea salt aerosol particles. 2. Outside of summer, irregular, larger aerosol particles are mainly found at lower altitudes. 3. Further examination of meteorological factors influencing aerosol optical properties reveals a notable correlation between sea surface temperature, wind speed, relative humidity, and aerosol optical thickness.
Aerosol scattering and absorption coefficients are important parameters that characterize the optical properties of aerosols, which have significant impacts on the radiation balance, air quality, and climate change of the Earth. In order to further improve the understanding of the relationship between aerosol optical properties and meteorological parameters in the offshore areas of Guangdong Maoming, the scattering and absorption coefficients of aerosols as well as meteorological parameters such as temperature, humidity, pressure, wind speed, wind direction, and visibility were measured. In this study, a prediction model of aerosol scattering and absorption coefficients based on the CatBoost algorithm was proposed using the measured data. Firstly, the measured data was preprocessed, and then a CatBoost algorithm model based on ensemble learning was constructed and trained. The Optuna framework was used to optimize the hyperparameters of the model to obtain the final aerosol scattering and absorption coefficient prediction model. Finally, the machine learning model was used to predict the scattering and absorption coefficients of aerosols in the offshore areas of Maoming. The model was compared with XGBoost and LightGBM algorithm models, and the mean squared error (MSE) and mean absolute error (MAE) were used as evaluation metrics to assess the accuracy of the model predictions. Based on the evaluation metrics, the CatBoost algorithm model based on Optuna automatic hyperparameter optimization performed the best among several models. The experimental results showed that when the training and testing data came from the same region, the MAE of the CatBoost algorithm model based on Optuna hyperparameter optimization was about 5.33, and the MSE was about 48.764, achieving a prediction accuracy of 90.88% for aerosol scattering and absorption coefficients.
Models related to long and short-term memory networks have demonstrated superior performance in short-term prediction, but their prediction ability becomes limited in long sequence time series forecasting (LSTF), and prediction time increases. To address these issues, this paper optimizes the Transformer and Informer models in the following ways: (1) input representation optimization, by adding a time embedding layer representing global timestamps and a positional embedding layer to improve the model's prediction ability for aerosol extinction coefficient (AEC); (2) self-attention mechanism optimization, by using probabilistic self-attention mechanism and self-attention distillation mechanism to reduce memory usage and enhance the model's local modeling ability through convolutional aggregation operations; (3) generative decoding, using dynamic decoding to enhance the model's long sequence prediction ability. Based on these optimizations, a new LSTF model for AEC is proposed in this paper. Experimental results on the atmosphere parameters of the Maoming (APM) dataset and weather dataset show that the proposed model has significant improvements in accuracy, memory usage, and runtime speed compared to other similar Transformer models. In the accuracy experiment, compared to the Transformer model, the MAE of this model on APM dataset decreased from 0.237 to 0.103, and the MSE decreased from 0.345 to 241. In the memory usage experiment, the model can effectively alleviate memory overflow problems when the input length is greater than 720. In the runtime speed experiment, when the input length is 672, the training time per round decreased from 15.32 seconds to 12.39 seconds. These experiments demonstrate the effectiveness and reliability of the proposed model, providing a new approach and method for long sequence prediction of AEC.
KEYWORDS: Solar radiation models, Solar radiation, Temperature metrology, Thermal modeling, Infrared radiation, Atmospheric modeling, Emissivity, 3D modeling
To estimate spatial distribution of thermal characteristics of stratospheric airships, this paper considers the complex thermodynamic environment in which the airships operate, and establishes a computational model for the thermal characteristics of the airships, including thermal equilibrium equations, direct solar radiation, scattered solar radiation, Earth-reflected radiation, atmospheric infrared radiation, Earth infrared radiation, radiation heat transfer and convective heat transfer between skin units. With this model, theoretical simulations of temperature fields were performed for the airships. The simulation results show that the skin temperature of stratospheric airships are mainly affected by the intensity of solar radiation, which is lower at night and higher during the day. Under floating conditions, the skin temperature field exhibits high non-uniformity and significant temporal variations. The skin solar absorptivity of the stratospheric airship has a significant effect on the skin temperature, as reducing the solar absorptivity from 0.5 to 0.2 decreases the maximum skin temperature from 322.94K to 263.98K, with a decrease of 58.96K. The skin surface infrared emissivity is another factor which has a significant effect on the skin temperature, as increasing the surface infrared emissivity from 0.5 to 0.8 reduces the maximum skin temperature from 297.35K to 274.74K, with a decrease of 22.61K. Different seasons have a certain influence on the skin surface temperature of stratospheric airships, with a temperature difference of about 15K between the summer and the winter solstices, mainly due to the difference in solar radiation intensity received by the skin of the airship, which affects the temperature variation of the skin. The theoretical model established in this paper provide a useful tool for multi-physics simulations and analyses of stratospheric airships.
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.
Knowledge of the atmospheric optical turbulence profile (AOTP) is critical for atmospheric optics studies. Meteorological sounding of long-term AOTP observations at seas often comes at an outrageous cost. It is necessary to establish a mathematical model driven by conventional meteorological parameters to predicate the AOTPs at high altitudes. Conventional meteorological parameters TUH (i.e., temperature, wind speed and relative humidity), have an important impact on the sea surface turbulence. AOTPs together with TUHs in Maoming were obtained. Based on the artificial neural network (NN) algorithm, an NN model is established according to the data to predict the upper atmospheric turbulence profile. The AOTPs measurements were used to validate the model predictions with the existing estimation theory. Cross-validation between these methods are performed and evaluated with mean absolute error (MAE), mean variance (MSE) and root mean square variance (RMSE). The results show that the predicted values simulated by the NN algorithm agree well with the real values, which proves that it is feasible and reliable to use the NN to simulate the atmospheric turbulence profile.
The complex refractive index of aerosol particles has a vital influence on the radiation effect of aerosols. From July to October 2020, a long-term observation of marine aerosols in the Pacific Ocean was carried out by a surveying vessel.Based on the number concentration of marine aerosol particles measured by optical particle counter (OPC), the extinction coefficient and scattering coefficient of marine aerosol measured by single scattering albedo monitor (CAPS), combined with meteorological data, and through Mie scattering theory, the influence of the change of real and imaginary parts of marine aerosol particle refractive index on particle single scattering albedo is studied. The measurement results show that the range of single scattering albedo of marine aerosol is about 0.7-0.9. The inversion results show that the real part of aerosol refractive index varies from 1.335 to 1.45 and the imaginary part varies from 0.011 to 0.018.
As an important part of the atmospheric environment, aerosols play a critical role in the study of the relationship between light and radiation. However, due to the complex spatiotemporal distribution of aerosols, it is much difficult to measure their microphysical properties and to determine their optical properties in coastal areas. In this paper, basic meteorological elements (e.g., wind speed, temperature, humidity) are simulated with the numerical weather forecasting (WRF) model. Then, the coastal aerosol model (CAM) together with the observation data is used to simulate the aerosol particle size distribution (APSD) and extinction coefficient for the coastal environment of Qingdao. Finally, data measured by the automatic weather station and particle counter in the coastal area are compared to their corresponding simulations. According to the comparisons results, temperature simulations were higher from an overall perspective (<2°C) with the correlation coefficient larger than 0.96; humidity simulations were comparatively lower on the 11th and 12th day (<10%) than those onthe 13th day (<20%), but the correlation coefficient was still larger than 0.8. With the meterological parameters simulations, the CAM model was used to predict the APSDs. It is founded that simulations for large particles are generally larger, while those for giant particles are generally smaller, but the simulated temperature, humidity, APSD and extinction coefficient are very consistent with their corresponding measurements. The method established in this paper is promising for the simulation and forecast of both the meteorological elements and aerosol microphysical properties.
The altitude of atmospheric medium involved in atmospheric optics has a height range of 100km, and the most complicated variation of atmospheric properties is mainly in the atmospheric boundary layer (ABL). The variety of ABL height is of considerable significance to the distribution of aerosol, cloud, and other processes. Since the research of Chinese marine ABL analysis is limited, in this study, we improved the algorithm by using 532nm total attenuated backscattering (TAB) for retrieving atmospheric boundary layer height (ABLH) from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and verified the results gained from micro-pulse lidar (MPL) and radiosonde over the South China Sea. Finally, we used the validated ABLH algorithm model to retrieve the ABLH against CALIPSO data from Mar. 2018 to Feb.2019 over the South China Sea.
Above-low-cloud aerosol (ACA) has important impacts on low clouds bellow. Based on the satellite data from 2007 to 2010, this study analyzed the relationship between ACA optical depth (OD), ACA occurrences and low cloud integrated color ratio (CR) over tropic Atlantic region where ACA frequently occurs. The results show that, the integrated attenuated CR (IACR) of low cloud is about 30%-50% larger over smoke region in smoke outbreak seasons than other regions or seasons. However, the IACR of low cloud over dust region shows small difference between dust outbreak seasons and other seasons. It indicates that above-low-cloud smoke aerosol can introduce stronger color effect than dust. The integrated corrected CR (ICCR) of low cloud tends to decrease with increasing above-cloud dust OD, while the low cloud ICCR shows weak relationship with above-low-cloud smoke OD. And, the above-low-cloud dust aerosol could introduce strong microphysics effect, that is, the low cloud droplet size may decrease with increasing burden of dust aerosol above.
Lidar has been widely used in remote sensing of atmospheric environment because of its high spatial-temporal resolution and detection sensitivity. As the main noise source in lidar detection, solar background radiation is an important factor to determine target from background. The background noise, which is estimated by taking the average value of the lidar echo signal at a certain height, is usually removed directly. However, the background noise also contains some useful information on the whole layer of the atmosphere. In this paper, atmospheric radiation transmission model software MODTRAN 5.0 was used to simulate the lidar background noise under clear sky condition, combined with micro-pulse lidar (MPL) and meteorological element sounding data. The daytime background noise received by lidar were simulated by standard model method and user-defined model method. The standard model method uses standard atmospheric and aerosol model, which is the common way in traditional background radiation simulation. The user-defined model method uses aerosol and meteorological data measured in Maoming, Guangdong Province in October 2018 to build a user-defined atmospheric model. Results shows that the overall trends of the simulated background radiation from two methods are quite similar to the MPL observation. The user-defined model method can produce more consistent results with the observation than the standard model method, mainly due to that standard model cannot be completely consistent with the real experimental environment. The simulation results in this paper can be used to improve the daytime MPL retrieval, and can also be applied to the retrieving of aerosol particle size information and optical characteristics of cloud in further study.
This paper studied the marine-type aerosol distribution characteristics with the Wide-range Particle Spectrometer (WPS) obsevations boared on ”Shen Kuo” ship, over the South China Sea from June 21 to July 2, 2019. Particle spectral distributions at different time, fitted by the log-normal distribution method, and compared with the fine particle measurements in Hefei. Results show that the particle distributions over the South China Sea mainly show peaks around 95nm and 480nm respectively, while peaks around 26nm and 100nm in Hefei. The maximum concentration of fine particles in Hefei can reach 13×103 /cm3 , which is much higher than that over the South China Sea with a peak concentration of 6×103 /cm3 .
In order to facilitate scientific applications of aerosol products from geostationary satellite such as Himawari-8 (H-8), the H-8 Level 3 version 030 aerosol data sets from coastal areas were evaluated in this work. Level 2.0 aerosol products from January 2017 to December 2017 collected from 10 Aerosol Robotic Network (AERONET) sites, located in the offshore areas of the South China Sea and the Taiwan Strait, were selected for comparisons. The results showed that the H-8 aerosol product is mostly underestimated when aerosol optical thickness (AOT) is larger then 0.2, while overestimated when AOT is less than 0.2. And that the trends shown by H-8 AOT are basically consistent with the ground observations from AERONET.
Based on the Temperature Independent Spectral Index (TISI) method, the inversion of land surface emissivity was performed with MODIS infrared data over the Taklimakan Desert. In the process of land surface emissivity retrieval, the RossThick-LiSparseR BRDF model and the modified Minneart’s BRDF model were used to calculate the surface albedo. The retrieved emissivities were compared with MODIS emissivity products. The results show that the inversion error is small when the RossThick-LiSparseR model is been used, the average errors of the channels 31 and 32 are 0.0084 and 0.0023, respectively, and the emissivity decreases with the increase of the observation angle. By using the modified Minneart’s BRDF model, the average error of channels 31、32 are 0.0364,0.0259 respectively.
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