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.
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.
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 the "roof of the world", the Tibetan Plateau (TP, in short) exhibits the distinctive "heat island" characteristics compared to the same latitude region, and plays a decisive role in the atmospheric thermal structure of the TP and surroundings. ERA5 reanalysis data from January 2017 to December 2020 are used to analyze the meridional distribution characteristics of the average skin temperature and the potential temperature lapse rate at coldest point tropopause (CPT) in the highaltitude areas of the TP in summer. The Pearson correlation coefficient between the measured data and the reanalyzed data in the Da Qaidam area (95°21’E, 37° 51’N, 3180m above sea level (ASL)) in August 2020 is 0.88, indicating good usability of the reanalyzed data. The average skin temperature of TP in summer shows a feature of "high in the south and low in the north", which is ~20° higher than the atmospheric temperature of surrounding low-altitude area at the same altitude. The distribution of heat sources on the TP not only affects the location and intensity of the South Asian High, but also aggravates the thermal difference between land and sea, which promotes the formation of the Asian summer monsoon. The strong heat source in the southern TP, on the one hand, directly affects the atmospheric thermal structure over the southern TP through enhancing upward transportation; on the other hand, indirectly affects the high-altitude atmospheric thermal structure of the region north to TP through the background transportation of westerly and summer monsoons. The potential temperature lapse rate at CPT over the high-altitude area of TP also has significant characteristics of north-south differences, indicating that the "heat source effect" can regulate the intensity of atmospheric turbulence.
Atmospheric stability characterizes the intensity of vertical movement near the ground. Monin- Obukhov length is the most commonly used also most important stability parameter in boundary layer theory, which can be calculated by the dimensional analysis method on the basis of the similarity theory of the near-surface layer. The M-O parameter ζ is often used to characterize the stability (the ratio of height to M-O length). When the atmospheric stratification is neutral, ζ=0; when the atmospheric stratification is stable, ζ>0, and the larger the value, the more stable the atmosphere; When the atmosphere stratification is unstable, ζ<0, and the smaller the value, the more unstable the atmosphere. Using the three-dimensional wind speed data of the ship-borne three-dimensional ultrasonic anemometer and the meteorological data observed by the automatic weather station, combined with similar theory, to analyze the atmospheric stability parameter ζ above the sea surface. The results show that the atmospheric stability has an obvious diurnal variation trend, the night atmosphere is mostly stable, and the atmospheric turbulence is vigorous at noon. Besides, using three-dimensional ultrasonic wind speed data combined with virtual temperature correction, the atmospheric refractive index structure constant is calculated and compared with the measured value of the micro-thermal meter. This paper also analyzes the values of friction velocity 𝒖𝒖∗, characteristic temperature 𝑻𝑻∗ , characteristic humidity 𝑸𝑸∗ and atmospheric refractive index structure constant Cn2 under different stability conditions to understand the changing characteristics of optical turbulence on the underlying surface of the ocean.
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.
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 .
Tibetan Plateau, known as the third pole of the Earth, has an important impact on the atmospheric circulation and weather in East Asia. The detection of meteorological elements in the near-surface layer of the area is of great significance for the in-depth study of the meteorological mechanism of the boundary layer. In this paper, we used multi-rotor UAV (Unmanned Aerial Vehicle) combined with conventional observations to independently design a measurement platform with various sensors, data storage and transmission components, measuring conventional meteorological parameters, and temperature structure constants (Ct2 ). The suitability analysis of the platform was carried out through drone sonde and kite-balloon sonde experiments in Lhasa area (91°08′07′′E, 29°39′36′′N). Comparative analysis of two kinds of data shows that 403MHz wireless transmission adopted by the two methods is stable, and two sounding results of temperature, air pressure and Ct2 have good consistency between each other, with acceptable normal ranges of error. This work provides a new boundary layer detection method, and also has positive significance for scientific research work in meteorology and environmental monitoring.
Because of the special topography surrounded by sea on three sides, the coastal area of Qingdao is greatly affected by the aerosol of sea salt sources, and has a significant maritime climate. Based on the meteorological observation data measured by the Qingdao seaside from September to October 2019, the correlations between the atmospheric conventional meteorological parameters of the typical sea area and the influence of meteorological parameters on the atmospheric aerosol optical parameters were analyzed. The results indicate that,(1) the relative humidity has opposite daily variation to temperature, and the change of atmospheric pressure slightly lags behind the relative humidity;(2) there is a relationship between the change of weather and meteorological parameters, that is, the parameters such as temperature and relative humidity, visibility and particle number density distribution on sunny days are significantly different from those in rainy days;(3) visibility is positively correlated with the daily variation of wind speed and temperature, and is negatively correlated with the daily variation of relative humidity. It shows that conventional meteorological parameters such as relative humidity, temperature, and atmospheric pressure are closely related to optical parameters such as particle size distribution and visibility. The above statistical analysis results have reference value for the establishment of the regional aerosol model in the coastal area of Qingdao.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.