Methods for estimate the dynamic and thermal characteristics of smoke plumes

Space observations of the propagation of smoke flares from the chimneys of industrial enterprises provide information on the physical characteristics of the emitted gas-air mixtures. Models for estimating the parameters of the rise of impurities under the influence of dynamic and thermal factors are proposed. The basic relations in the estimation models are the solutions of the equations of hydrothermodynamics of the atmosphere. The case of neutral atmospheric stratification is considered in detail. Using satellite information and meteorological observation data, a numerical study of the stage of ascent of smoke jets from the chimneys of the Gusinoozerskaya State District Power Plant was carried out.

Plant Hardiness Zone Map in Korea and an Analysis of the Distribution of Evergreen Trees in Zone 7b

Background and objective: This study was conducted to establish a Plant Hardiness Zone (PHZ) map, investigate the effect of global warming on changes in PHZ, and elucidate the difference in the distribution of evergreen trees between the central and southern region within hardiness Zone 7b in Korea. Methods: Mean annual extreme minimum temperature (EMT) and related temperature fluctuation data for 40 years (1981 to 2020) in each of the meteorological observation points were extracted from the Open MET Data Portal of the Korea Meteorological Administration. Using EMT data from 60 meteorological observation points, PHZs were classified according to temperature range in the USDA Plant Hardiness Zone Map. Changes in PHZs for each decade related to the effects of global warming were analyzed. Temperature fluctuation before and after the day of EMT were analyzed for 4 areas of Seoul, Suwon, Suncheon, and Jinju falling under Zone 7b. For statistical analysis, descriptive statistics and ANOVA were performed using the IBM SPSS 22 Statistics software package. Results: Plant hardiness zones in Korea ranged from 6a to 9b. Over four decades, changes to warmer PHZ occurred in 10 areas, especially in colder ones. Based on the analysis of daily temperature fluctuation, the duration of sub-zero temperatures was at least 2 days in Seoul and Suwon, while daily maximum temperatures were above zero in Suncheon and Jinju before and after EMT day. Conclusion: It was found that the duration of sub-zero temperatures in a given area is an important factor affecting the distribution of evergreen trees in PHZ 7b.

BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data

Complete and high-resolution temperature observation data are important input parameters for agrometeorological disaster monitoring and ecosystem modelling. Due to the limitation of field meteorological observation conditions, observation data are commonly missing, and an appropriate data imputation method is necessary in meteorological data applications. In this paper, we focus on filling long gaps in meteorological observation data at field sites. A deep learning-based model, BiLSTM-I, is proposed to impute missing half-hourly temperature observations with high accuracy by considering temperature observations obtained manually at a low frequency. An encoder-decoder structure is adopted by BiLSTM-I, which is conducive to fully learning the potential distribution pattern of data. In addition, the BiLSTM-I model error function incorporates the difference between the final estimates and true observations. Therefore, the error function evaluates the imputation results more directly, and the model convergence error and the imputation accuracy are directly related, thus ensuring that the imputation error can be minimized at the time the model converges. The experimental analysis results show that the BiLSTM-I model designed in this paper is superior to other methods. For a test set with a time interval gap of 30 days, or a time interval gap of 60 days, the root mean square errors (RMSEs) remain stable, indicating the model’s excellent generalization ability for different missing value gaps. Although the model is only applied to temperature data imputation in this study, it also has the potential to be applied to other meteorological dataset-filling scenarios.

Toward Development of a Framework for Prediction System of Local-Scale Atmospheric Dispersion Based on a Coupling of LES-Database and On-Site Meteorological Observation

An accurate analysis of local-scale atmospheric dispersion of radioactive materials is important for safety and consequence assessments and emergency responses to accidental release from nuclear facilities. It is necessary to predict the three-dimensional distribution of the plume in consideration of turbulent effects induced by individual buildings and meteorological conditions. In this study, first, we conducted with meteorological observations by a Doppler LiDAR and simple plume release experiments by a mist-spraying system at the site of Japan Atomic Energy Agency. Then, we developed a framework for prediction system of local-scale atmospheric dispersion based on a coupling of large-eddy simulation (LES) database and on-site meteorological observation. The LES-database was also created by pre-calculating high-resolution turbulent flows in the target site at mean wind directions of class interval 10°. We provided the meteorological observed data with the LES-database in consideration of building conditions and calculated the three-dimensional distribution of the plume with a Lagrangian dispersion model. Compared to the instantaneous shots of the plume taken by a digital camera, it was shown that the mist plume transport direction was accurately simulated. It was concluded that our proposed framework for prediction system based on a coupling of LES-database and on-site meteorological observation is effective.