Measurement report: Vehicle-based multi-lidar observational study of the effect of meteorological elements on the three-dimensional distribution of particles in the western Guangdong–Hong Kong–Macao Greater Bay Area

The distribution of meteorological elements has always been an important factor in determining the horizontal and vertical distribution of particles in the atmosphere. To study the effect of meteorological elements on the three-dimensional distribution structure of particles, mobile vehicle lidar and fixed-location observations were collected in the western Guangdong–Hong Kong–Macao Greater Bay Area of China during September and October in 2019 and 2020. Vertical aerosol extinction coefficient, depolarization ratio, and wind and temperature profiles were measured using a micro pulse lidar, a Raman scattering lidar, and a Doppler wind profile lidar installed on a mobile monitoring vehicle. The mechanism of how wind and temperature in the boundary layer affects the horizontal and vertical distribution of particles was analysed. The results show that particles were mostly distributed in downstream areas on days with moderate wind speed in the boundary layer, whereas they were distributed homogeneously on days with weaker wind. There are three typical types of vertical distribution of particles in the western Guangdong–Hong Kong–Macao Greater Bay Area (GBA): surface single layer, elevated single layer, and double layer. Analysis of wind profiles and Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) backward trajectory reveals different sources of particles for the three types. Particles concentrating near the temperature inversion and multiple inversions could cause more than one peak in the extinction coefficient profile. There were two mechanisms affecting the distribution of particulate matter in the upper and lower boundary layers. Based on this observational study, a general model of meteorological elements affecting the vertical distribution of urban particulate matter is proposed.

A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

Accurate forecasting of future meteorological elements is critical and has profoundly affected human life in many aspects from rainstorm warning to flight safety. The conventional numerical weather prediction (NWP) sometimes leads to unsatisfactory performance due to inappropriate initial state settings. In this paper, a short-term weather forecasting model based on wavelet packet denoising and Catboost is proposed, which takes advantage of the fusion information combining the historical observation data with the prior knowledge from NWP. The feature selection and spatiotemporal feather addition are also explored to further improve performance. The proposed method is evaluated on the datasets provided by Beijing weather stations. Experimental results demonstrate that compared with many deep-learning or machine-learning methods such as LSTM, Seq2Seq, and random forest, the proposed Catboost model incorporated with wavelet packet denoising can achieve shorter convergence time and higher prediction accuracy.

Characteristics of Surface Temperature and Its Relationship with Meteorological Elements in China in the Last 73 Years

Climate, as the natural environment on which human life depends, is intricately linked to human society. This paper focuses on the characteristics of temperature and its relationship with meteorological elements in China in the last 73 years. The data of this research is from NCEP/NCAR Reanalysis Monthly Means. This study adopts the Empirical Orthogonal Function (EOF) and Singular Value Decomposition (SVD) methods to study the surface temperature characteristics within China, and the synergistic variation between surface temperature and precipitable water content, wind field, and relative humidity in China. The results show that 1980s is a turning point for changes in surface temperature, precipitable water content, wind field, and relative humidity in China. Before 1980s, the temperature in China is low, while after this period, the temperature in China is high and China’s exposure to global warming has increased. Temperature is dominated by negative potential-phase oscillations with relative humidity and wind fields. In the north, temperature and precipitable water content have negative potential-phase oscillations, while temperature and precipitable water content have positive potential-phase oscillations in the south. In the central region of Xinjiang, temperature and precipitable water content have weak negative potential-phase oscillations, while temperature and wind field have positive potential-phase oscillations.

Application of meteorological element combination-driven SWAT model based on meteorological datasets in alpine basin

Thus far, reanalysis-based meteorological products have drawn little attention to the influence of meteorological elements of products on hydrological modeling. This study aims to evaluate the hydrological application potential of the precipitation, temperature, and solar radiation of the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and Climate Forecast System Reanalysis (CFSR) in an alpine basin. The precipitation, temperature, and solar radiation of the gauge-observed meteorological dataset (GD), CFSR, and CMADS were cross-combined, and 20 scenarios were constructed to drive the SWAT model. From the comprehensive comparisons of all scenarios, we drew the following conclusions: (1) among the three meteorological elements, precipitation has the greatest impact on the simulation results, and using GD precipitation from sparse stations yielded better performance than CMADS and CFSR; (2) although the SWAT modeling driven by CMADS and CFSR performed poorly, with CMADS underestimation and CFSR overestimation, the temperature and solar radiation of CMADS and CFSR can be an alternative data source for streamflow simulation; (3) models using GD precipitation, CFSR temperature, and CFSR solar radiation as input yielded the best performance in streamflow simulation, suggesting that these data sources can be applied to this data-scarce alpine region.

Variation Characteristics Mathematical Calculation of O3 and Its Relationship with Meteorological Factors by Big Data Technology

Based on the data of environmental monitoring stations and meteorological stations in Qinhuangdao from May 2017 to May 2020, the variation characteristics of O3 and precursors (NO2 and CO) as well as their relationship with meteorological elements were analyzed. The results showed that the daily average concentration of O3-8 h in Qinhuangdao increased year by year. The monthly average concentration of O3-8 h was high in summer and low in winter, and the peak appeared in June. The diurnal variation of O3 concentration was unimodal structure, and the concentration increased in the afternoon, but it decreased at night. The concentration of NO2 and CO was inversely correlated with O3, and the peak value of NO2 in March could be related to frequent cold air activity and increased burning of loose coal. The meteorological elements favorable for the occurrence of ozone pollution weather in Chengde were total solar radiation irradiance greater than 1000W/m2, the daily maximum temperature greater than 33 °C, and the daily minimum relative humidity less than 40% and 65%∽80%, southerly wind or southwest wind.

Development of air quality mobile tools for observation

Mobile weather stations are needed because of their better coverage balance than stationary stations. Center for Climate Risk and Opportunity Management in Southeast Asia Pacific (CCROM-SEAP) of Bogor Agricultural University (Institut Pertanian Bogor or IPB University) developed a low-cost mini observation system using Espressif ESP32 DOIT Development Kit V1 module, which based on the internet of things (IoT) to monitor real-time meteorological elements (such as temperature, humidity, and pressure), CO2, PM2.5, and PM10 concentration for Bogor (Center of Bogor City). With Firebase (database service by Google) integration, the system records data every 2 minutes and sent automatically to Firebase. We also create an unpublished android application called ServMo for exporting JSON to CSV format. The results show this system has a good performance for real-time monitoring purposes for a better balance of measurements coverage.

Detecting the relations between meteorological elements and alpha and beta activity concentration at Al-Tuwaitha site, Baghdad

In this study, 50 samples of air particulates collected from different places in- and outside the Al-Tuwaitha nuclear site, south of Baghdad were used to measure daily gross alpha and beta activity concentrations (AAC and BAC) for the period from 28 January 2015 to 13 April 2017. At the same time, several meteorological factors such as air temperature, wind speed, wind direction, air pressure, relative humidity, and solar radiation, were also measured. Air stability classes were also derived from wind speed and solar radiation. AAC/BAC variations in the surface air layer were discussed in relation to these factors. The results show that there are inverse relations between AAC/BAC and wind speed and temperature, linear relations between AAC/ABC and air pressure and weak relations between AAC/BAC and relative humidity and solar radiation. Lastly, AAC/BAC measurements in unstable air are as large as in neutral air.

Dynamic changes in moisture content and applicability analysis of a typical litter prediction model in Yunnan Province

Background Forest fire risk predictions are based on the most conservation daily predictions, and the lowest litter moisture content of each day is often used to predict the day’s fire risk. Yunnan Province is the area with the most frequent and serious forest fires in China, but there is almost no research on the dynamic changes and model predictions of the litter moisture content in this area. Therefore, to reduce the occurrence of forest fires and improve the accuracy of forest fire risk predictions, it is necessary to understand these dynamic changes and establish an appropriate prediction model for the typical litter moisture content in Yunnan Province. Method During the fire prevention period, daily dynamic changes in the litter moisture content are obtained by monitoring the daily step size, and the relationships between the litter moisture content and meteorological elements are analyzed. In this study, the meteorological element regression method, moisture code method and direction estimation method are selected to establish litter moisture content prediction models, and the applicability of each model is analyzed. Results We found that dynamic changes in the litter moisture content have obvious lags compared with meteorological elements, and the litter moisture content is mainly related to the air temperature, relative humidity and wind speed. With an increase in the sampling interval of meteorological elements, the significances of these correlations first increase and then decrease. The moisture content value obtained by directly using the moisture code method in the Fire Weather Index (FWI) significantly different from the measured value, so this method is not applicable. The mean absolute error (MAE) and mean relative error (MRE) values obtained with the meteorological element regression method are 2.97% and 14.06%, those from the moisture code method are 3.27% and 14.07%, and those from the direct estimation method are 2.82% and 12.76%, respectively. Conclusions The direct estimation method has the lowest error and the strongest extrapolation ability; this method can meet the needs of daily fire forecasting. Therefore, it is feasible to use the direct estimation method to predict litter moisture contents in Yunnan Province.

Studies of meteorological elements and climate change in the Uruçuí river basin/Brazil

The objective is to show the variability of meteorological elements in the hydrographic basin area of the hydrographic basin of the Uruçuí Preto River–PI/Brazil, aiming to contribute to sustainable development in the productive areas of agriculture, laser, and hydrology. The meteorological elements studied are air temperature and relative humidity and their fluctuations, thermal amplitude, wind (intensity and direction), total insolation, cloud cover, evaporation, evapotranspiration, and rainfall. The data were from the 1960-1990 series, acquired by the Superintendency of the Development of the Northeast and by the Technical Assistance and Rural Extension Company of Piauí. The maximum annual temperature is 32.1°C, its minimum 20.0°C, with an average annual temperature of 26.1°C. A climatic classification was used according to the KÖPPEN systems, where two climatic types are distinguished in the Uruçuí Preto/PI river basin, the Aw, tropical hot and humid, with rain in summer and dry in winter; Bsh, warm semi-arid, with summer rains and dry winter. The variation of the thermal amplitude is from 11.9 to 14.9ºC. The average relative humidity of the air was 47 to 79%; the average annual precipitation was 937.7 mm; it was observed that the annual march of relative humidity follows the annual distribution of precipitation because the precipitation was the feeding process from natural sources of water vapor and moisture. Total Sunstroke in the BHRUP area ranges from 2520 to 2750 hours. It is concluded that the maximum annual temperatures increased during the period, which can cause several socioeconomic problems, and human health.