Mesospheric Gravity Wave Potential Energy Density Observed by Rayleigh Lidar above Golmud (36.25° N, 94.54° E), Tibetan Plateau

Rayleigh lidar data in 2013–2015 is used to describe the characteristics of gravity wave potential energy density in the mesosphere above Golmud (36.25° N, 94.54° E) of the Tibetan Plateau. In this study, the vertical profiles of the atmospheric gravity wave potential energy density between 50–80 km above the region are presented, including the potential energy mass density Epm and the potential energy volume density Epv. It shows the mathematical characteristics of the atmospheric gravity wave potential energy density vertical distribution, which also indicate the gravity waves are obviously dissipated in the lower mesosphere and close to conservative growth in the upper mesosphere (the turning point is around 61 km). A total of 1174 h of data covers seasonal changes, which reveals the seasonal characteristics of the potential energy density. The Epm increases faster with altitude in summer than others. All seasons of the potential energy density profiles show that gravity waves are dissipated in the lower mesosphere, among which spring and winter are the most severe and summer is weakest. The Epm is higher in spring and winter below 55 km. Above 55 km, it is the maximum in winter, followed by summer. Then, the AGWs activities between the location with mid–latitudes and different longitudes are compared and discussed

Mesospheric Gravity Wave Potential Energy Density Observed by Rayleigh Lidar above Golmud (36.25° N, 94.54° E), Tibetan Plateau

Rayleigh lidar data in 2013–2015 is used to describe the characteristics of gravity wave potential energy density in the mesosphere above Golmud (36.25° N, 94.54° E) of the Tibetan Plateau. In this study, the vertical profiles of the atmospheric gravity wave potential energy density between 50–80 km above the region are presented, including the potential energy mass density Epm and the potential energy volume density Epv. It shows the mathematical characteristics of the atmospheric gravity wave potential energy density vertical distribution, which also indicate the gravity waves are obviously dissipated in the lower mesosphere and close to conservative growth in the upper mesosphere (the turning point is around 61 km). A total of 1174 h of data covers seasonal changes, which reveals the seasonal characteristics of the potential energy density. The Epm increases faster with altitude in summer than others. All seasons of the potential energy density profiles show that gravity waves are dissipated in the lower mesosphere, among which spring and winter are the most severe and summer is weakest. The Epm is higher in spring and winter below 55 km. Above 55 km, it is the maximum in winter, followed by summer. Then, the AGWs activities between the location with mid–latitudes and different longitudes are compared and discussed

Seasonal Variations of High-Frequency Gravity Wave Momentum Fluxes and Their Forcing toward Zonal Winds in the Mesosphere and Lower Thermosphere over Langfang, China (39.4° N, 116.7° E)

Meteor radar data collected over Langfang, China (39.4° N, 116.7° E) were used to estimate the momentum flux of short-period (less than 2 h) gravity waves (GWs) in the mesosphere and lower thermosphere (MLT), using the Hocking (2005) analysis technique. Seasonal variations in GW momentum flux exhibited annual oscillation (AO), semiannual oscillation (SAO), and quasi-4-month oscillation. Quantitative estimations of GW forcing toward the mean zonal flow were provided using the determined GW momentum flux. The mean flow acceleration estimated from the divergence of this flux was compared with the observed acceleration of zonal winds displaying SAO and quasi-4-month oscillations. These comparisons were used to analyze the contribution of zonal momentum fluxes of SAO and quasi-4-month oscillations to zonal winds. The estimated acceleration from high-frequency GWs was in the same direction as the observed acceleration of zonal winds for quasi-4-month oscillation winds, with GWs contributing more than 69%. The estimated acceleration due to Coriolis forces to the zonal wind was studied; the findings were opposite to the estimated acceleration of high-frequency GWs for quasi-4-month oscillation winds. The significance of this study lies in estimating and quantifying the contribution of the GW momentum fluxes to zonal winds with quasi-4-month periods over mid-latitude regions for the first time

Research on the Measurement Accuracy of Shipborne Rayleigh Scattering Lidar

This paper aims to study the measurement accuracy of Rayleigh scattering lidar (light detection and ranging) based on a ship platform and analyze the influence of the laser beam uncertainty on the temperature inversion results. Taking the ship platform roll data as a reference, the Rayleigh scattering lidar oscillating model is simplified to a sine function, and the inversion accuracy of atmospheric temperature is analyzed under different settled observation angles and different roll angles. When the settled observation angle is 0° and the roll angle amplitudes are 10°, 20°, and 30°, the maximum deviations of the temperature within the height range of 30–80 km are 3.47 K, 13.73 K, and 22.78 K, respectively, and the average deviations are 2.35 K, 9.09 K, and 12.95 K, respectively. When the observation angle is set to 30° and the roll angle amplitudes are 10°, 20°, and 30°, the maximum deviations of the temperature within the height range of 30–80 km are 11.75 K, 27.49 K, and 53.50 K, respectively, and the average deviations are 11.05 K, 13.88 K, and 16.12 K, respectively. The results of this paper show that ship platform rolling greatly influences the measurement of atmospheric temperature, which provides a certain data reference for the construction and use of Rayleigh scattering lidar in the ship platform

Atmospheric Gravity Wave Potential Energy Observed by Rayleigh Lidar above Jiuquan (40° N, 95° E), China

Two years of observational data from the 532 nm Rayleigh lidar were used to study the vertical profile characteristics of atmospheric gravity wave potential energy density (GWPED) between 40–80 km above Jiuquan (40° N, 95° E) for the first time. The atmospheric gravity waves (AGWs) characteristics are presented in terms of the atmospheric relative temperature perturbation, along with the estimated annual and seasonal GWPED with high spatial and temporal resolution (0.5 km and 1 h). The annual potential energy mass density Epm and volume density Epv vertical profiles show that the GWPED in the upper mesosphere is close to the adiabatic growth rate. The seasonal vertical profiles result shows that Epm is higher in autumn–winter than in spring–summer in all the observed altitudes. The GWPED approaches adiabatic growth above 61 and 65 km in spring–summer and autumn–winter, respectively. The AGWs severely dissipate below the turning altitudes and transfer energy into the background atmosphere. The GWPED scale heights show that the AGWs dissipation rate of spring–summer is close to that of autumn–winter. Furthermore, based on the wind data from SD–WACCM, the influence of critical level filtering on AGWs is discussed. It plays an important role in affecting the seasonal variation in GWPED

Molecular Methods for Identification and Quantification of Foodborne Pathogens

Foodborne pathogens that enter the human food chain are a significant threat worldwide to human health. Timely and cost-effective detection of them became challenging for many countries that want to improve their detection and control of foodborne illness. We summarize simple, rapid, specific, and highly effective molecular technology that is used to detect and identify foodborne pathogens, including polymerase chain reaction, isothermal amplification, loop-mediated isothermal amplification, nucleic acid sequence-based amplification, as well as gene chip and gene probe technology. The principles of their operation, the research supporting their application, and the advantages and disadvantages of each technology are summarized

Effects of Chloropicrin, Dimethyl Disulfide and Metham Sodium Applied Simultaneously on Soil-Born Bacteria and Fungi

The area used to grow high-value crops is currently decreasing because production in the same soil for many years increases soil-borne diseases that reduce crop yield and quality as well as farmer income. Soil-borne disease is effectively controlled by soil fumigation prior to planting. In this study, the five different types of soils that had been used to grow tomatoes, watermelon, cucumber, ginseng and ginger were collected from field plots with high incidence of soil-borne diseases. This experiment adopts the indoor fumigation method to conduct triple fumigation of chloropicrin(PIC), metham sodium(MS) and dimethyl disulfide(DMDS) on different soil collected to examine changes in the soil microbial community, including pathogenic fungi and bacteria and beneficial microorganisms in order to clarify the impact on the overall structure of soil microbial community while controlling complex and multiple pathogens. High-throughput gene sequencing was used to detect bacterial and fungal taxonomic changes in the treated soils. Triple fumigation significantly reduced the abundance of at least five kinds of pathogenic fungi, Fusarium oxysporum, Mortierella, Neocosmospora, Nitrospira Alternaria and significantly increased the abundance of two kinds of beneficial species, Bacillus and Trichoderma. The research result observed increases and decreases in the biodiversity and richness of beneficial and pathogenic bacteria and fungi in response to triple fumigation of soil that had been used to grow tomatoes, watermelon, cucumber, ginseng and ginger. The most significant effect was observed in the experimental field of Panax notoginseng in Wenshan, Yunnan. Triple fumigation showed good potential to decrease pathogenic bacteria and fungi in soils and improve the disease resistance of soils, and that it has a good application prospect in the field of soil disinfestation

Detection and Identification Methods and Control Techniques for Crop Seed Diseases

Seeds comprise an important way in which plant pathogens are introduced into new areas, and serve as carriers for their survival from one planting season to another. Seed health is a recognized factor in modern agricultural science, and affects ideal plant populations and good harvests. Seed disease is one of the most important biological constraints in seed production worldwide. Effective and rapid detection and identification methods for seed disease comprise an important step in crop management, and a measure to protect seeds from pathogens. The detection of seed diseases is usually divided into three categories: traditional detection, immunological detection, and bioinformatics-based detection. The detection methods used for different types of pathogens also vary. For the prevention and control of seed diseases, appropriate methods should also be adopted, such as physical methods, chemical methods, and biological methods. They can be used alone or in combination to achieve the purpose of disease prevention and control. Therefore, this article reviews some important crop seed diseases, their detection and identification methods, and control techniques, in order to provide a theoretical basis for the comprehensive prevention and effective control of seed diseases

Rayleigh Lidar Signal Denoising Method Combined with WT, EEMD and LOWESS to Improve Retrieval Accuracy

Lidar is an active remote sensing technology that has many advantages, but the echo lidar signal is extremely susceptible to noise and complex atmospheric environment, which affects the effective detection range and retrieval accuracy. In this paper, a wavelet transform (WT) and locally weighted scatterplot smoothing (LOWESS) based on ensemble empirical mode decomposition (EEMD) for Rayleigh lidar signal denoising was proposed. The WT method was used to remove the noise in the signal with a signal-to-noise ratio (SNR) higher than 16 dB. The EEMD method was applied to decompose the remaining signal into a series of intrinsic modal functions (IMFs), and then detrended fluctuation analysis (DFA) was conducted to determine the threshold for distinguishing whether noise or signal was the main component of the IMFs. Moreover, the LOWESS method was adopted to remove the noise in the IMFs component containing the signal, and thus, finely extract the signal. The simulation results showed that the denoising effect of the proposed WT-EEMD-LOWESS method was superior to EEMD-WT, EEMD-SVD and VMD-WOA. Finally, the use of WT-EEMD-LOWESS on the measured lidar signal led to significant improvement in retrieval accuracy. The maximum error of density and temperature retrievals was decreased from 1.36% and 125.79 K to 1.1% and 13.84 K, respectively

Rayleigh Lidar Signal Denoising Method Combined with WT, EEMD and LOWESS to Improve Retrieval Accuracy

Lidar is an active remote sensing technology that has many advantages, but the echo lidar signal is extremely susceptible to noise and complex atmospheric environment, which affects the effective detection range and retrieval accuracy. In this paper, a wavelet transform (WT) and locally weighted scatterplot smoothing (LOWESS) based on ensemble empirical mode decomposition (EEMD) for Rayleigh lidar signal denoising was proposed. The WT method was used to remove the noise in the signal with a signal-to-noise ratio (SNR) higher than 16 dB. The EEMD method was applied to decompose the remaining signal into a series of intrinsic modal functions (IMFs), and then detrended fluctuation analysis (DFA) was conducted to determine the threshold for distinguishing whether noise or signal was the main component of the IMFs. Moreover, the LOWESS method was adopted to remove the noise in the IMFs component containing the signal, and thus, finely extract the signal. The simulation results showed that the denoising effect of the proposed WT-EEMD-LOWESS method was superior to EEMD-WT, EEMD-SVD and VMD-WOA. Finally, the use of WT-EEMD-LOWESS on the measured lidar signal led to significant improvement in retrieval accuracy. The maximum error of density and temperature retrievals was decreased from 1.36% and 125.79 K to 1.1% and 13.84 K, respectively