Conventional Meteorological Observations Research Articles

Distribution Characteristics of the Short-term Heavy Rainfall of an MCC in Shandong Province

This study employs a comprehensive approach, utilizing conventional meteorological observation data, Fengyun satellite cloud imagery, and ERA5 reanalysis data to examine a short-term heavy rainfall event and its underlying causes in Shandong Province from August 14 to 16, 2020. The findings reveal that: (1) This event represents a typical Mesoscale Convective Complex (MCC)-induced rainfall process, with the primary precipitation zone in central and southern Shandong. The most intense hourly rainfall is observed along the side of the MCC with the steepest Tropical Brightness Temperature Blackbody (TBB) gradient. The heaviest hourly rainfall happens right around the time of the lowest TBB interval. (2) The rainfall event is marked by a vertical atmospheric structure with a high-altitude westerly jet and a low-level southwest jet, which makes it easy for water vapour to move and shear lines to form. Favourable conditions for water vapour convergence and high specific humidity are noted. During episodes of heavy precipitation, there is a marked upward motion in the lower troposphere, aligning with the vertical pattern of low-level convergence and high-level divergence.

Analysis of the Causes and Wind Field Structure of a Dry Microburst in a Weak Weather Background

Dry microbursts in weak weather backgrounds, due to their small scale and general lack of precipitation, are often difficult to observe using weather radar. On the night of 26 April 2023, a dry microburst occurred at the Yinchuan Airport. Based on conventional meteorological observations, Automated Weather Observing System (AWOS) data, and the Doppler Wind Lidar data, an analysis was conducted on the causes and wind field structure of this microburst. It was found that (1) the sounding data indicated a DCAPE value of 880 J·kg−1, which is important for forecasting the potential for dry microburst events; (2) the foehn from the Helan Mountains contributed to the occurrence of microburst weather at the Yinchuan Airport; (3) the Doppler Wind Lidar wind data showed distinct characteristics of the wind field during this microburst event, including a symmetric horizontal wind field structure, significant vertical downdraft velocities (reaching −5.76 m·s−1), and low-level wind shear over the airport runway and its vicinity; and (4) effective monitoring of such microburst weather events with the Doppler Wind Lidar wind measurements is crucial for ensuring aviation safety.

A WRF/WRF-Hydro Coupled Forecasting System with Real-Time Precipitation–Runoff Updating Based on 3Dvar Data Assimilation and Deep Learning

This study established a WRF/WRF-Hydro coupled forecasting system for precipitation–runoff forecasting in the Daqing River basin in northern China. To fully enhance the forecasting skill of the coupled system, real-time updating was performed for both the WRF precipitation forecast and WRF-Hydro forecasted runoff. Three-dimensional variational (3Dvar) multi-source data assimilation was implemented using the WRF model by incorporating hourly weather radar reflectivity and conventional meteorological observations to improve the accuracy of the forecasted precipitation. A deep learning approach, i.e., long short-term memory (LSTM) networks, was adopted to improve the accuracy of the WRF-Hydro forecasted flow. The results showed that hourly data assimilation had a positive impact on the range and trends of the WRF precipitation forecasts. The quality of the WRF precipitation outputs had a significant impact on the performance of WRF-Hydro in forecasting the flow at the catchment outlet. With the runoff driven by precipitation forecasts being updated by 3Dvar data assimilation, the error of flood peak flow was decreased by 3.02–57.42%, the error of flood volume was decreased by 6.34–39.30%, and the Nash efficiency coefficient was increased by 0.15–0.52. The implementation of LSTM can effectively reduce the forecasting errors of the coupled system, particularly those of the time-to-peak and peak flow volumes.

ИЗМЕНЕНИЯ ИСПАРЕНИЯ ПО ДАННЫМ ИНСТРУМЕНТАЛЬНЫХ НАБЛЮДЕНИЙ И ЕГО РЕАКЦИЯ НА ИЗМЕНЕНИЕ КЛИМАТА В ПРОВИНЦИИ ХЭЙЛУНЦЗЯН

In this study, instrument-measured evaporation and conventional meteorological observations from 80 stations in Heilongjiang Province from 1961 to 2020 were collected. Using these data, methods such as linear trend estimation, cumulative anomaly, multiple linear regression, and Morlet wavelet analysis were employed to analyze the changes of instrument-measured evaporation annually and seasonally in Heilongjiang Province. Additionally, we analyzed its response to climate change. The results showed that from 1961 to 2020, annual pan evaporation in Heilongjiang Province showed a significant downward trend, decreasing at 10.5 mm per decade. Evaporation decreased significantly in spring, while the magnitude of this decrease in summer and autumn was relatively small, and evaporation increased significantly in winter. In the periodic change, annual and seasonal evaporation have significant periods on short time scales. Among them, PE y and PE Su mainly have a short period of 3-4 years, PE Sp has short periods of about 3 years and 6 years, PE A mainly has short periods of 2 years and 3 years, and PE W mainly has short periods of about 4 years. The geographical correlation of evaporation was significant, with evaporation decreasing as latitude, longitude and altitude increased. Additionally, we found that changes in annual and seasonal pan evaporation were positively correlated to changes in average temperature, hours of sunshine, and wind speed, and negatively correlated to changes relative humidity. Our analysis indicates that a significant decrease in wind speed and hours of sunshine is the dominant factor leading to the decrease in evaporation throughout the year as well as in spring, summer, and autumn, while higher temperatures and lower relative humidity may lead to a significant increase in evaporation in winter.

Analysis of BC Pollution Characteristics under PM2.5 and O3 Pollution Conditions in Nanjing from 2015 to 2020

Using an AE-33 Aethalometer, surface air pollution monitoring data, radiosonde data, and conventional meteorological observation data, the characteristics and influencing factors of black carbon (BC) pollution under PM2.5 and O3 pollution in Nanjing were comprehensively analyzed. The results show that the air quality saw an apparent trend of improvement from 2015 to 2020, and the number of days with excellent air quality increased by 38.2% from 2015 to 2020. The number of days when the dominant pollutant was PM2.5 decreased each year to only 18 days in 2020, with an annual rate of decline of 16.0% from 2015 to 2020. The number of days when the dominant pollutant was O3 increased, reaching a maximum for the 6-year period of 78 days in 2019, with an annual rate of increase of 11.1% in 2015–2019. The average mass concentration of BC when the dominant pollutant was PM2.5 in slight, moderate, and heavy pollution decreased in 2015–2016 and then showed an increasing yearly trend in 2016–2018, with annual rates of increase of 73.8%, 105.5%, and 156.3%, respectively, reaching a maximum in 2018 and then starting to decrease thereafter. With PM2.5, the slight pollution and moderate pollution BC mass concentrations were mainly influenced by the height of the inversion layer. The average BC mass concentrations in the case of slight and moderate pollution with O3 as the dominant pollutant decreased significantly from 2015 to 2016, and then increased yearly from 2016 to 2019, with annual rates of increase of 112.2% and 138.6%, respectively, reaching a maximum in 2019 and then decreasing from 2020. The BC mass concentration was significantly negatively correlated with wind speed in both light and moderate O3 pollution, with correlation coefficients of −0.79 and −0.68, respectively. The seasonal variation and dominant influencing factors of BC differed when PM2.5 and O3 were the dominant pollutants. When PM2.5 was the dominant pollutant, the seasonal variation in the BC for slight pollution was winter > autumn > summer > spring, and for moderate pollution and heavy pollution was autumn > winter > spring, which were mainly affected by the inversion stratification difference and wind speed. When O3 was the dominant pollutant, the seasonal variation in BC under slight pollution was autumn > summer > spring, and for moderate pollution, it was spring > summer > autumn, which were mainly affected by the wind speed. Studying the evolution of BC in air pollution under different dominant pollutants is important to further improve the capability and level of global climate change research and predictions and can provide a scientific basis for assessing their impact on the environment, health, and climate.

Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China

Abstract. Fine particulate matter (PM2.5) has altered the radiation balance on Earth and raised environmental and health risks for decades but has only been monitored widely since 2013 in China. Historical long-term PM2.5 records with high temporal resolution are essential but lacking for both research and environmental management. Here, we reconstruct a site-based PM2.5 dataset at 6 h intervals from 1960 to 2020 that combines long-term visibility, conventional meteorological observations, emissions, and elevation. The PM2.5 concentration at each site is estimated based on an advanced machine learning model, LightGBM, that takes advantage of spatial features from 20 surrounding meteorological stations. Our model's performance is comparable to or even better than those of previous studies in by-year cross validation (CV) (R2=0.7) and spatial CV (R2=0.76) and is more advantageous in long-term records and high temporal resolution. This model also reconstructs a 0.25∘ × 0.25∘, 6-hourly, gridded PM2.5 dataset by incorporating spatial features. The results show PM2.5 pollution worsens gradually or maintains before 2010 from an interdecadal scale but mitigates in the following decade. Although the turning points vary in different regions, PM2.5 mass concentrations in key regions decreased significantly after 2013 due to clean air actions. In particular, the annual average value of PM2.5 in 2020 is nearly the lowest since 1960. These two PM2.5 datasets (publicly available at https://doi.org/10.5281/zenodo.6372847, Zhong et al., 2022) provide spatiotemporal variations at high resolution, which lay the foundation for research studies associated with air pollution, climate change, and atmospheric chemical reanalysis.

AVALIAÇÃO DA PRECIPITAÇÃO DO SATÉLITE TRMM 3B43V7 NO PANTANAL DE MATO GROSSO DO SUL NOS ANOS DE 1998 A 2019

Remote sensing can assist in the acquisition of scarce surface data. The analyzes for validation of the precipitation product estimated by the TRMM satellite (Tropical Rainfall Measuring Mission) were carried out with the precipitation data observed on the surface during the period from 1998 to 2019. For this purpose, precipitation data from the Pantanal biome meteorological stations were used, located between the 16 and 22°S parallels and the 55 and 58°W meridians, and compared with the data from the TRMM 3B42 V7 product algorithms. Statistical analysis was performed based on the correlation coefficient, root mean square error (RMSE), and relative bias (BIAS) between the monthly precipitation data observed on the surface and the estimated precipitation data. The results found for product 3B43 V7 indicated that the precipitation estimates were representative when compared to the surface observations. However, when compared for the rainy and dry periods, there was underestimation and overestimation, respectively, of the product. The product 3B42 V7 satisfactorily represents the precipitation that occurs on the surface. Keywords: Tropical Rainfall, Remote Sensing, Conventional Meteorological Observation Precipitation Estimate.

Study on Happening and Extincting Mechanism of a Squall Line along the Eastern Helan Mountain

In the afternoon of June 17, 2020, a squall line occurred over the eastern region of Helan Mountain in Ningxia, accompanied by lightning, strong wind and short-term heavy precipitation, which affected north and middle part of Ningxia and Wuhai of Inner Mongolia. Basing on the Doppler radar data and conventional meteorological observation data, the weather process was analyzed. the results show that: (1) the cold air from the middle and high latitudes intersected with the near surface warming air in Alashan of Inner Mongolia, which promoted the development of convection and induced the formation of squall line; (2) the temperature and pressure field at high and low level were asymmetrical and the weather system presented a forward tilting structure, which was conducive to the occurrence of strong convective weathers such as squall line; (3) the squall line strengthened when moving across Helan Mountain due to the topographic uplifting and the strong vertical wind shear;(4) After moving into north and middle part of Ningxia, the convergence and divergence configuration of the squall line weakened, the circulation energy faded and the convective structure and circulation field became incomplete gradually, the inclined sinking airflow in the back of the squall line gradually weakened due to the precipitation.

Improving the downscaled springtime temperature in Central Asia through assimilating meteorological and snow cover observations

Central Asia is vulnerable to climate change due to its scarce water resources and fragile ecosystems. However, the limited number of meteorological observations in the region restrict the study of its climate, hydrology and ecology. In order to improve the downscaled springtime temperature in Central Asia, this study explored the impact of atmospheric and snow data assimilation on climate simulations in Central Asia based on the Weather Research and Forecast (WRF) model and the WRF Data Assimilation (DA) system. The results based on climate simulations in Central Asia during the spring of 2017 show that the WRF model surface temperature simulation has a significant cold bias in Central Asia due to underestimation of snow melt. By assimilating conventional meteorological observations, the cold bias in Central Asia was reduced. This improvement is the result of both the direct effect of the analysis increment, and feedback effects from snow and atmosphere. In addition to the assimilation of atmospheric data, snow melt in Central Asia was better simulated through the assimilation of Japan Aerospace Exploration Agency (JAXA) Satellite Monitoring for Environmental Studies (JASMES) snow cover data. This further reduced the cold bias of the springtime temperature in Central Asia. Compared with an experiment that only assimilated atmospheric observations, the experiment that assimilated both snow and atmospheric data reduced the increase in temperature from the analysis and simulated a warmer land surface. This resulted in more sensible heat flux from the surface to the atmosphere and stronger sublimation and evaporation and thus improved the simulation of soil moisture.

Analysis on the Characteristics and Cause of a Compound Pollution Process in Changsha in 2017

Using the conventional meteorological observations, the air Air quality monitoring data, the L-band wind-profiling radar data, the NECP reanalysis data, and the simulation results of the HYSPLIT4 model, the compound pollution process from November 2 to 5, 2017 in Changsha is comprehensively analyzed in this study, including the sources and transport paths of pollutions, the Meteorological conditions and atmospheric boundary layer characteristics. The results showed that the process of air pollution in Changsha is a compound pollution process with high concentration of O3 and fine PM2.5 coexisting. It reached serious pollution from 08:00 to 11 : 00 on 4, PM2.5 was 269.3 ug·m−3 at 09:00 on 4 November. The characteristics of the two phases of this pollution process were clearly different. The first phase was mainly result of accumulation of local pollutants, and the second one was mainly result of outside resource. The maximum wind were light breezein on this pollution process, gentle breeze mainly occurred in the second stage of pollution, and the maximum wind velocity was 4.4m/s. It appearred multi-layer inversion usually, temperature stratification was stabled, and the inversion layer thickness reached the maximum value 909m at 20:00 on 3 November. The boundary layer convergenced and ascented movement, horizontal transport, vertical wind field distribution and atmospheric layer provided the favorable conditions for the air pollution process. The results showed that short-range transport of pollutants is the maily reason based on the HYSPLIT4 model, the northeast airflow was dominant at 100-1000m height. The vary of wind profile data and CO and SO2 emission ratio showed that the different pollution characteristics of two phases, the maximum ratio was more than 100. It has an important indicator for weather forecast in Changsha.

Analysis on the impact of two winter precipitation episodes on PM2.5 in Beijing

In this paper, the circulation background, the characteristics of meteorological elements configuration in the boundary layer and the stable meteorological conditions in two precipitation episodes (during February 19–21, 2015 and February 10–13, 2016, respectively) within the Beijing-Tianjin-Hebei region are compared and analyzed. Data from conventional meteorological observations, air quality monitoring, reanalysis and numerical models are used. The results show that before the two precipitation processes in 2015 and 2016, the circulations in the middle and high latitudes of Asia and Europe demonstrate “two troughs and one ridge”. Besides, the weather is stable and the pollutant concentration is relatively high. During the precipitation, the circulation is relatively stable for the episode in 2015, and no obvious change in the synoptic system is observed. However, during the episode in 2016, the formation of blocking high and the enhancement of the average ridge in western Asia cause obvious change in the circulation. The simulation results show that significant removal can be detected in both cases, and the PM2.5 wet deposition fluxes are 647 g/ha and 486 g/ha, respectively, with the removal in 2015 slightly stronger than that in 2016. The removal and dissipation of pollutants is determined by the atmospheric diffusion conditions and the precipitation, especially in the former episode. In the case of February 2016, good diffusion conditions and the precipitation demonstrate obvious PM2.5 removal effect. In the case of February 2015, the longstanding calm wind with high humidity and the physical quantity configuration in the lower mixed layer lead to the poor pollutant removal.

Numerical simulation and cause analysis for a severe mixed pollution of smog and haze in the Pearl River Delta in Winter

Based on the simulation results of the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ), the meteorological elements, boundary layer characteristics and temporal and spatial distribution characteristics of particulate matter during the heavy haze-dense fog-haze pollution process from 1 to 12 January 2017 were analyzed with the aid of the data from conventional meteorological observation and environmental monitoring station. Also, the causes of this pollution have been presented considering the terrain effects. The results show that, during the first process (day 1 to day 3), the upwind transports pollutants to Shaoguan, Qingyuan and other provinces, and the local surface wind velocity is lower than 3 m·s−1, which increased concentration of pollutants in the Pearl River Delta region and formed moderate smog and haze pollution. During the second process (day 4 to day 6), the average surface wind speed is lower, and the static wind is lower than 2 m·s−1 with the influence of terrain effect (even lasted for 5-16 hours). Besides, the influence of the strong convergence field of small wind increases the accumulation and convergence of pollutants on the ground, and simultaneously, the thickness of the ground inversion layer is further intensified and the height of the boundary layer is as low as 100 meters. At the same time, the relative humidity increases to nearly 100%, resulting in continuous visibility of fewer than 500 meters for 10 hours. Additionally, it was found that on the day of heavy pollution in Guangzhou, the main source is coarse nuclear particles, and the corresponding actual situation is that the concentration of NO2 was higher than that in other places, which reflects the influence of high concentration of human activities on air pollution. As the northeasterly winds continued to strengthen, pollutants across the region continued to be transported to the Pearl River Delta. Through the convergence of wind farms in Zhaoqing, Foshan, and Guangzhou, cross-regional pollutants and local pollutants emitted by the Pearl River Delta are again accumulated in the pearl river delta central area, forming a third moderate haze pollution process. It is concluded that the influence of strong convergence field of small wind under 2 m·s−1 (even 5-16 hours static wind), the increase of ground temperature inversion, the height of boundary layer as low as 100 m, the increase of near-ground humidity are superposed, and the combined effect of topography are the main meteorological causes of mixed pollution of fog and haze in the Pearl River Delta in winter.

Analysis of the carbon dioxide mole fraction variation and its transmission characteristics in Taiyuan

Based on the concentrations of CO2, PM2.5 and PM1.0, and conventional meteorological observation data from 2016 to 2018 at Taiyuan station, which belongs to the Shanxi greenhouse gas observation network, the CO2 concentration monthly and daily distribution characteristics, the weekend effect, and the variation characteristics on haze days and non-haze days, are analyzed. By using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (backward trajectory model) and surface wind data, the transmission characteristics of atmospheric CO2 in Taiyuan are studied in various seasons. The results show that, in Taiyuan, the CO2 mole fraction in autumn and winter is higher than that in spring and summer, and on haze days is higher than that on non-haze days. The diurnal variation characteristic of CO2 mole fraction in each season is ‘single peak and single valley’ with the peak value around 0700 (hereafter refers to Beijing Time) and the valley value around 1600. The CO2 mole fraction on workdays is slightly higher than that on non-workdays and obviously different around 0800 of the early peak. Horizontal diffusion can reduce the CO2 mole fraction, while breezy weather is not beneficial to CO2 diffusion. The wind direction and speed in the upper levels are different from those near the surface, and the close air masses in the southwest–west–northwest sector raise the CO2 concentration in Taiyuan obviously. This indicates that the CO2 in Taiyuan is mainly contributed by local sources.

The Use of Thermal Infra-Red Imagery to Elucidate the Dynamics and Processes Occurring in Fog

Improving our ability to predict fog accurately is currently a high priority for Numerical Weather Prediction models. Such an endeavour requires numerous types of observations of real fog as a means to both better understand it and also provide an assessment of model performance. We consider the use of thermal infra-red imagery, used in conjunction with other meteorological observations, for the purposes of studying fog. Two cameras were used—a FLIR Systems Inc. A655sc and a FLIR Systems Inc. A65sc—which were set up to capture one image per minute. Images were then combined to provide video footage of nocturnal fog events. Results show that the imagery from such cameras can provide great insight into fog processes and dynamics, identifying interesting features not previously seen. Furthermore, comparison of imagery with conventional meteorological observations showed that the observations were often not capable of being used to delineate all of the processes affecting fog, due to their incomplete and local nature.

Using social media to measure impacts of named storm events in the United Kingdom and Ireland

AbstractDespite increasing use of impact‐based weather warnings, the social impacts of extreme weather events lie beyond the reach of conventional meteorological observations and remain difficult to quantify. This presents a challenge for validation of warnings and weather impact models. This study considers the application of social sensing, the systematic analysis of unsolicited social media data to observe real‐world events, to determine the impacts of named storms in the United Kingdom and Ireland during the winter storm season 2017–2018. User posts on Twitter are analysed to show that social sensing can robustly detect and locate storm events. Comprehensive filtering of tweets containing weather keywords reveals that ~3% of tweets are relevant to severe weather events and, for those, locations could be derived for about 75%. Impacts of storms on Twitter users are explored using the text content of storm‐related tweets to assess changes in sentiment and topics of discussion over the period before, during and after each storm event. Sentiment shows a consistent response to storms, with an increase in expressed negative emotion. Topics of discussion move from warnings as the storm approaches, to local observations and reportage during the storm, to accounts of damage/disruption and sharing of news reports following the event. There is a high level of humour expressed throughout. This study demonstrates a novel methodology for identifying tweets which can be used to assess the impacts of storms and other extreme weather events. Further development could lead to improved understanding of social impacts of storms and impact model validation.

Deep Learning Architecture for Estimating Hourly Ground-Level PM2.5 Using Satellite Remote Sensing

The prediction of PM2.5 concentration is a canonical predictive challenge due to the distribution of $PM_{2.5}$ appears serious spatiotemporal variability at multiple scales. Currently, using satellite-based remote sensing data to estimate ground-level PM2.5 is a promising method for providing spatiotemporal continuous information of PM2.5. In this letter, we proposed a deep neural network (DNN)-based PM2.5 prediction model to capture the spatiotemporal variability of ground-level PM2.5 using the remote sensing aerosol optical depth (AOD) data from the Himawari-8 satellite along with the conventional meteorological observation variables (denoted as PM25-DNN). The PM25-DNN model was trained and tested using the data from Beijing-Tianjin-Hebei region of China in 2017, and we compared the prediction performance between the PM25-DNN and the current state-of-the-art methods in this field. The results show that the PM25-DNN outperforms the other models with the cross-validated coefficient of determination ($\text{R}^{2}$ ), root-mean-square error (RMSE), mean prediction error (MPE), and relative prediction error (RPE) were 0.84, $19.9~\mu \text{g}/\text{m}^{3}$ , $11.89~\mu \text{g}/\text{m}^{3}$ , and 41.21%, respectively. Then, the trained PM25-DNN model was applied to estimate the hourly gridded PM2.5 with 1-km spatial resolution. Our results indicate that the DNN architecture can capture the essential spatiotemporal distribution associated with PM2.5 only using AOD data and conventional meteorological observational variables without more handcrafted features. The proposed PM25-DNN model can greatly improve the accuracy of PM2.5 estimation, and it provides a new perspective for PM2.5 monitoring using end-to-end deep learning method.

Study on microclimate observation network for urban unit: A case study in a campus of Shenzhen, China

Urban units are city components that have relatively pure functions while carry a large number of urban residents. The quality of an urban unit's microclimate environment is closely related to the health and comfort of its population. In this study, we develop a technology on microclimate observation for urban units, which is experimentally applied in a campus in Shenzhen, China. We have arrived at the following conclusions: (1) The application of precision integration technology and small sensors greatly reduces the physical size of the microclimate observation instrument. With the help of advanced algorithms for data quality control, the cost of detection and measuring instruments can be greatly reduced even when accuracy is maintained so that the instruments can be densely deployed in urban units. (2) The urban unit microclimate observation network, as a supplement to the traditional meteorological observation system, makes up for the deficiencies of coarse resolution and limited capability of the traditional observation system on describing urban heterogeneity, and can be used to more precisely depict the heterogeneity of the microclimate within the urban units. Utilizing the technology of Internet of things, it provides data support for people, who work, live and study in the urban units, to take appropriate measures to regulate microclimate environment, and also provides external parameters for automatic adjustment and control of smart air conditioners and dehumidifiers. (3) Temperature monitoring data show that the detected daily temperature ranges of the indoor stations within urban unit are relatively small, and the daily average temperatures observed by the indoor stations are quite different from those observed by the automatic weather stations located in open spaces, indicating that conventional meteorological observation has little reference value to the indoor environment of urban units. At the same time, the observed data of the different indoor monitoring points are quite different from each other, which are closely related to their specific environments. (4) Analysis of relative humidity data shows that the rules of variation of indoor relative humidity are quite different from those of open space, and indoor crowd and monitoring point environment may affect the indoor relative humidity.

A Meteorological Supersite for Aviation and Cold Weather Applications

The goal of this study is to better understand atmospheric boundary layer processes and parameters, and to evaluate physical processes for aviation applications using data from a supersite observing site. Various meteorological sensors, including a weather and environmental unmanned aerial vehicle (WE-UAV), and a fog and snow tower (FSOS) observations are part of the project. The PanAm University of Ontario Institute of Technology (UOIT) Meteorological Supersite (PUMS) observations are being collected from April 2015 to date. The FSOS tower gathers observations related to rain, snow, fog, and visibility, aerosols, solar radiation, and wind and turbulence, as well as surface and sky temperature. The FSOSs are located at three locations at about 450–800 m away from the PUMS supersite. The WE-UAV measurements representing aerosol, wind speed and direction, as well as temperature (T) and relative humidity (RH) are provided during clear weather conditions. Other measurements at the PUMS site include cloud backscattering profiles from CL51 ceilometer, MWR observations of liquid water content (LWC), T, and RH, and Microwave Rain Radar (MRR) reflectivity profile, as well as the present weather type, snow water depth, icing rate, 3D-ultrasonic wind and turbulence, and conventional meteorological observations from compact weather stations, e.g., WXTs. The results based on important weather event studies, representing fog, snow, rain, blowing snow, wind gust, planetary boundary layer (PBL) wind research for UAV, and icing conditions are given. The microphysical parameterizations and analysis processes for each event are provided, but the results should not be generalized for all weather events and be used cautiously. Results suggested that integrated observing systems based on data from a supersite as well as satellite sites can provide better information applicable to aviation meteorology, including PBL weather research, validation of numerical weather model predictions, and remote-sensing retrievals. Overall, the results from the five cases are provided and challenges related to observations applicable to aviation meteorology are discussed.

Analysis of Meteorological Conditions for Fog and Haze in the Eastern and Central Regions of China during January, 2013

Based on the data of conventional meteorological observation, NCEP reanalysis data and atmospheric composition observation, a comprehensive analysis of the three kinds of persistent fog and haze in eastern China in January 2013 was carried out. The results show that the process of persistent fog and haze is in the background of static weather, and the zonal circulation in the middle and high latitudes is not conducive to the south of the cold air. In the eastern part of China, near-surface wind speed is low under the controlled of pressure field, which is conducive to the formation and maintenance of haze. The formation of inversion layer, the height of the mixed layer, the stratified structure of the upper dry layer, the ground wind speed and so on can represent the static stability of the atmosphere. In the actual forecast, fog and haze can be distinguished from the angle of relative humidity, PM2.5 concentration, diurnal variation characteristics, mixed layer height and energy structure, industrial structure and local and surrounding economic development level.

1330号台风“海燕”路径北翘的原因及其对华南的影响

利用常规气象观测资料、间隔为6小时的1˚ × 1˚NCEP再分析资料和卫星云图，对1330号“海燕”台风路径北翘东移的原因，以及其对华南地区所带来的强降水过程进行分析。结果表明：台风的路径和西太平洋副高的强度及位置演变密切相关，副高西伸加强时，台风位于副高南侧，受东风气流引导向西行，10日起，台风位于海南省南部，受冷空气影响，副高减弱东退，台风逐渐调整至副高西侧，10日20时，台风受南风气流引导而转向北行。台风登陆前，受台风倒槽影响，我国华南地区有充沛的水汽和强烈的上升运动，降水与850 hPa涡度场、散度场有较好的对应关系，强降水过程中有明显的水汽输送带和水汽辐合与动力条件相配合。台风登陆后，螺旋雨带降水逐渐减弱，广西东南部云系再生，从而产生又一波降水过程。 Based on the conventional meteorological observation data and the 1˚ × 1˚ NCEP reanalysis data and satellite cloud images with a time interval of 6 hours, the reason for the eastward migration of typhoon trail of “Haiyan” No. 1330 was analyzed. And the impact of typhoon in southern China which led to strong precipitation was also analyzed. The results show that the typhoon path is closely related to the intensity and position evolution of the western Pacific subtropical high. When the subtropical high is strengthened, the typhoon is located on the south side of the subtropical high, guided by the east wind flow westward. From 10 onwards, the typhoon is located in the south of Hainan Province, the typhoon is affected by cold air, subtropical high weakened eastward, and the typhoon gradually adjusts to the west side of the subtropical high. At 20 o’clock on the 10th, the typhoon was led by the south wind and turned northbound. Before typhoon landing, by the typhoon inverted trough, China’s southern region has abundant water vapor and a strong upward movement; precipitation and 850 hPa vorticity field, divergence field has a good correspondence. During the heavy rainfall, there is obvious water vapor conveyor belt, water vapor convergence and dynamic condition. When typhoon landed, the spiral rain gradually reduced and the southeastern Guangxi cloud regenerated, resulting in another wave of precipitation process.