Meteorological Observation Data Research Articles

Operational Forecasting of Wind Speed for an Self-Contained Power Assembly of a Traction Substation

Currently, the prospects of creating hybrid power assemblies using renewable energy sources, including wind energy, and energy storage systems based on hydrogen energy technologies are being considered. To control such an energy storage system, it is necessary to perform operational renewable sources generation forecasting, particularly forecasting of wind power assemblies. Their production depends on the speed and direction of the wind. The article presents the results of solving the problem of operational forecasting of wind speed for a hybrid power assembly project aimed at increasing the capacity of the railway section between Yaya and Izhmorskaya stations (Kemerovo region of the Russian Federation). Hourly data of wind speeds and directions for 15 years have been analyzed, a neural network model has been built, and a compact architecture of a multilayer perceptron has been proposed for short-term forecasting of wind speed and direction for 1 and 6 hours ahead. The model that has been developed allows minimizing the risks of overfitting and loss of forecasting accuracy due to changes in the operating conditions of the model over time. A specific feature of this work is the stability investigation of the model trained on the data of long-term observations to long-term changes, as well as the analysis of the possibilities of improving the accuracy of forecasting due to regular further training of the model on newly available data. The nature of the influence of the size of the training sample and the self-adaptation of the model on the accuracy of forecasting and the stability of its work on the horizon of several years has been established. It is shown that in order to ensure high accuracy and stability of the neural network model of wind speed forecasting, long-term meteorological observations data are required.

Spatiotemporal evolution of aeolian dust in China: An insight into the synoptic records of 1984–2020 and nationwide practices to combat desertification

AbstractAeolian dust is closely related to land degradation, desertification, and sand and dust storm weather, and is a significant factor influencing the Earth's biogeochemical cycles. Here we present an investigation of the recent synoptic features of atmospheric dust in China and analyze its spatiotemporal change based upon meteorological observation data and satellite products. It shows that aeolian dust frequency in China has decreased gradually from 1984 to 2020. The outbreak frequency of dust storms declined significantly by 97.7%, followed by severe dust storm, dust in suspension, and blowing dust with 88.8%, 75%, and 64.3% reduction, respectively. The main dust sources influencing China are the Mongolian Gobi Desert, the Taklimakan Desert, the Hexi Corridor, the Alxa Plateau Desert, the Qaidam basin Desert, and the northeast‐southwest stretching semiarid farming‐pasture ecotone. Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) may play an important role in teleconnection with aeolian dust event occurrence in China. Their correlation coefficients are −0.6 and 0.37, respectively. For more than 40 years, China has made huge investments in ecological restoration projects, and enacted national policies and laws to mitigate desertification. Significant environmental improvement has consequently ensued in China in the early 21st century, and the aeolian dust events were reduced to a record minimum in 2020. In contrast to the growing nationwide greenness, China is now facing up to an incremental dust risk generated from extraterritorial source regions such as the Mongolian Gobi Desert and Central Asian desiccated salt lakes.

Characteristics of Ozone Pollution and High-impact Meteorological Factors in Urban Cities: A Case of Suzhou

The problem of urban ozone (O3) pollution has become prominent in recent years. However, the meteorological factors associated with O3 pollution remain unclear. Analyzing the characteristics of O3 pollution in Suzhou, as a typical urban city, and exploring the high-impact meteorological factors with O3 pollution are crucial to the prevention and control of air pollution in this region. This study used correlation analysis and machine learning methods to analyze the variation in O3 concentration and the relationship between meteorological driving factors in Suzhou based on the O3 concentration data provided by Suzhou Environmental Monitoring Center and the contemporaneous meteorological observation data in Suzhou from April to September in 2015 to 2020. The results showed that: ① O3 pollution exceeding the standard rate was more than 20% in ozone seasons during the past six years; further, pollution days of O3 and the number of pollution days of O3 as the primary pollutant increased yearly. Evidently, the problem of O3 pollution has become increasingly prominent. ② The diurnal variations in O3 were unimodal with the valley point at 07:00 and the highest peak between 15:00 and 16:00. Similar trends were found in diurnal variations of both air temperature and solar radiation, but the daily highest peak came earlier than that of O3. The results also showed an apparent weekend effect of O3 concentration in 2017 and 2019 and a significant correlation between O3 concentration and solar irradiance during the week. In addition, the monthly variation in O3 concentration and pollution exceeding the standard rate was bimodal. ③The occurrence of ozone pollution was affected by various meteorological conditions. The maximum number of days appeared when daily sunshine hours lasted longer than 7 hours, with a daily maximum air temperature around 30℃, solar irradiance ranging from 350 to 440 kW·m-2, and relative humidity ranging from 50% to 75%, at which time the intensity of pollution was the strongest. When the wind speed of easterly wind was less than 1.5 m·s-1, or the wind speed of southwest wind was less than 3.5 m·s-1, moderate ozone pollution occurred. ④ An optimal prediction model of O3 concentration was established based on machine learning, which had good predictive ability for O3 concentration in April, May, July, and September but did not perform well when O3 concentration exceeded 200 μg·m-3. Meanwhile, it was found that solar radiation had the most obvious effect on O3 concentration, followed by relative humidity, whereas the temperature and wind were less important than the former two factors.

Evaluation of the regional climate model for the forest area of Yunnan in China

Climate change is becoming increasingly severe. Today, several studies have found that climate change substantially influences the increasing number of forest fires. Regional climate models (RCMs) are currently a vital tool for climate forecasting in researching how to combat forest fires. As China’s forest fire area, Yunnan province has frequent forest fires that generate significant losses, so it is a crucial area for forest fire prevention in China. Therefore, this study uses meteorological observational data from 25 stations in Yunnan over the period 2004–2018 to compares and evaluates the Regional Climate Forecast Model (RegCM) and Weather Research and Forecasting model (WRF) in multiple dimensions. The optimal RCM is then determined for the forest area of Yunnan. The results show that the deviations of RegCM predictions from the spatial mean of the real temperature are less than 3°C, whereas the deviations of WRF are all greater than 3°C. In addition, the RegCM correlation coefficient exceeds 0.8, whereas the WRF correlation coefficient exceeds 0.75. In terms of precipitation, the deviation of RegCM predictions for the whole territory is less than 2 mm, whereas the overall deviation of WRF predictions is great. The correlation coefficient for RegCM and WRF are both less than 0.5, but the RegCM correlation coefficient exceeds that of the WRF. We thus conclude that RegCM is more suitable for predicting the climate of the forest area of Yunnan. This study also provides references for related climate forecasting and research into forest fire dynamics in general.

Особенности многолетней изменчивости увлажнения ландшафтов Акмолинской области Республики Казахстан по данным спутниковой съемки

The aim of the study is to identify features of long-term variability of the moisture state of the vegetation and soil cover of the steppe zone of northern Kazakhstan. To identify the peculiarities of the variability of the state of natural humidification of landscapes (vegetation and soil) during the growing season of the long-term time interval (from 1976 to 2020), data from satellite images of the territory of the Ishim River Valley and the city of Atbasar, located in the Akmola region of the Republic of Kazakhstan, were analyzed. Based on the data of long-term meteorological observations and on the results of decryption of satellite images, the variability of the NDMI humidity index (normalized difference index of moisture in vegetation and soils) for 44 years for the period 1976‑2020 was characterized. The NDMI humidity index has increased values and intensity of color in the images at the air temperature below the norm (or near the norm) and the amount of precipitation above the norm for this territory. The NDMI humidity index has lower values at air temperatures above normal (or near normal) and the sum of precipitation below normal for a given territory. The low degree of moisture in vegetation and soil at the time of satellite survey is preceded by the arid previous 30-day period (also vice versa). A color scale of gradation of the NDMI humidity index values for use in assessing the humidity of the landscapes of Akmola region and adjacent territories of the northern part of Kazakhstan is proposed.

Spatio-temporal comparison of snow depth between passive microwave remote sensing inversion data and meteorological station observation data

Spatio-temporal comparison of snow depth between passive microwave remote sensing inversion data and meteorological station observation data

ДО ПИТАННЯ УТОЧНЕННЯ КРИТЕРІЇВ ДОСТОВІРНОСТІ, ЯКІ ВИКОРИСТОВУЮТЬ ПРИ АВТОМАТИЗОВАНОМУ ПРОСТОРОВО-ЧАСОВОМУ КОНТРОЛІ ДАНИХ МЕТЕОРОЛОГІЧНИХ СПОСТЕРЕЖЕНЬ

In order to ensure a reliability of data on a state of surface layer of atmosphere, data of meteorological observations conducted on the network of the Hydrometeorological Service of Ukraine are subject to regular multi-level technical and critical control. The entire set of errors in data of meteorological measurements, according to a nature of their manifestation, are divided into two groups: 1) random errors associated with instantaneous pulsations of measured meteorological valuables and their distortion during their further collection and processing; 2) systematic errors, which depend on defect in a devices design, their technical condition, as well as errors caused by violation of measurement technique and an influence of local features on measured meteorological valuables. In order to separate measurement errors from the manifestation of natural features of weather processes, all observational data coming from meteorological stations are subject to regular critical control, during which their reliability, that is, suitability for further use, is established. There are two main types of critical control of measurement data – intra-station and spatio-temporal. The basis of intra-station control is an analysis of values of interrelated meteorological values measured on one station. This is done at hydrometeorological stations with a help of special computer programs. Spatial-temporal control consists in comparing averaged over a certain period of time (decade, month) values of spatial fields of meteorological variables around a station, the data of which is checked/controlled. Local distortions of the field of meteorological values under an influence of local natural factors can be partially eliminated if we compare not values of meteorological values themselves, but their deviations from a climatic norm. Carrying out manual spatio-temporal control is a long and routine work, especially when it comes to analyzing a change in time of data observational inconsistencies. This made it necessary to conduct a study on improving the method of automated spatio-temporal data control of meteorological observations, and to present obtained results in a form of a normative document to be implemented in the activitiy of hydrometeorological organizations of the State Emergency Service of Ukraine. Such research was carried out at the Ukrainian Hydrometeorological Institute for 2020-2022. The purpose of the publication is to present scientific and methodological foundations of development and main provisions of the normative document “Clarification of reliability criteria used during automated spatio-temporal control of data from meteorological observations of stations. Methodical recommendations”, as well as the computer program “MeteoControl”, which forms a database of non-connections and calculates statistical characteristics, are needed for analyzing the array of non-connections and making a decision on the feasibility of changing the reliability criteria. The developed regulatory document provides a number of recommendations for specifying reliability criteria of meteorological observation data, in particular: 1) specification of a reliability criteria for all meteorological variables that are determined on a network of stations should be carried out at least once every 5 years; 2) before clarifying reliability criteria, it is necessary to assess an extent to which a current reliability criteria reveal a data, as well as to establish how many stations and how often in recent years have discrepancies that exceed a current reliability criterion; 3) before making a decision to change a reliability criterion for all stations, it is advisable to find out how, in connection with an introduction of new reliability criterion, a number of stations that will require additional analysis will change, or vice versa – whose data will be considered reliable despite to their dubious or erroneous values.

北京主要气传致敏花粉年浓度峰值日期预测

PDF HTML阅读 XML下载 导出引用 引用提醒 北京主要气传致敏花粉年浓度峰值日期预测 DOI: 10.5846/stxb202202220416 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 北京市科技计划课题（Z191100009119013） Prediction of date of annual maximum concentration of main airborne allergenic pollen in Beijing Author: Affiliation: Fund Project: Beijing Municipal Science and Technology Project(Z191100009119013) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:花粉是我国北方引发过敏性鼻炎最主要过敏原，花粉症发病期与花粉浓度高峰期吻合。基于北京地区2012至2020年花粉季多站、逐日分类花粉浓度观测数据分析，得出北京地区花粉浓度在3月上旬至5月中旬（可进一步划分为3月中旬至4月上旬和4月下旬至5月上旬两个高峰期）和8月中旬至9月中旬分别存在两个高峰期，第一个高峰期内优势致敏花粉种类为柏科、杨柳科和松科，第二个高峰期内优势致敏花粉种类为桑科、菊科蒿属和藜科。根据优势致敏花粉年浓度峰值日期观测数据，使用与花粉采样站点位置相匹配的逐日气象观测数据累积值，基于作物模型概念和模糊逻辑原理建立了北京地区主要气传致敏花粉年浓度峰值日期预测模型。经检验，柏科、杨柳科、松科、桑科、菊科蒿属和藜科花粉模型预测准确率分别为87.8%、80.0%、64.4%、86.7%、78.8%和81.8%。基于北京地区主要气传致敏花粉年浓度峰值日期预测模型可为本地花粉症防治提供理论参考。 Abstract:The prevalence and severity of pollen-induced pollinosis has been increasing yearly worldwide in recent years. Pollen is the main allergen causing allergic rhinitis in North China and the onset period of airborne allergenic pollen-induced pollinosis coincides with the peak period of pollen concentration. As one of the mega-cities in the northern region of China, Beijing is becoming increasingly aware of the problems caused by allergenic pollen. Based on the analysis of 12 pollen sampling stations daily classified pollen concentration observation data during pollen season in Beijing from 2012 to 2020, there were two peak periods of pollen concentration in Beijing from early March to mid-May (it could be further divided into two peak periods from mid-March to early April and from late April to early May) and from mid-August to mid-September, respectively. The dominant allergenic pollen species in the first peak period were Cupressaceae, Salicaceae (from early March to mid-April, the annual average concentration accounted for 39.1% and 18.2% respectively) and Pinaceae (from mid-April to early May, the annual average concentration accounted for 18.2%), and the dominant allergenic pollen species in the second peak period were Moraceae, Artemisia and Chenopodiaceae (from mid-August to mid-September, the annual average concentration accounted for 34.4%, 30.4% and 12.7% respectively). The annual maximum concentration of dominant airborne allergenic pollen in Beijing varied significantly among stations and pollen seasons, and fluctuated significantly. In contrast, the variation of the dates of annual maximum pollen concentration of the same species is relatively stable, and its prediction research work is more meaningful for pollen-induced pollinosis control. Based on the observation data of annual maximum concentration of dominant airborne allergenic pollen and the cumulative value of daily meteorological observation data matched with the location of pollen sampling stations, a prediction model of date of annual maximum concentration of main airborne allergenic pollen in Beijing is established based on the principle of crop growth model and fuzzy logic. The results showed that the prediction accuracy of pollen models of Cupressaceae, Salicaceae, Pinaceae, Moraceae, Artemisia and Chenopodiaceae were 87.8%, 80.0%, 64.4%, 86.7%, 78.8% and 81.8%, respectively. Using a model based on fuzzy logic principle and driven by the cumulative values of daily meteorological elements for pollen annual maximum concentration date, combined with the high-resolution regional numerical weather prediction model in Beijing, we can make a reasonable prediction of the time of maximum pollen concentration of different airborne allergenic pollens and provide a theoretical reference for the prevention and control of local airborne allergenic pollen-induced pollinosis. 参考文献 相似文献 引证文献

A dataset of carbon and water fluxes and micrometerological elements in Danzhou rubber plantation (2010-2018)

Rubber plantation is one of the main artificial vegetation types in Hainan Island. As an important forest ecosystem in China’s tropical regions, rubber plantation plays an important role in the regional carbon and water cycle. Measuring the carbon and water fluxes is critical for estimating the carbon and water balance and energy exchange of rubber plantation. Since November 2009, near-surface CO2 and H2O fluxes observation have been carried out in Danzhou rubber plantation, including eddy covariance system and meteorological observation system. Up to now, it has accumulated flux and meteorological observation data for 12 years. This dataset organizes the rubber plantation flux and conventional meteorological data in Danzhou from 2010 to 2018, including net ecosystem productivity, ecosystem respiration, gross ecosystem productivity, latent heat flux, sensible heat flux, evapotranspiration, air temperature, air relative humidity, vapor pressure, wind speed, wind direction, atmospheric pressure, total radiation, net radiation, photosynthetically active radiation, soil temperature, soil water content and precipitation. This dataset can provide basis and reference for ecosystem evaluation and sustainable development planning in economic and social.

Analysis of Agglomerate Fog Meteorological Characteristics in Anhui Province Based on Traffic Accident Data

Using the data of five agglomerate-fog-related traffic accidents during 2014–2017 and fog-related expressway accidents during 2013–2018 assessed by the Anhui provincial traffic authorities and corresponding meteorological observation data, this article analyzes the spatio-temporal characteristics, geographical environment and weather background of agglomerate-fog-related accidents in detail and identifies the characteristics of changes in terms of visibility, humidity, temperature and wind when agglomerate fog occurs. Furthermore, an agglomerate fog forecast index has been established in Anhui Province to provide a reference basis for the agglomerate fog forecast, early warning and prevention.

Comparative results of the average daily net radiation (Rnd) from meteorological observation data and Landsat–8 remote sensing imagery areas of Hoa Binh province

Comparative results of the average daily net radiation (Rnd) from meteorological observation data and Landsat–8 remote sensing imagery areas of Hoa Binh province

Climate Patterns Affecting Cold Season Air Pollution of Ulaanbaatar City, Mongolia

Many studies have been conducted on air pollution in Ulaanbaatar city. However, most have focused on the sources of pollutants and their characteristics and distribution. Although the location of the city subjects it to unavoidable natural conditions where air pollution accumulates during the cold season, nature-based solutions have not yet been considered in the projects implemented to mitigate air pollution levels. Therefore, this study aims to determine the combined influence of geography and atmospheric factors on cold season air pollution. The spatiotemporal variations in the variables were investigated using meteorological observation data from 1991 to 2020 in the different land-use areas. Then, atmospheric stagnation conditions and air pollution potential parameters were estimated from daily radiosonde data. Subsequently, the temporal variations in air pollutants were studied and correlated with estimates of the above parameters. In the Ulaanbaatar depression, the stable cold air lake (colder than −13.5 °C), windless (34–66% of all observations), and poor turbulent mixing conditions were formed under the near-surface temperature inversion layer in the cold season. Moreover, due to the mountain topography, the winds toward the city center from all sides cause polluted air to accumulate in the city center for long periods. Air pollution potential was categorized as very high and high (<4000 m2·s−1), in the city in winter, indicating the worst air quality. Thus, further urban planning policy should consider these nature factors.

A new method for calculating average visibility from the relationship between extinction coefficient and visibility

Abstract. Visibility data are fundamental meteorological observation data widely used in many fields. When using visibility data, it is often necessary to calculate the average visibility, which used to be the arithmetic average of the visibility data directly. In this study, we first analyze the relationship between the visibility, the extinction coefficient, and the atmospheric compositions. Then we propose to use the harmonic average of visibility data as the average visibility, which can better reflect changes in atmospheric extinction coefficients and aerosol concentrations. It is recommended to use the harmonic average visibility in the studies of climate change, atmospheric radiation, air pollution, environmental health, etc.

Study on Radiative Flux of Road Resolution during Winter Based on Local Weather and Topography

Large-scale traffic accidents caused by black ice on roads have increased rapidly; hence, there is an urgent need to prepare safety measures for their prevention. Here, we used local weather road observations and the linkage between weather prediction and a radiation flux model (LDAPS-SOLWEIG) to calculate prediction information regarding habitual shade areas, sky view factor (SVF), and downward shortwave radiative flux by road direction and lane. Using the LDAPS-SOLWEIG model system, a set of real-time weather prediction data (temperature, humidity, wind speed, and insolation at 1.5 km resolution) was applied, and 5 m resolution radiative flux prediction data, with road resolution blocked by local weather and topography, were calculated. We found that the habitual shaded area can be divided by the direction and lane of the road according to the height and shape of the terrain around the road. The downward shortwave radiation flux data from local meteorological observation data and that calculated from the LDAPS-SOLWEIG model system were compared. When road-freezing occurred on a case day, the RMSE was 20.41 W·m−2, MB was −5.04 W·m−2, and r was 0.78. The calculated information, habitual shaded area, and SVF can highlight road sections vulnerable to winter freezing and can be helpful in the special management of these areas.

Interaction between cloud–radiation, atmospheric dynamics and thermodynamics based on observational data from GoAmazon 2014/15 and a cloud-resolving model

Abstract. Observational meteorological data from the field experiment GoAmazon 2014/15 and data from numerical simulations with the cloud-resolving model (CRM) called the System for Atmospheric Modeling (SAM) are used to study the interaction between the cloudiness–radiation as well as the atmospheric dynamics and thermodynamics variables for a site located in the central Amazon region (−3.2∘ S, −60.6∘ W) during the wet and dry periods. The main aims are to (a) analyze the temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux as well as (b) to determine the relationship between the integrated cloud fraction, radiative fluxes and large-scale variable anomalies as a function of the previous day's average. The temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux from SAM simulations showed physical consistency with the observations from GoAmazon 2014/15. Shallow and deep convection clouds show to have a meaningful impact on radiation fluxes in the Amazon region during wet and dry periods. Anomalies of large-scale variables (relative to the previous day's average) are physically associated with cloud formation, evolution and dissipation. SAM consistently simulated these results, where the cloud fraction vertical profile shows a pattern very close to the observed data (cloud type). Additionally, the integrated cloud fraction and large-scale variable anomalies, as a function of the previous day's average, have a good correlation. These results suggest that the memory of the large-scale dynamics from the previous day can be used to estimate the cloud fraction as well as the water content, which is a variable of the cloud itself. In general, the SAM satisfactorily simulated the interaction between cloud–radiation as well as dynamic and thermodynamic variables of the atmosphere during the periods of this study, being able to obtain atmospheric variables that are impossible to obtain in an observational way.

Artificial Intelligent For Rainfall Estimation In Tropical Region : A Survey

Rainfall monitoring in real-time is a mandatory in tropical areas such as Indonesia. As a country with various topographical conditions ranging from low-lying urban areas, highlands, to mountainous valleys, Indonesia is prone to hydrometeorological disasters in the form of flash floods and landslides. The strategic geographical position at the equator, between the Pacific and Indian oceans, and surrounded by vast oceans, combined with various natural phenomena related to the dynamics of the atmosphere and the ocean, makes high-density rainfall observations indispensable for both disaster mitigation and climate monitoring. As a vast tropical and archipelagic country, Indonesia currently has around 1000 automatic rainfall sensors and still requires more sensors to increase the spatial resolution of the observation network. Increasing the density of the observation network using both rain gauges and weather radar poses a problem of high operational costs. Therefore, several alternative rainfall observation systems are required. In the last decade, there have been several studies related to rainfall measurements using artificial intelligence from various meteorological variables, including the exploitation of microwave signals from radio telecommunications links, both terrestrial and satellite using high frequency bands. In this survey paper, we review and discuss research articles related to rainfall estimation using state-of-the-art methods in artificial intelligence using meteorological observation data, remote sensing, terrestrial and satellite microwave communication links. In conclusion, we present several future research challenges that can be applied to increase the density of rainfall observation networks.

Effects of Seasonal Variation on Spatial and Temporal Distributions of Ozone in Northeast China.

The levels of tropospheric ozone (O3) are closely related to regional meteorological conditions, precursor emissions, and geographical environments, which have a significant negative impact on human health. The concentrations of O3 were relatively low, while the spatial distribution was strongly heterogeneous in Northeast China; however, little is known about how the influencing factors affect the distribution of O3 in Northeast China. Here, the O3 concentration, meteorological observation data, precursors (NO2), and vegetation coverage data from 41 monitoring cities in Northeast China from 2017 to 2020 were collected and analyzed. The spatial-temporal distributions and evolution characteristics of O3 concentrations were investigated using statistical analysis, kriging interpolation, spatial autocorrelation analysis, cold-hot spot analysis, and geographic detectors, and the effects of meteorological factors, NO2, and green land area on O3 concentrations were evaluated seasonally and spatially. The results showed that O3 pollution in Northeast China was generally at a relatively low level and showed a decreasing trend during 2017-2020, with the highest concentrations in the spring and the lowest concentrations in the autumn and winter. May-July had relatively high O3 concentrations, and the over-standard rates were also the highest (>10%). The spatial distribution showed that the O3 concentration was relatively high in the south and low in the northeast across the study area. A globally significant positive correlation was derived from the spatial autocorrelation analysis. The cold-hot spot analysis showed that O3 concentrations exhibited spatial agglomerations of hot spots in the south and cold spots in the north. In Northeast China, the south had hot spots with high O3 pollution, the north had cold spots with excellent O3 levels, and the central region did not exhibit strong spatial agglomerations. A weak significant negative correlation between O3 and NO2 indicated that the emissions of NOx derived from human activities have weak effects on the O3 concentrations, and wind speed and sunshine duration had little effect on spatial differentiation of the O3 concentrations. Spatial variability in O3 concentrations in the spring and autumn was mainly driven by temperature, but in the summer, the influence of temperature was weakened by the relative humidity and precipitation; no factor had strong explanatory power in the winter. The temperature was the only controlling factor in hot spots with high O3 concentrations. In cold spots with low O3 concentrations, the relative humidity and green land area jointly affected the spatial distributions of O3.

Spatial and Temporal Characteristics of Water Use Efficiency in Typical Ecosystems on the Loess Plateau in the Last 20 Years, with Drivers and Implications for Ecological Restoration

The water use efficiency (WUE) is an essential indicator of carbon–water coupling between terrestrial ecosystems and the atmosphere, and it is an important parameter for studying ecosystem responses to global climate change. A comprehensive understanding of the water–carbon coupling process in the Loess Plateau can reflect the balance between the “carbon absorption” and “water consumption” in vegetation, which drives the ecosystem succession process. In recent years, scholars have gained a more comprehensive understanding of the WUE and the driving factors of the Loess Plateau. However, there is still a need to study the carbon and water coupling mechanisms of different land use types in the Loess Plateau region. In this article, based on the gross primary productivity (GPP), evapotranspiration (ET), surface cover remote sensing products, and meteorological observation data, the trend of WUE changes for different vegetation types in the Loess Plateau from 2001 to 2020 and the correlations with the Normalized Difference Vegetation Index (NDVI), precipitation, and temperature values were analyzed using the Theil–Sen median (SEN) trend analysis method and correlation coefficient analysis method. The spatial distribution patterns of the changes with the drought index showed that the multi-year average WUE value of the Loess Plateau was 1.24 g C mm−1 H2O, and the mean WUE values in different seasons were ranked as follows: summer > autumn > spring. The WUE growth rates of all vegetation types showed a decreasing trend with the increase in drought index, and the size of the WUE response rate for each vegetation type to drought was ranked as follows: grassland > forest > shrub > crop. The annual average WUE increase rate of the Loess Plateau was 0.02 g C mm−1 H2O yr−1, of which 93.36% of the area showed an increasing trend. The NDVI was the dominant factor affecting the spatial and temporal variations in WUE rates in the Loess Plateau, and the correlation between the NDVI and WUE was strongest in summer. In the more arid regional ecosystems, the WUE was negatively correlated with the precipitation and temperature, but in summer the precipitation had a positive effect on the WUE. The correlation of grassland and shrub WUE rates with temperature was more sensitive to the drought index than that of the forest and crop areas, but there was also a threshold effect. Therefore, when vegetation restoration is carried out in arid and semi-arid regions, the carbon and water coupling mechanisms of different vegetation types and the reasonable allocation of regional water resources should be fully considered.

Influence of Seasonal Air–Sea Interaction on the Interannual Variation of the NPP of Terrestrial Natural Vegetation in China

Based on Moderate Resolution Imaging Spectoradiometer (MODIS) remote sensing data, meteorological observation data, multisource atmospheric circulation, and sea surface temperature (SST) data from NCEP/NCAR reanalysis, we estimated the net primary productivity (NPP) of terrestrial natural vegetation in China according to the CASA model and analyzed the linear trend and interannual fluctuation of NPP, as well as the spatial distribution characteristics of the annual NPP response to climatic factors. The obtained results revealed the impact of air–sea interaction on interannual NPP variability in key climatic areas. In China, the annual NPP of natural vegetation, linear NPP trend, and interannual NPP fluctuation showed significant regional characteristics. The annual NPP exhibited a significant increasing trend and interannual fluctuation in North China and Northeast China, with spatially consistent responses from NPP to precipitation and temperature. On the seasonal time scale, NPP in the key climatic area (105~135° E, 35~55° N) exhibited a strong response to both summer precipitation and mean temperature. In the summer atmospheric circulation, the circulation anomaly area is mainly distributed in the northeast cold vortex area in the middle- and high-latitude westerlies in East Asia and in the Sea of Okhotsk with dipole circulation. In the SST of the preceding winter and spring, the key SST anomaly area was the Kuroshio region, with an impact of the Kuroshio SST anomaly on the interannual variation in annual NPP in the key climatic area. The cold vortex in Northeast China played a pivotal role in the influence of the SST anomaly in the Kuroshio region on atmospheric circulation anomalies, resulting in abnormal summer precipitation in the key climatic region and affecting the annual accumulation of NPP of natural vegetation.

Characteristics of Ozone Pollution and the Impacts of Related Meteorological Factors in Shanxi Province, China

Based on environmental monitoring data and meteorological observation data of the Chinese major energy province, Shanxi, from 2015 to 2020, using the satellite remote sensing data of Atmospheric Infrared Sounder Instrument (AIRS) and Ozone Monitoring Instrument (OMI) in 2017, we analyzed the characteristics of surface ozone (O3) pollution and its correlation with meteorological factors, as well as the vertical distribution of O3 in typical pollution cities in Shanxi Province. The results showed that surface O3 became the primary pollutant in Shanxi. Surface O3 has shown a zonal distribution with a high level in the south and a low level in the north region since 2017. Surface O3 pollution was severe in 2019, and the maximum daily 8 h running average of O3 (MDA8 O3) decreased, but annual mean O3 in northern and central regions showed a slow rising trend in 2020. Comprehensive analyses of the influence of meteorological factors on surface O3 indicated that O3 pollution in Linfen, Yuncheng and Taiyuan was mainly caused by local photochemical reactions, while that in Jincheng, Xinzhou, Lvliang and Yangquan resulted from regional transports. O3 volume mixing ratios (VMR) in the middle and lower troposphere generally increased with altitude, peaking at 120 ppbv at approximately 400 hPa. The positive vertical gradient of O3 in the boundary layer was obvious in Taiyuan in summer and significant in the surface layer in Taiyuan and Linfen during winter and spring, which was associated with greater atmospheric dynamic stability and suppressed vertical mixing. Due to the lack of direct detection of O3 in the lower troposphere in this region, O3 vertical distribution retrieved by satellite observation is critical for the study of vertical mixing and transport of local O3, as well as its regional transport characteristics.