Search

In network real‐time kinematic positioning of multi‐reference station, the spatial and temporal distribution of tropospheric delay is affected by both horizontal and elevation. The traditional modeling strategy of regional troposphere takes more consideration of the horizontal factor, and the incomplete consideration of the elevation factor will lead to the problem of reduced modeling accuracy, especially in the face of the scene with large regional height deviation. Based on the traditional linear interpolation method (LIM), a simple and effective height‐deviation compensation‐based linear interpolation method (HCLIM) for regional tropospheric is proposed. The modeling accuracy of troposphere and the positioning accuracy of user RTK in large height deviation region are significantly improved. The method was verified based on six experimental subnets with large height deviations from a provincial continuously operating GNSS reference stations network in central China. The results showed that: For GPS satellite modeling, compared with the traditional LIM method, the average modeling accuracy improvement rate of HCLIM method is (84.5%, 75.5%, 59.3%, 26.7%) in the elevation angle range of (10–30°/30–40°/40–50°/50–90°). For BDS satellite, the average modeling accuracy improvement rate of HCLIM method in the above four elevation angles is (83.3%, 70%, 50%, 23.5%). For the positioning performance of user RTK, The horizontal positioning accuracy and RTK fixing rate were similar under the two methods, while HCLIM method showed only slight improvement. However, in the U direction, LIM method showed obvious systematic bias, while HCLIM method showed consistent positioning accuracy, which was improved to 82.8% compared with LIM method. Plain Language Summary: In GNSS relative positioning, the positioning performance of user largely depends on the extent to which atmospheric deviations (ionospheric delay or tropospheric delay) are eliminated or weakened. Compared with the ionosphere, the temporal and spatial characteristics of troposphere are significantly different. It is part of the lower atmosphere, extends from the Earth's surface to an altitude of about 20 km, and contains most of the atmospheric water vapor. The delay of satellite signals through the troposphere is affected by both horizontal and elevation. The traditional tropospheric regional modeling strategy only uses plane coordinates for interpolation, ignoring the influence of elevation factors or the consideration of height‐deviation is not perfect. When the height difference between the reference station and the rover station is large, the modeling accuracy will obviously be reduced. The large modeling residual may bring systematic deviation to the user coordinates, especially the elevation direction component. In this paper, we propose a regional tropospheric linear interpolation method (HCLIM) considering height deviation compensation. This method is simple and effective, The modeling accuracy of troposphere and the positioning accuracy of user RTK in large height difference region are significantly improved. Key Points: A simple and effective height‐deviation compensation‐based linear interpolation method (HCLIM) for regional tropospheric is proposedThe HCLIM method greatly improves the tropospheric modeling accuracy of each elevation angle range in the large height difference site areaThe HCLIM method significantly improves the systematic bias in elevation direction of user RTK positioning due to modeling residuals [ABSTRACT FROM AUTHOR]

Thermodynamic and liquid water profiles can be retrieved by a ground-based microwave radiometer (MWR) in nearly all weather conditions, which is useful for detecting mesoscale phenomena. This paper reviews the advances in remote sensing of atmospheric profiles and cloud properties by MWR in central China. Comparative studies indicate that MWR retrieval accuracy is different under various skies, especially those that decay under precipitation. The off-zenith method is proven to be capable of reducing the impact of precipitation and snow on MWR retrieval accuracy. Application studies demonstrate that MWR retrievals are helpful for early warning of rainstorms, hailstorms, and thunderstorms. Moreover, MWR retrievals provide a way to study cloud properties. The temporal variations of cloud occurrence frequency (COF) and liquid water path (LWP) are different for low, middle, and high clouds, and the vertical distribution of COF is also different in autumn and other seasons. Note that MWR can infer valid retrievals over the eastern Tibetan Plateau due to the weak precipitation over there. Also, cloud properties over the eastern Tibetan Plateau present differences from those over central China, and this is related to the different characteristics of atmospheric water vapor between these two regions. To bring more benefits for mechanism study and early warning of severe weather and numerical weather prediction, the decayed accuracy of MWR zenith retrievals under precipitation should be resolved. And combining MWR with other instruments is necessary for MWR application in detecting multi-layer clouds and ice clouds. [ABSTRACT FROM AUTHOR]

Using multiple observational and reanalysis data, this paper investigates the impact of the interdecadal shift in summer Arctic Oscillation (AO) on precipitation in East Asia, by removing ENSO influences. The results indicate that the lower-layer activity center of summer AO in Atlantic shifted eastward after the mid-1980s. This regime shift of summer AO has a significant impact on precipitation in East Asia. Before the mid-1980s, the key regions in which precipitation was affected by AO in East Asia were northern East Asia and Northeastern China and adjacent regions. After the mid-1980s, the key regions in which precipitation was affected by AO in East Asia were central Inner Mongolia and Southern China. The mechanism of precipitation changes can be attributed to changes in atmospheric circulation and water vapor transport related to AO changes. After the mid-1980s, the influence of AO on geopotential height over northern East Asia weakened; meanwhile, the impact of AO on geopotential height over China increased. Consistent with the changes in atmospheric circulation, water vapor transport in East Asia also underwent interdecadal changes before and after the mid-1980s. The differences in atmospheric circulation and water vapor transport in East Asia can be traced back to the North Atlantic. Before the mid-1980s, wave activity flux related to summer AO tended to propagate in high latitudes and subtropics; after the mid-1980s, the wave activity flux changed in its subtropical path and propagated eastward from the North Atlantic through the Middle East to China, significantly affecting the summer precipitation in China. [ABSTRACT FROM AUTHOR]

Water vapor is an important part of the atmospheric system and plays a key role in the global water cycle, energy balance, extreme weather, and long-term climate research. It is of great significance to fully grasp the spatial and temporal distribution characteristics of water vapor for the study of various climate models. The traditional water vapor detection technology mainly has the problems of low spatial-temporal resolution and poor capture of fine changes. In recent decades, the rapid development of intelligent sensor technology and Walktrap algorithm has made it a reliable means to obtain and analyze the characteristics of water vapor. However, the acquisition of atmospheric water vapor requires two key parameters: station pressure (PS) and weighted average temperature (TM). Since most traditional stations are mainly used for geodetic research, few are equipped with meteorological intelligent sensors. Therefore, based on the traditional meteorological stations, this paper improves the equipment of the traditional stations and endows the traditional stations with precise and sensitive intelligent sensor equipment to obtain the regional water vapor characteristic data of El Niño and a province in the east of China, combined with the advanced Walktrap algorithm, in order to use the above equipment and technology to realize the correlation analysis of El Niño and regional water vapor characteristics. The results show that through the water vapor information calculated by 12 coastal stations, the relationship between monthly average PWV and seawater surface temperature is studied, and the correlation degree between the relevant characteristics is as high as 94.6%. [ABSTRACT FROM AUTHOR]

Objectives: Atmospheric water vapor is one of the important components in troposphere. It is of great significance to study the factors and accuracy of influencing water vapor. This paper mainly studies the influencing factors of precipitable water vapor (PWV) in the Loess Plateau and evaluates its actual accuracy. Methods: Firstly, the pressure, temperature data of the fifth-generation atmospheric reanalysis data of EC-MWF (ERA5) and global navigation satellite system (GNSS) derived zenith troposphere delay (ZTD) are evaluated. Secondly, according to the pressure and temperature data of ERA5 and ZTD data of crustal movement observation network of China (CMONOC), the PWV with 1-hour time resolution is calculated, and the theoretical error of PWV is deduced by using error propagation theory, and then the accuracy of PWV in the Loess Plateau is analyzed by comparing with the actual calculation error of PWV. Results: The results show that the average root mean square(RMS) and bias of GNSS-derived ZTD calculated by GAMIT/GLOBK and PANDA are 4.05 mm and −0.46 mm, respectively. The average RMS and bias of ERA5-provided pressure and temperature are 3.36 hPa/1.97 K and − 0.01 hPa/0.04 K, respectively. The theoretical error of PWV in the Loess Plateau is 1.51 mm, and the actual error is 1.94 mm. Conclusions: The accuracy of PWV calculated in this paper is high, which is of great significance to the investigation of water vapor distribution and climate monitoring. [ABSTRACT FROM AUTHOR]

Atmospheric water vapor is one of the important parameters for weather and climate studies. Generally, atmospheric water vapor can be monitored by some techniques, such as the Global Navigation Satellite System (GNSS), radiosonde (RS), remote sensing and numerical weather forecast (NWF). However, the comprehensive retrieval and application of precipitable water vapor (PWV) using multi techniques has been hardly performed before, which becomes the focus of this study. A comprehensive PWV retrieval and application platform (CPRAP) is first established by combing the ground-based (GNSS), space-based (Fengyun-3A, Sentinel-3A) and reanalysis-based (the fifth-generation reanalysis dataset of the European Centre for Medium-Range Weather Forecasting, ERA5) techniques. Additionally, its applications are then extended to drought and rainfall monitoring using the CPRAP-derived PWV. The statistical result shows that PWV derived from ground-based GNSS has high accuracy in China, with the root mean square (RMS), Bias and mean absolute error (MAE) of 2.15, 0.05 and 1.65 mm, respectively, when the RS-derived PWV is regarded as the reference. In addition, the accuracy of PWV derived from the space-based (FY-3A and Sentinel-3A) techniques technique is also validated and the RMS, Bias and MAE of a Medium Resolution Spectral Imager (MERSI) onboard Fengyun-3A (FY-3A) and an Ocean and Land Color Instrument (OLCI) onboard Sentinel-3A are 4.46/0.56/3.61 mm and 2.95/0.01/1.37 mm, respectively. Then, the performance of ERA5-derived PWV is evaluated based on GNSS-derived and RS-derived PWV. The result also shows good accuracy of ERA5-provided PWV with the averaged RMS, Bias and MAE of 1.86/0.11/1.48 mm and 0.90/−0.05/1.51 mm, respectively. Finally, the PWV data derived from the established CPRAP are further used for drought and rainfall monitoring. The applied results reveal that the calculated the standardized precipitation evapotranspiration index (SPEI) using the CPRAP-derived PWV can monitor the drought and the correlation coefficient ranges from 0.83 to 0.9 when compared with the SPEI. Furthermore, in this paper correlation analysis between PWV derived from the CPRAP and rainfall, and its potential for rainfall monitoring was also validated. Such results verify the significance of the established CPRAP for weather and climate studies. [ABSTRACT FROM AUTHOR]

Extraordinary floods are linked with heavy rainstorm systems. Among various systems, their synoptic features can be quite different. The understanding of extreme rainstorms by their causative processes may assist in flood frequency analysis and support the evaluation of any changes in flood occurrence and magnitudes. This paper aims to identify the most dominant meteorological factors for extreme rainstorms, using the ERA5 hourly reanalysis dataset in Henan, central China as a case study. Past 72 h extreme precipitation events are investigated, and six potential factors are considered in this study, including precipitable water (PW), the average temperature (Tavg) of and the temperature difference (Tdiff) between the value at 850 hPa and 500 hPa, relative humidity (RH), convective available potential energy (CAPE), and vertical wind velocity (Wind). The drivers of each event and the dominant factor at a given location are identified using the proposed metrics based on the cumulative distribution function (CDF). In Henan, central China, Wind and PW are dominant factors in summer, while CAPE and Wind are highly related factors in winter. For Zhengzhou city particularly, Wind is the key driver for summer extreme rainstorms, while CAPE plays a key role in winter extreme precipitation events. It indicates that the strong transport of water vapor in summer and atmospheric instability in winter should receive more attention from the managers and planners of water resources. On the contrary, temperature-related factors have the least contribution to the occurrence of extreme events in the study area. The analysis of dominant factors can provide insights for further flood estimations and forecasts. [ABSTRACT FROM AUTHOR]

The use of remote sensing technique to monitor atmospheric water vapor is significant for weather and climate studies. However, the general methods of retrieving precipitable water vapor (PWV) with high precision and high resolution using remote sensing satellite has hardly been investigated, which becomes the focus of this paper. A general remote sensing PWV retrieval (GRPR) method that uses level-1 near-infrared (NIR) data sympathized by a global navigation satellite system (GNSS) is proposed. In this method, the atmospheric transmittance coefficients are determined by combining the radiative transmission model and the central wavelength interpolation method instead of directly using the traditional empirical values. Next, the PWV derived from remote sensing NIR data is obtained using the adaptive seasonal exponent model rather than the traditional transmittance–water vapor lookup table method. Furthermore, the accuracy of remote sensing NIR PWV is further calibrated by establishing the seasonal relationship between PWV residual and elevation. The corresponding NIR data from the Fengyun-3A (FY3A) satellite over the period of 2013–2015 in China are selected to validate the proposed method. Statistical results show the good performance of GRPR method for internal and external accuracies with root mean square (RMS) improvement rates of 76.8% and 72.4%, respectively, compared with the FY3A level-2 PWV products. In addition, the proposed method has good robustness and is almost unaffected by the PWV magnitude at different seasons. Proposed GRPR method is also applied for PWV retrieval at different time scales, further showing its superiority of retrieving PWV with high precision and high resolution. These results indicate the good application prospect of the GRPR method proposed in this study for generating remote sensing PWV products. • The optimal atmospheric transmittance coefficients are determined in this study. • An adaptive seasonal exponent model is developed to retrieve remote sensing PWV. • The accuracy of remote sensing NIR PWV is further calibrated using DEM data. • GRPR method is applied for remote sensing PWV retrieval at different time scales. [ABSTRACT FROM AUTHOR]

An obvious long‐term drying trend in recent early springs (February–March–April) is observed over southeastern China (SEC). Here, we attribute this drying to the weakened subtropical westerlies and deflected by the Tibetan Plateau (TP). Climatologically, the low‐level southwesterlies at the southeastern margin of the TP, a branch of the upstream subtropical westerly jet deflected by the TP terrain, bring water vapor to SEC and the southerlies move upward over SEC mainly through isentropic gliding mechanism, inducing persistent precipitation in early spring. However, the subtropical westerlies weakened significantly in recent decades due potentially to the decreased Eurasian snow cover. Consequently, an easterly trend appears along the southern margin of the TP with anomalous northeasterlies over SEC. These northeasterlies suppress both moisture supply and upward motions over SEC, and reduce regional early spring precipitation. Our results highlight the interaction between the TP terrain and the weakened subtropical westerlies that leads to the drying SEC. Plain Language Summary: Spring precipitation in southeastern China (SEC) is a major rainband during the pre‐flood season in East Asia, which is significant for agricultural production and social economy. However, in the recent few decades, a robust long‐term drying trend has occurred over SEC in early spring. In this study, we propose a new mechanism for the decreased SEC precipitation and highlight the important influence of the weakened subtropical westerlies and their interaction with the Tibetan Plateau (TP). Deflected by the TP large terrain, the upstream weakened subtropical westerlies induce weakened westerlies and southwesterlies along the southern and southeastern margins of the TP, respectively. As a result, the weakened southwesterlies at the southeastern TP not only reduce the moisture transport downstream, but also suppress the ascending motions over SEC through the isentropic gliding mechanism. Both the water vapor and atmospheric circulation conditions finally induce the drying SEC in recent early springs. Key Points: An early spring drying trend has occurred in southeastern China (SEC), much of this can be attributed to the weakened subtropical westerliesDeflected by the Tibetan Plateau (TP), the weakened subtropical westerlies decelerate downstream westerlies along the TP's southern marginThe decelerated westerlies at the southeastern TP suppress moisture supply and rising motions over SEC, both processes cause the drying SEC [ABSTRACT FROM AUTHOR]

Regional haze often occurs after the New Year holiday. To explore the characteristics of PM2.5 pollutions under the influence of the New Year's Day effect, this study analyzed the spatiotemporal changes relating to PM2.5 during and around the New Year's Day holiday in China from 2015 to 2022, and used the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model to study the effects of human activities and meteorological factors on PM2.5 pollutions, as well as the differences in the contributions of different industries to PM2.5 pollutions. The results show that for the entire study period (i.e., before, during, and after the New Year's Day holiday) from 2015 to 2022, the average concentrations of PM2.5 in China decreased by 41.9% overall. In 2019~2022, the New Year's Day effect was significant, meaning that the average concentrations of PM2.5 increased by 18.9~46.8 μg/m3 from before to after the New Year's Day holiday, with its peak occurring (64.3~74.9 μg/m3) after the holiday. In terms of spatial differences, the average concentrations of PM2.5 were higher in the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and central China. Moreover, the Beijing–Tianjin–Hebei region and its surrounding areas, the Chengdu–Chongqing region, the Fenwei Plain, and the middle reaches of the Yangtze River region were greatly affected by the New Year's Day effect. Human activities led to higher increases in PM2.5 in Henan, Hubei, Hebei, and Anhui on 3 and 4 January 2022. If the haze was accompanied by cloudy days or weak precipitation, the accumulation of surface water vapor and atmospheric aerosols further increased the possibility of heavy pollution. It was found that, for the entire study period, PM2.5 generated by residential sources contributed the vast majority (60~100 μg/m3) of PM2.5 concentrations, and that the main industry sources that caused changes in time distributions were industrial and transportation sources. [ABSTRACT FROM AUTHOR]

Research on endophytic fungi in desert plants, particularly the epiphytic or endophytic fungi of leaves, remains limited. In the extremely arid regions of northwest China, the ultra-xerophytic desert plant Haloxylon ammodendron harbors white fungi on its assimilating branches during autumn. The hyphae of these fungi intertwine, both internally and externally, comprising superficial, bridging, and endophytic types. The superficial hyphae attach to the surface of the assimilating branches and continuously grow and intersect, forming a thick layer of felt-like hyphae. This thick, felt-like layer of hyphae facilitates the adsorption of atmospheric water vapor on the surface of the hyphae or the assimilating branches, allowing H. ammodendron to capture atmospheric moisture, even under low humidity. Some superficial hyphae penetrate the cuticle into the epidermis, becoming bridging hyphae, which can rapidly transport water from the outside of the epidermis to the inside. The endophytic hyphae shuttle within the epidermis, achieving rapid water transfer within the epidermis of the assimilating branches. The presence of these three types of hyphae not only enables the assimilating branches of H. ammodendron to achieve rapid water absorption and transmission, but also facilitates the uptake of atmospheric water vapor under low humidity conditions. We discuss the mechanism by which the hyphae promote water absorption from the perspectives of hyphal composition, the formation of felt-like structures, and environmental conditions. We consider the presence of fungal hyphae on the surface of the H. ammodendron assimilating branches as an inevitable ecological process in arid environments. This study provides important theoretical insights into the mechanisms underlying the strong drought resistance of desert plants in extremely arid regions and offers strategies for desertification control. [ABSTRACT FROM AUTHOR]

Microwave sensors possess the capacity to effectively penetrate through clouds and fog and are widely used in obtaining soil moisture, atmospheric water vapor, and surface temperature measurements. Long time-series datasets play a pivotal role in climate change studies. Unfortunately, the lifespan of operational satellites often falls short of the needs of these extensive datasets. Hence, comparing and cross-calibrating sensors with similar configurations is paramount. The Microwave Radiation Imager (MWRI) onboard Fengyun-3D (FY-3D) is the latest generation of satellite-based microwave remote sensing instruments in China, and its data quality and application prospects have attracted widespread attention. To comprehensively assess the data quality of MWRI, a comparison of the orbital brightness temperature (TB) data between FY-3D/MWRI and Global Change Observation Mission 1st-Water (GCOM-W1)/Advanced Microwave Scanning Radiometer 2 (AMSR2) is conducted, and then a calibration model is established. The results indicate a strong correlation between the two sensors, with a correlation coefficient exceeding 0.9 across all channels. The mean bias ranges from −1.5 K to 0.15 K. Notably, the bias of vertical polarization is more pronounced than that of horizontal polarization. The TB distribution patterns and temporal evolutions are highly consistent for both sensors, particularly under snow and ice. The small intercepts and close-to-1 slopes obtained during calibration further demonstrate the minor data differences between the two sensors. However, the calibration process effectively reduces the existing errors, and the calibrated FY-3D/MWRI TB data are closer to GCOM-W1/AMSR2, with a mean bias approximately equal to 0 K and a correlation coefficient exceeding 0.99. The excellent consistency of the TB data between the two sensors provides a vital data basis for retrieving surface parameters and establishing long time-series datasets. [ABSTRACT FROM AUTHOR]

Zhejiang province in China experienced an extreme climate phenomenon in August 2014 with temperature rises, sunshine duration decreases, and precipitation increases, particularly, the successive heavy rainfall events occurring from 16 to 20 August 2014 that contributed to this climate anomaly. This study investigates the spatial-temporal variation characteristics of precipitable water vapor (PWV) and the normalized difference vegetation index (NDVI) associated with this phenomenon. Multiple sources of PWV values derived from the Global Positioning System (GPS), Radiosonde (RS) and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data are used with different spatiotemporal resolutions. The monthly averaged PWV in August 2014 exceeded the 95% percentiles of climatological value (53 mm) while the monthly averaged temperature was less than the 5% percentiles of climatological value (26.6 °C). Before the extreme precipitation, the PWV increased from the yearly averaged value of about 35 mm to more than 60 mm and gradually returned to the August climatological average of 50 mm after the precipitation ended. A large-scale atmospheric water vapor was partially conveyed by the warm wet air current of anticyclones which originated over the South China Sea (25° N, 130° E) and the Western Pacific Ocean. The monthly NDVI variation over the past 34 years (1982–2015) was investigated in this paper and the significant impact of extreme climate on vegetation growth in August 2014 was found. The extreme negative temperature anomaly and positive PWV anomaly are the major climate-driven factors affecting vegetation growth in the north and south of Zhejiang province with correlation coefficients of 0.83 and 0.72, respectively, while the extreme precipitation does not show any apparent impact on NDVI. [ABSTRACT FROM AUTHOR]

Atmospheric water vapour transport is one of the most active components of the hydrological cycle, which shows great effects on the regional climate variability, while it is largely affected by the large‐scale circulation systems. El Niño–Southern Oscillation (ENSO) shows a remarkable influence on the atmospheric water circulation via disrupting the normal/regular water vapour transport processes, producing the anomaly water vapour fluxes input from ocean to land (Qnet), and leading to regional precipitation anomalies. However, their quantitative impact has not been fully studied. This study quantified the effect of ENSO on the water vapour transport from ocean to land and identified its impact on tropical precipitation anomalies. The results showed that the global detrended Qnet displayed a significantly negative correlation with the strength of ENSO. The notable impact of ENSO on Qnet was largely affected by the different patterns of the atmospheric moisture circulation during El Niño and La Niña and was responsible for the change of the spatial distribution of precipitation over the Tropics. El Niño reduced precipitation over the Malay Archipelago and increased precipitation over south China, especially in boreal winter, while La Niña enhanced precipitation over the Malay Archipelago. This study identified the impacts of ENSO on the circulation and water vapour transport process and improved our understanding of the influencing mechanism of ENSO on regional precipitation anomalies, which has important significance on regional water resource management and disaster risk assessment, especially as the extreme climate events have become increasingly frequent. [ABSTRACT FROM AUTHOR]

The influence of the underlying surface on the atmospheric environment over rivers and lakes is not fully understood. To improve our understanding, this study targeted the Twain-Hu Basin (THB) in central China, with a unique underlying surface comprising a dense "water network" over rivers and lakes. In this study, the Weather Research and Forecasting Model with Chemistry (WRF-Chem) was used to simulate the impact of this dense "water network" on a wintertime heavy PM2.5 pollution event in the THB. On this basis, the regulating effects of density and area of the lake groups, with centralized big lakes (CBLs) and discrete small lakes (DSLs), on PM2.5 concentrations over the underlying surface of the dense "water network" in the THB were clarified, and the relative contributions of thermal factors and water vapor factors in the atmospheric boundary layer to the variation of PM2.5 concentrations were evaluated. The results show that the underlying surface of dense "water networks" in the THB generally decreases the PM2.5 concentrations, but the influences of different lake-group types are not uniform in spatial distribution. The CBLs can reduce the PM2.5 concentrations over the lake and its surroundings by 4.90–17.68% during the day and night. The ability of DSLs in reducing PM2.5 pollution is relatively weak, with the reversed contribution between −5.63% and 1.56%. Thermal factors and water vapor–related factors are the key meteorological drivers affecting the variation of PM2.5 concentrations over the underlying surface of dense "water networks". The warming and humidification effects of such underlying surfaces contribute positively and negatively to the "purification" of air pollution, respectively. The relative contributions of thermal factors and water vapor–related factors are 52.48% and 43.91% for CBLs and 65.96% and 27.31% for DSLs, respectively. The "purification" effect of the underlying surface with a dense "water network" in the THB on regional air pollution highlights the importance of environmental protection of inland rivers and lakes in regional environmental governance. In further studies on the atmospheric environment, long-term studies are necessary, including fine measurements in terms of meteorology and the environment and more comprehensive simulations under different scenarios. [ABSTRACT FROM AUTHOR]

The ability of plants to absorb unsaturated atmospheric water vapor is a controversial topic. To study how vegetation in arid areas survives under limited water resources, this study uses Tamarisk in the Ulan Buh Desert of China as an example. The in-situ observation of a newly designed Lysimeter and sap flow meter system were used to monitor the precipitation infiltration and the utilization efficiency of Tamarisk of atmospheric vapor. The results show that the annual precipitation of 84 mm in arid areas could still result in deep soil recharge (DSR) with a recharge rate of 5 mm/year. Furthermore, DSR is detectable even in the winter, and the 5-year average DSR was 5.77% of the annual precipitation. It appears that the small precipitation events are critically important for the survival of Tamarisk. When the atmospheric relative humidity reaches 70%, Tamarisk leaves can absorb the unsaturated atmospheric vapor, which accounts for 13.2% of the annual precipitation amount. To adapt to the arid environment, Tamarisk can harvest its water supply from several sources including atmospheric vapor and micro-precipitation events (whose precipitation is below the measurement limit of 0.2 mm of the precipitation gauge) and can still permit a certain amount of recharge to replenish the deep soil moisture. Such an ecohydrological dynamic is of great significance to desert vegetation. [ABSTRACT FROM AUTHOR]

Southwest China is with complex topography including basins, mountains, hills and plains, where abrupt heavy rainfall events (AHREs) occur frequently and are difficult to quantitatively estimate due to limited ground‐based observations. Using the data of ground rain gauges and GPM dual‐frequency precipitation radar during April to September in 2014–2020, this study investigates vertical structures of AHREs over southwest China. Both mass‐weighted mean diameter (Dm) and reflectivity (Ze) of AHREs increase rapidly in ice‐phase process but slowly in liquid‐phase process. For convective rainfall of AHREs, abundant water vapour and strong atmospheric convective motion cause higher Dm and Ze but lower generalized intercept parameter (dBNw) than those for stratiform rainfall. Moreover, ice‐phase process is active while liquid‐phase process is weak in the mountains, but the situation is opposite in the plains, which results in large‐size and low‐concentration raindrops in the mountains while small‐size and high‐concentration raindrops in the plains. Furthermore, statistical models of vertical profile of reflectivity (VPR) indicate that VPR patterns are affected by surface rain intensity and present different trends around the 0°C level between stratiform and convective rainfalls. In addition, higher rain top height in the mountains is conducive to ice‐phase process while lower terrain height in the plains is favourable for liquid‐phase process. Therefore, the VPR pattern depends on rain type, terrain and rainfall intensity, and its fine model is beneficial for understanding microphysical process of AHREs and improving quantitative precipitation estimation by ground‐based radars. [ABSTRACT FROM AUTHOR]

For better modeling the vertical distribution and variations of water vapor, a 10-year time-series of a newly derived water vapor parameter (termed IRPWV), defined as the ratio of water vapor density (WVD) to the total precipitable water vapor (TPWV), was statistically analyzed. This research showed that the vertical distribution of IRPWV presents a periodic pattern and is highly correlated with the relative magnitude of its corresponding TPWV when compared with the other TPWVs in the same time range. Six TPWV ranges were first chosen to determine the relative magnitude and then used to classify the IRPWV vertical distributions of TPWV. For the periodic variations in each of the six classified IRPWV vertical distribution time-series, a temporal IRPWV model was developed accordingly with six sets of coefficients. The new models were validated by comparing their predictions against the reference values from sounding data at 12 radiosonde stations in China, and their performance was also evaluated against that of the commonly used exponential model. Results showed that, first, the proportions of the height range that had reduced annual root mean square error (RMSE) of WVD in all height ranges within all TPWV ranges were over 75% at the 12 stations. Then, the annual RMSEs of the WVD for all the stations were reduced by at least 11%, 20%, 43%, 48%, 40%, 38%, 32%, 35%, 32%, and 28% in each of the 10 selected height ranges, respectively. [ABSTRACT FROM AUTHOR]

Atmospheric precipitable water vapor (PWV) is a key variable for weather forecast and climate research. Various techniques (e.g., radiosondes, global navigation satellite system, satellite remote sensing and reanalysis products by data assimilation) can be used to measure (or retrieve) PWV. However, gathering PWV data with high spatial and temporal resolutions remains a challenge. In this study, we propose a new empirical PWV grid model (called ASV-PWV) using the zenith wet delay from the Askne model and improved by the spherical harmonic function and vertical correction. Our method is convenient and enables the user to gain PWV data with only four input parameters (e.g., the longitude and latitude, time, and atmospheric pressure of the desired position). Profiles of 20 radiosonde stations in Qinghai Tibet Plateau, China, along with the latest publicly available C-PWVC2 model are used to validate the local performance. The PWV data from ASV-PWV and C-PWVC2 is generally consistent with radiosonde (the average annual bias is −0.44 mm for ASV-PWV and −1.36 mm for C-PWVC2, the root mean square error (RMSE) is 3.44 mm for ASV-PWV and 2.51 mm for C-PWVC2, respectively). Our ASV-PWV performs better than C-PWVC2 in terms of seasonal characteristics. In general, a sound consistency exists between PWV values of ASV-PWV and the fifth generation of European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis (ERA5) (total 7381 grid points in 2020). The average annual bias and RMSE are −0.73 mm and 4.28 mm, respectively. ASV-PWV has a similar performance as ERA5 reanalysis products, indicating that ASV-PWV is a potentially alternative option for rapidly gaining PWV. [ABSTRACT FROM AUTHOR]

The Ejin Oasis is located in the lower reaches of the Heihe River Basin of northwestern China. It is one of the most arid regions in the world, and Populus euphratica Oliv. is the foundation species of the desert riparian forests there. The photosynthesis of P. euphratica is one of the first physiological processes that is most likely to be affected by the extremely arid climate conditions. The factors impacting photosynthesis can be divided into stomatal and non-stomatal limitations. In order to investigate whether the photosynthesis of P. euphratica was limited and, if so, whether this limitation was caused by drought stress in the P. euphratica Forest Reserve on the Ejin River, we analyzed stomatal, non-stomatal, and relative stomatal limitations (reflecting the relative importance of the stoma in controlling the processes of photosynthesis) of photosynthesis. The results show that, at the beginning of the midday depression of photosynthesis, the values of stomatal limitation of photosynthesis (Ls) peaked, with its predominance being supported by sub-stomatal CO2 concentrations (Ci) being at a minimum. Thereafter, Ls decreased and non-stomatal limitation (Ci/stomatal conductance (gs)) increased sharply, indicating that the non-stomatal limitation of photosynthesis was predominant. Both Ls and relative stomatal limitation of photosynthesis increased in the morning, and then decreased, whereas Ci/gs showed the opposite trend. We concluded that P. euphratica did not experience drought stress by analyzing leaf water potential, groundwater table, and the decoupling coefficient (a parameter characterizing the coupling degree between vegetation canopy and atmospheric water vapor flux); however, the Ls values of P. euphratica were much greater than those of other species. This was likely because P. euphratica has a relatively conservative water use strategy even when growing under favorable water conditions. Extremely high temperatures caused the closure of the stoma to reduce transpiration, resulting in more intense stomatal limitations of photosynthesis. [ABSTRACT FROM AUTHOR]

The Loess Plateau is one of the three most severely affected geological disaster areas in China. Water sensitivity is the most significant feature of the loess. Under the action of continuous heavy rainfall, rainwater infiltrates the loess, resulting in a rapid increase in soil saturation and changes in soil moisture. This affects the shear strength of the soil and induces shallow loess landslides. Therefore, it is significant to our country's disaster prevention and mitigation efforts to effectively monitor the deformation and inducement of such landslides. At present, the global navigation satellite system (GNSS) is widely used in the field of landslide disaster monitoring as a technical means to directly obtain real-time three-dimensional vector deformation of the surface. At the same time, GNSS can also provide a steady stream of L-band microwave signals to obtain surface environmental information, such as soil moisture around the station. In past landslide disaster monitoring research, GNSS was only used to provide three-dimensional deformation information, and its ability to provide environmental information around the station was almost completely ignored. This study proposes a ground-based GNSS remote sensing comprehensive monitoring system integrating "three-dimensional deformation and soil moisture content" combined with a rainfall-type shallow loess landslide event in Linxia City. The ability of ground-based GNSS to comprehensively monitor shallow loess landslide disasters was analysed. Experiments show that GNSS can provide high-precision deformation time series characteristics and monitor the changes in soil moisture content around the station at the same time; the two have a certain response relationship, which can comprehensively evaluate the stability of shallow loess landslides. As heavy rainfall is a key factor affecting the change in soil water content, this study adds the atmospheric water vapour content calculated by ground-based GNSS refraction remote sensing in the discussion chapter and analyses the relationship between precipitable water vapour and rainfall in this area to give full play to ground-based GNSS remote sensing. In the role of landslide disaster monitoring, we hope to build a more comprehensive ground-based GNSS remote sensing monitoring system to better serve the monitoring of landslide disasters. [ABSTRACT FROM AUTHOR]

Correlation analysis between precipitable water vapor (PWV) and precipitation over China was conducted by combining high-quality PWV data based on 1999–2015 ground-based global positioning system (GPS) observations with the measurements at matched meteorological stations in the same period. The mean correlation coefficient at all the stations is approximately 0.73, indicating that there is a significant positive correlation between PWV content and precipitation measurements, and the comparison of correlation among different climate types suggests that the distribution characteristics of the correlation coefficients are distinctively related to different climate types. There is also some positive correlation between PWV and precipitation long-term trends, with the correlation coefficients of monthly anomalies ranging generally from 0.2 to 0.6. Furthermore, the intensity of both PWV and precipitation extremes shows a long-term upward trend overall, with the most-intense events showing more significant increases. The extreme precipitation–temperature scaling rate of changes can reach above Clausius–Clapeyron (CC) scaling, whereas that of the extreme PWV-temperature is sub-CC overall, with regional differences in the specific scaling values. The correlation analysis in this work is of great significance for long-term climate analysis and extreme weather understanding, which provides a valuable reference for better utilizing the advantages of PWV data to carry out the studies above. Significance Statement: Atmospheric water vapor is crucial to the climate system, especially in the context of global warming, and accurate knowledge of the correlation between precipitable water vapor (PWV) and precipitation is of great significance for long-term climate analysis and extreme precipitation weather forecasting. We take full advantage of the long-term homogeneity of ground-based GPS to conduct long-term correlation analysis between GPS-derived PWV and precipitation over China. Results show a significant positive correlation between them, and the degree of correlation is related to different climate types. The correlation of monthly anomalies is also positive, and, over the long-term, both water vapor and precipitation extremes have been increasing in intensity, with more significant increases occurring in the most-intense events. Extreme precipitation might increase beyond thermodynamic expectations, whereas PWV increases below expectations. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) is a key factor for evaluating soil fertility. Rapidly monitoring organic matter content in desert soil can provide a scientific basis for the rational development and utilization of reserve arable land resources. Although spectral inversion accuracy for SOM under laboratory-controlled conditions is high, it is time-consuming and costly compared to the in situ spectroscopic determination method. However, in situ spectroscopy causes losses in accuracy due to interference from external environmental factors (e.g., the surface roughness of soil, changes in weather conditions, atmospheric water vapor, etc.). Therefore, reducing or removing the interference of external environmental factors to improve the accuracy of in situ spectroscopy for estimating SOM is challenging. In this study, visible and near-infrared (Vis-NIR) in situ spectral data were collected from 135 topsoil (0–20 cm) samples in a desert area of northwestern China, and organic matter content was measured. Three spectral pre-processing methods—the standard normal transform (SNV), reciprocal logarithm (log(1/R)) and normalization (NOR)—combined with three feature variable selection methods—the particle swarm algorithm (PSO), ant colony algorithm (ACO) and simulated annealing (SA) algorithm—were used to filter the spectral feature bands of SOM, and then partial least squares regression (PLSR), a back propagation neural network (BPNN) and a convolutional neural network (CNN) were used to construct the estimation models of SOM. The results indicated that the SNV could enhance the spectral information related to SOM and improve the accuracy of model estimation, and it was one of the most effective spectral pretreatment methods. Compared with the model constructed with the full-band spectroscopy method, the feature variable selection method could effectively improve the estimation accuracy of the Vis-NIR in situ spectroscopy model. The most obvious improvement was found with PSO, where R2 and RPD were improved by more than 0.34 and 0.16, respectively, and RMSE was reduced by more than 0.29 g kg−1. The accuracy of the CNN model was higher than that of the BPNN and PLSR models, both for the inversion model of SOM built from full-band spectral data and the bands selected by the characteristic variable selection method. SNV-PSO-CNN is the optimal hybrid model for in situ spectral measurement of SOM (R2 = 0.71, RPD = 1.88, RMSE = 1.67 g kg−1) and can realize the quantitative in situ spectral inversion of SOM in desert soils. [ABSTRACT FROM AUTHOR]

The Liupan Mountain (LPM) area is located on the northeast margin of the Qinghai–Tibet Plateau, which is the western part of the second ladder of China's terrain. It is also an intersection area of two air currents, which are caused by the combined action of the Qinghai–Tibet Plateau, the middle and lower levels of the westerly belt and the edge of the monsoon area. LPM is one of the main air water vapour transport pathways in Northwest China as well as a main water conservation area for nearly ten million people. Research on atmospheric precipitable water vapour (PWV) variation characteristics in LPM is beneficial for understanding the mechanisms of orographic precipitation and improving the effects of weather modification. Based on the data from 10 Global Navigation Satellite System Meteorology (GNSS/MET) stations for 6 years and the data of automatic weather stations in the LPM, the temporal and spatial variation characteristics of PWV in the LPM were analysed, and the differences in PWV in the 24 h before and after precipitation were compared in this study. The results showed that the hourly, monthly and seasonal variations in PWV displayed obvious patterns. PWV increased rapidly 10 h ahead of precipitation, while it decreased rapidly within 10 h after precipitation, which was slower than that before precipitation. In terms of spatial distribution, PWV was larger in the south than in the north and larger in the east than in the west. Although the precipitation on the LPM peak was the highest in the whole LPM area, its PWV was always the lowest, indicating that the PWV was obviously affected by the air temperature. This showed that under the same water vapour condition, precipitation was more likely to form in the area with low temperature, and the precipitation was larger, which also provided a train of thought for improving the method of artificial precipitation enhancement by using the condensation catalyst. [ABSTRACT FROM AUTHOR]

The Yellow River Basin is essential to China's economic and social development and ecological security. In order to assess the temporal and spatial distribution of cloud water in the Yellow River Basin, we analyzed the distribution characteristics of water vapor content and cloud water content using ERA5 monthly averaged data from 1980 to 2019. The results showed that the high-value area of the annual average atmospheric water vapor content distribution was concentrated above the North China Plain in the eastern part of the basin, and the value was mostly between 21 and 24 mm. The low-value areas were mainly centered above the high mountain areas in the western part of the basin, and the value mostly fell between 3 and 6 mm. The seasonal distribution characteristics of the annual average water vapor content were relatively consistent with the annual average distribution characteristics. The high-value cloud ice water content area was in the northeastern part of the Qinghai-Tibet Plateau (4.5 g·m−2), while the low-value area appeared on the Loess Plateau (2 g·m−2). The high-value area of cloud liquid water content was on the north side of the West Qinling Mountains (12 g·m−2). The low-value area appeared on the Loess Plateau and the northern part of the Qinghai-Tibet Plateau (3 g·m−2). The cloud water content was higher in the eastern region than in the western region in the overall spatial distribution, and the content of cloud liquid water was higher than that of cloud ice water. The average annual atmospheric water vapor content was increasing, and the annual average content of cloud ice water and cloud liquid water was declining. The change in the total amount and spatial distribution of cloud water was not obvious in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Fengyun-3E (FY-3E) is the world's first operational meteorological satellite on dawn-dusk orbit. In order to realize the generality of algorithms for different satellites and provide a basis for subsequent inversion studies on remote sensing parameters, such as land surface temperature, fire, and atmospheric water vapour, we perform a cross-comparison of the brightness temperature (TB) of the six infrared (IR) channels between the Medium Resolution Spectral Imager Level L onboard the FY-3E satellite (FY-3E/MERSI-LL) and the Advanced Himawari Imager onboard the Himawari-8 satellite (Himawari-8/AHI). The statistical indicators, correlation coefficient, root mean square error (RMSE) and mean bias, are used to analyse the consistency and differences of the channel parameters between the MERSI-LL and AHI in terms of overall data, different regions and different TB. The results show that the TB data observed by the FY-3E/MERSI-LL and Himawari-8/AHI are generally consistent, with correlation coefficients of more than 0.99, mean biases of less than 1.60 K and RMSEs of less than 1.90 K. In terms of different channels, the TB in channel IR 038 has the best consistency between the two sensors, while the results from the MERSI-LL are higher than those of the AHI for channels IR038, IR086, IR108 and IR120, and lower than those of the AHI for channel IR041. Analysing the situation in different regions, we find that the MERSI-LL and AHI data are highly consistent and relatively stable in the Huang-Huai Region, with low RMSEs, low mean biases and high correlation coefficients (more than 0.95) at all channels. For the different TB ranges, the RMSEs and mean biases for the TB ranges of 270–280 K and 280–290 K between the MERSI-LL and AHI are small, and the data in the 290–300 K TB range has great differences and fluctuations between the two sensors. [ABSTRACT FROM AUTHOR]

Atmospheric water vapor plays a key role in the water cycle, especially on the Tibetan Plateau, where precipitation is an invaluable contributor to water resources. To better understand which water vapor source areas influence precipitation on the northeastern Tibetan Plateau (NETP), we applied the flexible particle dispersion method (FLEXPART) to simulate water vapor trajectories and water vapor source contribution related to precipitation events during the snow season from 1979 to 2017 on the NETP. The results show that continental water vapor source areas contributed 92.33% of the water vapor to precipitation events on the NETP, which was obviously greater than the water vapor contribution from oceanic areas. One key continental water vapor source area, the Tibetan Plateau without the study area, contributed 66.13% of the water vapor to the precipitation, and central Asia supplied 8.69%, ranking second. Comparing the distributions of the water vapor contributions to extensive and regional precipitation events revealed that the only difference between extensive and regional precipitation events is in the magnitudes of the water vapor contributions, and the spatial distributions of the water vapor contributions are extremely similar. Central and southern China obviously supplied more water vapor to extensive precipitation events than to regional precipitation events. These results help us better understand the recent drastic precipitation changes on the NETP. Significance Statement: We sought to understand how water vapor influences precipitation over the northeastern Tibetan Plateau and which water vapor source areas play a key role in the water vapor supply. Therefore, we applied a numerical model to investigate the relationship between water vapor and precipitation from 1979 to 2017 during the snow season. Continental water vapor source areas contributed considerably more water vapor than oceanic water vapor source areas. The most important continental water vapor contributor was the Tibetan Plateau without the northeastern Tibetan Plateau area, and the second highest contributor was central Asia. Future work should focus on how water vapor impacts the precipitation changes in this wetter and warming area. [ABSTRACT FROM AUTHOR]

As the latest members of Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HuanjingJianzai-2 (HJ-2) series satellites were launched on 27 September 2020 by China and are usually abbreviated as HJ-2 A/B satellites. The polarized scanning atmospheric corrector (PSAC) is one of main sensors onboard HJ-2 A/B satellites, which is mainly used to monitor atmospheric components such as water vapor and aerosols. In this study, a columnar water vapor (CWV) retrieval algorithm using two bands (865 and 910 nm) is developed for PSAC. The validation results of PSAC CWV data based on ground-based CWV data derived from Aerosol Robotic Network (AERONET) show that PSAC CWV data has a high accuracy, and all statistical parameters of PSAC CWV data are better than those of Moderate-resolution Imaging Spectroradiometer (MODIS) CWV data released by NASA. Overall, there is no obvious overestimation or underestimation in PSAC CWV data. The root mean square error (RMSE), mean absolute error (MAE), relative error (RE), and percentage of CWV data with error within ± (0.05 + 0.10 ∗ C W V A E R O N E T ) (PER10) of PSAC CWV data are 0.17 cm, 0.13 cm, 0.08, and 78.19%, respectively. The RMSE, MAE, RE, and PER10 of MODIS CWV data are 0.59 cm, 0.48 cm, 0.28, and 16.55%, respectively. Compared with MODIS CWV data, PSAC CWV data shows a 71% decrease in RMSE, a 73% decrease in MAE, a 71% decrease in RE, and a 372% increase in PER10. In addition, the results of day-to-day comparisons between PSAC CWV data and AERONET data show that PSAC CWV data can effectively characterize the change trend of CWV. [ABSTRACT FROM AUTHOR]

The effect of soil moisture (SM) on precipitation is an important issue in the land–atmosphere interaction and shows largely regional differences. In this study, the SM of the ERA-Interim reanalysis and precipitation data of the weather stations were used to investigate their relationship over eastern China during July and August. Moreover, the WRF model was applied to further validate the effect of SM on rainfall. In the observations, a significantly negative relationship was found that, when the soil over southern China is wet (dry) in July, the rainfall decreases (increases) over the Huang–Huai–River basin (hereafter HHR) in August. In the model results, the soil can "memorize" its wet anomaly over southern China from July to August. In August, the wet soil increases the latent heat flux at surface and the air moisture at lower levels of the atmosphere, which is generally unstable due to the summer monsoon. Thus, upward motion is prevailing over southern China in August, and the increased surface air moisture is transported upwards. After that, the condensation of water vapor is enhanced at the middle and upper levels, increasing the release of latent heat in the atmosphere. The heat release forms a cyclonic circulation at the lower levels over eastern China, and induces the transport and convergence of water vapor increased over southern China in August. This further strengthens the upward motion over southern China and the cyclonic circulation at the lower levels. Therefore, positive feedback appears between water vapor transport and atmospheric circulation. Meanwhile, the cyclonic circulation over southern China results in a response of water vapor divergence and a downward motion over HHR. Consequently, the negative anomalies of precipitation occur over HHR in August. When the July soil is dry over southern China, the opposite results can be found through the similar mechanism. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Land surface temperature (LST) is an essential parameter widely used in environmental studies. The Medium Resolution Spectral Imager II (MERSI-II) boarded on the second generation Chinese polar-orbiting meteorological satellite, Fengyun-3D (FY-3D), provides a new opportunity for LST retrieval at a spatial resolution of 250 m that is higher than that of the already widely used Moderate Resolution Imaging Spectrometer (MODIS) LST data of 1000 m. However, there is no operational LST product from FY-3D MERSI-II data available for free access. Therefore, in this study, we developed an improved two-factor split-window algorithm (TFSWA) of LST retrieval from this data source as it has two thermal-infrared (TIR) bands. The essential coefficients of the TFSWA algorithm have been carefully and precisely estimated for the FY-3D MERSI-II TIR thermal bands. A new approach for estimating land surface emissivity has been developed using the ASTER Global Emissivity Database (ASTER GED) and the International Geosphere-Biosphere Program (IGBP) data. A model to estimate the atmospheric water vapor content (AWVC) from the three atmospheric water vapor absorption bands (bands 16, 17, and 18) has been developed as AWVC has been recognized as the most important factor determining the variation of AT. Using MODTRAN 5.2, the equations for the AT estimate from the retrieved AWVC were established. In addition, the AT of the pixels at the far edge of FY-3D MERSI-II data may be strongly affected by the increase of the optical path. Viewing zenith angle (VZA) correction equations were proposed in the study to correct this effect on AT estimation. Field data from four stations were applied to validate the improved TFSWA in the study. Cross-validation with MODIS LST (MYD11) was also conducted to evaluate the improved TFSWA. The cross-validation result indicates that the FY-3D MERSI-II LST from the improved TFSWA are comparable with MODIS LST while the correlation coefficients between FY-3D MERSI-II LST and MODIS LST over the Mid-East China region are in the range of 0.84~0.98 for different seasons and land cover types. Validation with 318 field LST samples indicates that the average MAE and R2 of the scenes at the four stations are about 1.97 K and 0.98, respectively. Thus, it could be concluded that the improved TFSWA developed in the study can be a good algorithm for LST retrieval from FY-3D MERSI-II data with acceptable accuracy. [ABSTRACT FROM AUTHOR]

This study conducted land cover classification and inversion analysis to estimate land surface temperature, soil moisture, specific humidity, atmospheric water vapor density, and relative humidity using remote sensing and multi-source mining data. Using 1990–2020 data from the Shendong mining area in Inner Mongolia, China, the eco-environmental evolution and the ecological cumulative effects (ECE) of mining operations were characterized and analyzed at a long-term scale. The results show that while the eco-environment was generally stable, mining activities affected the eco-environment at the initial stage (1990–2000) to a certain degree. During the rapid development stage of coal mining, the eco-environment was severely damaged, and the ECE were significant at the temporal scale. The absolute value of the change rate of ecological parameters was increasing. Due to an increased focus on ecological restoration, starting in 2010, the environmental indicators gradually stabilized and the eco-environment improved considerably, ushering in a period of stability for coal mining activities. The absolute value of the change rate of ecological parameters became stable. Analysis of the change in eco-environmental indicators with distance and comparison to the contrast area showed the ECE characteristics from mining disturbance at the spatial scale. This study shows that remote sensing technology can be used to characterize the ECE from mining operations and analyze eco-environmental indicators, providing crucial information in support of ecological protection and restoration, particularly in coal mining areas. [ABSTRACT FROM AUTHOR]

Microwave land surface emissivity (MLSE) is an important geophysical parameter to determine the microwave radiative transfer over land and has broad applications in satellite remote sensing of atmospheric parameters (e.g., precipitation, cloud properties), land surface parameters (e.g., soil moisture, vegetation properties), and the parameters of interactions between atmosphere and terrestrial ecosystem (e.g., evapotranspiration rate, gross primary production rate). In this study, MLSE in China under both clear and cloudy sky conditions was retrieved using satellite passive microwave measurements from Aqua Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), combined with visible/infrared observations from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere reanalysis dataset of ERA-20C. Attenuations from atmospheric oxygen and water vapor, as well as the emissions and scatterings from cloud particles are taken into account using a microwave radiation transfer model to do atmosphere corrections. All cloud parameters needed are derived from MODIS visible and infrared instantaneous measurements. Ancillary surface skin temperature as well as atmospheric temperature-humidity profiles are collected from ECMWF reanalysis data. Quality control and sensitivity analyses were conducted for the input variables of surface skin temperature, air temperature, and atmospheric humidity. The ground-based validations show acceptable biases of primary input parameters (skin temperature, 2 m air temperature, near surface relative humidity, rain flag) for retrieving using. The subsequent sensitivity tests suggest that 10 K bias of skin temperature or observed brightness temperature may result in a 4% (~0.04) or 7% (0.07) retrieving error in MLSE at 23.5 GHz. A nonlinear sensitivity in the same magnitude is found for air temperature perturbation, while the sensitivity is less than 1% for 300 g/m2 error in cloud water path. Results show that our algorithm can successfully retrieve MLSE over 90% of the satellite detected land surface area in a typical cloudy day (cloud fraction of 64%), which is considerably higher than that of the 29% area by the clear-sky only algorithms. The spatial distribution of MLSE in China is highly dependent on the land surface types and topography. The retrieved MLSE is assessed by compared with other existing clear-sky AMSR-E emissivity products and the vegetation optical depth (VOD) product. Overall, high consistencies are shown for the MLSE retrieved in this study with other AMSR-E emissivity products across China though noticeable discrepancies are observed in Tibetan Plateau and Qinling-Taihang Mountains due to different sources of input skin temperature. In addition, the retrieved MLSE exhibits strong positive correlations in spatial patterns with microwave vegetation optical depth reported in the literature. [ABSTRACT FROM AUTHOR]