Multi-source Observation Data Research Articles

Time series prediction method for multi-source observation data

Traditional time series analysis focuses on modeling and predicting time series data obtained from a single observation source. However, in a confrontational environment, one party often creates biased data to interfere with the other party's predictions. To mitigate the observation error inherent in single-source data, there is a growing emphasis on the importance of utilizing and studying multi-source observation data, which motivates this paper to propose methods for analyzing and predicting this kind of data. First, this paper proves that the accuracy of the estimation can be improved by introducing multiple sets of time-invariant data with bias. Subsequently, when this approach is extended to include time-varying data, these findings continue to hold. For time-varying data with bias, this paper proposes a comprehensive prediction procedure to provide the uncertainty distribution for the forthcoming moment. Finally, three examples are proposed to illustrate the effectiveness and efficiency of the methods.

Analysis of a Rainstorm Process in Nanjing Based on Multi-Source Observational Data and Lagrangian Method

Using multi-source observation data including automatic stations, radar, satellite, new detection equipment, and the Fifth Generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA-5) data, along with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) platform, an analysis was conducted on a rainstorm process that occurred in Nanjing on 15 June 2020, with the aim of providing reference for future urban flood control planning and heavy rainfall forecasting and early warning. The results showed that this rainstorm process was generated under the background of an eastward-moving northeast cold vortex and a southward retreat of the Western Pacific Subtropical High. Intense precipitation occurred near the region of large top brightness temperature (TBB) gradient values or the center of low TBB values on the northern side of the convective cloud cluster. During the heavy precipitation period, the differential propagation phase shift rate (KDP), differential reflectivity factor (ZDR), and zero-lag correlation coefficient (ρHV) detected by the S-band dual-polarization radar all increased significantly. The vertical structure of the wind field detected by the wind profile radar provided a good indication of changes in precipitation intensity, showing a strong correspondence between the timing of maximum precipitation and the intrusion of upper-level cold air. The abrupt increase in the integrated liquid water content observed by the microwave radiometer can serve as an important indicator of the onset of stronger precipitation. During the Meiyu season in Nanjing, convective precipitation was mainly composed of small to medium raindrops with diameters less than 3 mm, with falling velocities of raindrops mainly clustering between 2 and 6 m·s−1. The rainstorm process featured four water vapor transport channels: the mid-latitude westerly channel, the Indian Ocean channel, the South China Sea channel, and the Pacific Ocean channel. During heavy rainfall, the Pacific Ocean water vapor channel was the main channel at the middle and lower levels, while the South China Sea water vapor channel was the main channel at the upper level, both accounting for a trajectory proportion of 34.2%.

Estimating evapotranspiration and drought dynamics of winter wheat under climate change: A case study in Huang-Huai-Hai region, China

Drought is one of the vital meteorological disasters that influence crop growth. Timely and accurately estimating the drought dynamics of crops is valuable for decision-maker to formulate scientific management measures of agricultural drought risk. In this study, the evapotranspiration and drought dynamics of winter wheat from 1981 to 2020 in the Huang-Huai-Hai (HHH) region of China were evaluated based on long-term multi-source observation data. Four key developmental stages of winter wheat were given attentions: growth before winter stage, overwintering stage, stage of greening-heading, and stage of filling-maturity. The crop water deficit index (CWDI) on a daily scale was established for quantitatively appraising the impacts of drought on winter wheat. Our results indicated that interannual variation in reference crop evapotranspiration (ET0) during the growth season of winter wheat from 1981 to 2020 in the HHH region showed a slight increase trend, with an average of 602.4 mm and obvious spatial differences of decreasing from the Northeast to the Southwest. Over the past forty years, the winter wheat in the HHH region was most severely affected by severe drought, followed by moderate drought, and finally mild drought. In addition, the impacts of drought on winter wheat at different critical growth stages varied greatly. For the growth before winter stage, the winter wheat was mainly threatened by mild, moderate, and severe droughts. For the overwintering stage, the winter wheat was mainly threatened by moderate, severe, and extreme droughts. For the greening-heading stage, the winter wheat was mainly threatened by mild, moderate, severe, and extreme droughts. For the filling-maturity stage, the winter wheat was mainly threatened by mild and moderate droughts. Finally, the impacts of drought on winter wheat during 1981–2020 in the HHH region were revealed to differ extraordinarily in space. In particular, the areas of winter wheat affected by severe drought significantly decreased. However, the areas of winter wheat affected by moderate drought clearly expanded. Our findings provide new insights for further improving climate change impact studies and agricultural drought defense capabilities adapting to continuous environmental change.

Characteristics of Atmospheric Rivers and the Impact of Urban Roof Roughness on Precipitation during the “23.7” Extreme Rainstorm against the Background of Climate Warming

In July 2023, Baoding in Hebei Province experienced unprecedented torrential rainfall, breaking historical records and causing severe flooding. However, our understanding of the multi-scale circulation systems and physical mechanisms driving this extreme precipitation event remains incomplete. This study utilizes multi-source observational data and the Weather Research and Forecasting (WRF) numerical model to conduct a weather diagnosis and numerical simulation of this extreme rainfall event, focusing on the impact of atmospheric rivers (ARS) and urban rooftop roughness on the precipitation process against the background of climate warming. The study found that this extremely heavy rainstorm occurred in the circulation background formed by the factors of subtropical high ectopics, typhoon residual vortex retention, double typhoon water-vapor transmission, and stable high-level divergence. The ARS provided abundant moisture, with its vapor pathway significantly altered following the landfall of Typhoon Doksuri. The interaction between the ARS and the Taihang Mountains was crucial in triggering and intensifying the rainstorm in the foothills. Urbanization significantly affected the distribution of precipitation, with moderate urban roughness enhancing rainfall in and around the city, whereas excessive roughness suppressed it. These results contribute to a deeper understanding of the mechanisms behind extreme precipitation under climate change and provide a scientific basis for improving the forecasting and mitigation of such events.

The different dynamic influences of Typhoon Kalmaegi on two pre-existing anticyclonic ocean eddies

Abstract. Using multi-source observational data and GLORYS12V1 reanalysis data, we conduct a comparative analysis of different responses of two warm eddies, AE1 and AE2 in the northern South China Sea, to Typhoon Kalmaegi during September 2014. The findings of our research are as follows: (1) for horizontal distribution, the area and the sea surface temperature (SST) of AE1 and AE2 decrease by about 31 % (36 %) and 0.4 °C (0.6 °C). The amplitude, Rossby number (Ro = relative vorticity / Coriolis parameter) and eddy kinetic energy (EKE) of AE1 increase by 1.3 cm (5.7 %), 1.4×10-2 (20.6 %) and 107.2 cm2 s−2 (49.2 %) after the typhoon, respectively, while AE2 weakens and the amplitude, Rossby number and EKE decrease by 3.1 cm (14.6 %), 1.6×10-2 (26.2 %) and 38.5 cm2 s−2 (20.2 %), respectively. (2) In the vertical direction, AE1 demonstrates enhanced convergence, leading to an increase in temperature and a decrease in salinity above 150 m. The response below the mixed-layer depth (MLD) is particularly prominent (1.3 °C). In contrast, AE2 experiences cooling and a decrease in salinity above the MLD. Below the MLD, it exhibits a subsurface temperature drop and salinity increase due to the upwelling of cold water induced by the suction effect of the typhoon. (3) The disparity in the responses of the two warm eddies can be attributed to their different positions relative to Typhoon Kalmaegi. Under the influence of negative wind stress curl outside the maximum wind radius (Rmax) of the typhoon, triggering negative Ekman pumping velocity (EPV) and quasi-geostrophic adjustment of the eddy, the warm eddy AE1, with its center to the left of the typhoon's path, further enhances the converging sinking of the upper warm water, resulting in its intensification. On the other hand, the warm eddy AE2, situated closer to the center of the typhoon, weakens due to the cold suction caused by the strong positive wind stress curl within the typhoon's Rmax. The same polarity eddies may have different response to typhoons. The distance between eddies and typhoons, eddy intensity, and the background field need to be considered.

Analysis of lightning activity characteristics and its relationship with precipitation in a strong convective weather process in Jiangsu Province

Based on multi-source observation data such as lightning locator, atmospheric electric field instrument and hourly precipitation data from automatic stations, the lightning characteristics of the strong convective weather process under the influence of the northeast cold vortex in Nanjing Jiangsu Province on 15 June 2020 was analyzed in depth, and the relationship between the lightning activity and the precipitation was obtained. The results show that this strong convective weather process is formed by the cold air from the back of the northeast cold vortex along the front of the high-pressure ridge and the back of the transverse through southward to the middle and lower reaches of the Yangtze River, which makes the cold and warm air currents converge along the south of the Yangtze River to form the heavy precipitation, and is accompanied by the strong lightning activity in the process of this heavy precipitation. The local thunderstorm electric field in Nanjing is mainly characterized by negative increasing type, positive and negative alternating type, and multi-single thunderstorm electric field, and the thunderstorm activity is mainly dominated by negative ground flashes. During this strong thunderstorm, the stronger precipitation periods corresponded to the stronger lightning frequency periods, and the lightning and precipitation fallout areas showed a consistent spatial distribution. The correlation between the lightning activity and precipitation was analyzed by linear fitting, and the correlation between them is 0.946.

Comparative Analysis of Aerosol Vertical Characteristics over the North China Plain Based on Multi-Source Observation Data

In this paper, multi-source observation, such as aircraft, ground-based remote sensing, and satellite-retrieved data, has been utilized to compare and analyze the vertical characteristics of aerosol optical properties and the planetary boundary layer height (HPBL) over the North China Plain (NCP) region during May–June 2016. Aircraft observations show the vertical profiles of aerosol absorption coefficients (σabs), scattering coefficients (σsca), and extinction coefficients (σext) gradually decrease with altitude, with their maximum values near HPBL. The vertical profiles of σext depended most on the vertical distribution of measured σsca, indicating a significant contribution of scattering aerosols. In addition, the prominent characteristic of the inverse relationship between σext and moisture profile could serve as a reference for predicting air quality in the NCP region. The lower layer pollution during the field experiment was likely caused by the accumulation of fine-mode aerosols, characterized by the vertical distribution of the Ångström exponent and the Aerosol Robotic Network (AERONET) products. Typically, HPBL derived from aircraft and surface Micro Pulse Lidar (MPL) was approximate, while the predicted HPBL by meteorological data indicates an underestimation of ~192 m. Aerosol optical depth (AOD) calculated from aircraft and ground-based remote sensing (such as MPL and AERONET) experienced a strong correlation, and both of them exhibited a similar tendency. However, the AOD retrieved from satellites was significantly larger than that from aircraft and ground-based remote sensing. Overall, the inversion algorithm, cloud identification algorithm, representativeness of the space, and time of the observation may lead to an overestimation or underestimation of AOD under certain circumstances. This study may serve as a re-evaluation of AOD retrieved from multi-source observations and provide a reference to uncover the actual atmospheric environment in the NCP regions.

Distinguishing Convective and Stratiform Cloud Precipitation Using Multi-Source Observation Data Over Southern Mountainous Areas in Ningxia

Distinguishing Convective and Stratiform Cloud Precipitation Using Multi-Source Observation Data Over Southern Mountainous Areas in Ningxia

Coupling Simulation and Prediction of Sustainable Utilization of Water Resources in an Arid Inland River Basin under Climate Change

The arid endorheic basin of northwest China is characterized by rich land resources, water shortage, and a fragile ecological environment. The establishment of a credible coupling model of groundwater and surface water based on multi-source observation data is an effective means to study the change in basin water cycles and the sustainable utilization of water resources in the past and future. Based on the latest understanding of hydrogeological conditions, hydrology and water resource utilization data in the middle reaches of the Heihe River Basin (HRB), this paper constructs an up-to-date coupling model of surface water and groundwater to study the water balance change of the basin. The water resources data series under historical replay and CMIP5 climate model prediction are constructed to predict future changes in water resources. The study shows that, under the joint influence of natural conditions and human activities, the average annual recharge of groundwater in the study area from 1990 to 2020 is 17.98 × 108 m3/a, the average annual discharge is 18.62 × 108 m3/a, and the difference between recharge and discharge is −0.64 × 108 m3/a. The total groundwater storage is −19.99 × 108 m3, of which the groundwater storage from 1990 to 2001 was −17.52 × 108 m3 and from 2002 to 2020 was −2.47 × 108 m3. Abundant water from 2002 to 2020 in the basin significantly improved the loss of groundwater storage. Under the prediction of historical reappearance and the CMIP5 CNRM-CM5 model RCP4.5 and RCP8.5 pathways, the groundwater level of the Heihe River–Liyuanhe River inclined plain falls first because the HRB has just experienced a wet season and then rises according to future climate change. The groundwater level of the inclined plain east of the Heihe River and Yanchi basin decreases continuously because of the change in water cycle caused by human activities. The erosion accumulation plain is located in the groundwater discharge zone, and the water level is basically stable. Under the conditions of water resource development and utilization, the runoff of Zhengyixia hydrological station cannot meet the requirements of the “97 Water Dividing Plan” of the State Council in most years in the future, and the ecological and production water in the lower reaches of HRB cannot be effectively guaranteed. With the implementation of water-saving irrigation under the RCP4.5 and RCP8.5 scenarios, the runoff of Zhengyixia can meet the “97 Water Diversion Plan”. It is suggested to further improve the level of agricultural water savings in the middle reaches of the HRB and control the reasonable scale of cultivated land in order to reduce water consumption in the middle reaches of the HRB and implement sustainable utilization of water resources in the HRB.

Physical informed neural network improving the WRF-CHEM results of air pollution using satellite-based remote sensing data

Accurate measurement of air gases concentration are crucial for effective air quality management and minimizing the harm caused by pollution events, as well as providing guidance for healthy travel. We developed develop a physical informed deep learning model that combines a traditional atmospheric chemical transport model and a data-based deep learning model. The model uses multisource observation data to constrain and optimize the results of atmospheric pollution gases concentrations. The accuracy and spatial resolution in central and eastern China (20–45°N, 100–125°E) have been improved compared to the original Weather Research and Forecasting model coupled to Chemistry (WRF-CHEM) results by incorporating constraints from ground-based and satellite observations. Compared with the WRF-CHEM results, our model increases the verification Pearson coefficient (R) of NO2 and CNEMC sites from 0.56 to 0.8, and the RMSE decreases from 25.33 to 15.54 μg cm−3. For O3 results, the model increased the verification R of the CNEMC from 0.57 to 0.76, and the RMSE decreased from 24.55 to 20.22 cm−3. We employed independent MAXDOAS monitoring to authenticate the HCHO results. The R was 0.78, while the validation results for satellite HCHO and MAXDOAS exhibited a R of 0.63 and the validation results for WRF-CHEM and MAXDOAS demonstrated a R of 0.11. Additionally, the RMSE was 6.97E15 mole cm−2, whereas the validation results for satellite HCHO and MAX-DOAS had an RMSE of 7.16E15 mole cm−2. By utilizing satellite remote sensing to effectively capture the spatial distribution of pollutants, and by combining satellite data with other sources of monitoring data and optimizing our models, we have improved the accuracy of our results and have overcome the limitation of cloud coverage. As a result, we are now able to better understand the space-time distribution of pollutants in different regions.

Estimation of daily evapotranspiration in gully area scrub ecosystems on Loess Plateau of China based on multisource observation data

Evapotranspiration is an important process in the hydrologic cycle. In this study, different types of daily evapotranspiration were accurately estimated and comparatively analyzed for 2020–2021 in the gully area of Loess Plateau, China with multisource observation data. The daily actual evapotranspiration was directly observed by the eddy covariance system and then compared with the daily evapotranspiration simulated by HYDRUS-1D, and they showed reasonable agreement and similar variability characteristics it with a correlation coefficient of 0.71. The FAO-56 Penman-Monteith equation was used to calculate the reference evapotranspiration and compared with the pan evaporation in the study area. The reference evapotranspiration has a single-peak pattern and seasonal variation, similar to the actual evapotranspiration process during the annual period. The crop coefficient had seasonal-variation characteristics and remained 0.8 in the mid-growth period. The correlation analysis between evapotranspiration and environmental factors at different time scales suggested that energy was the dominant factor affecting evapotranspiration in the study area. The validation results of the complementary relationship of evaporation indicated a significant asymmetric complementary relationship in the study area. Both generalized nonlinear complementary relationship functions have good applicability, but the performance of the sigmoid function was slightly better than that of the polynomial function. The results provided a reference for local water resource management and agricultural production.

Estimation of hydraulic conductivity in a watershed using sparse multi-source data via Gaussian process regression and Bayesian experimental design

Enhanced water management systems depend on accurate estimation of subsurface hydraulic properties. However, geologic formations can vary significantly, so information from a single source (e.g., widely spaced boreholes) is insufficient in characterizing subsurface aquifer properties. Therefore, multiple sources of information are needed to complement the hydrogeology understanding of a region. This study presents a numerical framework in which information from different measurement sources is combined to characterize the 3D random field in a multi-fidelity prediction model. Coupled with the model, a Bayesian experimental design was used to determine the best future sampling locations. The Upper Sangamon watershed in east-central Illinois was selected as the case study site, where the multi-fidelity Gaussian process model was used to estimate the hydraulic conductivity in the region of interest. Multi-source observation data were obtained from electrical resistivity and borehole pumping tests. The accuracy of the model prediction is dependent on the locations and the distribution of both high- and low-fidelity data. Furthermore, the multi-fidelity model was compared with the single-fidelity model. The uncertainties and confidence in the measurements and parameter estimates were quantified and used to design future cycles of data collection to further improve the confidence intervals.

Hyperspectral and LiDAR Data Classification Based on Structural Optimization Transmission.

With the development of the sensor technology, complementary data of different sources can be easily obtained for various applications. Despite the availability of adequate multisource observation data, for example, hyperspectral image (HSI) and light detection and ranging (LiDAR) data, existing methods may lack effective processing on structural information transmission and physical properties alignment, weakening the complementary ability of multiple sources in the collaborative classification task. The complementary information collaboration manner and the redundancy exclusion operator need to be redesigned for strengthening the semantic relatedness of multisources. As a remedy, we propose a structural optimization transmission framework, namely, structural optimization transmission network (SOT-Net), for collaborative land-cover classification of HSI and LiDAR data. Specifically, the SOT-Net is developed with three key modules: 1) cross-attention module; 2) dual-modes propagation module; and 3) dynamic structure optimization module. Based on above designs, SOT-Net can take full advantage of the reflectance-specific information of HSI and the detailed edge (structure) representations of multisource data. The inferred transmission plan, which integrates a self-alignment regularizer into the classification task, enhances the robustness of the feature extraction and classification process. Experiments show consistent outperformance of SOT-Net over baselines across three benchmark remote sensing datasets, and the results also demonstrate that the proposed framework can yield satisfying classification result even with small-size training samples.

Impact Analysis of Super Typhoon 2114 ‘Chanthu’ on the Air Quality of Coastal Cities in Southeast China Based on Multi-Source Measurements

The northward typhoon configuration along the southeast coast of China (TCN-SEC) is one of the key circulation patterns influencing the coastal cities in southeast China (CCSE). Here, we analyzed the air quality in CCSE during the high-incidence typhoon period from 2019 to 2021. Multi-source measurements were carried out to explore the impact of super typhoon 2114 ‘Chanthu’ on the air quality in CCSE. The results showed that the TCN-SEC and its surrounding weather situation had a favorable impact on the increase in pollutant concentration in CCSE, especially on the increase in O3 concentration. From 13 September to 17 September 2021, affected by the cyclonic shear in the south of super typhoon 2114 ‘Chanthu,’ the strong wind near the ground, stable relative humidity, strong precipitation, and the significantly reduced wind speed had a substantial effect on PM10, PM2.5, SO2, and NO2 concentrations. Calm and light air near the ground, weak precipitation, high daily maximum temperatures, and minimum relative humidity may provide favorable meteorological conditions for the accumulation of O3 precursors and photochemical reactions during the day, resulting in the daily peak values of O3 exceeding 160 μg/m3. The evolution of wind, relative humidity, and boundary layer height could play an important role in the variations in PM10 and PM2.5 concentrations by influencing pollutant accumulation or diffusion. It was suggested that the atmospheric structure of horizontal stability and vertical mixing below 1500 m could play a significant role in the accumulation and vertical distribution of ozone. The results highlight the important role of typhoons in the regional environment and provide a scientific basis for further application of multi-source observation data, as well as air pollution control.

Multisource information fusion-based environment perception and dynamic model of underwater vehicle in irregular ocean environment

The irregular ocean environment brings difficulties to observation and affects the movement state of underwater vehicles. Multisource information fusion can provide more reliable results on information-rich datasets. This paper proposes a multisource information fusion-based environment perception and dynamics model for underwater vehicles in irregular ocean environments. First, the multisource observation data is decomposed by the latent feature disentanglement learning model into the latent stable and dynamical features. The dynamical feature is predicted by the retrospect regression method and then integrated with the latent stable feature to predict the ocean field. After that, the environment-vehicle coupling effect is explored by thermodynamics and statics simulation. Then, a real-time dynamic model of the underwater vehicle is constructed by combining multisource information from physical principles, environmental perception, and sensor observations. Finally, extensive experiments are performed on a novel underwater glider and a publicly available South China Sea dataset to verify the proposed method.

Tropical cyclone size estimation based on deep learning using infrared and microwave satellite data

Tropical cyclone (TC) size is an important parameter for estimating TC risks such as wind damage, rainfall distribution, and storm surge. Satellite observation data are the primary data used to estimate TC size. Traditional methods of TC size estimation rely on a priori knowledge of the meteorological domain and emerging deep learning-based methods do not consider the considerable blurring and background noise in TC cloud systems and the application of multisource observation data. In this paper, we propose TC-Resnet, a deep learning-based model that estimates 34-kt wind radii (R34, commonly used as a measure of TC size) objectively by combining infrared and microwave satellite data. We regarded the resnet-50 model as the basic framework and embedded a convolution layer with a 5 × 5 convolution kernel on the shortcut branch in its residual block for downsampling to avoid the information loss problem of the original model. We also introduced a combined channel-spatial dual attention mechanism to suppress the background noise of TC cloud systems. In an R34 estimation experiment based on a global TC dataset containing 2003–2017 data, TC-Resnet outperformed existing methods of TC size estimation, obtaining a mean absolute error of 11.287 nmi and a Pearson correlation coefficient of 0.907.

Geo-Intelligent Retrieval Framework Based on Machine Learning in the Cloud Environment: A Case Study of Soil Moisture Retrieval

Soil moisture is one of the important parameters in Earth system models. In recent years, the retrieval based on machine learning and data fusion of multi-source satellite observation data has become one of the effective methods to obtain soil moisture information at a large scale. However, most retrieval studies need to download remote sensing original data first, then preprocess, train the retrieval models, and finally generate products in the offline environment. In order to meet the requirements of long temporal series of large-scale area retrieval, and with the widespread use of machine learning in retrieval studies, the amount of remote sensing data and necessary computing resources are gradually increasing. Moreover, studies usually use a single machine learning retrieval model for the entire study area, which lacks the consideration of geographical differences and spatial heterogeneity of soil moisture. Therefore, we established a geo-intelligent soil moisture retrieval framework completely based on the cloud environment. In this study, a variety of machine learning algorithms were used to fuse multi-source observation data mainly including MODIS data and other auxiliary data, and the Continental United States (CONUS) was taken as the experimental area to generate soil moisture data with a resolution of 500m. In addition, this study combines geographical correlation with machine learning models to cope with the spatial heterogeneity of surface soil moisture. Overall, on the basis of site-based validation, the retrieval model trained under the framework performed well, with estimation accuracy of 0.716 and 0.0383 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> in terms of coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and unbiased root mean square error (ubRMSE). The establishment of the cloud retrieval framework provides convenience for the whole retrieval process and also provides a new idea for other retrieval studies of geoscience parameters.

Effect of snow cover on water and heat transfer in alpine meadows in the source region of Yellow River

Extreme snowfall events have been increasing in the Tibetan Plateau, causing greater variations in the snow cover conditions. However, the soil water-heat transfer under different snow conditions has rarely been characterized in detail. Here, by using the multi-source observation data of five years, we analyzed the influences of snow cover on water-heat transfer in alpine meadows of the source region of the Yellow River. The main findings are as follows: In the deep soil, the yearly warming rate from spring to summer was much faster than the cooling rate from autumn to winter, while in the shallow soil, conversely, the former was slower than the latter. Snow cover not only decreased the average soil temperature but also inhibited the occurrence of extremely low temperatures in the soil. The insulation effect of snow was mainly in the mid-frozen period. It was insufficient to balance out the heat lost by the high albedo during early and late frozen periods. In years with more snow, different depths of the soil featured similar thawing dates and plenty of soil voids due to small solid water content and high gravel content, together creating favorable conditions for the snowmelt infiltration, which passed through the frozen layer and infiltrated into the soil of 3.20 m or deeper. In years with less snow, the long-term freezing-thawing cycles aggravated the evaporation and loss of surface soil water in spring. Under different snow cover conditions, the difference in the sensible heat flux was much larger than the latent heat flux in winter and early spring. This study provides a refined physical image of soil water-heat transfer under extreme snow cover conditions in the Tibetan Plateau, which is expected to light the snow cover-frozen soil interaction in the mid-latitude and high-elevation areas.

Characteristics of Environmental Parameters of Compound and Single Type Severe Convection in Hunan

Based on the multi-source observation data and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data in Hunan from 2002 to 2021, this study statistically analyzes the climatic characteristics of compound severe convection. The distribution characteristics and prediction thresholds of environmental parameters of compound severe convective weather, thermal hail, thunderstorm gale and short-term heavy precipitation events in Hunan, are compared and analyzed. The results show that: (1) The primary type of compound severe convection is a thunderstorm gale that accompanies short-term heavy precipitation. The compound severe convection mainly occurs in spring and summer, with the most common occurrence in July. There are seasonal differences in the types of compound severe convection. The primary type in spring is a thunderstorm gale with hail and hail with short-term heavy precipitation. The primary type in summer is a thunderstorm gale with short-term heavy precipitation. The probability of compound severe convection is the highest in southeastern Hunan, and the lowest in southwestern Hunan. (2) The values of the critical physical parameters required for the occurrence of thermal hail, such as the thermal instability stratification, CAPE and 0–6 km vertical wind shear, are the highest, followed by compound severe convection. Compound severe convection also requires high water vapor, which is only inferior to short-term heavy precipitation. (3) The critical physical parameters also differ in spring and summer. For the temperature difference between 850 and 500 hPa, the seasonal difference is not apparent, indicating that all types of severe convective weather need strong convective instability. CAPE is generally higher in summer than in spring, while the surface-specific humidity and 0–6 km vertical wind shear are higher in summer than in spring.

Difference in PM2.5 Pollution and Transport Characteristics Between Urban and Suburban Areas

Based on multi-source observation data, such as lidar ceilometer, aircraft AMDAR, and conventional sites, combined with numerical simulation (CAMx-PSAT), this study took the typical cities of the Beijing-Tianjin-Hebei region-Beijing (BJ) urban area and suburbs (Miyun) and Shijiazhuang (SJZ) urban area and suburbs (Pingshan) as the case study areas. The differences in boundary layer height between urban areas and suburbs (ΔPBLH), surface PM2.5 mass concentration (ΔSurf_PM2.5), vertical PM2.5 mass concentration (ΔVert_PM2.5), and transmission flux intensity and height distribution characteristics were analyzed. The results showed:due to factors such as anthropogenic heat sources, short-wave radiation, and thermal turbulence, the annual average planetary boundary layer height in urban areas was 8%-29% higher than that in the suburbs, and in different seasons, the monthly average planetary boundary layer height in urban areas was 2% (April in SJZ)-47% (July in BJ) higher than that in the suburbs. Due to the combined effects of anthropogenic emissions, inversions, and atmospheric turbulence, the annual averageρ(PM2.5) in urban areas between 0-1260 m was higher than that in suburbs by 0.1 (SJZ)-29.7 (BJ) μg·m-3 and decreased with the increase in height. The annual average total net flux intensity in urban areas was much greater than that in suburbs, with outflows in urban areas and inflows in suburbs; due to the urban low pressure and the suburban high pressure, suburban thermal circulation was formed. The annual average total net flux intensity in BJ (44.77 t·d-1) was greater than that in SJZ (34.44 t·d-1). Affected by wind speed and PM2.5 mass concentration, between 0-1260 m, the fluxes in urban areas and suburbs and surrounding areas showed an obvious trend of increasing net flux intensity with the increase in height above the ground. Furthermore, the transmission exchange between urban areas and suburbs and surrounding areas in January and April had the most obvious impact on the environment. The intensity of the maximum net flux in the lower urban areas and the suburbs in different seasons was significantly different, and the difference between the two was 2.23-4.48 times; however, the height characteristic difference in the intensity of the maximum net flux was small, mainly located at 611-1260 m.