Ground Observation Data Research Articles

A novel finer soil strength mapping framework based on machine learning and remote sensing images

Soil strength is an important factor for assessing the vehicle trafficability in the wilds and making reliable off-road path planning. Rating Cone index (RCI) has been widely used as an indicator of soil strength for mobility assessment. Currently, regional RCI are mainly obtained by using Soil Moisture Strength Prediction (SMSP) Model based on the Unified Soil Classification System (USCS) soil types and soil moisture at critical depth. However, USCS classification is inaccessible directly in most soil databases. And current soil moisture products are of coarse spatial resolution and only reliable in soil surface layer. It is costly to arrange ground survey for USCS soil type and the fine soil moisture in deeper layers cannot be obtained directly. To fill this gap, a novel framework is proposed to generate finer RCI-based soil strength map. To gain USCS soil classification in an un-surveyed study site, RF, GBDT, XGBoost and LCE ensemble learning models were trained with data from gSSURGO Database which contains USCS soil types and soil properties, and then make prediction on data from SoilGrids Database by using same properties. Then, to obtain the finer soil moisture of critical depth, these tree-based models were constructed with ground observation data, Sentinel-2 images, topographical data and soil properties with higher spatial resolution. The SMSP model is finally used to generate the finer daily soil strength map for the study site. The proposed framework highly improved the resolution and reliability of existing soil strength map generating methods, and can provide detailed soil strength information.

A dataset of biomass of the alpine shrub meadow (2003–2020) and the alpine Kobresia humilis (2002–2020) observed at the CO2 flux monitoring station in Haibei, China

The Qinghai-Tibet Plateau (QTP) is a hotspot and sensitive region affected by global climate change. The alpine shrub and alpine Kobresia humilis meadow are the primary types of alpine meadow found in this area, which play an important role in the sustainable development of the QTP and even the global alpine meadow ecosystem. Biomass, as a direct index reflecting the production function of grassland ecosystem, can be used to effectively study the relationship between grassland ecosystem function and other influencing factors. The precise measurement and assessment of the biomass of the alpine shrub and alpine Kobresia humilis meadow are crucial elements for gaining a scientific understanding of the ecological functions of the alpine meadow on the QTP. The National Field Scientific Observation and Research Station of Alpine Grassland Ecosystem located in Haibei Prefecture, Qinghai Province (Haibei Station) has been conducting scientific monitoring of the biomass of alpine Potentilla fruticosa shrub and alpine Kobresia humilis meadow respectively since 2002 and 2003. Haibei Station has continuously accumulated the original data of these two ecosystems for 17 and 18 years respectively. In order to precisely evaluate the carbon sink potential of the alpine shrub and alpine Kobresia humilis meadow ecosystems in the QTP and even the global, and accurately predict the long-term response of the ecosystem to climate change, we published the biomass data of the alpine shrub (2003-2020) and alpine Kobresia humilis meadow (2002-2020) in the Haibei Flux Monitoring Station. This dataset includes aboveground biomass and underground biomass of the alpine shrub and alpine Kobresia humilis meadow. It can provide ground observation data support for scientific cognition, remote sensing inversion and model verification of the spatial and temporal dynamics of ecosystem biomass in alpine meadows, and contribute to the healthy and sustainable development of alpine meadows.

Analysis and Research on the Differences in Observed Data of Sand–Dust Weather between China and Mongolia

The difference in meteorological factors (such as weather phenomena, wind speed, and visibility) of sand–dust weather between China and Mongolia from 2011 to 2021 was analyzed using meteorological observational data and international exchange of meteorological observation data. Additionally, consistency analysis was performed by integrating satellite retrieval products with meteorological observation data. The results showed that the average annual frequency of sand–dust weather in Mongolia was significantly higher than that in China. In China, the sand–dust weather was mainly characterized by floating dust or blowing dust, while in Mongolia, it was primarily characterized by blowing dust or a sand and dust storm. The average annual wind speed and visibility during sand–dust weather in Mongolia were relatively higher than those in China. Based on the dust grade standard of China, when the floating dust occurred in Mongolia, there were cases with wind speed > level 3 and visibility > 10 km; when the blowing dust or sand and dust storm occurred in Mongolia, there were cases with wind speed ≤ level 3 and visibility > 10 km. In China, the sand–dust weather mainly occurred in the spring, while the sand-dust weather occurred frequently throughout the year in Mongolia. The number of days with dust lasting for 2 days or more in Mongolia exceeded that of China, and Mongolia had a significant impact on the sand–dust weather in China. According to the ground observation data and satellite retrieve products during the dust events, all dust events that significantly affected China and Mongolia during the same period from 2021 to 2022 were classified into three categories; among them, the proportion of types of large-scale sand–dust weather phenomena observed by both satellite and ground observation stations was significantly higher (6 times). By integrating ground observation data and satellite retrieval products and following the dust grade standard of China, the consistent correction of sand–dust weather phenomena was carried out. This laid the foundation for the future development of international dust grade standards and provided technological support for improved dust forecasting services in the Asian region.

Atmospheric Oxidation Capacity and Its Impact on the Secondary Inorganic Components of PM2.5 in Recent Years in Beijing: Enlightenment for PM2.5 Pollution Control in the Future

In recent years, the concentrations of PM2.5 in urban ambient air in China have been declining; however, the strong atmospheric oxidation capacity (AOC) represents challenges to the further reduction of PM2.5 concentration and the continuous improvement of ambient air quality in China in the future, since the overall AOC is still at a high level. For this paper, based on ground observation data recorded in Beijing from 2016 to 2019, the variation in AOC was characterized according to the concentration of odd oxygen (OX = O3 + NO2). The concentrations of the primary and secondary components of PM2.5 were analyzed using empirical formulas, the correlation between AOC and the concentrations of secondary PM2.5 and the secondary inorganic components (SO42−, NO3−, NH4+, and SNA) in Beijing were explored, the impact of atmospheric photochemical reaction activity on the generation of atmospheric secondary particles was evaluated, and the impact of atmospheric oxidation variations on PM2.5 concentrations and SNA in Beijing was investigated. The results revealed that OX concentrations reached their peak in 2016 and reached their lowest point in 2019. The OX concentrations followed a descending seasonal trend of summer, spring, autumn, and winter, along with a spatial descending trend from urban observation stations to suburban stations and background stations. The degree of photochemical activity and the magnitude of the AOC have a large influence on the production of atmospheric secondary particles. When the photochemical reaction was more active and the AOC was stronger, the mass concentrations of the secondary generated PM2.5 fraction were higher and accounted for a higher proportion of the total PM2.5 mass concentrations. In the PM2.5 fraction, SNA accounted for 50.7% to 94.4% of the total mass concentrations of water-soluble inorganic ions in the field observations. Higher concentrations of the atmospheric oxidant OX in ambient air corresponded to a higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), suggesting that the increase in AOC could promote the increase of PM2.5 concentration. Based on a relationship analysis of SOR, NOR, and OX, it was inferred that the relationship between OX and SOR and the relationship between OX and NOR were both nonlinear. Therefore, when establishing PM2.5 control strategies in Beijing in the future, the impact of the AOC on PM2.5 generation should be fully considered, and favorable measures should be taken to properly regulate the AOC, which would be more effective when carrying out further control measures regarding PM2.5 pollution.

High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data

This study utilized TROPOMI remote sensing data, MODIS remote sensing data, ground observation data, and other ancillary data to construct a high-resolution spatiotemporal distribution and evaluation of ground-level NO2 concentrations in the Beijing–Tianjin–Hebei (BTH) region using the Geographic Temporal Neural Network Weighted Regression (GTNNWR) model. Through this model, we obtained the daily distribution of ground-level nitrogen dioxide (NO2) concentrations in the Beijing–Tianjin–Hebei region at a resolution of 500 m for the period of 2019–2022. The research results exhibited higher accuracy and more detailed features compared to other models, enabling a more accurate reflection of the spatial distribution and temporal variations of ground-level NO2 concentrations in the region, while retaining more details and trends and excluding the influence of noisy data. Furthermore, we conducted an evaluation analysis considering important events such as public health incidents and the Winter Olympics. The results demonstrated that the GTNNWR model outperformed the Random Forest (RF), Convolutional Neural Network (CNN), and Geographic Neural Network Weighted Regression (GNNWR) models in performance metrics such as R2, RMSE, MAE, and MAPE, showcasing greater reliability when considering spatiotemporal heterogeneity and spatiotemporal non-stationarity. This study provides crucial data support and reference for atmospheric environmental management and pollution prevention and control in the Beijing–Tianjin–Hebei region.

Validation of precipitation reanalysis products for rainfall-runoff modelling in Slovenia

Abstract. Observational data scarcity often limits the potential of rainfall-runoff modelling around the globe. In ungauged catchments, earth observations or reanalysis products could be used to replace missing ground-based station data. However, performance of different datasets needs to be thoroughly tested, especially at finer temporal resolutions such as hourly time steps. This study evaluates the performance of ERA5-Land and COSMO-REA6 precipitation reanalysis products (PRPs) using 16 meso-scale catchments (41–460 km2) located in Slovenia, Europe. These two PRPs are firstly compared with a gridded precipitation dataset that was constructed based on ground observational data. Secondly, a comparison of the temperature data of these reanalysis products with station-based air temperature data is conducted. Thirdly, several data combinations are defined and used as input for the rainfall-runoff modelling using the GR4H model. A special focus is on the application of an additional snow module. Both tested PRPs underestimate, for at least 20 %, extreme rainfall events that are the driving force of natural hazards such as floods. In terms of air temperature, both tested reanalysis products show similar deviations from the observational dataset. Additionally, air temperature deviations are smaller in winter compared to summer. In terms of rainfall-runoff modelling, the ERA5-Land yields slightly better performance than COSMO-REA6. If a recalibration with PRP has been carried out, the performance is similar compared to the simulations where station-based data were used as input. Model recalibration proves to be essential in providing relatively sufficient rainfall-runoff modelling results. Hence, tested PRPs could be used as an alternative to the station-based data in case precipitation or air temperature data are lacking, but model calibration using discharge data would be needed to improve the performance.

Spatial patterns of China's carbon sinks estimated from the fusion of remote sensing and field-observed net primary productivity and heterotrophic respiration

Accurately assessing the carbon sink and spatial distribution pattern of China's terrestrial ecosystems is of great significance to the implementation of climate change and carbon neutrality strategy. However, the views of various studies are still very controversial due to the differences in carbon sink estimation methods and data sources. In this study, vegetation net primary productivity (NPP) and ecosystem heterotrophic respiration (Rh) estimation models were constructed based on machine learning methods by fusing multisource data, such as remote sensing and ground observation data. The magnitude and spatial pattern of carbon sink in China from 2000 to 2018 were then revealed, and the carbon sink capacity of various ecosystems was quantitatively assessed. The main conclusions include the following: (1) The use of scale-matched carbon input and output data can help reduce the system error in carbon sink estimation. (2) China's terrestrial ecosystem carbon sink since the twenty-first century is approximately 0.458 Pg C/yr, which is equivalent to 22.72% of China's anthropogenic carbon emissions. (3) Deciduous forest has a higher carbon sink capacity than evergreen forest, while coniferous forest has a more stable carbon sink capacity than broad-leaved forest. The magnitude and spatial distribution of carbon sink in China reported in this study provides a scientific reference for achieving carbon neutrality and sustainable development.

A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms.

Eutrophication happens when water bodies are enriched by minerals and nutrients. Dense blooms of noxious are the most obvious effect of eutrophication that harms water quality, and by increasing toxic substances damage the water ecosystem. Therefore, it is critical to monitor and investigate the development process of eutrophication. The concentration of chlorophyll-a (chl-a) in water bodies is an essential indicator of eutrophication in them. Previous studies in predicting chlorophyll-a concentrations suffered from low spatial resolution and discrepancies between predicted and observed values. In this paper, we used various remote sensing and ground observation data and proposed a novel machine learning-based framework, a random forest inversion model, to provide the spatial distribution of chl-a in 2m spatial resolution. The results showed our model outperformed other base models, and the goodness of fit improved by over 36.6% while MSE and MAE decreased by over 15.17% and over 21.26% respectively. Moreover, we compared the feasibility of GF-1 and Sentinel-2 remote sensing data in chl-a concentration prediction. We found that better prediction results can be obtained by using GF-1 data, with the goodness of fit reaching 93.1% and MSE only 3.589. The proposed method and findings of this study can be used in future water management studies and as an aid for decision-makers in this field.

Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation

The fast motion of low Earth orbit (LEO) satellites provides rapid geometric changes in a short time, which can accelerate the initialization of precise point positioning (PPP). The rapid convergence of ambiguity parameters is conducive to the rapid success of ambiguity fixing. This paper presents the performance of single- and four-system combined PPP Ambiguity Resolution (AR), enhanced with an ambiguity-float solution LEO. Two LEO constellations were designed: L was a typical polar orbit constellation, with a higher number of visible satellites at high latitudes than at low and middle latitudes; and M was designed to compensate for the lack of visible satellites at low and middle latitudes. The ground observation data of the LEO satellites at the MGEX stations were simulated. Because the global navigation satellite systems (GNSSs) were fully operational, the GNSS data were real observation data from the MGEX stations. Based on the daily observation datasets collected at 258 stations in the global MGEX observation network over three days (from January 1 to January 3 2022), in addition to the LEO simulation data, we evaluated the positioning performance of LEO ambiguity-float solution-enhanced PPP ambiguity resolution and compared it with LEO-enhanced PPP. The L+M mixed constellation was able to reduce the time to first fix (TTFF) of the four-system combined PPP-AR to 5 min, and four LEO satellites were sufficient to achieve this. L+M mixed constellation was able to redu ce the convergence time of the four-system combined PPP to 2 min. Unlike PPP-AR, PPP required more LEO satellites for augmentation to saturate.

Evaluation of Remote Sensing and Reanalysis Products for Global Soil Moisture Characteristics

Soil moisture (SM) exists at the land-atmosphere interface and serves as a key driving variable that affects global water balance and vegetation growth. Its importance in climate and earth system studies necessitates a comprehensive evaluation and comparison of mainstream global remote sensing/reanalysis SM products. In this study, we conducted a thorough verification of ten global remote sensing/reanalysis SM products: SMAP DCA, SMAP SCA-H, SMAP SCA-V, SMAP-IB, SMOS IC, SMOS L3, LPRM_C1, LPRM_C2, LPRM_X, and ERA5-Land. The verification was based on ground observation data from the International SM Network (ISMN), considering both static factors (such as climate zone, land cover type, and soil type) and dynamic factors (including SM, leaf area index, and land surface temperature). Our goal was to assess the accuracy and applicability of these products. We analyzed the spatial and temporal distribution characteristics of global SM and discussed the vegetation effect on SM products. Additionally, we examined the global high-frequency fluctuations in the SMAP L-VOD product, along with their correlation with the normalized difference vegetation index, leaf area index, and vegetation water content. Our findings revealed that product quality was higher in regions located in tropical and arid zones, closed shrubs, loose rocky soil, and gray soil with low soil moisture, low leaf area index, and high average land surface temperature. Among the evaluated products, SMAP-IB, SMAP DCA, SMAP SCA-H, SMAP SCA-V, and ERA5-Land consistently performed better, demonstrating a good ability to capture the spatial and temporal variations in SM and showing a correlation of approximately 0.60 with ISMN. SMOS IC and SMOS L3 followed in performance, while LPRM_C1, LPRM_C2, and LPRM_X exhibited relatively poor results in SM inversion. These findings serve as a valuable reference for improving satellite/reanalysis SM products and conducting global-scale SM studies.

Monitoring the Impact of Heat Damage on Summer Maize on the Huanghuaihai Plain, China

As an important food crop, summer maize is widely planted all over the world. Monitoring its growth and output is of great significance for world food security. With the trend of global warming and deterioration, the frequency of high temperature and heat damage affecting summer corn has been increasing in the past ten years. Therefore, there is an increasing demand for monitoring the high temperature and heat damage of summer maize. At present, there are nearly a hundred indices or methods for research on high temperature and heat damage. However, research based on the vegetation index cannot fully describe the damage caused by high-temperature thermal damage, and there is an obvious asynchrony effect. Research based on hyperspectral remote sensing has many inconveniences in data acquisition and complex physical model construction. Therefore, this study uses remote sensing data, including MODIS surface reflection data, MODIS land surface temperature products, as well as ground observation data and statistical data, combined with multiple remote sensing indices and land surface temperature, to construct a remote sensing index, LSHDI (land surface heat damage index). The LSHDI first searches for a location with the worst vegetation growth conditions in the three-dimensional feature space based on the LST (land surface temperature), the normalized difference vegetation index (NDVI), and the land surface water index (LSWI). Then, it calculates the distance between each point and this location to measure the degree of vegetation affected by high temperature and heat damage. Finally, because there is no reliable disaster verification dataset that has been published at present, this study uses soil moisture as a reference to explain the performance and stability of the LSHDI. The results showed that their coefficient of determination was above 0.5 and reached a significance level of 0.01. The LSHDI can well-reflect the high temperature and heat damage of land surface vegetation and can provide important data support and references for agricultural management departments.

Data-to-data translation-based nowcasting of specific sea fog using geostationary weather satellite observation

Dense sea fog events are responsible for many traffic accidents and fatalities. Research has demonstrated the difficulty in distinguishing sea fog from clouds. This study presents a sea fog nowcasting method that uses brightness temperature (BT) at a mid-wave infrared (MWIR) 3.7 μm bands and the BT difference between MWIR and long-wave infrared 10.8 μm bands of communication, ocean, and meteorological satellite data through a data-to-data (D2D) translation based on a conditional generative adversarial networks technique. Sixty fog events that occurred in the Yellow Sea from 2017 to 2020 were used to develop the model. The D2D prediction model for predicting sea fog from 15-min to 2-h ahead with 15 min intervals was trained by stacking individual prediction time datasets in an array of 512 × 512 × 4 pixels for training and 512 × 512 × 8 pixels for testing. Otsu's thresholding method was used to determine sea fog pixels. The sea fog prediction model showed excellent statistical scores, such as the probability of detection = 0.923, false alarm ratio = 0.032, critical success index = 0.895, Heidke skill score = 0.909, and post agreement = 0.968 for 1 h-ahead of a sea fog forecast. Consequently, our study can make a scientific and operational contribution to fog forecasting as useful fog-related data, in addition to other numerical prediction data and ground observation data.

A remote sensing based method for assessing the impact of O3 on the net primary productivity of terrestrial ecosystems in China

O3 pollution in China has been increasing in recent years, but the process of O3 impact on net primary productivity of terrestrial ecosystems remains unclear. We attempts to explore a remote sensing-based method to assess the impact of O3 on NPP of China’s terrestrial ecosystems by combining MODIS NPP and the latest ground observation data of O3 concentration. By comparing the NPP data of MODIS image pixels with the 6-year average AOT40 data of corresponding pixels, we extracted the signal data that highlighted the effects of O3 on NPP and established the response relationships between AOT40 and NPP. It was found that NPP was significantly negatively correlated with AOT40 in farmland and grassland ecosystems in China (farmland: r = 0.8674, p < 0.003, grassland: r = 0.7181, p < 0.03). Then the response relationships were adopted to evaluate the effect in China in 2014. The results showed that the estimated percentage of O3-induced NPP decline was in the range of 5%–35%. Among them, the most significant declines were found in farmland ecosystems, with the vast majority of declines in 10%–35%. The decrease rate of evergreen coniferous forests ranked second, mostly in the range of 15%–20%. The grassland ecosystems declined at a lower rate, almost between 5% and 15%. And the evergreen broad-leaved forests has the lowest decline rate, most distributed in 0%–5%. The highest percentage decreases were mainly found in the Beijing-Tianjin-Hebei region and Shandong Province. And the decline rate of farmland ecosystems was significantly higher than other ecosystem types.

Correction method by introducing cloud cover forecast factor in model temperature forecast

Objective temperature forecast products can achieve better forecast quality by using one-dimensional regression correction directly based on the present model temperature forecast product, and the forecast accuracy can be further improved by adding appropriate auxiliary factors. In this paper, ECMWF forecast products and ground observation data from Fujian are used to revise the surface temperature at 2 m by introducing a cloud cover forecast factor based on the model temperature forecast correction method. Analysis shows that the forecast deviation of daily maximum and minimum temperature after the revision of a single-factor forecast is obviously correlated with cloud cover. A variety of prediction schemes are designed, and the final scheme is determined through comparative testing. The following conclusions are drawn: all schemes based on cloud cover grouping can improve forecast performance, and the total cloud cover scheme is generally better than the low cloud cover scheme. There is a good positive correlation between the forecast deviation of maximum temperature and the mean total cloud cover; that is, the more cloud cover, the bigger the deviation. The minimum temperature is negatively correlated with cloud cover when the cloud cover is less than 40% and positively correlated for the rest. The absolute forecast deviations of the maximum and minimum temperatures are larger when the total cloud cover is less. Whether for Tmax or Tmin forecast, the binary regression scheme after grouping consistently showed the best performance, with the lowest MAE. The final scheme was used to forecast the maximum and minimum temperature in 2021, and most verification indicators showed improvement in most forecast periods. The forecast accuracy for the 36-h daily maximum and minimum temperature is 81.312% and 91.480%, respectively, which is 2.4%–2.6% higher than the single-factor regression scheme. The forecast skill scores (FSS) reach 0.065 and 0.086, indicating that the method can effectively improve forecast quality in a stable manner and can be used for practical forecasting.

Study of winter haze pollution events in Jinan (China)

A comprehensive study of the formation process of winter haze pollution in Jinan, China, was conducted using ground and satellite observation data from January 2 to January 13, 2022. According to the variations in the pollutant concentrations, three haze pollution events occurred in the Jinan area during this period. Compared to haze pollution event 1, event 2 lasted longer and exhibited a cumulative growth state with hourly PM2.5 values peaking at 250 μg/m3. This was primarily caused by a mixture of local and nearby pollutants emitted in Jinan. Haze pollution event 3 was shorter and showed a similar trend to event 2, but the event dissipated rapidly after it was generated. Moreover, unfavorable weather factors, such as lower wind speeds, a stable inversion layer, and higher relative humidity, contributed to pollution accumulation and haze event formations. In addition, important photochemical processes also influenced haze pollution formation. The results of this study revealed the haze pollution formation process in Jinan. These results are helpful for promoting the study of regional climate changes and provide a reference for the government to prevent and control haze pollution in eastern China.

Uncertainty analysis of wind speed of wind turbine

AbstractThe uncertainty of wind speed has a great impact on the reliability of wind turbines. However, previous studies usually used average wind speed or rated wind speed as inputs, ignoring the impact of time and space distribution on wind speed. Based on the hourly ground observation data of meteorological stations, four observation stations are taken as the research object of the uncertainty of wind speed spatial distribution. The four observation stations are divided into single day and one week, and seven data analysis indicators and normal distribution models are used day and night to analyze the uncertainty of wind speed time distribution. The results show that both spatial distribution and temporal distribution are important factors that cause the uncertainty of wind speed. Due to different geographical locations of four stations, their wind speed time domain diagrams are different, and the maximum wind speed, fluctuation frequency and fluctuation amplitude of wind speed at each observation station are different; The wind speed distribution of a single day is quite different from that of a week. The data of a single day and a week are analyzed by day and night, which is quite different from the overall data analysis. The necessity of wind speed uncertainty research of wind turbine considering the temporal and spatial distribution is verified, and it is concluded that normal distribution is not suitable for fitting the distribution model of four stations under study, which lays a foundation for determining the wind speed distribution model, analyzing the power characteristics of wind turbine, evaluating the generation capacity of wind turbine, and analyzing the reliability of wind turbine.

A study on the forecast model of winter precipitation type in Liaocheng based on physical parameters

AbstractIn this study, 46 physical parameters are first calculated by using the hourly ground observational data of Liaocheng Airport station and the daily sounding data of Xingtai station and Zhangqiu station in the winters of 2016–2020. After the significance test of the correlation coefficients between the physical parameters and the precipitation types, six physical parameters that could be used in the operational weather forecast at Zhangqiu station and Xingtai station are selected by the methods of box‐plot and skill score based on the distribution characteristics of physical parameters under different precipitation types. Moreover, the judgment criteria for the winter precipitation forecast in Liaocheng are obtained by analyzing the indication effect and thresholds of each physical parameter in different precipitation types. In addition, a forecast model of winter precipitation type in Liaocheng is established by using the extended membership function transformation method with the sounding data from the nearest upstream and downstream stations. The verification with observations shows that the forecast accuracy is above 85% and the model performs quite well. We hope this study can provide a good reference for related operational forecasts in the areas without local sounding data or numerical forecast product receiving.

Global analysis of the correlation and propagation among meteorological, agricultural, surface water, and groundwater droughts

Groundwater drought monitoring relies on ground observation data, which cannot be used to reflect large-scale droughts in groundwater resources. The Gravity Recovery and Climate Experiment Satellite (GRACE) improved the situation and provided a new solution for groundwater drought research. However, the propagation relationship among global different drought types has not been fully explored. We employed CRU precipitation data, MERR2 reanalysis soil moisture data, GLDAS and GRACE data to calculate SPI (Standardized precipitation index), SSI (Standardized soil moisture index), SRI (Standardized runoff index), and GDI (Groundwater drought index), characterizing meteorological, agricultural, surface water and groundwater droughts, respectively. The Pearson correlation coefficient was adopted to study the propagation time of these four types of droughts. The results showed that the average propagation times for the different drought types are meteorological drought to surface water drought (3.5 months), meteorological drought to agricultural drought (5.7 months), agricultural drought to groundwater drought (12.97 months), surface water drought to groundwater drought (13.78 months), and meteorological drought to groundwater drought (14.47 months) from longest to shortest. (2) Climate conditions had a significant impact on the propagation time of different drought types. Low temperatures in cold climates resulted in the longest drought propagation time, while dry summer climates in temperate climates reduced drought propagation time. There were weaker propagation relationships in arid climates. In tropical climates, precipitation may not be the main driving factor for drought propagation. (3) Different land cover types show significant differences in the propagation of groundwater droughts, with forests having a longer propagation time from meteorological drought to agricultural drought or surface water drought than grassland and cropland, and forests having the shortest propagation time when meteorological drought, agricultural drought, and surface water drought is propagated to groundwater drought. Woody plants have deeper root systems than herbaceous plants and can draw up deeper groundwater. Forests have greater water storage capacity and weaker groundwater recharge than grasslands and croplands, resulting in forests being more resistant to agricultural and surface water droughts and less resistant to groundwater droughts during meteorological droughts. This study can help to clarify the propagation laws among different drought types and understand the internal mechanisms that affect the development of drought during the water cycle.

Water vapour multi-vortex structure under the interactions of typhoons and mid-low latitude systems during extreme precipitation in North China

With diverse atmospheric circulation and complex mechanisms, typhoon remote rainstorms (TRR) are closely related to a variety of mid-low latitude systems. The anomalous signatures in such processes can improve forecasting heavy rainfall associated with typhoons and fill the gaps in current physical conceptual models of TRR. Using the optimal typhoon path dataset, ground observation data, and the National Centres for Environmental Prediction (NCEP) reanalysis dataset, the impact of anomalous water vapour transport on excessive rainfall in North China is investigated. The vector empirical orthogonal function (VEOF) analysis is applied to typhoon precipitation events from 1970 to 2021, and three major modes are extracted to reflect the anomalous water vapour transport for typhoon precipitation. Mode 1 reflects a moisture circulation pattern in North China due to the coexistence of typhoons and remote and direct precipitation, with the highest probability of heavy precipitation. The differences in remote precipitation areas and intensities are attributed to the location of typhoons in the western North Pacific and the shifting of the subtropical high ridge. To characterise the water vapour transport circulation of TRR in North China, we proposed the ‘multi-vortex’ idea. The transmission of the multi-vortex ensures a continuous supply of TRR water vapour, and the enhancement of the multi-vortex is significantly linked to the enhancement of remote precipitation. Three extreme northern rainstorms, 75·8 Henan, 7·21 Beijing, and 7·20 Zhengzhou rainstorms, have anomalous multi-vortex water vapour convergence, similar to Mode 1. The mechanism of water vapour driven by multi-vortex in the three severe rainstorm events is more extreme than in usual TRR events in North China. The stronger Indian low vortex and relatively southerly subtropical highs can intensify the southwest branch of the double water vapour transport branch, whereas the strengthening of typhoons in the western North Pacific facilitates water vapour transport through the channel on the southwestern side of the subtropical highs and then transports it to key rainstorm areas, promoting TRR development. Therefore, the combination of subtropical highs, typhoons, and the anomalous multi-vortex structure may help in the establishment of key indicators of extreme precipitation in North China.

Analysis of the urban turbidity island effect: a case study in Beijing City, China

The urban turbidity island (UTI) effect is an important research topic in urban climate studies. It is closely related to urban visibility and the health of urban residents; however, it has received little attention in previous research. This study analyzes the temporal and spatial distribution characteristics of the UTI effect through the combined use of satellite remote sensing and ground observation data. Specifically, absolute and relative urban turbidity island intensity (UTII_A and UTII_R) indices are proposed and calculated for 2000–2020 by using aerosol data products and atmospheric fine particle mass concentration inversion products, which are represented by aerosol optical depth (AOD), PM1, PM2.5, and PM10. The results show the following: (a) there has been a clear footprint of the UTI effect in Beijing since 2000, generally consistent with trends of urban sprawl; (b) there are great differences in the interannual distribution of AOD, normalized AOD and PM values in urban and suburban areas; and (c) there are seasonal differences in the UTI distribution and air pollutant concentrations. The differences among indices between urban and suburban areas are mainly caused by heat island-induced air convection, complex structures in urban areas and regional weather conditions. Importantly, the interannual distribution of AOD and UTII_A of PM values decreased from 2014 to 2020, indicating that the government’s air pollution control policy has significantly improved air quality. Analysis from this study could support the formulation of urban planning and control policies to guide human activities.