Meteorological Monitoring Research Articles

Multivariate Hydrological Modeling Based on Long Short-Term Memory Networks for Water Level Forecasting

In the Department of Chocó, flooding poses a recurrent and significant challenge due to heavy rainfall and the dense network of rivers characterizing the region. However, the lack of adequate infrastructure to prevent and predict floods exacerbates this situation. The absence of early warning systems, the scarcity of meteorological and hydrological monitoring stations, and deficiencies in urban planning contribute to the vulnerability of communities to these phenomena. It is imperative to invest in flood prediction and prevention infrastructure, including advanced monitoring systems, the development of hydrological prediction models, and the construction of hydraulic infrastructure, to reduce risk and protect vulnerable communities in Chocó. Additionally, raising public awareness of the associated risks and encouraging the adoption of mitigation and preparedness measures throughout the population are essential. This study introduces a novel approach for the multivariate prediction of hydrological variables, specifically focusing on water level forecasts for two hydrological stations along the Atrato River in Colombia. The model, utilizing a specialized type of recurrent neural network (RNN) called the long short-term memory (LSTM) network, integrates data from hydrological variables, such as the flow, precipitation, and level. With a model architecture featuring four inputs and two outputs, where flow and precipitation serve as inputs and the level serves as the output for each station, the LSTM model is adept at capturing the complex dynamics and cross-correlations among these variables. Validation involves comparing the LSTM model’s performance with linear and nonlinear Autoregressive with Exogenous Input (NARX) models, considering factors such as the estimation error and computational time. Furthermore, this study explores different scenarios for water level prediction, aiming to utilize the proposed approach as an effective flood early warning system.

A Novel Stacking Ensemble Learning Approach for Predicting PM2.5 Levels in Dense Urban Environments Using Meteorological Variables: A Case Study in Macau

Air pollution, particularly particulate matter such as PM2.5 and PM10, has become a focal point of global concern due to its significant impact on air quality and human health. Macau, as one of the most densely populated cities in the world, faces severe air quality challenges. We leveraged daily pollution data from 2015 to 2023 and hourly meteorological pollution monitoring data from 2020 to 2022 in Macau to conduct an in-depth analysis of the temporal trends of and seasonal variations in PM2.5 and PM10, as well as their relationships with meteorological factors. The findings reveal that PM10 concentrations peak during dawn and early morning, whereas PM2.5 distributions are comparatively uniform. PM concentrations significantly increase in winter and decrease in summer, with relative humidity, temperature, and sea-level atmospheric pressure identified as key meteorological determinants. To enhance prediction accuracy, a Stacking-based ensemble learning model was developed, employing LSTM and XGBoost as base learners and LightGBM as the meta-learner for predicting PM2.5 concentrations. This model outperforms traditional methods such as LSTM, CNN, RF, and XGB across multiple performance metrics.

Elastic Self-Recovering Hybrid Nanogenerator for Water Wave Energy Harvesting and Marine Environmental Monitoring.

To achieve large-scale development of triboelectric nanogenerators (TENGs) for water wave energy harvesting and powering the colossal sensors widely distributed in the ocean, facile and scalable TENGs with high output are urgently required. Here, an elastic self-recovering hybrid nanogenerator (ES-HNG) is proposed for water wave energy harvesting and marine environmental monitoring. The elastic skeletal support of the ES-HNG is manufactured using three-dimensional (3D) printing technology, which is more conducive to the large-scale integration of the ES-HNG. Moreover, the combination of a TENG and an electromagnetic generator (EMG) optimizes the utilization of device space, leading to enhanced energy harvesting efficiency. Experimental results demonstrate that the TENG achieves a peak power output of 42.68 mW, and the EMG reaches a peak power output of 4.40 mW. Furthermore, various marine environment monitoring sensors, such as a self-powered wireless meteorological monitoring system, a wireless alarm system, and a water quality monitoring pen, have been successfully powered by the sophisticated ES-HNG. This work introduces an ES-HNG for water wave energy harvesting, which demonstrates potential in marine environment monitoring and offers a new solution for the sustainable development of the marine internet of things.

Comprehensive assessment of five near-real-time satellite precipitation products in the Lower Yangtze River Basin and the Lixiahe region, China: Dual perspectives from time series and extreme events

Near real-time satellite precipitation products (NSPPs) have significant potential in meteorological hydrological monitoring and forecasting due to their timeliness. Previous evaluation-oriented studies have either focused on the uncertainty of time series or on the monitoring capability of extreme events. There is limited research that comprehensively considers both aspects to provide a systematic knowledge. Moreover, there is still insufficient understanding of the accuracy of hourly precipitation estimates. This study conducts a comprehensive evaluation between five NSPPs (IMERG Early, IMERG Late, GSMaP NRT, GSMaP NRT Gauge and PERSIANN CCS) from two main perspectives at hourly scale: an assessment of time series spanning from 2018 to 2022 over the Lower Yangtze River Basin and the Lixiahe region and an examination of the four extreme events over the Tai Lake Basin and the Chao Lake Basin. HP-metrics are proposed to evaluate bias in the location of extreme rainfall, rainy area, and precipitation amount over time, comprehensively analyzing performance in terms of extreme events. The results show that, for general and seasonal assessment, IMERG Late generally performed best with good statistical metrics, with the highest CSI (0.343), CC (0.445), KGE ‘(0.295) in annual results, and the best agreement in capturing convective rain diurnal curves (summer CC = 0.820). GSMaP showed a better detection capability especially in spring and summer, and also performed better in capturing frequency curves, but always showed large estimation errors. IMERG and GSMaP detected worse CC region close to the water body. And the effect of high terrain on GSMaP NRT is more obvious in winter. For the evaluation in detecting the extreme events, IMERG and GSMaP overestimated the area and intensity and had a certain ability to discriminate the location of extreme precipitation, based on the temporal variation of HP-metrics. The rapid expansion or contraction of the extent of the extreme precipitation region could degrade the detection capability of HP-π. The comprehensive understanding of NSPPs provides valuable insights for upgrading satellite retrieval algorithms and making informed selection for precipitation products in meteorological hydrological applications.

An exploration of urban air health navigation system based on dynamic exposure risk forecast of ambient PM2.5

Under international advocacy for a low-carbon and healthy lifestyle, ambient PM2.5 pollution poses a dilemma for urban residents who wish to engage in outdoor exercise and adopt active low-carbon commuting. In this study, an Urban Air Health Navigation System (UAHNS) was designed and proposed to assist users by recommending routes with the least PM2.5 exposure and dynamically issuing early risk warnings based on topologized digital maps, an application programming interface (API), an eXtreme Gradient Boosting (XGBoost) model, and two-step spatial interpolation. A test of the UAHNS’s functions and applications was carried out in Wuhan city. The results showed that, compared with trained random forest (RF), LightGBM, Adaboost models, etc., the XGBoost model performed better, with an R2 exceeding 0.90 and an RMSE of approximately 15.74 μg/m3, based on data from national air and meteorological monitoring stations. Further, the two-step spatial interpolation model was adopted to dynamically generate pollution distribution at a spatial resolution of 300 m*300 m. Then, an exposure comparison was performed under randomly selected commuting routes and times in Wuhan, showing the recommended routes for lower PM2.5 exposure made effectively help. And the route difference ratios of about 14.9 % and 16.9 % for riding and walking, respectively. Finally, the UAHNS platform was integrally realized for Wuhan, consisting of a real-time PM2.5 query, a one-hour PM2.5 prediction function at any location, health navigation on city map, and a personalized health information query.

A self-regulation blades wind energy harvester system for self-powered wireless monitoring sensors

Using wind energy in the natural environment provides a promising solution for wireless sensor power supply for ecological, meteorological, environmental, and infrastructure monitoring. However, the uncertainty and disorder of natural wind restrict the further development of wind energy harvester systems and self-powered wireless sensor technology. Hence, this paper proposes a self-regulation blade wind energy harvester system (SBWEHS) for self-powered wireless monitoring sensors in remote field areas with power shortages. The system is mainly composed of three parts: wind harvesting mechanism, generator module, and energy storage module. The device can control the blade overlap ratio according to the wind speed while generating electricity to maximize the power coefficient. The system can control the blade’s closure in bad weather to protect the device. Based on the computational fluid dynamics technology of Ansys Fluent software, this study evaluated the impact of wind speed and blade overlap ratio on the two-stage blades. Experiments revealed that when the overlap ratio of the blades is fixed at 0.2 and the wind speed is set at 16 m/s, the maximum average power will reach 0.79 W, which fulfills the power requirements of wireless sensors. These results illustrate that the SBWEHS can effectively supply power for wireless monitoring sensors, especially in remote natural environments.

Study on Optimization of Placement Method for Transmission Line Monitoring Devices Based on Probability of Meteorological Disasters

Abstract To ensure the reliability and safety of the power grid system, it is of great significance to carry out electric meteorological monitoring for transmission channels. In this paper, a method for optimizing the placement of transmission line monitoring devices based on the probability of meteorological disasters is proposed. By considering the climatic characteristics near the transmission line, the probability of occurrence of adverse meteorological faults for each transmission tower in the transmission line is analyzed and calculated by using existing meteorological and GIS elevation data. Meanwhile, the historical failure data and voltage levels of transmission lines are combined to achieve the optimization of the installation position of monitoring devices in transmission channels. This method was applied to a 220 kV transmission line for practical analysis, achieving dense monitoring in areas with high failure rates and sparse monitoring in areas with low failure rates.

Analysis of Haze Problem in Beijing-Tianjin-Hebei Region of China: Causes and Solutions

This paper analyzes the causes and countermeasures of severe haze in the Beijing-Tianjin-Hebei region, based on the status quo of haze in China, especially in the Beijing-Tianjin-Hebei region. This paper analyzes the causes of severe haze in the Beijing-Tianjin-Hebei region, based on the complexity and multi-factor nature of the haze problem in the Beijing-Tianjin-Hebei region, it is difficult to control. The government needs to adopt comprehensive policy measures, including strengthening the management of industrial and transportation emissions, promoting the use of clean energy, optimizing the energy structure, improving domestic pollution control, and strengthening meteorological early warning and monitoring, so as to gradually reduce the problem of smog, improve air quality, and protect people's health. It is considered that quantifying the role of anthropogenic emissions and climate change is the premise of rational control of air pollution, and it is proposed to implement stricter and more specific air pollution prevention and control policies. Some solutions are listed that require joint efforts from all sectors of society to effectively reduce the smog pollution problem in the Beijing-Tianjin-Hebei region, improve air quality and protect people's health.

Evaluating the accuracy of APHRODITE and CHIRPS satellite-based Precipitation products for meteorological drought monitoring

Evaluating the accuracy of APHRODITE and CHIRPS satellite-based Precipitation products for meteorological drought monitoring

A markov chain based photovoltaic power simulation method

Photovoltaic power generation has volatility and randomness, which affects the safety and reliability of the power system. In order to achieve accurate simulation of photovoltaic output, this paper proposes a photovoltaic power timing simulation method based on rolling sampled Markov chain model. Firstly, establish a photovoltaic output model and analyze the actual output characteristics; Then, based on the first-order Markov chain model, the relationship between adjacent days is considered, and a multistate transition probability matrix is established to construct an annual time series output model; Finally, based on the annual output data of a photovoltaic power station and historical meteorological monitoring data, an example is simulated to verify the effectiveness of the proposed method.

Data-driven approach for air pollutant concentrations forecasting: A window-based multi-output GBRT approach

Air pollutant concentrations forecasting is essential for detrimental public exposure prevention, air quality enhancement, and effective environmental policy shaping. Recently, air pollutant concentrations forecasting based on machine learning has been considerably advanced with the development of data-driven approaches. However, multi-source data constraints and architectural complexity in machine learning approaches limit the use of machine learning in actual scenarios. Herein we present a window-based multi-output gradient boosting regression tree (WM-GBRT) approach to predict air pollutant concentrations for the next 48 h at each monitoring station, considering air pollutant monitoring data, meteorological monitoring data, NECP-GFS data, and GEOS-CF data. The key innovation of our approach lies in the flexible input window configuration and the implementation of an multi-step independent strategy. The experiments were conducted in China and compared to existing data-driven baselines, we show that our approach achieved the superior performance in forecasting concentrations of six air pollutants, with a considerable improvement of 6.94 % (PM2.5), 12.3 % (PM10), 3.84 % (O3), 30.6 % (SO2), 9.84 % (NO2), and 93.9 % (CO) when compared to the second-best baselines. This simple yet efficient approach can be easily applied, providing a reliable technical method for predicting air pollutant concentrations.

Analysis of extreme precipitation variation characteristics in mountain grasslands of arid and semi-arid regions in China

Introduction: With global warming, the disaster losses caused by extreme precipitation events are increasing. The poor natural conditions and climate change make the arid and semi-arid mountainous grassland area a sensitive region of climate change. The study on the spatio-temporal variation characteristics of extreme precipitation events in this region is helpful to improve the ability of climate prediction and disaster prevention and reduction in grassland.Methods: Based on the daily precipitation data of four meteorological monitoring stations in the Yinshanbeilu from 1970 to 2020, the trend analysis, M-K test and wavelet analysis were used to select seven typical extreme precipitation indicators to analyze the temporal and spatial characteristics of extreme precipitation.Results and Discussion: The results showed that the precipitation in the Yinshanbeilu increased in the past 51a, and the number of heavy rain days increased significantly. The significance test of CDD and CWD showed that the number of continuous dry days and continuous wet days decreased abruptly. The spatial analysis showed that the high value areas of R95p, R95d and PRCRTOT were all located in Siziwang Banner, and it could be concluded that the extreme precipitation risk was the highest in Siziwang Banner, while the low value areas of SDII, Rx1day, R95p and PRCRTOT were all located in Sonid Right Banner, which could be inferred that the Sonid Right Banner was relatively dry. The first main cycle of the seven indexes of extreme precipitation almost runs through the whole time series, and the starting point of the minimum main cycle changes inconsistent. In addition to the number of consecutive dry days, the other indices have a good correlation with annual precipitation, flood season and monthly precipitation from June to September, and July is the peak period of extreme precipitation events.

Research on Drought Monitoring Based on Deep Learning: A Case Study of the Huang-Huai-Hai Region in China

As climate change intensifies, drought has become a major global engineering and environmental challenge. In critical areas such as agricultural production, accurate drought monitoring is vital for the sustainable development of regional agriculture. Currently, despite extensive use of traditional meteorological stations and remote sensing methods, these approaches have proven to be inadequate in capturing the full extent of drought information and adequately reflecting spatial characteristics. Therefore, to improve the accuracy of drought forecasts and achieve predictions across extensive areas, this paper employs deep learning models, specifically introducing an attention-weighted long short-term memory network model (AW-LSTM), constructs a composite drought monitoring index (CDMI) and validates the model. Results show that: (1) The AW-LSTM model significantly outperforms traditional long short-term memory (LSTM), support vector machine (SVM) and artificial neural network (ANN) models in drought monitoring, offering not only better applicability in meteorological and agricultural drought monitoring but also the ability to accurately predict drought events one month in advance compared to machine learning models, providing a new method for precise and comprehensive regional drought assessment. (2) The Huang-Huai-Hai Plain has shown significant regional variations in drought conditions across different years and months, with the drought situation gradually worsening in the northern part of Hebei Province, Beijing, Tianjin, the southern part of Huai North and the central part of Henan Province from 2001 to 2022, while drought conditions in the northern part of Huai North, southern Shandong Province, western Henan Province and southwestern Hebei Province have been alleviated. (3) During the sowing (June) and harvesting (September) periods for summer maize, the likelihood of drought occurrences is higher, necessitating flexible adjustments to agricultural production strategies to adapt to varying drought conditions.

Deep learning tool: reconstruction of long missing climate data based on spatio-temporal multilayer perceptron

Long-term monitoring of climate data is significant for grasping the law and development trend of climate change and guaranteeing food security. However, some weather stations lack monitoring data for even decades. In this study, 62 years of historical monitoring data from 105 weather stations in Xinjiang were used for missing sequence prediction, validating proposed data reconstruction tool. First of all, study area was divided into three parts according to the climatic characteristics and geographical locations. A spatio-temporal multilayer perceptron (MLP) was established to reconstruct meteorological data with three time scales (Short term, cycle and long term) and one spatio dimension as inputing (rolling predictions, one step predicts one day), filling in long sequence blank data. By designing an end-to-end model to autonomously detect the locations of missing data and make rolling predictions,we obtained complete meteorological monitoring data of Xinjiang from 1961 to 2022. Seven kinds of parameter reconstructed include maximum temperature (Max_T), minimum temperature (Min_T), mean temperature (Ave _ T), average water vapor pressure (Ave _ WVP), relative humidity (Ave _ RH), average wind speed (10 m Ave _ WS), and sunshine duration (Sun_H). Contrasted the prediction accuracy of the model with general MLP and LSTM, results shows that, in the seven types of parameters, designed spatio-temporal MLP decreases MAE and MSE by 7.61% and 4.80% respectively. The quality of reconstructed data was evaluated by calculating correlation coefficient with the monitored sequences of nearest station,determining the applicable meteorological parameters of the model according to the results. Results show that,proposed model reached satisfied average correlation coefficient for Max_T, Min_T, Ave _ T and Ave _ WVP parameters are 0.969, 0.961, 0.971 and 0.942 respectively. The average correlation coefficient of Sun_H and Ave _ RH are 0.720 and 0.789. Although it is difficult to predict extreme values, it can still capture the period and trend; the reconstruction effect of 10 m Ave _ WS is poor, with the average similarity of 0.488. Proposed method is applicable to reconstruct Max_T, Min_T, Ave _ T and Ave _ WVP, but not recommended to reconstruct Sun_H, Ave _ RH and Ave _ WS.

Analysis of the Low-Frequency Debris Flow Disaster Induced by a Local Rainstorm on 12 July 2022, in Pingwu County, China

Low-frequency debris flows often lead to severe disasters due to large energy releases and strong concealment. However, the understanding of formation conditions, movement processes, and disaster-causing mechanisms of low-frequency debris flow is still limited, especially regarding occurrences within the large catchment (>50 km2). This study presents a typical case of large-scale, low-frequency debris flow occurring in the Heishui catchment (102.65 km2), Pingwu County, China. The movement process, disaster characteristics, and causes of the Heishui debris flow were analyzed in detail through field investigations and remote sensing interpretation. The results indicated that the Heishui debris flow is a large-scale, low-frequency, dilute debris flow with a recurrence period of over 100 years. The debris flow was primarily initiated from the right branch gully, Longchi gully, triggered by a local rainstorm with a maximum hourly rainfall return period of over 20 years. The main cause of casualties and building damage is attributed to large energy releases from boulder blockages and outbursts that occurred in the middle part of the main channel. This led to a sudden increase in peak discharge to 1287 m3/s, with a volume of 3.5 × 105 m3 of solid materials being transported to the outlet of the gully. It is essential to enhance the identification of debris flows by comprehensively considering tributary gullies’ susceptibility and strengthening joint meteorological and hydrological monitoring networks in the middle and upper reaches within large catchments. This preliminary work contributes towards improving prevention and mitigation strategies for low-frequency debris flows occurring within large catchments.

Research on Regional Early Warning of Flash Flood Disasters in Small Watershed Based on Multi-source Early Warning Data Fusion

The research on the early warning method of flash floods serves as an important groundwork for the prediction and early warning of flash flood disasters. Being restricted by various objective factors such as lack of data and complicated formation mechanism of flash flood disasters in small watersheds, accurate and targeted early warning has consistently been a weak link within the flash flood control system. In this regard, focusing on a series of problems, such as "short risk forecasting period", "frequent missing and false reports" and "poor early warning accuracy", this research explores a hierarchical partition management system that fulfills the demands of early warning based on the analysis of watershed lag time and risk situation. Meanwhile, based on the comprehensive consideration of the characteristics of various multi-source early warning data, such as meteorological forecast rainfall and monitoring rainfall water level, this research proposes corresponding early warning modes for different types of early warning objects. On these grounds, this research applies the proposed early warning system to Xiaguan Creek in Shangyu District, Zhejiang Province. Relevant findings indicate that the effective extension of the forecasting period, coupled with the reduction of the phenomenon of "missing and false reports", provides scientific support for the early warning and transfer decision related to flash floods.

Wind Aggregation Enhanced Triboelectric-Electromagnetic Hybrid Generator with Slit Effect.

It is of great significance to establish a low-cost, high-efficiency, self-powered micrometeorological monitoring system for agriculture, animal husbandry, and transportation. However, each additional detection element in the meteorological monitoring system increases the power consumption of the whole system by about 0.7 W. As a renewable energy technology, a triboelectric nanogenerator has the advantages of low price and self-powered sensing. To reduce the power consumption of the micrometeorological monitoring system, this work introduces an innovative solution: the wind-gathering enhanced triboelectric-electromagnetic hybrid generator (WGE-TEHG). Coupling the thin-film vibrating triboelectric nanogenerator (TENG) and electromagnetic generator (EMG), the TENG is used to monitor wind direction and the EMG is used to monitor wind speed and provide energy needed by the system. In particular, the TENG can be used as a self-powered sensor to reduce the power consumption of the sensing system. Besides, the TENG is used to produce slit effect to enhance the output performance of EMG. The experimental results show that the WGE-TEHG can build a self-powered natural environment micrometeorological sensing system. It can monitor the wind direction, wind speed, temperature, and relative humidity. This research has great application value for the self-powered sensing implementation of a hybrid TENG and EMG.

Recent-year variations in O3 pollution with high-temperature suppression over central China

By analyzing environmental and meteorological monitoring data over recent years of 2015–2022, the Twain-Hu Basin (THB) in central China was identified as a regional O3 pollution center over China with the highest increasing trend at 1.10 %⸱yr−1 in interannual variations of O3 concentrations with deteriorating O3 pollution over recent years. We explored the spatiotemporal variations in O3 pollution in the THB with ozone suppression (OS) under high air temperature over metropolitan, small urban, and mountainous areas. The bipolarized interannual trends in interannual O3 variations in urban and mountainous areas over central China were characterized with the increasing and decreasing 90th percentiles of the daily maximum 8-h (MDA8-90) O3 concentrations respectively in polluted urban areas and clean mountainous areas over recent eight years. The changes of the near-surface O3 concentrations with air temperature exhibited the inflection points of OS from increasing to decreasing O3 at air temperature of 30.5 °C in mountainous areas, 32.5 °C in small urban areas, and 34.5 °C in metropolitan areas, and the intensity of OS was estimated in the ranking with mountainous areas (−2.30 μg⸱m−3⸱°C−1) > small urban areas (−1.96 μg⸱m−3⸱°C−1) > metropolitan areas (−1.54 μg⸱m−3⸱°C−1), indicating that the OS was more significant over the lower-O3 mountainous areas. This study has implications for understanding O3 pollution variations with the meteorological drivers.

An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications

Cloud cover assessment is crucial for meteorology, Earth observation, and environmental monitoring, providing valuable data for weather forecasting, climate modeling, and remote sensing activities. Depending on the specific purpose, identifying and accounting for pixels affected by clouds is essential in spectral remote sensing imagery. In applications such as land monitoring and various remote sensing activities, detecting/removing cloud-contaminated pixels is crucial to ensuring the accuracy of advanced processing of satellite imagery. Typically, the objective of cloud masking is to produce an image where every pixel in a satellite spectral image is categorized as either clear or cloudy. Nevertheless, there is also a prevalent approach in the literature that yields a multi-class output. With the progress in Machine and Deep Learning, coupled with the accelerated capabilities of GPUs, and the abundance of available remote sensing data, novel opportunities and methods for cloud detection have emerged, improving the accuracy and the efficiency of the algorithms. This paper provides a review of these last methods for cloud masking in multispectral satellite imagery, with emphasis on the Deep Learning approach, highlighting their benefits and challenges.

A Review of Research on the Application and Development of Meteorological Monitoring in the Field of Agriculture

The application of meteorological monitoring technology in the agricultural field has become an indispensable and important component of agricultural production and management processes.In this paper, the application of meteorological monitoring in the agricultural field is summarized,including the types of current meteorological monitoring technologies,data acquisition and processing methods,as well as their practical application effects in different agricultural environments,including meteorological stations,satellite remote sensing,unmanned aerial vehicles(UAVs),and sensor networks [4].Then,aspects of meteorological data acquisition and processing methods,data collection,transmission,and storage are explored.Next,the application effects of meteorological monitoring technology in crop cultivation,yield estimation, plant diseases and insect pests,livestock husbandry,and greenhouse agriculture are discussed in detail for different types of agricultural environments.Finally,the development trends and future research directions of meteorological monitoring technology are discussed.