Meteorological Station Data Research Articles

Augmented Reality for Air Quality Monitoring: Case Study in the Marche Region (Italy)

Abstract. Augmented Reality (AR) allows the real world to be enhanced through the overlapping of digital information generated by an algorithm or software amplifying the user's perception. In this research, the potential of AR is exploited by applying it to the problem of air pollution visualization to raise awareness of the issue. It is well known that the principal sources of air pollution include industry, domestic heating, traffic, production processes and more others. According to European Environment Agency, air pollution is the largest environmental health risk in Europe in 2022 affecting respiratory and cardiovascular diseases and premature deaths. A suitable combination of GIS-based procedures for data analysis and georeferencing, AR systems for the visualization and exploitation of the results, can be a strategic driver. The research presented in this paper has been experimented in the Marche Region, in the central part of Italy, and in particular the city of Ancona. The pollutant data collected come from 17 Agenzia Regionale per la Protezione Ambientale delle Marche (ARPAM) meteorological ground-based stations located in Marche Region. The data processing have been executed using the software ArcGis Pro instead of a 3D City Model has been acquired from the online 3D ArcGIS platform. According to the Dlgs.155/2010 regulation, the AQI index (Air Quality Index) has been provided by the interpolation of the meteorological ground stations data. The visualization phase has been tackled exploiting in a WebGIS platform and an APP by AR. A 3D immersive visualization is made using five AQI level of air quality (excellent, good, fair, poor, bad) in order to increase the urban sensing.

ПОВТОРЯЕМОСТЬ АДВЕКТИВНО-РАДИАЦИОННЫХ ТУМАНОВ НАД АПШЕРОНСКИМ ПОЛУОСТРОВОМ (АЗЕРБАЙДЖАН)

The article examines the physical and meteorological characteristics of advective-radiation fogs formed on the Apsheron Peninsula and nearby islands, including the dependence of atmospheric elements that influence the formation of fogs on the meteorological visibility range, monthly and long-term fog trends. For this purpose, the initial data of aviation meteorological stations in the water area for 1999...2022 were used. The absence of a large morphometric unit on the Apsheron Peninsula and the archipelago, surroundings by the sea, and air masses affecting the area throughout the year determine its synoptic conditions. With the help of physical-meteorological and mathematical-statistical analyses, it has been established that the bulk of advective-radiation fogs formed in the Apsheron waters are repeated in march...april. With this type of fog, it has been established that the range of meteorological visibility varies depending on wind speed and sky conditions. During the period of advective-radiation fogs, south-eastern winds are most often observed in the water area. Analyzes show that an increase in average monthly and annual air temperatures on the peninsula has recently affected the frequency of advective-radiation fogs. Studying the spatiotemporal distributions of fogs can create conditions for the effective organization of aviation work.

Evaluations of ground surface heat fluxes and its potential vegetation effects in the permafrost region of northeastern China

Ground surface-air temperature difference (GATD) is the primary source of sensible heat flux from the ground surface, and vegetation inevitably affects it by altering the ground surface characteristics. However, the relationship between vegetation and GATD in the permafrost region has not been well-elucidated. Using meteorological station data, National Oceanic and Atmospheric Administration Climate Data Record Normalized Difference Vegetation Index (NOAA CDR NDVI) data, and some auxiliary data, this study aims to investigate the patterns of GATD in the Northeast China permafrost region and quantify the response of GATD to vegetation changes. The study employs trend analysis, Geodetector, and correlation analysis methods. The results show that the GATD in the Northeast China permafrost region decreased from west to east and the annual mean GATD exhibited an upward from 1982 to 2020. The relationship between NDVI and GATD trend is non-linear, in areas with low vegetation, vegetation changes amplify the GATD trend, while in areas with high vegetation, vegetation changes inhibit the GATD trend. Furthermore, the rate of GATD increase is higher in the discontinuous permafrost (DP) region than in the sporadic permafrost (SP) and isolated permafrost (IP) regions, suggesting that the DP region may have experienced a faster ground surface state change process. The findings of this study will provide new insights for evaluating permafrost degradation and studying the resulting ecological environmental effects in the permafrost region.

Accurate solar radiation site adaptation: Harnessing satellite data and in situ measurements

Accurate solar radiation data are essential to optimize solar energy systems and assess their feasibility. In this study, we propose a site-adaptation procedure based on a machine learning model trained to enhance the accuracy of solar radiation data using a combination of the National Solar Radiation Database (NSRDB) and in situ data collected in southern Colombia. The NSRDB provides high temporal and spatial resolution data, while in situ data offer accurate localized measurements specific to the study area. Our machine learning models were trained to learn the relationships between NSRDB data and in situ meteorological station data. The results demonstrate promising predictive capabilities, with the extreme grading boosting model effectively reducing mean absolute error, while a neural network model trained with the triplet loss function proved effective in minimizing mean bias error (MBE) and improving correlation between model-adjusted and in situ collected data. These findings make significant contributions to the field of solar radiation prediction, highlighting the effectiveness of amalgamating NSRDB and in situ data for precise solar radiation estimation, and promote the advancement of solar energy system design and decision-making processes.

Cold and Wet Island Effect in Mountainous Areas: A Case Study of the Maxian Mountains, Northwest China

The Maxian Mountains, characterized by high altitudes and abundant vegetation, create a cooler and more humid environment compared to the surrounding areas, and are highly susceptible to climate change. In order to study the cold and wet island effects in the Maxian Mountains, air temperature and relative humidity (RH) were analyzed using meteorological station data. Additionally, spatial variations were examined by retrieving Land Surface Temperature (LST) and the Temperature Vegetation Dryness Index (TVDI) from 2001 to 2021. The most pronounced cold island effect was observed in the mountainous area during summer, mainly in May and July. The most significant wet island effect was observed from March to May, with an average relative humidity difference of 24.72%. The cold island area index, as an indicator of the cold island effect, revealed an increasing trend in the summer cold island effect in recent years. The cooling intensity ranged from 5 to 10 °C, with variations observed between 500 and 1000 m. A 30% increase in wet island effects in summer was observed, with a humidification intensity within a range of 500 m. Geodetector analysis identified vegetation cover as the primary factor affecting the thermal environment in mountainous areas. The increase in vegetation in mountainous areas was identified as the main reason for enhancing the cold and wet island effects. The findings emphasize the role of vegetation in enhancing cold and wet island effects, which is crucial for understanding and preserving mountainous regions.

Using the Data of Meteorological and Satellite Station in Determining the Intensity of Rainfall in the Kan Basin

Using the Data of Meteorological and Satellite Station in Determining the Intensity of Rainfall in the Kan Basin

Analysis of Land Water Balance in Various Rainfall Conditions and Its Utilization to Determine Planting Patterns of Food Crops in the Eastern Part of Seram District of Seram Island

Soil water balance calculation is one of the methods used to estimate the dynamics of soil water content during plant growth. This study aims to describe rainfall conditions and analyze soil water balance as well as determine the growing season and composition of food crop combination patterns based on the available growing season in East Seram Island. This research uses rainfall data for 30 years of observation period 1992-2021 from Geser Meteorological Station (Data analysis with the following stages: (i) rainfall analysis (ii) calculation of average rainfall (iii) determination of rainfall with a 75% chance (iv) calculation of soil water balance using the Thornthwaite and Mather method (v) determination of the growing season and cropping pattern. The results showed that the average rainfall in East Seram was 2,194 mm/year with a water deficit (D) occurring in October and November, then the rainfall had a 75% chance of being exceeded at 1,439 mm/year with a water deficit of 228 mm/year which lasted for eight months, namely August - March. The growing season under normal rainfall conditions was available throughout the year, while under high rainfall conditions, a 75% chance of the growing season was available for five months, namely May and October. The cropping patterns and crop combinations used were monoculture, polyculture, and intercropping with combinations of cassava, maize, sweet potato, groundnut, mung bean, cassava-corn, cassava-peanuts/green beans, maize-peanuts, cassava-corn-peanuts/green beans, cassava-corn-horticultural crops.

ANALYSIS OF THE CALCULATION OF REFERENCE EVAPOTRANSPIRATION ACCORDING TO THE DATA OF THE STATE METEOROLOGICAL STATION

Since direct measurement of reference evapotranspiration (ET0) is a complex, time-consuming and expensive process, the most common procedure is to estimate ET0 from climate data. The purpose of this study was to perform reference evapotranspiration calculations based on the data of the state meteorological station Askania-Nova and compare them with the actual ET0 data obtained using an automatic Internet meteorological station. The data for the study were taken from the state meteorological station Askania-Nova (township Askania-Nova, Kakhovsky district, Kherson region, 46.45°N 33.88°E) and the automatic Internet meteorological station iMetos IMT 300 from the company "Pessl Instruments", which is located at the meteorological site of the Askaniysk DSDS (Tavrychanka village, Kakhovsky district, Kherson region, 46.55°N, 33.83°E). Standard evapotranspiration was calculated using the Penman-Monteith method (FAO56-RM). To assess the accuracy of ET0 calculations, mean absolute percent error (MAPE), root mean square error (RMSE) and Standard Error of Estimate (SEE ) were determined. According to the results of the comparison of indicators from two meteorological stations, it was found that the smallest errors are inherent in the daily average and maximum temperature and relative air humidity (MAPE<10%), for the minimum temperature and relative air humidity, the MAPE errors are 18,1 and 13,7%, respectively. The MAPE error for water vapor pressure deficit and solar radiation is 20,2 and 26,3%, respectively. The largest MAPE error of 40,3% was established for wind speed measurements. The average MAPE error between the calculated ET0, based on the meteorological data of the Askania-Nova station, and the actual ET0 data obtained from the automatic Internet meteorological station iMetos is 16,8%, RMSE – 0,65 mm, SEE – 0,56 mm. Applying a coefficient of 0,92 when calculating ET0 reduces the errors of MAPE, RMSE, and SEE by 3,2%, 0,15 mm, and 0,05 mm, respectively, for all calculation periods. For the May-August period, the MAPE error was 10,7%, which brings the calculations close to high accuracy (MAPE <10%). Based on the results of the calculations, it was established that on average over the years of research, the actual ET0 was 68 mm less than the calculated one. The absolute errors of determination of ETc depended on the crop and the average over the years of research ranged from 33 mm (winter wheat) to 68 mm (early tomatoes). The application of the refined value of ET0 in calculations reduces the absolute errors in the determination of c over the years of research, this error did not exceed 6 mm (early tomato). Research results confirm the possibility of using meteorological indicators obtained from state meteorological stations to calculate ET0. To increase the accuracy of calculations, it is necessary to use a refinement coefficient.

Extreme weather characteristics and influences on urban ecosystem services in Wuhan Urban Agglomeration

In recent years, there has been a pronounced increase in the frequency of extreme weather events. To comprehensively examine the impact of extreme weather on ecosystem services within the Wuhan Urban Agglomeration (WUA), this study utilized meteorological station data, the Mann-Kendall (MK) test, and the Standardized Precipitation-Evapotranspiration Index (SPEI) to quantify the variation trends in heatwaves (HW) and droughts from 1961 to 2020. Then the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model was employed to evaluate and compare the differences in water yield and climate regulation ecosystem services under various HW, droughts, and HW-drought combination scenarios. The results show that over the past 60 years, the temperature, duration, and frequency of HW have significantly increased in the WUA. Specifically, the highest HW temperature, total HW days, HW frequency, and average HW temperature showed changing trend of +0.17 ℃/decade, +1.4 day/decade, +0.19 event/decade, and +0.07 ℃/decade, respectively. The year 2000 was identified as a mutation year for HW, characterized by increased frequency and heightened severity thereafter. The SPEI value exhibited an insignificant upward trend, with 1980 marked as a mutation year, indicating a decreasing trend in drought occurrences after 1980. Heatwaves have a weakening effect on both water yield and climate regulation services, while drought significantly weakened water yield and had a relatively modest effect on climate regulation. During HW-drought composite period, the average monthly water yield showed a notable discrepancy of 60 mm compared to humid years. Besides, as heatwaves intensify, the area of low aggregation for ecosystem services expands, whereas the area of high aggregation decreases. This study provides a preliminary understanding of the impact of urban extreme weather on urban ecosystem services under changing climatic conditions.

The Construction and Application of a Model for Evaluating Tourism Climate Suitability in Terraced Agricultural Cultural Heritage Sites: A Case Study of Longji Terraced Fields in China

As one of the globally significant agricultural cultural heritages, Longji Terraced Fields in Longsheng, Guangxi, China, attract numerous tourists. This study aims to describe the weather phenomena and climate change characteristics of Longji Terraced Fields in recent years to reveal their impact on the tourism economy. Utilizing meteorological station data and considering the actual situation in Longsheng, Guilin, the existing models for evaluating tourism climate comfort are improved. The tourism climate comfort of Longji Terraced Fields from 2002 to 2022 is discussed. The results show that the improved model can better reflect the local situation. The results show that the current Holiday Climate Index and Modified Climate Index for Tourism are not suitable for evaluating the Longji Terraces. Adjustments were made to these indices to account for the high annual precipitation and relative humidity of Longsheng. Combining extensive questionnaire surveys, it was found that the improved evaluation model better reflects tourists’ perceptions of climate comfort. Analysis indicates that when the modified model value is above 70, tourist satisfaction exceeds 80%. The most comfortable tourism periods for the Longji Terraces are August, September, and October, while the least comfortable periods are January, February, and March. This study helps to understand the seasonal variations in tourism climate comfort at Longji Terraced Fields and provides a scientific basis for local tourism industry responses to climate change, thereby increasing tourism revenue.

Mapping local climates in highly heterogeneous mountain regions: Interpolation of meteorological station data vs. downscaling of macroclimate grids

Topography modulates air and soil temperature and gives rise to local climates that may strongly deviate from regional macroclimates. These local climatic conditions determine the distribution of many organisms but are hardly accounted for by coarse-grained macroclimate grids. Downscaling macroclimate grids or interpolating high-resolution climate surfaces from weather station data has thus become a staple in ecological research. Against this background, topographically complex territories pose a challenge, as downscaling and interpolation techniques are susceptible to high topographic heterogeneity. Simultaneously, these regions will act as refugia and preserve biodiversity amidst the challenges of climate change. This contrast renders choosing the technique to derive local climatic conditions under high topographic complexity critically relevant.In this study, we compared how interpolation and downscaling techniques predict the local climates of topographically heterogeneous territories. We interpolated monthly average temperatures from weather stations in a Southern Italy mountain region and downscaled WorldClim and CHELSA macroclimatic grids from their native 30 arcsecs to 10 m resolution. We carried out the interpolation and downscaling procedures by employing four commonly used statistical algorithms and by including physiographic descriptors (altitude, northness, eastness, distance to the coast, and monthly average of daily clear-sky insolation time) known to determine local climatic conditions. We compared the techniques' predictive performance via leave-one-out cross-validation. We extended the comparison over the study area to identify where the techniques most strongly diverged and evaluated how interpolation and downscaling techniques fare against environmental extrapolation.Although the interpolations scored the best values across all error metrics, interpolating weather station data and downscaling WorldClim yielded essentially equivalent predictions of local climates in the cross-validations. Differences between the two techniques emerged during the spatial comparison, where downscaling WorldClim produced biased and less detailed monthly climate surfaces. On the contrary, weather station interpolation was affected by environmental extrapolation in the study area's most internal and topographically heterogeneous landscapes. Downscaling CHELSA severely over-smoothed local climatic conditions during both cross-validations and spatial comparisons, possibly due to lacking climate variability in the native product itself.We demonstrated that technique and data source choice affect the prediction of local climates in topographically heterogeneous territories. Despite its limitations, we argue that interpolating weather station data better accounted for local-scale topographic effects. These effects form an integral part of the microrefugia that will shelter mountain communities from warming climates. We thus recommend carefully considering the techniques chosen to unravel the intricate climatic mosaics of topographically heterogeneous territories.

Long-Term Changes in the Permafrost Temperature and Surface Frost Number in Northeast China

The permafrost in Northeast China is experiencing rapid degradation due to the influence of climate change and human activities, profoundly impacting the local ecological environment and engineering construction. Understanding the spatiotemporal dynamics of long-term permafrost in this region is crucial; however, systematic research on this topic remains scarce. This study combines meteorological station data, MODIS land surface temperature (LST) datasets, and borehole locations to apply the surface frost number (SFn) model. This approach enables the simulation and estimation of the spatial distribution and changes in the area of the surface frost number without vegetation effects (SFnv) and permafrost temperature (PT) in Northeast China from 1971 to 2020. The area of the SFnv > 0.49 within the permafrost region decreased substantially from approximately 44.353 × 104 km2 to 19.909 × 104 km2 between 1971 and 2020, with a notable change in 1988. The area of permafrost calculated using PT < 0 was slightly smaller, declining from 39.388 × 104 km2 to 29.852 × 104 km2. There was also a significant increase in the area with PT ranging from −1 °C to 0 °C, indicating a decline in permafrost stability. Approximately 10.926 × 104 km2 of stable permafrost has been transformed into semi-stable and unstable permafrost. Moreover, from 1982 to 2020, the NDVI was negatively correlated with the area of stable permafrost and positively correlated with the area of transitional or unstable permafrost. Vegetation cover decreased as transitional or unstable permafrost degraded. These findings provide valuable information for permafrost research and engineering development in cold regions, as well as for future planning and adaptation strategies.

Saastamoinen and Marini-Murray's Study of the Tropospheric Delay Model in Shijiazhuang

In addition to the non-dispertic medium, the tropospheric medium changes with time, and it is difficult to use a simple and unified mathematical model to determine the degree of change, especially in the long baseline relative positioning, the tropospheric delay between two stations often shows a weak spatial correlation. In this paper, this study will briefly introduce the main methods to reduce or eliminate various kinds of errors in order to build a real-time tropospheric delay model. The main body of this paper is to introduce and discuss the existing direct calculation model and mapping function of tropospheric delay. Based on the meteorological data of Shijiazhuang meteorological station provided by the official website of China Meteorological Administration, the appropriate tropospheric delay correction model and projection function (Saastamoinen +Marini-Murray model) are selected. The hourly tropospheric delay correction model along the propagation path direction in Shijiazhuang city on April 1, 2023 is established, and the accuracy and reliability of the model are analyzed.

Analysis of the climatic trends and heat stress periods for ruminants rearing in Bangladesh

The production of ruminant livestock is greatly impacted by climate change, as it is anticipated to jeopardise food security due to the increasing heat stress experienced by the animals, which can be measured using the Temperature Humidity Index (THI). The objective of our study was to analyze climatic patterns, identify influential variables and evaluate heat stress episodes through the utilization of the THI to establish a rearing system for ruminants in Bangladesh. The THI value was determined by analyzing meteorological station data spanning from 1995 to 2022 across various climatic zones in Bangladesh. The Mann–Kendall evaluation was used to analyze the THI patterns throughout the study. Our findings indicated that heat stress problems are expected to occur in Bangladesh when THI for ruminant rearing exceeds 74, particularly from February to December. The severity of heat stress in THIruminant 71–90 varied significantly, ranging from normal to extremely severe. We observed that June (90) was the hottest month in the west central region, while January (71) was the coldest in the northwest area. When examining the impact of climatic factors on the THI, we found that air temperature has the highest influence, while relative humidity had the second-highest influence on THI in all areas of Bangladesh. Sunlight length and wind speed influenced the yearly THI marginally but not seasonally. Our findings highlighted a seasonal threat associated with heat stress in the climatic conditions of Bangladesh. It is essential to identify heat stress in ruminants, especially considering the continuing global warming issue. Our results recommend the implementation of heat stress mitigation strategies for ruminant farmers in Bangladesh.

Seasonal Scale Climatic Factors on Grassland Phenology in Arid and Semi-Arid Zones

Influenced by climate change, significant alterations in vegetation phenology have been observed globally. Grassland phenology is highly sensitive to climate change. However, research on the variations in grassland phenology and its responses to seasonal climatic changes in arid and semi-arid regions remains scarce. This study, utilizing Solar-Induced Chlorophyll Fluorescence (SIF) data, meteorological station data, and grassland type data, employs trend analysis and time series analysis to explore the trends of seasonal climatic variability and the sensitivity response of grassland phenology in Xinjiang to seasonal climates. The findings reveal the following: (1) The region experiences more pronounced warming in winter and spring than in summer and autumn, with ground temperature increments outpacing those of air temperatures. The summer season registers the peak in precipitation volume and rate of increase, where mountainous zones accrue more rainfall compared to basins and plains. The distribution of sunshine duration is characterized by higher values in eastern areas than in the west and more in the plains than in mountainous regions, potentially due to escalating cloudiness, which has contributed to a diminishing trend in sunshine hours across Xinjiang over the past 20 years. (2) Over the past two decades, the perennial greening phase of Xinjiang grasslands has predominantly occurred in early May, showing an overall trend of occurring earlier by approximately 5.47 days per decade, while the yellowing phase mainly occurs at the end of September and the beginning of October, demonstrating a delaying trend (6.61 days/decade). The average length of the growing season is 145 days, generally showing a slightly increasing trend (11.97 days/decade). (3) In spring, the rise in air and ground temperatures, along with increased sunshine duration, all promote grassland growth, leading to an earlier greening phase. Conversely, in autumn, increases in air temperature, ground temperature, and sunshine duration can inhibit grassland growth, resulting in an earlier yellowing phase. Increased precipitation in summer and autumn can delay the yellowing phase and extend the length of the grassland growing season. This research provides new insights into the factors influencing large-scale grassland phenology and offers references for grassland adaptation to future climate changes.

Characteristics of Soil Temperature Change in Lhasa in the Face of Climate Change

Soil temperature is an important index of climate change, and the analysis of soil temperature change is of great significance for understanding climate change and ecohydrological processes. This study was based on the measured meteorological data of a meteorological station, combined with the soil temperature data of 0–10, 10–40, 40–100 and 100–200 cm from the Global Land Data Assimilation System (GLDAS-NOAH). The Mann–Kendall test, wavelet analysis, linear tendency estimation and other methods were used to analyze the variability, periodicity and trend of soil temperature in Lhasa from 2006 to 2022. The results showed that the soil temperature of different soil layers had abrupt changes in annual and seasonal time series, and all showed a warming phenomenon after abrupt changes. In terms of periodicity, the average annual soil temperature of different soil layers has similar periodic changes, and the periodic oscillation is strong around 10a, which is the main cycle of soil temperature change. The soil temperature in Lhasa showed a significant rising trend in the interannual and seasonal time series, and the average annual rising trend of soil temperature was greater than that of air temperature. The correlation between soil temperature and mean air temperature (MAT), maximum air temperature (Tmax), minimum air temperature (Tmin) and snow depth (SD) was investigated by Pearson correlation analysis. Soil temperature in spring, autumn and winter had a strong correlation with MAT, Tmax and Tmin, showing a significant positive correlation. The negative correlation between soil temperature and SD in 0–40 cm soil in spring and winter was more severe. The research results show that Lhasa has experienced a rise in air temperature and soil temperature in the past 17 years, and reveal the specific changes in soil temperature in Lhasa against the background of climate change. These findings have reference significance for understanding the impact of climate change on the natural environment.

FIRE WEATHER INDEX AND FOREST FIRE DANGER MAPPING: INSIGHTS FROM A CASE STUDY IN ANTALYA - MANAVGAT FOREST, TURKIYE

Abstract. Forest fires in Türkiye, like in other regions, have detrimental effects on wildlife habitats, water quality, air pollution, climate change, and the economy. These fires become particularly concerning during the dry summer months. In 2021, forest fires affected over 150 thousand hectares of land across the country, with the Manavgat district in Antalya province alone witnessing the burning of approximately 60 thousand hectares of forest area. This study aims to assess the applicability and suitability of Fire Weather Index (FWI) data derived from meteorological station data in the Antalya region, as well as EFFIS FWI data generated using satellite-based meteorological information, for fire danger mapping during the Manavgat forest fire that occurred between 28 July and 6 August 2021. Additionally, correlation analyses were performed between the two FWI datasets and other relevant variables, including the difference in Normalized Burn Ratio (dNBR), the difference in Land Surface Temperature (dLST), and Fire Radiative Power (FRP) data detected from MODIS and VIIRS satellites. The results of the correlation analysis indicated that the FWI values obtained using in-situ meteorology station data showed much higher correlations than FWI values obtained from EFFIS, with the highest correlation (73%) observed with dLST data. Consequently, the fire danger map was created using the in-situ meteorological data, given its stronger correlation. The results prominently revealed a widespread high-risk level across the entire Antalya province, with the Manavgat district classified into the "Extreme" and "Very Extreme" FWI classes, emphasizing the critical importance of utilizing in-situ meteorological data for precise fire danger assessments and proactive fire management strategies.

THE EFFECT OF SEASONAL VARIATION ON GNSS ZENITH TROPOSPHERIC DELAY

Abstract. The Global Navigation Satellite System (GNSS) signal experiences delays caused by the atmosphere, leading to the lengthening of the geometric path of the ray, commonly referred to as tropospheric delay. This delay is a significant source of error in GNSS positioning, contributing to a bias in the height component of several centimeters, even when meteorological data are simultaneously recorded and used in tropospheric models. In this study, considering seasonal variations, we investigated the impact of tropospheric delay on the GNSS height component. GNSS stations, part of the Turkish RTK CORS Network known as TUSAGA-Active (Turkish National Permanent GNSS Network Active), covered different heights over the 2014–2019 period. Daily coordinates of GNSS stations and tropospheric zenith delay were obtained through the GAMIT/GLOBK software solution.In the study, temperature, pressure, and relative humidity data of meteorological stations at different heights were converted to mean sea level. By using these values, interpolation estimates were made for the continuous GNSS stations in the same region with the IDW method. The most significant delay in GNSS signals occurs in July and August. This effect, which causes periodic changes in the zenith delay, varies inversely with the station's height. With the increase in the amount of water vapor in the atmosphere in parallel with the rise in the temperature in the summer months, it is seen that the stations at a low height are more exposed to the tropospheric effect than the stations at higher heights. In addition, GNSS stations' reduced meteorological values (temperature, pressure and relative humidity) show that the zenith delay values changed directly proportional to the temperature and inversely proportional to the pressure and relative humidity.

Short-term photovoltaic power prediction model based on hierarchical clustering of K-means++ algorithm and deep learning hybrid model

In order to further improve the accuracy of photovoltaic (PV) power prediction and the stability of power system, a short-term PV power prediction model based on hierarchical clustering of K-means++ algorithm and deep learning hybrid model is proposed in this paper. First, hierarchical clustering of the K-means++ algorithm is used to cluster historical data into different weather scenes according to different seasons. Second, a hybrid model combining convolutional neural network (CNN), squeeze-and-excitation attention mechanism (SEAM), and bidirectional long short-term memory (BILSTM) neural network is constructed to capture long-term dependencies in time series, and the improved pelican optimization algorithm (IPOA) is used to optimize the hyperparameters in the prediction model. Finally, an example for modeling analysis is conducted by using the actual output and meteorological data of a PV power station in the Ili region of Xinjiang, China. The effectiveness and accuracy of the proposed model are verified by comparing with LSTM, BILSTM, CNN-BILSTM, and POA-CNN-SEAM-BILSTM models, and the superiority of IPOA is verified by comparing with particle swarm optimization and whale optimization algorithm. The results show that the proposed model can obtain better results under different weather scenes in different seasons, and the prediction accuracy of the model optimized by IPOA is further improved.

Transformer based day-ahead cooling load forecasting of hub airport air-conditioning systems with thermal energy storage

The air conditioning system constitutes more than half of the total energy demand in hub airport buildings. To enhance the energy efficiency and to enable intelligent energy management, it is vital to build an accurate cooling load prediction model. However, the current models face challenges in dealing with dispersed load patterns and lack interpretability when black box approaches are adopted. To tackle these challenges, we propose a novel k-means-Temporal Fusion Transformer (TFT) based hybrid load prediction model. Specifically, the daily load patterns are grouped using an improved k-means clustering method that considers both input feature weights and dynamic time warping (DTW) distances. Additionally, the statistical features of the clustering output are inputted into the TFT. By further incorporating context information, the integration of data between different schema categories is achieved, thus reducing errors that may occur during the transition process. As a result, the prediction performance and interpretability are significantly improved. The Chongqing Jiangbei Airport T3A terminal is used as a case study, and experiments are conducted using cooling data from the No.1 energy station, as well as the airport traffic data and the meteorological station data. Results are compared with other mainstream models, confirming that the proposed day-ahead load forecasting model achieves improvements in several performance indicators, including MAE, MAPE, CV-RMSE, and R2, which are 384 kW, 3%, 5%, and 0.058 respectively.