Network Of Meteorological Stations Research Articles

Case studies of the winds in the urban area of Hong Kong – Microclimate station observations and high resolution numerical simulations

Hong Kong, renowned for its densely packed urban areas, poses unique challenges for understanding the effects of buildings on local meteorological conditions. To address this, the Hong Kong Observatory has started building a network of urban meteorological monitoring stations since 2017 for monitoring, analysing and studying urban microclimate. This paper presents an observational and numerical study focusing on wind measurements obtained from wind sensors installed on two smart lampposts in Tsim Sha Tsui, a major urban area in Hong Kong. Two representative high wind conditions in Hong Kong, Super Typhoon Saola in 2023 and a strong monsoon case characterized by prevailing easterly winds, are considered. With the use of high resolution computational fluid dynamic simulations, major features of actual observations can be reproduced. This suggests that district scale or even street scale weather services could be possible in the future with sufficient computational power.

Diagnosis for integrating the main Iberian networks of solar radiation meteorological stations

Diagnosis for integrating the main Iberian networks of solar radiation meteorological stations

Maximum Power Transfer of a Photovoltaic Microgeneration System Using PSO-Based Dynamic Modeling

This research aims to implement an already developed algorithm to obtain the maximum power transfer of a solar generation field based on a dynamic approach. The study addresses the sizing of the load to be supplied, which is a residential building. On the other hand, it also considers the field sizing as a function of the load and the operating characteristics of the selected inverter. The irradiance data correspond to the hourly record of a station that is part of the network of meteorological stations in Quito. Quito was chosen as the location for this research due to the optimization algorithm’s practical application and the availability of experimental equipment. The demand sizing is based on the regulations of the distribution company with jurisdiction in the area, which makes it a suitable test bed for the algorithm. The optimization algorithm is developed using Python (version 3.9), and the analysis of the behavior of the solar panels is performed by dynamic modeling using the Vensim software (version 10.1.2). Finally, comparative results are presented between using and not using the investigated circuit and algorithm in the photovoltaic system, obtaining an improvement in the generation over a system without the use of these improvements, validating these results by implementing them in a test system, obtaining ranges higher than 10% of the initially generated power.

Meteorological drought in the upper Noteć catchment area (Central Poland) in the light of NDVI and SPI indicators

The currently used standardised precipitation index (SPI) does not allow for a reliable assessment of the impact of drought due to the small and unevenly distributed network of meteorological stations. Hopes for developing methods to assess the impact of droughts are pinned on remote data acquisition and the use of analysis of aerial photographs and satellite scenes. The aim of this study is to assess the occurrence of drought based on the normalised difference vegetation index (NDVI) and SPI at multiple time scales (1-, 3-, and 6-, 9- and 12-month). NDVI values do not simply reflect meteorological drought. However, the spatial co-occurrence of meteorological drought with drought defined on the basis of the NDVI index was demonstrated. The study presents a new approach to identifying drought characterized by SPI and NDVI based on the bivariate choropleth map method, which can indicate the actual places of drought occurrence. The study was carried out for the upper Noteć catchment located in Central Poland.

Predicting Stroke Risk Based on ICD Codes Using Graph-Based Convolutional Neural Networks

In recent years, convolutional neural networks (CNNs) have emerged as highly efficient architectures for image and audio classification tasks, gaining widespread adoption in state-of-the-art methodologies. While CNNs excel in machine learning scenarios where the data representation exhibits a grid structure, they face challenges in generalizing to other data types. For instance, they struggle with data structured on 3D meshes (e.g., measurements from a network of meteorological stations) or data represented by graph structures (e.g., molecular graphs). To address such challenges, the scientific literature proposes novel graph-based convolutional network architectures, extending the classical convolution concept to data structures defined by graphs. In this paper, we use such a deep learning architecture to examine graphs defined using the ICD-10 codes appearing in the medical data of patients who suffered hemorrhagic stroke in Hungary in the period 2006–2012. The purpose of the analysis is to predict the risk of stroke by examining a patient’s ICD graph. Finally, we also compare the effectiveness of this method with classical machine learning classification methods. The results demonstrate that the graph-based method can predict the risk of stroke with an accuracy of over 73%, which is more than 10% higher than the classical methods.

Improving the operational forecasts of outdoor Universal Thermal Climate Index with post-processing

The Universal Thermal Climate Index (UTCI) is a thermal comfort index that describes how the human body experiences ambient conditions. It has units of temperature and considers physiological aspects of the human body. It takes into account the effect of air temperature, humidity, wind, radiation, and clothes. It is increasingly used in many countries as a measure of thermal comfort for outdoor conditions, and its value is calculated as part of the operational meteorological forecast. At the same time, forecasts of outdoor UTCI tend to have a relatively large error caused by the error of meteorological forecasts. In Slovenia, there is a relatively dense network of meteorological stations. Crucially, at these stations, global solar radiation measurements are performed continuously, which makes estimating the actual value of the UTCI more accurate compared to the situation where no radiation measurements are available. We used seven years of measurements in hourly resolution from 42 stations to first verify the operational UTCI forecast for the first forecast day and, secondly, to try to improve the forecast via post-processing. We used two machine-learning methods, linear regression, and neural networks. Both methods have successfully reduced the error in the operational UTCI forecasts. Both methods reduced the daily mean error from about 2.6∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}C to almost zero, while the daily mean absolute error decreased from 5∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}C to 3∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}C for the neural network and 3.5∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}C for linear regression. Both methods, especially the neural network, also substantially reduced the dependence of the error on the time of the day.

Trend and Seasonality of Hip Fractures in Catalonia, Spain: Exploring the Influence of Climate

To describe the secular trend and seasonality of the incidence of hip fracture (HF) and its relationship with climatic variables during the period 2010–2019 in Catalonia in people aged ≥ 65 years. The results were analyzed by sex, age groups (65–74, 75–84, and ≥ 85), and types of fracture (extracapsular and intracapsular). Data on sex, age, type of fracture, year, and month of hospitalization of patients admitted with a diagnosis of HF between January 1, 2010 and December 31, 2019 were collected. Crude and standardized HF incidence (HFi) rates were obtained. Data on the monthly mean of climatological variables (temperature, insolation, icy days, rain, relative humidity, atmospheric pressure, and wind force) were obtained from the network of meteorological stations in Catalonia. Time series analytical statistics were used to identify trends and seasonality. Linear regression and a seasonal autoregressive integrated moving average (ARIMA) were used to analyze the relationship of each climatic parameter with fracture rates. In addition, generalized additive models were used to ascertain the best predictive model. The total number of HF episodes was 90,149 (74.1% in women and 25.9% in men). The total number of HFs increased by 6.4% between 2010 and 2019. The median age (SD) was 84.5 (7.14) and 54% of patients were ≥ 85 years of age. Extracapsular fractures were the most common (55%). The standardized incidence rates decreased from 728.1/100,000 (95% CI 738.6–769.3) to 624.5/100,000 (95% CI 648.7–677.0), which represents a decrease of 14.2% (p < 0.05). The decline was greater at older ages. There were seasonal variations, with higher incidences in autumn (27.2%) and winter (25.7%) and lower rates in summer (23.5%) and spring (23.6%). Seasonality was more pronounced in elderly people and men. In the bivariate regression analysis, high temperatures and greater insolation were negatively associated with the HF rate, while the number of icy days, rainy days, and high relative humidity were associated with a higher incidence of fractures in all age groups and sexes. In the regression analysis using the seasonal ARIMA model, only insolation had a consistently significant association with overall HFi, after adjusting by trend and other climatic parameters. While the global number of HFs grew in Catalonia due to increases in the elderly population, the standardized HF rate decreased during the years 2010–2019. There was a seasonal trend, with predominance in the cold months and correlations with climatic parameters, especially with insolation.

Performance Evaluation of High-resolution Reanalysis Datasets Over North-central British Columbia

abstract Version 2.1 of Environment and Climate Change Canada’s Canadian Surface Reanalysis (CaSR), based on the Regional Deterministic Reforecast System (RDRS), was implemented in 2022 to provide temporally complete meteorological data over 1980–2018 covering Canada at 10 km spatial resolution. Similarly, the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-Land (ERA5-Land) dataset at ∼9 km spatial resolution became available. To assess their performance in complex topography, this paper undertakes spatiotemporal inter-comparisons between the RDRS and ERA5-Land reanalysis products with station-based data across British Columbia’s Skeena and Nechako watersheds for 1980–2018. Results reveal persistent cold biases, ranging from −6.2°C to −1.6°C basin-wide, in reanalysis mean annual air temperatures relative to observations, but biases vary in both space and time. Conversely, reanalysis total annual precipitation shows wet biases, ranging from 25% to 59% basin-wide. Analyses generally show wetting trends for observations and ERA5-Land while RDRS exhibits drying trends. Reanalysis datasets achieve better agreement overall with observations over the Nechako Watershed, likely due to its denser network of meteorological stations and less complex terrain than the Skeena Watershed. Despite some deficiencies, the RDRS and ERA5-Land reanalyses remain particularly useful products to assess regional climate variability and climatic change given their generally skilful representation of spatial patterns and temporal trends in meteorological variables across the Nechako and Skeena watersheds.

РОМЕНСЬКЕ ТОВАРИСТВО СІЛЬСЬКИХ ГОСПОДАРІВ, ЙОГО ФІЛІЇ ТА ДОСЛІДНІ ПОЛЯ (1896–1914)

The place and role of the Romenske Rural Cooperative (hereinafter referred to as the Cooperative) and its branches in the reconstruction of the agricultural sector in Posullya are being explored. The Cooperative, established at the end of the 19th century, was one of the first in the region and became a unique phenomenon in agricultural research. From the early months of its activity, the Cooperative contributed to innovative improvements and the dissemination of agricultural technology, implementing progressive forms of management. Its established branches and research fields served as a link between science and practice. The Cooperative comprehensively promoted the development of agriculture in the region. It conducted significant educational work, addressed issues of credit acquisition for peasants, established a center for agricultural surplus, which not only sold products but also provided services for renting complex equipment and machinery. It actively organized agricultural exhibitions and fairs, encouraged farmers with awards, disseminated scientific literature, created a network of meteorological stations, mobile libraries, advisory-veterinary points, and more. The society, founded with the participation of local civic initiative, and its activities can serve as an example of reforming agro-industrial production in the context of the specific Posullya region. The Cooperative made a significant contribution to the development of scientific research, becoming an exemplar in implementing the gained experience into agricultural production.

Cold units and their relationship with wheat grain yield in the Yaqui Valley, Sonora, Mexico

Global warming has the potential to cause the reduction of the cold period available during the winter, which may affect the production of wheat and deciduous fruit trees. Wheat requires accumulated cold units (CU) to switch from a vegetative period to a productive one, making the phenological cycle slower and promoting greater productive yield. The objective was to analyze the accumulation of CU and wheat yield during the last five fall-winter agricultural seasons (2018-2019 to 2022-2023) in the Yaqui Valley, Sonora, Mexico. Data of air temperature were obtained from 21 meteorological stations from the network of automatic meteorological stations of the state of Sonora, located in the Yaqui Valley. The number of CU (≤ 10 °C) was calculated for the wheat season (November 15 to April 30) from the years 2018 to 2023, and the grain yield was obtained from the reports by the Agri-Food and Fisheries Information Service. Descriptive statistics of central tendency, dispersion and the Shapiro-Wilk test were performed to determine the normality of the data; also, correlation analysis and the simple linear regression model by least squares were performed. The 2022-23 crop season showed the highest average grain yield (7.79 t ha-1) and the highest average number of CU (882) among the five seasons analyzed. Wheat seasons 2018-2019 and 2019-2020 had an average number of CU lower than 500. The Pearson correlation analysis showed a highly positive correspondence between CU and grain yield. The linear regression model showed that starting from a base of 434 CU, wheat production per hectare would be 5.90 t, and it will increase 214.3 kg for every additional 100 CU.

Modern changes in climatic characteristics and minimum flow of the rivers in the basin of lake Sevan

Formulation of the problem. Studies and data on modern changes in the winter minimum average monthly flow of the rivers of the Lake Sevan basin and their climatic characteristics have acquired great practical importance and considerable interest due to the increased use of water resources for the purposes of various types of economic activities, in particular, in connection with the development of the economy, hydropower, irrigation, population growth and rising living standards. Especially relevant are the calculations of the minimum flow in relation to solving the problems of water supply for settlements, industrial enterprises. The aim of the work. Assessment of modern changes in the winter minimum average monthly runoff of rivers in the Lake Sevan basin and their climatic characteristics. Methods. The following methods were used in the work: mathematical-statistical, extrapolation, interpolation, spatial analysis, analogy, correlation and cartography. Results. A physical map has been compiled with a network of meteorological stations and hydrological posts in the Lake Sevan basin. Close correlations were obtained between the values of the winter minimum average monthly runoff and the average runoff of the winter period for a hydrological year. These dependences can be used for preliminary estimates of the minimum runoff of the winter low-water period of unstudied rivers in the territory under consideration. In the course of the research, the winter minimum average monthly discharges were calculated by months and for the entire observation period, the average value of the minimum runoff for the winter low water, and the linear trend of the climatic characteristic. Based on the obtained results, a map of the distribution of the modul of the average minimum runoff of the winter low-water period and the coefficients of the linear trend of the winter air temperature were constructed. Examples of curves of long-term changes in precipitation amounts for the autumn and winter periods, the sum of positive temperatures and the number of days with a positive average daily air temperature in the winter period for a series of meteorological stations in the study area are presented. The rivers of the Lake Sevan basin are characterized by an uneven spatio-temporal distribution of winter minimum runoff and climatic characteristics, changes in the winter average monthly minimum runoff, precipitation amounts for the autumn and winter periods are multidirectional, and air temperature changes are only an upward trend. In the area under consideration, there is mainly (by 8 out of 12 stations under study) a tendency to decrease in the values of the minimum average monthly river flow. The conducted studies lead us to the conclusion that the main factor causing changes in the low-water runoff of the rivers in the Lake Sevan basin in winter is the air temperature, or rather, its increase. It follows from this that when calculating and forecasting winter runoff, it is necessary to relate the values of runoff and air temperature.

Significance and issues of automatic snow cover measurement – case study for the Šumava Mountains region

The study evaluates the contribution of automatic snow measuring stations for snow monitoring in the Šumava region. Using climatological maps, the results of spatial analysis of the maximum depth of the snow cover for six winter seasons were compared, namely first from the measured data of the basic CHMI stations and second from the data of all stations. The biggest differences occurred in the comparison at the highest locations on the windward side of the Šumava (&gt;1 200 m a.s.l.), where there is no corresponding network of basic meteorological stations. In the case of the snow-poor winter season 2020/21, these differences were relatively higher than in the 2018/19 season, with more extensive snow cover in all positions. The evaluation of the entire period (2017/18–2022/23) produced similar results, as snow-poor winter seasons prevailed here. We can therefore assume that the differences in snow characteristics will continue to be rather significant in the Šumava region. Accordingly, the importance of automatic snow measurement will also increase, especially in higher altitudes of the windward border area. In addition to the expansion of the measuring network and the significant refinement of information about the snow cover, especially in the mountains, the main benefit of data on the depth of snow from automatic stations is their operability and immediate use for other applications.

On the Use of Reanalysis Data to Reconstruct Missing Observed Daily Temperatures in Europe over a Lengthy Period of Time

In this study, a methodology that can reconstruct missing daily values of maximum and minimum temperatures over a long time period under the assumption of a sparse network of meteorological stations is described. To achieve this, a well-established software used for quality control, homogenization and the infilling of missing climatological series data, Climatol, is used to combine a mosaic of data, including daily observations from 15 European stations and daily data from two high-resolution reanalysis datasets, ERA5-Land and MESCAN-SURFEX; this is in order reconstruct daily values over the 2000–2018 period. By comparing frequently used indices, defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in studies of climate change assessment and goodness-of-fit measures, the reconstructed time series are evaluated against the observed ones. The analysis reveals that the ERA5-Land reconstructions outperform the MESCAN-SURFEX ones when compared to the observations in terms of biases, the various indices evaluated, and in terms of the goodness of fit for both the daily maximum and minimum temperatures. In addition, the magnitude and significance of the observed long-term temporal trends maintained in the reconstructions, in the majority of the stations examined, for both the daily maximum and daily minimum temperatures, is an issue of the greatest relevance in many climatic studies.

A statistical analysis of the distribution of convective windstorms and their radar characteristics in Hubei Province, China. Part 2

A statistical analysis of the distribution of convective windstorms and their radar characteristics in Hubei Province, China. Part 2

Three-reference-point based group ELECTRE III method for urban flood resilience evaluation

In order to cope with the growing problem of flood disasters, it is crucial to improve the urban flood resilience. Hence, this paper develops a novel evaluation methodology to find the weak links and capacity gaps of urban flood resilience. Firstly, according to the three periods of flood, the urban flood resilience evaluation indicator system is divided into the predictive capacity subsystem before the flood, the absorptive and coping capacity subsystem and the recovery capacity subsystem during the flood, and the adaptive capacity subsystem after the flood. This indicator system also creatively introduces the density of hydrological and meteorological station networks into the predictive capacity subsystem, and divides the adaptive capacity more meticulously into learning capacity at the governmental and individual-society levels. Secondly, a novel three-reference-point (T-R-P) based group ELECTRE III method is proposed to rank the flood resilience of different cities. This method integrates subjective and objective information to obtain more reasonable expert weights, and then sets thresholds with T-R-P to obtain more comprehensive and objective evaluation results. Furthermore, the proposed methodology is applied to six flood-prone cities in China, and its practicality and availability are verified through comparative analysis with other three evaluation methods and sensitivity analysis of the indicator weights. Finally, based on the results, targeted suggestions are made to enhance the flood resilience of the six cities.

Accuracy Assessment of a Satellite-Based Rain Estimation Algorithm Using a Network of Meteorological Stations over Epirus Region, Greece

The study concerns the quantitative evaluation of a satellite-based rain rate (RR) estimation algorithm using measurements from a network of ground-based meteorological stations across the Epirus Region, Greece, an area that receives among the maximum precipitation amounts over the country. The utilized version of the rain estimation algorithm uses the Meteosat-11 Brightness Temperature in five spectral regions ranging from 6.0 to 12.0 μm (channels 5–7, 9 and 10) to estimate the rain intensity on a pixel basis, after discriminating the rain/non-rain pixels with a simple thresholding method. The rain recordings of the meteorological stations’ network were spatiotemporally correlated with the satellite-based rain estimations, leading to a dataset of 2586 pairs of matched values. A statistical analysis of these pairs of values was conducted, revealing a Mean Error (ME) of −0.13 mm/h and a correlation coefficient (CC) of 0.52. The optimal computed Probability of False Detection (POFD), Probability of Detection (POD), the False Alarm Ratio (FAR) and the bias score (BIAS) are equal to 0.32, 0.88, 0.12 and 0.94, respectively. The study of the extreme values of the RR (the highest 10%) also shows satisfactory results (i.e., ME of 1.92 mm/h and CC of 0.75). The evaluation statistics are promising for operationally using this algorithm for rain estimation on a real-time basis.

Calibration of COSMO model in the Central-Eastern Mediterranean area adjusted over the domains of Greece and Israel

The goal of this work is to optimize five parameters of expected high impact on COSMO model performance in non-hydrostatic regime using a calibration method based on the so called Meta-Model. The domain of consideration covers the Central-Eastern Mediterranean area with a horizontal mesh of 0.030 (~3.3 km). The optimization technique considers observations at twenty-two Greek and sixty-four Israeli meteorological stations of 24 h accumulated precipitation, minimum and maximum 2-m dry and dew-point temperatures at sixty dates spread over the year 2019, five for every month, in order to comply with the seasonal variability. The optimum model parameters found have been evaluated by application on an independent set of sixty days chosen from the same year-period and in the same manner. An overall model performance score improvement of order 5% was obtained for both sets of test cases. Better verification scores were found regarding almost all meteorological fields considered with respect to average values, mean absolute and standard deviation errors, as well as threat scores regarding precipitation. The use of relatively sparse local observations, instead of the gridded data sets available to places with dense meteorological station networks where the methodology has been successfully applied in numerical weather prediction and climatology, makes this approach, upon its successful realization, suitable to a significantly larger community of model users with modest observation resources. This approach is also expected to be of value for domains with a large marine area fraction, like the domain chosen, where observations are sporadic, if available, by nature as well as to regions of low population density.

Optimizing Spatial Quality Control for a Dense Network of Meteorological Stations

Abstract Crowdsourced meteorological observations are becoming more prevalent and in some countries their spatial resolution already far exceeds that of traditional networks. However, due to the larger uncertainty associated with these observations, quality control (QC) is an essential step. Spatial QC methods are especially well suited for such dense networks since they utilize information from nearby stations. The performance of such methods usually depends on the choice of their parameters. There is, however, currently no specific procedure on how to choose the optimal settings of such spatial QC methods. In this study we present a framework for tuning a spatial QC method for a dense network of meteorological observations. The method uses artificial errors in order to perturb the observations to simulate the effect of having errors. A cost function, based on the hit and false alarm rate, for optimizing the spatial QC method is introduced. The parameters of the spatial QC method are then tuned such that the cost function is optimized. The application of the framework to the tuning of a spatial QC method for a dense network of crowdsourced observations in Denmark is presented. Our findings show that the optimal settings vary with the error magnitude, time of day, and station density. Furthermore, we show that when the station network is sparse, a better performance of the spatial QC method can be obtained by including crowdsourced observations from another denser network.

Modeling of the Calm Situations in the Atmosphere of Almaty

This article addresses modeling of the atmospheric boundary layer of the city of Almaty (Kazakhstan) in stagnant, environmentally unfavorable conditions using WRF Model. The city is located on the northern slope of Trans-Ili Alatau, where the rate of recurrence of calm and low-wind (1–2 m/sec) days reaches about 80%. All simulations were made for a period from 28.11.2016 to 05.12.2016, covering main synoptic situations of the stagnant atmosphere: the extent of Asian anticyclone, higher and lower pressure gradient fields. The model integrated three nested domains with grid sizes 9, 3 and 1 km, respectively. The initial boundary conditions were formed based on ERA5-reanalysis. Subject to the WRF model requirements, the land-use map with a standard USGS set (24 categories) was developed, to which 3 categories of the urban areas were added. The most relevant configuration of parameterization methods was selected: short-wave and long-wave radiation (Mlawer), surface layer (Monin-Obukhov similarity theory), urban area (BEP), boundary layer (Bougeault-Lacarrere), turbulence (Smagorinsky). The article demonstrates that the WRF model adequately reflects fundamental urban atmosphere patterns in the most unfavorable anticyclone periods of the autumn-winter season. It was established that the accuracy of estimates decreases with the transition to weak cyclonic activity. Based on the simulation results and remote sensing data, the territory in question is divided into four climatic zones to which a comparative method was applied; however for a detailed correlative analysis a denser network of meteorological stations is required. Calculations showed that the wind along the Ili river valley prevails in the northern part, regularly changing its western direction to eastern. Near the mountain area mountain-valley wind circulation prevails. The blocking inversion layer has a strong impact. The urban heat islands strongly depend on wind conditions. For example, a nocturnal heat island is cooled by the cold wind flow from the mountains.

Beijing — Port-Arthur — Vladivostok: Organization of the Academy of Sciences’ Magnetic and Meteorological Observatories in the Far East in the 19th–Early 20th Сenturies

The professional tool’s investigations of climate in the world began relatively recently. Since the second half of the XIX century Russia was one of the meteorological investigation’s leaders. Study of the history of meteorology in the Russian Far East and in China, systematization, and analysis of research results would be a great contribution to a better understanding of the importance of Russian research in the development of meteorology. The article discusses about the background of the creation of the observatory in Vladivostok. The author analyzed the chain of political and scientific decisions about the transfer of the magnetic and meteorological observatory of the Academy of Sciences from Beijing, through the plans of the creation an observatory in Port Arthur to the construction of an observatory in Vladivostok. In addition, the observatory’s budget and plans for its equipment are considered. Such materials are usually not the subject of close attention of researchers. In the historiography lacks an investigation about the role of the Russian Academy of Sciences to the development of a network of magnetic meteorological stations and observatories in the Russian Far East and in adjacent territories (Korea, China). The author introduced into scientific circulation new archival data from the Russian State Archive of the Navy and the St. Petersburg branch of the Archive of the Academy of Sciences.