Observed Series Data Research Articles

Improving the accuracy of wind speed spatial interpolation: A pre-processing algorithm for wind speed dynamic time warping interpolation

Wind power is one of the most vital renewable energy resources in the world. Wind energy production is directly correlated with the quality and quantity of wind speed data. Interpolation techniques can be employed to fill in the gaps in the current wind speed observation data series. However, existing methods for obtaining blank data do not pre-regulate the regional spatial wind speed sequence and instead rely on direct interpolation, which leads to low accuracy in the interpolation. This is not a problem with the model itself, but rather with the wind speed flowing in space and exhibiting sequence misalignment on the timeline. To address this issue, this study proposes a Wind Speed Dynamic Time Warping (WSDTW) algorithm based on Dynamic Time Warping (DTW) to match similar wind speed reduction sequences in terms of time error. We used the shape context descriptor to encode wind speed and introduced wind rose descriptors to represent wind direction initially. The matching cost of DTW was then optimized. Finally, five common interpolation methods were selected to evaluate the method. The research results indicate that interpolation after WSDTW matching and warping can significantly improve the accuracy of wind speed interpolation and reduce the spatial dependence of wind speed. This method demonstrates good stability and generalization, performing exceptionally well in situations where there are gaps in regional wind speed data or missing data.

Effects of land surface temperatures on vegetation phenology along urban–rural local climate zone gradients

ContextUrbanization and local urban climate have multiple impacts on vegetation phenology in urban and suburban areas. Understanding these effects and their interactions with the surface urban heating effect remains limited.ObjectiveWe employed a time series of Earth observation data to analyze land surface phenology (LSP) dynamics and related environmental drivers in the highly urbanized Pearl River Delta (PRD) region.MethodsFirst, local climate zone (LCZ) maps were generated from Earth observation datasets of 2000 and 2019. Second, LSP (i.e., start, end, and length of season) were extracted from vegetation indices for 2000–2019. Thirdly, land surface temperature (LST) was used as an explanatory variable based on the LCZ of cities. Finally, interannual trends of LSP and their association with LST were analyzed, depending on the distance gradient of vegetation to compact high-rise buildings.ResultsUrban surface characteristics showed that LSP in regions dominated by compact and high-rise urban areas presented significant spatiotemporal variation at the start and end of season than those dominated by open, mid-rise, and low-rise areas. The impacts of spring and autumn LST in the daytime on LSP were slightly more substantial than those in the nighttime. The association of decreasing spring LST in the daytime with a delayed start of season is especially pronounced in urban domains.ConclusionsThe results indicate that vegetated areas adjacent to urban domains presented greater spatiotemporal dynamics than suburban and rural regions. Our study emphasizes the dependence of spatiotemporal changes in vegetation phenology on the effects of urban surface warming.

Detection and evaluation of anthropogenic impacts on natural forest ecosystems from long-term tree-ring observations

Anthropogenic interventions lead to various direct and indirect impacts on natural ecosystems that are often hindered by natural long-term variability, and thus their detection and evaluation remain challenging. Ecological systems are strongly affected by climate variations that typically exhibit long-term correlations capable of imitating or hindering external trends in finite-time observations, thus complicating their detection and correct attribution to either anthropogenic interventions or natural variability. Here we focus on the quantitative assessment of the alterations in the tree-ring width (TRW) of four tree species in response to the changes in the soil water regime following a drainage experiment in a dwarf-shrub type peatland forest. We consider the long-term effects of the intervention, focusing on two characteristic quantities: the durations of clusters when significant discrepancies with relevant controls could be observed in every single consecutive year, and relative trends in the data reflecting long-term, gradual changes in the ecosystem. By extrapolating pre-drainage TRW dynamics and adjusting for recent climate variations using a multivariate model, we simulate surrogate data series that act as additional controls for the post-drainage time period. By comparing the long-term dynamics of the observational TRW data series against both natural and surrogate controls over several decades following the drainage experiment, we evaluate long-term alterations and gradual trends in the tree growth dynamics and reassess the statistical significance of these effects, taking into account long-term correlations in the natural TRW variations. Our results also indicate pronounced alterations in the drought stress response characterized by significant negative trends in the tree growth dynamics following the 2010 heatwave and associated flash drought in the drained area, while no similar effect could be observed in the undrained area, indicating that the increased productivity of the forest ecosystem following the drainage likely comes at the cost of its reduced drought stress resilience.

Assessing atmospheric gravity wave spectra in the presence of observational gaps

Abstract. We present a thorough investigation into the accuracy and reliability of gravity wave (GW) spectral estimation methods when dealing with observational gaps. GWs have a significant impact on atmospheric dynamics, exerting influence over weather and climate patterns. However, empirical atmospheric measurements often suffer from data gaps caused by various factors, leading to biased estimations of the spectral power-law exponent (slope) β. This exponent describes how the energy of GWs changes with frequency over a defined range of GW scales. In this study, we meticulously evaluate three commonly employed estimation methods: the fast Fourier transform (FFT), generalized Lomb–Scargle periodogram (GLS), and Haar structure function (HSF). We assess their performance using time series of synthetic observational data with varying levels of complexity, ranging from a signal with one frequency to a number of superposed sinusoids with randomly distributed wave parameters. By providing a comprehensive analysis of the advantages and limitations of these methods, our aim is to provide a valuable roadmap for selecting the most suitable approach for accurate estimations of β from sparse observational datasets.

Statistical Analysis of Local Extrema in Rough Sea Surfaces Based on Computer Simulation

Introduction. Generalized extreme value (GEV) distributions represent a universal description of the limiting distribution of the normalized local maxima statistics for independent and identically distributed data series. Extreme value distributions are commonly classified into three different types representing different functional forms and thus varying in shape, also known as types I, II, and III. Thus, attribution of some observational data series to a particular type of its local maxima distribution, as well as fitting of the distribution parameters, provides certain information about the laws governing the underlying natural or technogenic process. Radar-based remote sensing techniques represent a ubiquitous tool for analyzing large patterns of the sea surface and determining the parameters of the waves. In turn, understanding the laws governing the extreme values in the rough sea surface obtained from their radar images followed by evaluation of their distribution parameters, depending on the wind speed and direction, as well as the presence of surface currents and swells, can be useful for predicting wave height. Aim. Analysis of the functional forms governing the local extreme value distributions in a rough sea surface for the given wind and swell parameters based on computer simulations. Materials and methods. For the rough sea surface simulated by an additive harmonic synthesis procedure, the local extreme value distribution was fitted using the least-mean-squares technique. The fitted parameters were then used for their classification according to the three predetermined types. Results. Computer simulations of a rough sea surface with combined wind and swell waves were performed. It is shown that the distribution of local maxima in the absence of swell waves could be well approximated by theWeibull (type III GEV) distribution, with the parameters explicitly depending on the wind speed. At the same time, no significant dependence on the sea depth was observed. On the contrary, in the presence of additional swell waves, the distribution of local extrema could be rather attributed to the Fréchet (type II GEV) distribution, with the parameters additionally depending on the angle between the wind and swell waves. Conclusion. The laws governing the distributions of local wave extrema in rough seas are in a good agreement with the theoretical GEV approximations, with the distribution parameters being deductible from the key features of the waves. This indicates the predictability of wave height extrema from sea surface measurements, which can be performed based on remote radar observations.

Wavelet Analysis of Ozone Driving Factors Based on ~20 Years of Ozonesonde Measurements in Beijing

A long-term vertical ozone observational dataset has been provided during 2001–2019 by ozonesonde measurements in Beijing on the North China Plain. Previous studies using this dataset primarily focused on the vertical characteristics of climatological ozone and its variation; however, the driving factors of ozone variation have not been well discussed. In this study, by applying the wavelet analysis method (including continuous wavelet transform and cross wavelet) and sliding correlation coefficients to ~20 years of ozonesonde measurements collected in Beijing, we analyzed the dominant modes of ozone column variability within three height ranges over Beijing (total column ozone: TOT; stratospheric column ozone: SCO; and tropospheric column ozone: TCO). Moreover, we also preliminarily discussed the relationship between these three ozone columns and the El Niño Southern Oscillation (ENSO), Quasi-biennial Oscillation (QBO), and 11-year solar activity cycle. The results revealed that the ozone columns within the three height ranges predominantly adhered to interannual variability patterns, and the short-term variabilities in TOT and SCO may have been related to eruptive volcanic activity. In comparison to the TOT and SCO, the TCO was more susceptible to the forcing influences of high-frequency factors such as pollutant transport. Similar to the results in other mid-latitude regions, strong ENSO and QBO signals were revealed in the interannual ozone column variability over Beijing. The TOT and SCO showed positive anomalous responses to ENSO warm-phase events, and the peak of the ENSO warm phase led the winter peaks of the TOT and SCO by approximately 3–6 months. During the strong cold–warm transition phase in 2009–2012, the TOT and SCO showed a significant positive correlation with the ENSO index. The strong seasonality of the meridional circulation process driven by the QBO led to a significant positive correlation between the QBO index and the TOT and SCO in the interannual cycle, except for two periods of abnormal QBO fluctuations in 2010–2012 and 2015–2017, whereas the TCO showed a time-lagged correlation of approximately 3 months in the annual cycle relative to the QBO due to the influence of the thermodynamic tropopause. In addition, analysis of the F10.7 index and the ozone columns revealed that the ozone columns over Beijing exhibited lagged responses to the peaks of sunspot activity, and there was no obvious correlation between ozone columns and 11-year solar activity cycle. Given the complex driving mechanism of the climatic factors on local ozone variability, the preliminary results obtained in this study still require further validation using longer time series of observational data and the combination of chemical models and more auxiliary data.

A Close Binary Supermassive Black Hole Model for the Galaxy 3C 273

The data from the last 60 years on the programs of long-term multi-frequency monitoring of active galactic nucleus (AGN) 3C 273 were analyzed. A model is proposed for finding the parameters of close binary systems (CBSs) from supermassive black holes (SMBHs), including a harmonic analysis of observational data series obtained in the optical and radio ranges. The purpose of this research was to show that in the absence of optical information on AGNs, only radio data can be used and the necessary information on the physical objects can be obtained. Regarding the example of the blazar 3C 273, the following parameters were obtained: the masses of the companions; their orbital characteristics, such as the speeds of movement in orbits; the reserves of the kinetic energy of the system; and others. It was found that AGN 3C 273 can be a very massive binary system at the stage of evolution close to merging. Based on the obtained parameters, the characteristics of the gravitational waves (GWs) of this system, its lifetime before the merger, and the possible observation of 3C 273 using gravitational wave detectors were considered.

Method of Development of a New Regional Ionosphere Model (RIM) to Improve Static Single-Frequency Precise Point Positioning (SF-PPP) for Egypt Using Bernese GNSS Software

Due to the lack of coverage of IGS in Africa, especially over North Africa, and the construction revolution of infrastructure in Egypt, a geodetic CORS stations network was established in 2012. These CORS stations are operated by the Egyptian Surveying Authority (Egy. SA) and cover the whole of Egypt. The paper presents a fully developed regional ionosphere model (RIM) depending on the Egyptian CORS stations. The new model and the PPP solution were obtained using Bernese GNSS V. 5.2 software. An observation data series of eight days (DOY 201–208)/2019 was used in this study. Eighteen stations were used to develop the RIM model for each day; fifteen stations were used to validate the new RIM model. A static SF-PPP solution was obtained using the CODE-GIM and RIM models. Comparing the outcomes to the reference network solution, based on the recently developed RIM model, the solution showed a mean error of 0.06 m in the East direction, 0.13 m in the North direction, and 0.21 m in the height direction. In the East, North, and height directions, this solution improves the SF-PPP result achieved by the Global Ionosphere Maps (CODE-GIM) model by 60%, 68%, and 77%, respectively.

მინიმალური ხარჯების შედარებითი ანალიზი ეკოლოგიური ხარჯის განსაზღვრისათვის მდ. ალაზნის (შაქრიანი); მტკვრისა (ხერთვისი) და ფარავნის (ხერთვისი) მაგალითზე

Minimal runoff plays a crucial role in designing industrial and domestic water supply, to determine minimal discharge of water in rivers observation data of winter and summer-autumn seasons are used. There are selected the stations of rivers which have the longest observed data series. These rivers are Alazani (H/S shakriani), river Mtkvari (H/S Khertvisi), river Paravani (H/S Khertvisi). As a result of actual observed data of minimum discharges were allocated to the research rivers, such as: · 30-day minimum discharge (non-calendar year) · Instantaneous minimum discharge · Minimum discharge taken from daily average data. Nowadays, in Georgia there is no legal basis and corresponding normative-technical documentation for determining the normative values of discharge of water discharged for ecological purposes which of course is the cause of numerous problems in terms of conservation and use of water resources. Therefore, it is crucially important to take into consideration the complex relationship between minimum and environmental flows.

Construction of a Time-Variant Integrated Drought Index Based on the GAMLSS Approach and Copula Function

Construction of an integrated drought index is a fundamental task to conducting drought disaster risk management and developing drought resistance planning strategies. Given the evident non-consistent features during the drought evolution process, firstly, the GAMLSS approach was utilized to construct multiple combination scenarios of time-variant parameters and corresponding probability distribution functions. Then, the time-variant comprehensive drought index integrating the variable characteristics of precipitation and soil moisture was established by means of the copula function. Finally, the reliability of the time-variant comprehensive drought index was verified through its application in frequency analysis and return period determination of drought hazard system in Huaibei Plain, China. The application results demonstrated that: (1) The variation of the time-variant integrated drought indicator presented strong consistency with both soil moisture and precipitation during historical years in Huaibei Plain. (2) The overall variation of the drought hazard system characterized by drought duration and severity presented a gradual mitigation trend from west to east and north to south in Huaibei Plain, which agrees with the geographic differences and water resources availability distribution features. (3) Drought recognition results, including the frequency of drought events and typical drought processes with extreme grades, are in agreement with the practical statistics and observed data series. On the whole, the proposed time-variant integrated drought indicator is capable of extracting complex variation characteristics within the drought hazard evolution process, and can be further applied in drought monitoring, recognition and assessment research fields.

A scalable software package for time series reconstruction of remote sensing datasets on the Google Earth Engine platform

ABSTRACT Spatiotemporal residual noise in terrestrial earth observation products, often caused by unfavorable atmospheric conditions, impedes their broad applications. Most users prefer to use gap-filled remote sensing products with time series reconstruction (TSR) algorithms. Applying currently available implementations of TSR to large-volume datasets is time-consuming and challenging for non-professional users with limited computation or storage resources. This study introduces a new open-source software package entitled ‘HANTS-GEE’ that implements a well-known and robust TSR algorithm, i.e. Harmonic ANalysis of Time Series (HANTS), on the Google Earth Engine (GEE) platform for scalable reconstruction of terrestrial earth observation data. Reconstruction tasks can be conducted on user-defined spatiotemporal extents when raw datasets are available on GEE. According to site-based and regional-based case evaluation, the new tool can effectively eliminate cloud contamination in the time series of earth observation data. Compared with traditional PC-based HANTS implementation, the HANTS-GEE provides quite consistent reconstruction results for most terrestrial vegetated sites. The HANTS-GEE can provide scalable reconstruction services with accelerated processing speed and reduced internet data transmission volume, promoting algorithm usage by much broader user communities. To our knowledge, the software package is the first tool to support full-stack TSR processing for popular open-access satellite sensors on cloud platforms.

Predicting the Functional Dependence of the Sunspot Number in the Solar Activity Cycle Based on Elman Artificial Neural Network

The possibility of predicting the function of the time dependence of the sunspot number (SSN) inthe solar activity cycle is analyzed based on the application of the Elman artificial neural network platform tothe historical series of observational data. A method for normalizing the initial data for preliminary trainingof the ANN algorithm is proposed, in which a sequence of virtual idealized cycles is constructed using scaledduration coefficients and the amplitude of solar cycles. The correctness of the method is analyzed in a numericalexperiment based on modeling the time series of sunspots. The intervals of changing the adaptableparameters in the ANN operation are estimated and a mathematical criterion for choosing a solution is proposed.The significant asymmetry of its ascending and descending branches is a characteristic property of theconstructed functional dependence of the sunspot number cycle. A forecast of the time course for the current25th cycle of solar activity is presented and its correctness is discussed in comparison with other forecastresults and the available data of solar activity status monitoring

The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland

Abstract. Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are prone to inhomogeneities that can influence and even change trends if not taken into account. In order to assess the relevance of homogenisation for time-series analysis of daily snow depths, we investigated the effects of adjusting inhomogeneities in the extensive network of Swiss snow depth observations for trends and changes in extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depths in the period 1961–2021. Three homogenisation methods were compared for this task: Climatol and HOMER, which apply median-based adjustments, and the quantile-based interpQM. All three were run using the same input data with identical break points. We found that they agree well on trends of seasonal average snow depth, while differences are detectable for seasonal maxima and the corresponding extreme values. Differences between homogenised and non-homogenised series result mainly from the approach for generating reference series. The comparison of homogenised and original values for the 50-year return level of seasonal maximum snow depth showed that the quantile-based method had the smallest number of stations outside the 95 % confidence interval. Using a multiple-criteria approach, e.g. thresholds for series correlation (>0.7) as well as for vertical (<300 m) and horizontal (<100 km) distances, proved to be better suited than using correlation or distances alone. Overall, the homogenisation of snow depth series changed all positive trends for derived series of snow days to either no trend or negative trends and amplifying the negative mean trend, especially for stations >1500 m. The number of stations with a significant negative trend increased between 7 % and 21 % depending on the method, with the strongest changes occurring at high snow depths. The reduction in the 95 % confidence intervals of the absolute maximum snow depth of each station indicates a decrease in variation and an increase in confidence in the results.

Seguimiento de la biodiversidad en la era del Big Data

The monitoring and evaluation of the distribution, structure and functioning of biodiversity, as well as the effects exerted by different pressures and threats on its conservation and dynamics in a context of Global Change, constitute one of the greatest challenges of current science and a critical axis in the agendas of numerous government agencies. Given the importance of having reliable, representative and temporally dynamic data to respond to these challenges, Ecology, like other branches of Biology and Earth Sciences, has recently echoed the phenomenon known as "Big Data" or "Data Science". In Ecology, this massive dataset relates to several sources of information: a) time series of remote observation data; b) aggregation of local and regional field or experimental data; c) long-term monitoring networks with national and international field data; d) automated sensor networks, and, e) Citizen Science initiatives, among others. Together, Big Data amplifies the spatio-temporal scale of issues related to biodiversity monitoring and management, making it possible to cover aspects and scales that were unthinkable only a few years ago. However, its application in Ecology still presents great challenges and there is currently an intense debate about its applicability at scales related to the management of spaces and species. Through its pages, this review tries to present the current state of the science on monitoring biodiversity of terrestrial ecosystems and its sources of information, as well as its synergies, applications, analysis methodologies, challenges and opportunities in the era of Big Data.

Developing the Role of Earth Observation in Spatio-Temporal Mosquito Modelling to Identify Malaria Hot-Spots

Anopheles mosquitoes are the vectors of human malaria, a disease responsible for a significant burden of global disease and over half a million deaths in 2020. Here, methods using a time series of cost-free Earth Observation (EO) data, 45,844 in situ mosquito monitoring captures, and the cloud processing platform Google Earth Engine are developed to identify the biogeographical variables driving the abundance and distribution of three malaria vectors—Anopheles gambiae s.l., An. funestus, and An. paludis—in two highly endemic areas in the Democratic Republic of the Congo. EO-derived topographical and time series land surface temperature and rainfall data sets are analysed using Random Forests (RFs) to identify their relative importance in relation to the abundance of the three mosquito species, and they show how spatial and temporal distributions vary by site, by mosquito species, and by month. The observed relationships differed between species and study areas, with the overall number of biogeographical variables identified as important in relation to species abundance, being 30 for An. gambiae s.l. and An. funestus and 26 for An. paludis. Results indicate rainfall and land surface temperature to consistently be the variables of highest importance, with higher rainfall resulting in greater mosquito abundance through the creation of pools acting as mosquito larval habitats; however, proportional coverage of forest and grassland, as well as proximity to forests, are also consistently identified as important. Predictive application of the RF models generated monthly abundance maps for each species, identifying both spatial and temporal hot-spots of high abundance and, by proxy, increased malaria infection risk. Results indicate greater temporal variability in An. gambiae s.l. and An. paludis abundances in response to seasonal rainfall, whereas An. funestus is generally more temporally stable, with maximum predicted abundances of 122 for An. gambiae s.l., 283 for An. funestus, and 120 for An. paludis. Model validation produced R2 values of 0.717 for An. gambiae s.l., 0.861 for An. funestus, and 0.448 for An. paludis. Monthly abundance values were extracted for 248,089 individual buildings, demonstrating how species abundance, and therefore biting pressure, varies spatially and seasonally on a building-to-building basis. These methods advance previous broader regional mosquito mapping and can provide a crucial tool for designing bespoke control programs and for improving the targeting of resource-constrained disease control activities to reduce malaria transmission and subsequent mortality in endemic regions, in line with the WHO’s ‘High Burden to High Impact’ initiative. The developed method was designed to be widely applicable to other areas, where suitable in situ mosquito monitoring data are available. Training materials were also made freely available in multiple languages, enabling wider uptake and implementation of the methods by users without requiring prior expertise in EO.

Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States

Abstract. Accurate river streamflow forecasts are a vital tool in the fields of water security, flood preparation and agriculture, as well as in industry more generally. Traditional physics-based models used to produce streamflow forecasts have become increasingly sophisticated, with forecasts improving accordingly. However, the development of such models is often bound by two soft limits: empiricism – many physical relationships are represented empirical formulae; and data sparsity – long time series of observational data are often required for the calibration of these models. Artificial neural networks have previously been shown to be highly effective at simulating non-linear systems where knowledge of the underlying physical relationships is incomplete. However, they also suffer from issues related to data sparsity. Recently, hybrid forecasting systems, which combine the traditional physics-based approach with statistical forecasting techniques, have been investigated for use in hydrological applications. In this study, we test the efficacy of a type of neural network, the long short-term memory (LSTM), at predicting streamflow at 10 river gauge stations across various climatic regions of the western United States. The LSTM is trained on the catchment-mean meteorological and hydrological variables from the ERA5 and Global Flood Awareness System (GloFAS)–ERA5 reanalyses as well as historical streamflow observations. The performance of these hybrid forecasts is evaluated and compared with the performance of both raw and bias-corrected output from the Copernicus Emergency Management Service (CEMS) physics-based GloFAS. Two periods are considered, a testing phase (June 2019 to June 2020), during which the models were fed with ERA5 data to investigate how well they simulated streamflow at the 10 stations, and an operational phase (September 2020 to October 2021), during which the models were fed forecast variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), to investigate how well they could predict streamflow at lead times of up to 10 d. Implications and potential improvements to this work are discussed. In summary, this is the first time an LSTM has been used in a hybrid system to create a medium-range streamflow forecast, and in beating established physics-based models, shows promise for the future of neural networks in hydrological forecasting.

An analysis of the shadow effect for the short-offset transient electromagnetic method

The electromagnetic methods based on long-wire sources, whether in the time or frequency domain, are mainly detected in the far-source zone. Because the observation array is far away from the transmitting source, the amplitude and bandwidth of the signal have a large loss, which limits the detection depth and accuracy of these methods. In the near-source zone, the loss of signal amplitude and bandwidth is relatively small, conducive to improving the detection performance. However, the complex excitation field in the near-source zone makes the observation seriously affected by the source effect. This paper mainly researched the influence of the shadow effect (one of the source effects) on the observation of the short-offset transient electromagnetic method (SOTEM), which is a near-source time-domain method. The results show that the shadow effect has relatively limited influence only on the specific observation channels of the time series of observation data. Based on this, the research on eliminating shadow effects under single / multi-source, multi location and multi polarization excitation modes is carried out. The results show that using a single excitation source with multiple polarization directions or using excitation sources with different polarization directions can improve the detection accuracy of underground targets. All of these lays a foundation for further development of the SOTEM method.

Неблагоприятные и опасные гидрологические явления р. Яны в районе поселка Усть-Куйга

Purpose: Generalization of characteristics of the Yana Rive hydrological regimen and assessment of unfavorable and dangerous hydrological phenomena in the area of Ust-Kuiga township. The township is an important transport transshipment base between the three Yakut districts: Ust-Yanskiy, Verkhoyanskiy and Eveno-Bytantaiskiy, and is promising in terms of the development of the Sakha Republic main economic sectors, including development of gold deposits. Methods: Collection, systematization, processing and statistical analysis of published materials of hydrological observations at the Ust-Kuiga stand and at the nearest stands, located on the Yana River and having a long series of observation data. Results: Characteristics of water extreme levels and expenditures during different phases of hydrological regimen, data on ice phenomena and knowledge on the largest spate causing township flooding were obtained. It has been established that the number of years with open riverbed short duration (from 93 to 98 days), which’s the least favorable for cargo delivery by river transport, constitutes 20% for the years of 1938-1989. The cases of the Yana River flow complete cessation during a day or more constitute 40% (21 years) for the observation period (52 years). The riverbed freezing in winter and absence of water flow during 30 days or more was marked in 28% of cases. The longest period (109 days) without water flow was recorded in 1975. The largest spate in Ust-Kuiga happened in June 1996 when the whole township was flooded. By flooding height. it exceeded huge spate of 1968 by 1.3-1.4 m. Practical significance: The work results can find a use in engineering-hydrological research implementation, planning, construction and exploitation of economic facilities in the area of Ust-Kuiga township. The obtained hydrological information is in demand by river and road transports which provide for enterprises activities in the township.

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

Cumulative effects of air pollution and climate drivers on COVID-19 multiwaves in Bucharest, Romania

Over more than two years of global health crisis due to ongoing COVID-19 pandemic, Romania experienced a five-wave pattern. This study aims to assess the potential impact of environmental drivers on COVID-19 transmission in Bucharest, capital of Romania during the analyzed epidemic period. Through descriptive statistics and cross-correlation tests applied to time series of daily observational and geospatial data of major outdoor inhalable particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) or ≤ 10 µm (PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), Aerosol Optical Depth at 550 nm (AOD) and radon (222Rn), we investigated the COVID-19 waves patterns under different meteorological conditions. This study examined the contribution of individual climate variables on the ground level air pollutants concentrations and COVID-19 disease severity. As compared to the long-term average AOD over Bucharest from 2015 to 2019, for the same year periods, this study revealed major AOD level reduction by ~28 % during the spring lockdown of the first COVID-19 wave (15 March 2020–15 May 2020), and ~16 % during the third COVID-19 wave (1 February 2021–1 June 2021). This study found positive correlations between exposure to air pollutants PM2.5, PM10, NO2, SO2, CO and 222Rn, and significant negative correlations, especially for spring-summer periods between ground O3 levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance with COVID-19 incidence and deaths. For the analyzed time period 1 January 2020–1 April 2022, before and during each COVID-19 wave were recorded stagnant synoptic anticyclonic conditions favorable for SARS-CoV-2 virus spreading, with positive Omega surface charts composite average (Pa/s) at 850 mb during fall- winter seasons, clearly evidenced for the second, the fourth and the fifth waves. These findings are relevant for viral infections controls and health safety strategies design in highly polluted urban environments.