Climatological Studies Research Articles

Intra-Storm Pattern Recognition through Fuzzy Clustering

The identification and recognition of temporal rainfall patterns is important and useful not only for climatological studies, but mainly for supporting rainfall–runoff modeling and water resources management. Clustering techniques applied to rainfall data provide meaningful ways for producing concise and inclusive pattern classifications. In this paper, a timeseries of rainfall data coming from the Greek National Bank of Hydrological and Meteorological Information are delineated to independent rainstorms and subjected to cluster analysis, in order to identify and extract representative patterns. The computational process is a custom-developed, domain-specific algorithm that produces temporal rainfall patterns using common characteristics from the data via fuzzy clustering in which (a) every storm may belong to more than one cluster, allowing for some equivocation in the data, (b) the number of the clusters is not assumed known a priori but is determined solely from the data and, finally, (c) intra-storm and seasonal temporal distribution patterns are produced. Traditional classification methods include prior empirical knowledge, while the proposed method is fully unsupervised, not presupposing any external elements and giving results superior to the former.

Eksik sıcaklık verilerinin Yapay Sinir Ağları (YSA) ile tahmin edilmesi

Ensuring more reliable and quality meteorological and climatological studies by providing data continuity and widening the data range. For this reason, missing values in meteorological data such as temperature, precipitation, evaporation must be completed. In this study, an artificial neural network (ANN) model was used to complete missing temperature data in the Horasan meteorology station. To establish the ANN model, monthly average temperature values of neighboring stations having similar climatic characteristics and altitude with Horasan were used as input. The monthly average temperature values of the Horasan station were used as output. Approximately 70% of the data was used for training, about 15% for testing, and about 15% for verification in the ANN model. Various statistical parameters were compared to determine the best network architecture and best model. As a result, the model's high determination coefficient (R2 = 0.99) and low mean absolute error (MAE = 0.61) showed that the ANN model can be used effectively in estimating missing temperature data.

Spatial dynamic of land surface temperature in ciliwung watershed

Land surface temperature (LST) is one of critical element in urban climatology study, especially on urban heat island (UHI) mitigation and water balance. Urbanization in Ciliwung watershed continuously erodes open areas and considerably significant affect the surface temperature within watershed area. Therefore, the purpose of the study is to explore the potential contribution of land surface rising between elevations, vegetation and built-up index. Landsat 8/OLI satellite images use to derive LST, normalize differences vegetation index (NDVI) and normalized differences built-up index (NDBI) in study area on 2014 and 2018. Furthermore, elevation derive from DEMNAS raster provide by Geospatial Information Agency (BIG). In summary, there were LST increases from means temperature from 28.52 Celsius (2014) to 29.10 Celsius (2018) along with land cover changes (LCC). Meanwhile, LST spatial distribution is very closely related to the distribution of NDVI and NDBI. Statistical test results show high correlation (R-squared = 0.89 - 0.91) between LST, elevation, NDVI and NDVI. This indicates elevation, NDVI and NDBI factor play a significant role in LST dynamics.

Volcanic alert system by lightning detection using the WWLLN - ash cloud monitor

Electrical discharges are observed in many volcanic eruptions and they have often been used as indicators of such eruptions. Volcanic lightning is remarkably similar to those produced during thunderstorms and is called analogous to thunderstorm-like plume lightning. The WWLLN developed a program called “Ash Cloud Monitor” (ACM), in which alerts are issued for possible volcanic eruptions when lightning strokes are detected around a volcano. The ACM tool has demonstrated to be a very effective technique to be aware of volcanic eruptions. However, most of the alerts released by ACM belong to false alarms of volcanic activity, because, in general, the detected lightning is produced by thunderstorms near the volcano.In order to assess and improve the ACM to detect volcanic eruptions, reducing false alert emissions and improving the quick interpretation of them, we develop a web platform called Georayos-VolcanoAr with a new structure and a modified algorithm, with respect to the algorithm used by ACM, for the classification of alerts. The new algorithm considers an alert system with 3 levels: Red - Yellow - Green, with the Red alert being the highest level and decreasing towards Green. The Red alert was assigned to those volcanoes where only recorded lightning within a radius of 20 km or the lightning detected within a radius of 20 km is at least twice as much as that detected up to 100 km from the vent.The study focused on 32 volcanoes located in the Andes, close to the Argentine-Chilean border, and analyzed the results reported by the ACM network in terms of a climatological study of the lightning activity, thunderstorm days and predominant winds in that region. This analysis serves as a basis for a general recognition of the study zone in order to improve the interpretation of the distribution and generation of false alerts; as well as to help decision makers, among others, to have a reference that allows them to issue the warning.

Climatology of low-level temperature inversions over China based on high-resolution radiosonde measurements

Low-level temperature inversions play crucial roles in inhibiting vertical exchange of energy and mass, and may lead to air pollutants accumulation. The climatological study of them provides a fundamental overview of the static stability of atmosphere and is indispensable for air pollution controls. Based on high-vertical-resolution radiosondes at 00 and 12 UTC during 2011–2018, this work comprehensively examined the spatial and temporal variability of three characteristics (occurrences, strengths, and depths) of low-level inversions in China. Results revealed that inversions are prevalent (more than 80% of the observation records) over Eastern China. The northern part of Eastern China is dominated by the thick strong surface-based inversions with median thickness and strength of 200 m and 3 K, while the southern part by thin weak elevated ones of 80 m and 0.8 K. Tibetan Plateau experiences rare inversions (less than 50% of the time) which tend to be strong thick surface-based inversions with median depth and intensity of 170 m and 4 K. Inversion depths and strengths are positively correlated. The three properties of inversions present remarkable seasonal variations. Generally, inversions are strongest, thickest, and most frequent in winter and least so in summer, with median intensity and depth ranging from 0.3 K and 80 m to 5 K and 220 m. This paper also analyzed the monthly variations of inversions of four representative stations (Harbin, Urumqi, Beijing, and Chongqing) in details. These four cities are known for frequent severe air pollution events. The quantitative investigations of inversions may play a significant role in atmospheric environmental management.

ModIs Dust AeroSol (MIDAS): a global fine-resolution dust optical depth data set

Abstract. Monitoring and describing the spatiotemporal variability in dust aerosols is crucial for understanding their multiple effects, related feedbacks, and impacts within the Earth system. This study describes the development of the ModIs Dust AeroSol (MIDAS) data set. MIDAS provides columnar daily dust optical depth (DOD) at 550 nm at a global scale and fine spatial resolution (0.1∘ × 0.1∘) over a 15-year period (2003–2017). This new data set combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6.1; Level 2), along with DOD-to-AOD ratios provided by the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis to derive DOD on the MODIS native grid. The uncertainties of the MODIS AOD and MERRA-2 dust fraction, with respect to the AEronet RObotic NETwork (AERONET) and LIdar climatology of vertical Aerosol Structure for space-based lidar simulation (LIVAS), respectively, are taken into account for the estimation of the total DOD uncertainty. MERRA-2 dust fractions are in very good agreement with those of LIVAS across the dust belt in the tropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere, where dust sources are smaller. MIDAS, MERRA-2, and LIVAS DODs strongly agree when it comes to annual and seasonal spatial patterns, with colocated global DOD averages of 0.033, 0.031, and 0.029, respectively; however, deviations in dust loading are evident and regionally dependent. Overall, MIDAS is well correlated with AERONET-derived DODs (R=0.89) and only shows a small positive bias (0.004 or 2.7 %). Among the major dust areas of the planet, the highest R values (>0.9) are found at sites of North Africa, the Middle East, and Asia. MIDAS expands, complements, and upgrades the existing observational capabilities of dust aerosols, and it is suitable for dust climatological studies, model evaluation, and data assimilation.

Complexity entropy-analysis of monthly rainfall time series in northeastern Brazil

In this work we analyze predictability and complexity of monthly rainfall temporal series recorded from 1950 to 2012, at 133 gauging stations in Pernambuco state, northeastern Brazil. To this end we use the complexity entropy causality plane (CECP) and Fisher Shannon plane (FS) formed by information quantifiers permutation entropy, permutation statistical complexity, and Fisher information measure, which serve to evaluate randomness and structural organization of the underlying process. By comparing the locations of analyzed stations in CECP and FS and performing statistical significance test, we distinguish rainfall regime in the deep inland, drier Sertão region, from the intermediate inland Agreste, and coastal, tropical humid Zona da Mata regions. We also perform time dependent CECP and FS analysis and for Sertão region identify the periods of higher entropy (lower complexity and Fisher information measure) which are related to El Niño episodes and historical droughts in northeastern Brazil. Our work represents a contribution to establishing the use of information theory based methods in climatological studies.

Structure and Density of Sedimentary Basins in the Southern Part of the East-European Platform and Surrounding Area

Modern satellite gravity missions and ground gravimetry provide operational data models that can be used in various studies in geology, tectonics, and climatology, etc. In the present study, sedimentary basins in the southern part of the East European Platform and adjoining areas including the Caucasus are studied by employing the approach based on decompensative gravity anomalies. The new model of sediments, implying their thickness and density, demonstrates several important features of the sedimentary cover, which were not or differently imaged by previous studies. We found a significant redistribution of the low-dense sediments in the Black Sea. Another principal feature is the increased thickness of relatively low-dense sediments in the Eastern Greater Caucasus. The deepest part of the South Caspian basin is shifted to the north, close to the Apsheron Trough. In its present position, it is almost joined with the Terek–Caspian depression, which depth is also increased. The thickness of sediments is significantly decreased in the eastern Pre-Caspian basin. Therefore, the new sedimentary cover model gives a more detailed description of its thickness and density, reveals new features and helps in better understanding of the evolution of the basins, providing a background for further detailed studies of the region.

CLASSIFICAÇÃO CLIMÁTICA APLICADA AO ESTADO DE GOIÁS E AO DISTRITO FEDERAL, BRASIL: CLIMATE CLASSIFICATION APPLIED TO THE STATE OF GOIÁS AND THE FEDERAL DISTRICT, BRAZIL

Considered the basis for aid in environmental and economic planning, the climate classification defines the climate in several regions of the Planet. This article suggests a new approach to mapping climatic units for the State of Goias and the Federal District, analyzing the average temperature of the coldest month (TMMMF), the number of dry months, the influence of atmospheric systems and their implications on the amount of rain, and in the passages of cold fronts with the possibility of frost formation. This climate classification system has a hierarchy, and can be divided into Domains, Subdomains, Types, Subtypes and Meso / Topoclimates. To determine the climatic units, data were collected from 108 rainfall collection points of the National Water Agency (ANA) and 17 stations from the National Meteorological Institute (INMET). The CHELSA air temperature algorithm was used to cover areas with no weather stations. Two climatic domains were identified in the study area: Tropical and Mild Tropical; these were subdivided into climatic subdomains (with number of dry months), climatic types (location in the Brazilian territory) and climatic subtypes (more precise location within geomorphological aspects). The definition of climatic units in the state of Goias and the Federal District generates new contributions to regional studies in Detail Climatology. This new climate classification system is further improved, due to the large number of historical data available on the internet, and can be used in environmental diagnostics, teaching materials and agricultural analyzes.

АНТРОПОЛОГИЧЕСКИЙ ВЗГЛЯД НА ЭПИДЕМИИ ЧУМЫ В ИСТОРИЧЕСКОМ ПРОШЛОМ

At many times in their history, humans have faced major epidemics. The “Black Death”, which broke out in the middle of the 14th century, is believed to be the deadliest one. The total number of the deceased population and the scale in many ways had a dramatic impact on the course of subsequent historical events. Until recently, the myths dating back to the Middle Ages have prevailed in scientific circles. They have remained unrevised despite the latest achievements in the research on the plague bacterium Yersinia pestis, the history of climate and medicine, and the extensive published data, making it of an interest to both reconsider certain established dogmas and reconstruct the algorithm of the course of major plague epidemics from the historical past using the results of the available paleogenetic, epidemiological, and climatological studies. This triggered our discussion on the nonlinear nature of the course of plague pandemics. The analysis of the historical sources showed that the scale and speed of the spread of plague epidemics in new territories directly depended on the degree of inclusion of cities and regions of the Afro-Eurasian space in the economic system of the Great Silk Road, the proximity to various epidemic centers of plague. The decisive influence of the anthropogenic factor on the duration and main directions of the spread of plague epidemics was revealed. The multivariate onset and course of pandemic plague waves were recognized. Therefore, it is difficult to pinpoint the exact place where the spread of the plague epidemics ended or to identify their consequences.

气候变暖背景下杉木年轮密度对气候因子的响应

为探讨杉木年轮密度与气候因子的响应关系，采用树木年轮学方法，以60年生杉木种源林为研究对象，测定杉木整轮密度、早材密度、晚材密度、晚材最大密度和早材最小密度，分析在气候变暖条件下主要气候因子（温度、降水、相对湿度）对杉木年轮密度及其生长的影响。结果表明：杉木不同年轮密度指标均受到温度、降水和相对湿度的显著影响。早材密度与当年夏季最高温度、当年5月降水量，最大密度与当年10月、当年秋季降水量，最小密度与前一年秋季降水量、最小相对湿度呈显著负相关。滑动相关分析表明气候因子在短时间尺度上对杉木生长影响的稳定性有显著影响，其中杉木年轮最大密度与当年10月、秋季的平均相对湿度和最小相对湿度，最小密度与当年2、3月的平均相对湿度和前一年秋季的平均相对湿度、最小相对湿度、降水量的负相关关系最为稳定。杉木年轮最小密度对前一年气候要素的响应存在滞后效应，且晚材密度对当年春季的气候要素响应也存在滞后效应。研究结果对开展亚热带针叶树种年轮生态学和年轮气候学研究具有重要参考价值，建议选择武夷山的天然林获取更长年表用于重建古气候。;The objective of this study was to investigate the relationship between tree ring density and climatic factors of Chinese fir. The tree ring chronology methods were used to study 60 years old Chinese fir provenance forest in Sanming. The whole round density, earlywood density, latewood density, latewood maximum density and earlywood minimum density of Chinese fir were measured, and the effects of main climatic factors (temperature, precipitation and relative humidity) on the tree ring density and growth of Chinese fir were analyzed. The results showed that the tree ring density of Chinese fir was significantly influenced by temperature, precipitation and relative humidity. The earlywood density was negatively correlated with the highest temperature in summer and precipitation in May; the latewood maximum density was negatively correlated with precipitation in October and autumn; and earlywood the minimum density was negatively correlated with precipitation and minimum relative humidity in autumn. The sliding correlation analysis also showed that climate factors had a significant effect on the stability of Chinese fir growth in short time, in which the maximum density was related to the average relative humidity and the minimum relative humidity in autumn of the current year, while the negative correlation between the minimum density and the mean relative humidity in February and March of the current year as well as the mean relative humidity, the minimum relative humidity and the precipitation in autumn of the previous year. The response of earlywood minimum density of Chinese fir to climatic factors in previous year had lag effect, and the response of latewood density to climatic factors in spring of the current year also had lag effect. The results of this study have important reference value for the study of tree ring ecology and climatology of subtropical coniferous tree species. It is suggested that natural forests in Wuyi Mountain should be selected to obtain a longer chronology for paleoclimate reconstruction.

An Ionospheric Climate Index Based on GNSS

AbstractThe ionospheric total electron content (TEC) is a significant parameter for scientific studies of the ionosphere and space weather. Ground‐based Global Navigation Satellite System (GNSS) station network provides an opportunity for modeling the ionospheric TEC with high accuracy. Based on global ionospheric TEC maps, by using data from the International GNSS Service (IGS) stations for 21 years (from 1998 to 2018), we establish an ionospheric climate index (ICI) for representing the general state of the ionosphere. In addition, we build a multiple linear regression model for fitting ICI using the SSN, F10.7, Ap, and Dst indices and upper atmospheric wind field. We quantitatively analyze the contribution of these factors to ICI time series. ICI during the past six solar cycles, namely, from 1958 to 2018, are presented, which are assigned as the adjectives (cold, cool, mild, warm, hot, and sizzling) for ionospheric climatology studies. This new index is a very promising representative of day‐to‐day variations of middle‐ and low‐latitude ionosphere.

A Climatological Study of Southwesterly Flows and Heavy Precipitation in Taiwan during Mei-yu Seasons from 1979 to 2018

A Climatological Study of Southwesterly Flows and Heavy Precipitation in Taiwan during Mei-yu Seasons from 1979 to 2018

Instrumentación electrónica de una estación meteorológica automática

Climatological studies present a common restriction in decisionmaking based on meteorological data, since climate models often lack precise values of their parameters depending on the geographical point where the information is acquired, therefore, climate acquisition in specific geographical positions is an interesting possibility. The objective of the present study was to develop the electronic instrumentation of an automatic weather station (AWS) using a controlling medium with sensors that measure air and soil temperature (°C), relative humidity (%), solar radiation (Wm2 ) and wind speed (m/s). The acquired data were compared with commercial stations Vantage Pro (VP) and GroWeather (GW) of the Davis Instruments family where an R2 of 0.93 was obtained in Temperature and Relative Humidity of the air and an R2 of 0.94 for solar radiation. Thus, the developed AWS presents benefits in the quality of the acquired data that can be used for studies of mathematical modeling and generation of decision-making.

On long period trend estimates of upper-air extreme and sub-extreme temperatures by use of quantile regression

The traditional studies of climate change are based on estimation of trends in average values of climate variables (such as monthly, seasonal, or annual average values of temperature, air pressure, precipitation, wind speed, etc.). However, these estimates do not provide detailed information on changes in the distributions and are not sufficient to answer questions about changes in extreme and sub-extreme values, as well as questions on left and right “tails” of the distributions and changes in the measures of variability. A mechanism called quantile regression (QR) is an instrument for performing such a study. Unlike the traditional regression methods that are based on the Ordinary Least Squares (OLS) methodology, QR can provide detailed information about the structure of climate trends for a whole range of values, i.e. including extremal and sub-extremal values. While the known climatological studies of QR trends in extremes and sub-extremes are related to surface meteorological variables, such as surface temperature, it is essential to make similar QR-based studies of trends for the upper-air (UA) temperature. This paper contains results of such an analysis for the UA temperature based on a collection of radiosonde data for more than 30 years of observation. We discuss typical patterns of a detailed structure of climate trends for the upper-air temperature in the troposphere and in the lower stratosphere for certain geographic sectors of the Northern Hemisphere. The existing difficulties to obtain realistic results in such a study are shown to be related to various kinds of gaps in the radiosonde data. Therefore, some data quality requirements are vital for such upper-air studies.

令和元年東日本台風（台風1919）による大雨の気候学的評価 ―区内観測資料の利用―

令和元年東日本台風（台風1919）による大雨の気候学的評価 ―区内観測資料の利用―

Fog Forecast Using WRF Model Output for Solar Energy Applications

The occurrence of fog often causes errors in the prediction of the incident solar radiation and the power produced by photovoltaic cells. An accurate fog forecast would benefit solar energy producers and grid operators, who could take coordinated actions to reduce the impact of discontinuity, the main drawback of renewable energy sources. Considering that information on discontinuity is crucial to optimize power production estimation and plant management efficiency, in this work, a fog forecast method based on the output of the Weather Research and Forecasting (WRF) numerical model is presented. The areal extension and temporal duration of a fog event are not easy to predict. In fact, there are many physical processes and boundary conditions that cause fog development, such as the synoptic situation, air stability, wind speed, season, aerosol load, orographic influence, humidity and temperature. These make fog formation a complex and rather localized event. Thus, the results of a fog forecast method based on the output variables of the high spatial resolution WRF model strongly depend on the specific site under investigation. In this work, the thresholds are site-specifically designed so that the implemented method can be generalized to other sites after a preliminary meteorological and climatological study. The proposed method is able to predict fog in the 6–30 h interval after the model run start time; it has been evaluated against METeorological Aerodrome Report data relative to seven selected sites, obtaining an average accuracy of 0.96, probability of detection of 0.83, probability of false detection equal to 0.03 and probability of false alarm of 0.18. The output of the proposed fog forecast method can activate (or not) a specific fog postprocessing layer designed to correct the global horizontal irradiance forecasted by the WRF model in order to optimize the forecast of the irradiance reaching the photovoltaic panels surface.

A Ceilometer-Derived Climatology of the Convective Boundary Layer Over a Southern Hemisphere Subtropical City

Accurate measurements of the depth of the convective boundary layer (CBL) are fundamental for understanding and forecasting weather, air quality, and climate. However, the CBL depth (BLD) shows significant spatial and temporal variability, which is challenging to measure and model. Ceilometer instruments, which estimate the CBL depth from aerosol layers, are relatively cheap, have high temporal resolution, and have the potential for increased spatial coverage. Nevertheless, their performance in subtropical environments with low aerosol concentrations is unknown. Furthermore, climatological studies of the variability in the urban CBL depth are scarce, especially in the Southern Hemisphere, and very few studies have examined the relationship between BLD variability and synoptic conditions. Using three years of continuous ceilometer data, we present a novel climatology of the temporal variability in CBL depth over the Southern Hemisphere subtropical, coastal city of Auckland, New Zealand. The results indicate that the median daytime maximum CBL depth (BLDMAX) over the Auckland CBD is 1100 m a.g.l. (above ground level) during summer and 700 m a.g.l. during winter. Strong relationships are found between the maximum CBL depth and both solar radiation and soil moisture; however, no relationship is found between the maximum CBL depth and the synoptic conditions as determined by the New Zealand Kidson weather types synoptic classification. The absence of a relationship between the maximum CBL depth and Kidson weather types may explain the poor correlation previously observed between synoptic class and air pollution events in Auckland.

Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation

Reference evapotranspiration (ET0) is one of the most important parameters, which is required in many fields such as hydrological, agricultural, and climatological studies. Therefore, its estimation via reliable and accurate techniques is a necessity. The present study aims to estimate the monthly ET0 time series of six stations located in Iran. To achieve this objective, gene expression programming (GEP) and support vector regression (SVR) were used as standalone models. A novel hybrid model was then introduced through coupling the classical SVR with an optimization algorithm, namely intelligent water drops (IWD) (i.e., SVR−IWD). Two various types of scenarios were considered, including the climatic data- and antecedent ET0 data-based patterns. In the climatic data-based models, the effective climatic parameters were recognized by using two pre-processing techniques consisting of τ Kendall and entropy. It is worthy to mention that developing the hybrid SVR-IWD model as well as utilizing the τ Kendall and entropy approaches to discern the most influential weather parameters on ET0 are the innovations of current research. The results illustrated that the applied pre-processing methods introduced different climatic inputs to feed the models. The overall results of present study revealed that the proposed hybrid SVR-IWD model outperformed the standalone SVR one under both the considered scenarios when estimating the monthly ET0. In addition to the mentioned models, two types of empirical equations were also used including the Hargreaves−Samani (H−S) and Priestley−Taylor (P−T) in their original and calibrated versions. It was concluded that the calibrated versions showed superior performances compared to their original ones.

The NASA‐JAXA Global Precipitation Measurement mission – part II: New frontiers in precipitation science

The <scp>NASA‐JAXA</scp> Global Precipitation Measurement mission – part <scp>II</scp>: New frontiers in precipitation science