Collection Of Meteorological Data Research Articles

Contrails and Their Dependence on Meteorological Situations

Contrails created by aircraft are a very hot topic today because they contribute to the warming of the atmosphere. Air traffic density is very high, and current forecasts predict a further significant increase. Increased air traffic volume is associated with an increased occurrence of contrails and induced cirrus clouds. The scientific level of contrails and their impact on the Earth’s climate is surprisingly low. The scientific studies published so far are mainly based on global models, in situ measurements, and satellite observations of contrails. The research is based on observations of contrails in flight paths in the vicinity of Děčín and Prague, and the collection of flight and meteorological data. It focused on the influence of the meteorological situation on the formation of persistent contrails. The collected data on contrails and meteorological variables were statistically processed using machine learning methods for classification models. Several models were developed to predict and simulate the properties of contrails as a function of given air traffic and meteorological conditions. The Random Forests model produced the best results. Dependencies between meteorological conditions, formation, and contrail lifetime were found. The aim of the study was to identify the possibility of using available meteorological data to predict persistent contrails.

IoT-enabled Smart Weather Stations: Innovations, Challenges, and Future Directions

The evolution of the Internet of Things (IoT) has ushered in innovative approaches facilitated by breakthrough technologies. This paper presents a comprehensive review of recent advancements in smart weather stations, focusing on IoT-enabled solutions. The integration of Internet of Things (IoT) technologies has revolutionized traditional weather monitoring systems, enabling seamless data collection, analysis, and dissemination. Commercially available automated weather stations offer cost-effective solutions for comprehensive meteorological data collection. However, challenges such as limited local deployment and reliance on expensive options persist, hindering comprehensive monitoring efforts. The critical need for improved data collection methods is underscored to enhance the accuracy of weather forecasts and address evolving climatic conditions. Climate change impacts, including shifts in weather patterns and rising temperatures, highlight the importance of effective weather monitoring for agriculture, infrastructure, and national security. Additionally, the dependence on non-renewable energy sources for electricity generation emphasizes the environmental and economic implications of energy production. In response to these challenges, numerous IoT based smart weather station systems have been proposed by earlier researchers. The introduction of IoT-enabled smart weather stations represents a significant advancement in weather monitoring technology. These stations leverage IoT technologies to collect, analyse, and visualize meteorological data in real time. In order to identify the challenges and prospects in this area of technology, the purpose of this study is to present a thorough analysis of the suggested designs as well as to compare, evaluate, and assess the outcomes, contributing to the development of robust and efficient weather monitoring systems.

Remote Sensing and Field Measurements for the Analysis of the Thermal Environment in the “Bosco Verticale” Area in Milan City

The trend of urbanization nowadays has caused serious issues related to climate. One of the most important ones is that of the ‘Urban Heat Island (UHI)’ and it occurs in major cities throughout the world. The most important categories, and therefore the most studied ones, are the canopy urban heat island (CUHI) and surface heat island (SUHI). The aim and the novelty of the current study was to assess different remote sensing approaches to detect the thermal environment of an open area inside a large city. The study was undertaken in an urban area with green spaces, in the Bosco Verticale area in the city of Milan, during the spring and summer period of 2021. The area is characterized by different types of cover materials, which were investigated in terms of surface temperature under shaded and non-shaded conditions. Both field measurements and remote sensing techniques were applied. Remote sensing techniques included downscaling techniques and the usage of different split-window algorithms applied on the Landsat8 satellite sensor data. The land surface temperature (LST) extracted from remote sensing methods was compared with the surface temperature derived from in situ measurements. For the needs of the study, both in situ measurements and the collection of meteorological data from different fixed meteorological stations throughout the city of Milan were carried out. The results revealed the significance of greenery presence inside the urban environment, as a comparison of the meteorological data across the urban area of Milan showed that the areas with a low presence of greenery were found to be warmer than those with a higher presence of green elements. Concerning the field measurements in the study area, the results showed a significant reduction in both surface and air temperature in shaded places. On the other hand, the presence of conventional artificial materials in sunny areas led to relatively high values of both surface and air temperature. The downscaling method showed satisfying results in terms of average LST values; however, some discrepancies appeared in terms of the RMSE index. The application of split-window algorithms has shown that some forms of the ‘Generalized split-window algorithm’ and some forms of the ‘Jimenez-Munoz algorithm’ presented better performance among the studied algorithms. Comparing the LST values derived from the most representative algorithm, the ‘Du, Wan algorithm’, with those derived from downscaling methods, it was found to be quite close. However, under shaded conditions, the results derived from the ‘Split-window algorithm’ were found to be more precise. The application of remote sensing techniques in microscale in urban regions should be further studied in future, as they could be an essential tool for observing microclimatic conditions in urban areas and on building scale.

Soil moisture forecasting for precision irrigation management using real-time electricity consumption records

The installation of the smart electricity meters (SEMs) to the high-density pumping wells in the North China Plain (NCP) provided a method to control the groundwater withdraw. The record in real-time electricity consumption from SEMs also provided an alternative method to forecast soil moisture to make irrigation decisions to improve irrigation efficiency. The irrigation timing and the irrigation amount could be obtained on-line from the SEMs based on the coefficients obtained for electricity-to-water conversion (E-Wc). The real-time recordings in the irrigation timing and amounts were further used to forecast the next irrigation timing and amount based on the water rights per area of land, the lower limit of soil water contents for different crops at their different growing stages and online meteorological data. A smartphone app was developed based on this framework. The running of the app to simulate soil water contents based on the soil water balance equation was supported by databases on soil texture, crop coefficients, water rights, E-Wc and threshold soil water content values as well as the real-time collection of electricity consumption and meteorological data. The simulated real-time soil water contents were shown on the mobile phone screen and could be compared with the threshold values for making irrigation decisions for users. The accuracy in forecasting the soil water contents using the methods developed in this framework was tested under different irrigation schedules for winter wheat and summer maize during 2018–2019 and 2021–2022, and the app was tested for three locations starting from sowing winter wheat in October 2022 to April in 2023. The results showed that the app could be easily run, and precise real-time soil water contents were simulated. With the application of the pumping well renovation project in the NCP, most of the pumping wells were installed with the SEMs, and using SEMs for groundwater pumping control and irrigation decisions should be possible.

Effect of Drought and Pluvial Climates on the Production and Stability of Different Types of Peanut Cultivars in Guangdong, China

The production and breeding of peanuts was restricted by the frequently extreme climatic conditions in Guangdong province, China. To understand the influence of drought and pluvial climates on peanut traits and yield, a phenotypic investigation of seventy peanut cultivars was conducted from 2018 to 2022; comprehensive field meteorological data collection, and typical drought (2021) and pluvial (2022) climates were recorded. The results revealed that the cultivars achieved the highest single plant pod weight (SPPW) and single plant seed weight (SPSW) of 61.03 g and 45.84 g, respectively, in drought conditions, followed by the control, and finally the pluvial. The SPPW, SPSW and eight agronomy traits exhibited significant differences across the different climatic conditions. Correlation analysis revealed the yield traits and key yield-related traits were positively or negatively correlated with soil water content (SWC), total global radiation (TGR), total precipitation (TP) and total net radiation (TNR). The intermediate and Spanish type cultivars were more stable and productive than the other botanical types of cultivars, commercial varieties exhibited better performance than landraces, and seven cultivars were identified with good production potential, under drought and pluvial conditions. Our study showed that pluvial climate was detrimental to peanut yield, and the SPPW and SPSW were significantly influenced by climates with genotype differences.

IoT and Ensemble Long-Short-Term-Memory-Based Evapotranspiration Forecasting for Riyadh.

Evapotranspiration (ET) is the fundamental component of efficient water resource management. Accurate forecasting of ET is essential for efficient water utilization in agriculture. ET forecasting is a complex process due to the requirements of large meteorological variables. The recommended approach is based on the Internet of Things (IoT) and an ensemble-learning-based approach for meteorological data collection and ET forecasting with limited meteorological conditions. IoT is part of the recommended approach to collect real-time data on meteorological variables. The daily maximum temperature (T), mean humidity (Hm), and maximum wind speed (Ws) are used to forecast evapotranspiration (ET). Long short-term memory (LSTM) and ensemble LSTM with bagged and boosted approaches are implemented and evaluated for their accuracy in forecasting ET values using meteorological data from 2001 to 2023. The results demonstrate that the bagged LSTM approach accurately forecasts ET with limited meteorological conditions in Riyadh, Saudi Arabia, with the coefficient of determination (R2) of 0.94 compared to the boosted LSTM and off-the-shelf LSTM with R2 of 0.91 and 0.77, respectively. The bagged LSTM model is also more efficient with small values of root mean squared error (RMSE) and mean squared error (MSE) of 0.42 and 0.53 compared to the boosted LSTM and off-the-shelf LSTM models.

Spatial heterogeneity of human activities and its driving factors in karst areas of Southwest China over the past 20 years

Guizhou Province is located in the karst mountain regions of Southwest China, where the ecological environment is extremely fragile and particularly sensitive to human activities. Therefore, understanding the changing characteristics and driving factors of human activity in recent decades is urgent. In this study, least squares, correlation analysis, spatial autocorrelation analysis, and GeoDetector model were used. Based on a large collection of nightlight, topography, and meteorological data, combined with geographical information technology, we investigated the spatial distribution, intensity change, and degree of impact of human activity from 2000 to 2020. The results showed that during the study period, human activities intensity and range were expanding. The human activity areas expanded from 15,963 to 86,923 km2 at an increasing rate of 4,279.2 km2/a, which was 1,118.4 km2/a from 2000 to 2010 and 6,375.3 km2/a from 2010 to 2020. The human activity intensity increased from 0.009 to 0.018, with an increasing rate of 0.0006/a, which was 0.00028/a from 2000 to 2010 and 0.00073/a from 2010 to 2020. The rate of increase in the human activity intensity and the rate of expansion in the human activity range from 2010–2020 were significantly higher than that from 2000–2010, becoming more notable (p ≤ 0.01), especially after 2012. With the expansion of human activities, the center of gravity of human activity moved towards the northeast by 20.71 km. The human activities were primarily distributed in areas with the gentlest slopes (6–15°), middle and low altitudes (489–1,982 m), suitable temperatures (12.36–17.74°C), and abundant precipitation (1,001.99–1,276.99 mm). The research results using the GeoDetector model indicate that slope had the greatest impact on human activities with a q value of 0.1338; precipitation, elevation, and temperature had q values of 0.0626, 0.0253, and 0.0136, respectively. The combined impact between the precipitation and slope was the greatest with a q value of 0.1803. In Guizhou Province, under policy guidance, human activities that promoted vegetation change accounted for 79.60%. This study attempts to enhance sustainable development and provides valuable information on the environmental protection of karst mountain regions.

A MODIS–Landsat cloud-based spatiotemporal downscaling algorithm to estimate land surface temperature

ABSTRACT Land Surface Temperature (LST) is a key variable in the energy and water balance between the surface and the atmosphere. LST is typically retrieved from remote sensing data because of the lack of sufficient flux towers for meteorological data collection, which makes local availability of LST scarce. Remote sensing can provide data for large areas, but the spatial resolution of the data may be coarser than the temporal resolution (e.g. a daily revisit interval for satellite-based Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors), or conversely, the spatial resolution may be coarser than the temporal resolution (e.g. Landsat satellite images obtained every 16 days). This tradeoff between spatial and temporal resolutions has motivated researchers to develop methods and algorithms to create image collections with both high spatial and temporal resolutions. In this study, we used a cloud-based downscaling algorithm to create LST images with high temporal and spatial resolution for the southern region of Brazil using Google Earth Engine (GEE). A comparison of the downscaling estimates with thermal Landsat images showed that 65% of the root mean squared errors (RMSE) were lower than 2 K, and 77% of the correlation coefficients (R) were greater than 0.70, while 84% of the bias values were between −2 and 2 K. The downscaling estimates were also validated using in situ measurements. Overall, a comparison between in situ measurements and different LST retrieval methods differed slightly in accuracy, with average RMSE values between 1.55 and 4.32 K, bias between −1.07 and 3.96 K, and correlation coefficients of nearly 0.90. These results demonstrate that cloud computing can be used to retrieve LST with high spatiotemporal resolution.

Relationship between Climate Variables and Dengue Incidence in Argentina.

Climate change is an important driver of the increased spread of dengue from tropical and subtropical regions to temperate areas around the world. Climate variables such as temperature and precipitation influence the dengue vector's biology, physiology, abundance, and life cycle. Thus, an analysis is needed of changes in climate change and their possible relationships with dengue incidence and the growing occurrence of epidemics recorded in recent decades. This study aimed to assess the increasing incidence of dengue driven by climate change at the southern limits of dengue virus transmission in South America. We analyzed the evolution of climatological, epidemiological, and biological variables by comparing a period of time without the presence of dengue cases (1976-1997) to a more recent period of time in which dengue cases and important outbreaks occurred (1998-2020). In our analysis, we consider climate variables associated with temperature and precipitation, epidemiological variables such as the number of reported dengue cases and incidence of dengue, and biological variables such as the optimal temperature ranges for transmission of dengue vector. The presence of dengue cases and epidemic outbreaks are observed to be consistent with positive trends in temperature and anomalies from long-term means. Dengue cases do not seem to be associated with precipitation trends and anomalies. The number of days with optimal temperatures for dengue transmission increased from the period without dengue cases to the period with occurrences of dengue cases. The number of months with optimal transmission temperatures also increased between periods but to a lesser extent. The higher incidence of dengue virus and its expansion to different regions of Argentina seem to be associated with temperature increases in the country during the past two decades. The active surveillance of both the vector and associated arboviruses, together with continued meteorological data collection, will facilitate the assessment and prediction of future epidemics that use trends in the accelerated changes in climate. Such surveillance should go hand in hand with efforts to improve the understanding of the mechanisms driving the geographic expansion of dengue and other arboviruses beyond the current limits. https://doi.org/10.1289/EHP11616.

Age Prediction for Energy-Aware Communication in WSN Using Hybrid Optimization-Enabled Deep Belief Network

To perceive the data utilizing sensor nodes, wireless sensor network (WSN) consists of several nodes connected to a wireless channel. However, the sink node, also known as a base station (BS), provides power to the WSN and acts as an access node for a number of the network’s sensor devices. Weather monitoring, field surveillance, and the collection of meteorological data are just a few of the various uses for WSN. The energy of each node directly affects how long a wireless network will last. So, to increase the lifespan of WSN, effective routing is required. Using the suggested Taylor sea lion optimization-based deep belief network (TSLnO-based DBN), the ultimate purpose of this research is to build a method for energy-aware communication in WSN. In the setup stage, cluster head (CH) is chosen using a hybrid optimization technique called ant lion whale optimization (ALWO), which is created by fusing the whale optimization algorithm (WOA) and ant lion optimizer (ALO). It is important to note that CH’s selection criteria are solely based on fitness factors such as energy and distance. The second phase, known as the steady state step, is when the updating of energy and trust takes place. In the prediction phase, the network classifier is trained using a newly created optimization method called TSLnO, and the age of neighbor nodes is predicted by estimating the energy of neighbors using DBN. By combining the Taylor Series and the sea lion optimization (SLnO) method, the proposed TSLnO is produced. The communication/route discovery phase, which occurs in the fourth phase, is where the path through nearby nodes is chosen. The maintenance phase of the route is the fifth phase.

Desensitization method of meteorological data based on differential privacy protection

Artificial intelligence (AI) profoundly affects the research of meteorology. As an AI method, deep learning greatly improves the accuracy of weather forecasting. Deep learning training requires a lot of data, while the collection of meteorological data faces some problems, such as the long collection cycle, the high cost, and the desensitization requirement. We propose a deep learning model called MDPGAN (Meteorology Differential Privacy Generative Adversarial Network), which introduces a differential privacy framework to reduce the risk of identifying real data by querying synthetic data. The MDPGAN model can generate synthetic weather data with similar statistical characteristics to real weather data. The data generated by the MDPGAN model meets the requirements of data augmentation and data desensitization at the same time. In this paper, the meteorological data set of Kennedy Airport published by NOAA (National Oceanic and Atmospheric Administration) was used for the experiments of the MDPGAN model. The reliability and validity of generated meteorological data were analyzed and tested. The comparison between the generated data and the real data shows that they have similar statistical characteristics, and the synthetic data has achieved good results in the time series prediction of temperature changes. The MDPGAN model provides a convenient tool for the meteorology researches based on deep learning, which can automatically generate a large amount of safe and reliable data, especially suitable for the meteorology researches with small sample data.

The first evaluation of the FY-3D/MERSI-2 sensor’s thermal infrared capabilities for deriving land surface temperature in volcanic regions: a case study of Mount Etna

ABSTRACT In November 2017, the China Meteorological Administration launched a new polar orbiting satellite in its Fengyun (FY) series: FY-3D. With its main purpose being the collection of meteorological data, FY-3D featured a comprehensive payload that is equally exploitable by various Earth Science disciplines. One of its sensors, the MEdium Resolution Spectral Imager-2 (MERSI-2), provides visible and infrared imagery at spatial resolutions of 250 – 1000 m. These characteristics make MERSI-2 suitable for volcanological remote sensing and make it comparable to the National Aeronautics and Space Administration’s (NASA’s) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors which themselves, have been widely used in volcanological applications. This paper evaluates the first clear and near-coincident MODIS – MERSI-2 images of Mount Etna (Italy) during an active volcanic phase in 2019 and in turn, provides the first assessment of MERSI-2’s utility in observing volcanic activity in the Thermal InfraRed (TIR). To ensure the comparability of both scenes, data from each were converted to Land Surface Temperature (LST) and comparisons were encouraging, with an r2 of 0.92, a mean temperature discrepancy of 0.26 K and a root mean squared error of 0.75 K. Having ascertained comparability, we focussed on the absolute temperatures detected at the eruption site, with the highest being 317.3 K and 328.1 K for MODIS and MERSI-2, respectively. The 20 minute gap between the acquisitions is the most likely the cause of this temperature discrepancy, suggesting variations in lava effusion rates and activity were occurring at Mount Etna over such timescales. This study confirms the applicability of MERSI-2 for observing volcanic activity and emphasises the significance of TIR volcanic monitoring and the importance that additional spaceborne platforms might have in reducing temporal gaps between image acquisitions. Given its unique characteristics, future studies should investigate the applicability of MERSI-2 in more varied volcanic settings.

Long-Term Monitoring of Diel and Seasonal Rhythm of Dentex dentex at an Artificial Reef

Behavioral rhythms are a key aspect of species fitness, since optimize ecological activities of animals in response to a constantly changing environment. Cabled observatories enable researchers to collect long-term biological and environmental data in real-time, providing relevant information on coastal fishes’ ecological niches and their temporal regulation (i.e., phenology). In this framework, the platform OBSEA (an EMSO Testing-Site in the NW coastal Mediterranean) was used to monitor the 24-h and seasonal occurrence of an ecologically iconic (i.e., top-predator) coastal fish species, the common dentex (Dentex dentex). By coupling image acquisition with oceanographic and meteorological data collection at a high-frequency (30 min), we compiled 8-years’ time-series of fish counts, showing daytime peaks by waveform analysis. Peaks of occurrence followed the photophase limits as an indication of photoperiodic regulation of behavior. At the same time, we evidenced a seasonal trend of counts variations under the form of significant major and minor increases in August and May, respectively. A progressive multiannual trend of counts increase was also evidenced in agreement with the NW Mediterranean expansion of the species. In GLM and GAM modeling, counts not only showed significant correlation with solar irradiance but also with water temperature and wind speed, providing hints on the species reaction to projected climate change scenarios. Grouping behavior was reported mostly at daytime. Results were discussed assuming a possible link between count patterns and behavioral activity, which may influence video observations at different temporal scales.

Influence of short-term surface temperature dynamics on tree orchards energy balance fluxes

Land surface temperature (LST) plays an essential role in developing and applying precision agriculture protocols, especially for calculating crop evapotranspiration (ETc) by surface energy balance (SEB) approaches; and for determining crop water status. However, LST is quite dependent on the meteorological conditions, which can rapidly vary. This variability, together with the limited meterological data acquisition frequency in most weather stations, can lead to the miscalculation of the SEB components, especially relevant when used for irrigation purposes.The present study assessed the temporal dynamic of LST in a very short period of time (20-minutes) through the acquisition of multiple thermal imagery. Additionally, a combination of SEB approach with Eddy Covariance technique was performed for quantifying the effect that LST variations have on the sensible (H) and latent (LE) heat fluxes.Even under steady meteorological conditions, temporal variations in LST of 3.5 and 4.0 K were observed for tree canopy and sunny bare soil surfaces, respectively. These LST oscillations reached values of about 7.8 and 17.9 K for tree canopies and bare soil when heterogeneous meteorological conditions were observed (i.e. cloud presence). Such LST differences translated into H and LE differences of about 26 and 19%, respectively; with variations up to 5 (for H) and 2.7 times (for LE) under fast-varying meteorological conditions.The obtained results suggest the necessity of acquiring thermal imagery when steady meteorological conditions exist or, otherwise, ensuring the collection of instantaneous meteorological data for applying post-processing corrections. This is of importance when incorporating the obtained ETc maps into precision irrigation protocols.

Design of mathematical model for the prediction of rainfall

There are several soft computing methods in use to predict rainfall. Mainly two features were included to increase rainfall prediction: (1) using data pre-processing procedure and (2) using a modular approach. The projected pre-processing approach incorporated Moving Average (MA) and Singular Spectrum Analysis (SSA). The method of modular was poised of Support Vectors Regression l (SVR) models or the Rough Set Technique (RST). In the development for forecasting of rainfall, the RST was initially applied for the data- preprocessing. Modular models used to pre-process the preparation data into three distinct subsets (level-1, level-2, and level-3) from the scales of the trained data, and lastly, two SVRs were used in the level-2 and level-3 subsets. In contrast, Artificial Neural Network (ANN) or SVR was concerned with predicting and was useful in training the data. Each day’s rate of downpour proof, the level-1 subset tended to be modelled by the ANN because it was overwhelming in the training data. ANN is beneficial for huge-scale samples training because of its corresponding information processing configuration. This work explores the use of hybridization technique of RST and time series, where RST helps to find the essential attribute from the collection of raw meteorological data set then analyzing the important characteristic using time series and various classifiers. There are several prediction techniques available to predict based on meteorological data set to suitably predict a meteorological phenomenon. In this work, our objective is to find the most frequent meteorological phenomena which affects our state most. Purposefully, we consider the RST to predict the most significant meteorological phenomena by using the RST and predict the rainfall per day basis. Hybrid soft computing techniques and RST are used in the present work. We validate our claim using the Chi-square test.

Prediction of hourly solar radiation in Tamil Nadu using ANN model with different learning algorithms

The solar radiation is a critical metric for design and optimal operation of the solar energy system. Most of the locations available solar radiation records are not available due to high cost for buying and maintaining the measuring instruments. Aim of this paper is building an efficient neural network model that can reliably estimate solar radiation. In this work, different ANN models with three popular algorithms admired from the literature are investigated. The models were trained using meteorological data collected over a year from six different places from the hot area locations of Tamil Nadu, India. Based on minimal Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), the optimum ANN algorithm and model are determined (R). Furthermore, the research illustrates that the ANN model’s prediction performance is dependent on the entire set of data utilised to train the network. Also, this article aims at finding out the accurate number of hidden layer neurons for the developed model. The proposed ANN model offers improved accuracy and applicability for estimating hourly average global radiation for the purpose of designing or evaluating photovoltaic (PV) installations in areas without meteorological data collection facilities.

Meteorological Factors Influence the Presence of Fungi in the Air; A 14-Month Surveillance Study at an Adult Cystic Fibrosis Center.

BackgroundCystic fibrosis is an inherited disease that predisposes to progressive lung damage. Cystic fibrosis patients are particularly prone to developing pulmonary infections. Fungal species are commonly isolated in lower airway samples from patients with cystic fibrosis. Fungal spores are prevalent in the air.MethodsWe performed environmental air sampling surveillance at the Manchester Adult Cystic Fibrosis Centre, UK (MACFC) over a 14-month period to assess fungal growth inside and outside the CF center.ResultsAirborne counts of fungal spores peaked from May to October, both in outdoor and indoor samples. Collection of meteorological data allowed us to correlate fungal presence in the air with elevated temperatures and low wind speeds. Additionally, we demonstrated patient rooms containing windows had elevated fungal counts compared to rooms not directly connected to the outdoors.ConclusionsThis study suggests that airborne Aspergillus fumigatus spores were more abundant during the summer months of the survey period, which appeared to be driven by increased temperatures and lower wind speeds. Indoor counts directly correlated to outdoor A. fumigatus levels and were elevated in patient rooms that were directly connected to the outdoor environment via an openable window designed for ventilation purposes. Further studies are required to determine the clinical implications of these findings for cystic fibrosis patients who are predisposed to Aspergillus related diseases, and in particular whether there is seasonal influence on incidence of Aspergillus related conditions and if screening for such complications such be increased during summer months and precautions intensified for those with a known history of Aspergillus related disease.

Forecasting Algorithm Based on Temperature Error Prediction Using Kalman Filter for Management System Development

Abstract The study offers a new method of collection and processing of meteorological data from the meteorological service based on observations and correction of numerical weather forecast errors using a new prediction algorithm. This algorithm vastly increases the accuracy of the short-term forecast of outdoor air temperature, which is subject to uncertainty due to the stochastic nature of atmospheric processes. Processing of temperature data using Kalman filter provides the decrease in predicted temperature errors. The main setup methods of Kalman filter have been examined. The article also describes the implementation of accuracy improving algorithm of predicted temperature using Python.

Climate indices in historical climate reconstructions: a global state of the art

Abstract. Narrative evidence contained within historical documents and inscriptions provides an important record of climate variability for periods prior to the onset of systematic meteorological data collection. A common approach used by historical climatologists to convert such qualitative information into continuous quantitative proxy data is through the generation of ordinal-scale climate indices. There is, however, considerable variability in the types of phenomena reconstructed using an index approach and the practice of index development in different parts of the world. This review, written by members of the PAGES (Past Global Changes) CRIAS working group – a collective of climate historians and historical climatologists researching Climate Reconstructions and Impacts from the Archives of Societies – provides the first global synthesis of the use of the index approach in climate reconstruction. We begin by summarising the range of studies that have used indices for climate reconstruction across six continents (Europe, Asia, Africa, the Americas, and Australia) as well as the world's oceans. We then outline the different methods by which indices are developed in each of these regions, including a discussion of the processes adopted to verify and calibrate index series, and the measures used to express confidence and uncertainty. We conclude with a series of recommendations to guide the development of future index-based climate reconstructions to maximise their effectiveness for use by climate modellers and in multiproxy climate reconstructions.

Assessment of the intra-annual flow distribution of the Shu-Talas river basin over the past decade

Based on the collection and analysis of hydrological and meteorological data, the article estimates the distribution of intra-annual runoff within the Shu-Talas River basin over the past decades. To assess changes in the intra-annual distribution of runoff in the Shu-Talas water management basin, observation data for 10 active hydrological posts were collected from hydrological yearbooks. Limiting and non-limiting periods are established for the studied basin, including the period of predominance of meltwater runoff, the period of predominance of the rain source of food and the period of manifestation of ice phenomena. Empirical and analytical curves are constructed for each set period. According to these curves, three gradations of water content are defined: a high-water year (p<33.3%), an average year by water content (33.3%≤p≤66.7%), a low-water year (P>33.3%) and are classified by water content of years in three groups. The intra-annual distribution of runoff over the past decades was estimated, and the results of the studies were compared and analyzed with the results given in the" Surface Water Resources of the USSR " for the basin under consideration. The calculations carried out allow us to determine various water management parameters for the basin, to ensure the safety of settlements within the basin.