Aeromycology: studies of fungi in aeroplankton

Air is a natural environment for spores of many genera and species of fungi. Despite its small size and a significant dispersion they have a great impact on human health and different areas of our activities, such as agricultural production. The study on spores of fungi that belong to aeroplankton or bioaerosole is called aeromycology. The most frequent fungi present in the air are Cladosporium and Alternaria species. Their numbers are abundant regardless of latitude and height above the sea level and above the ground. They mostly originate from agricultural environment. Other frequently listed species of fungi, whose spores are present in the air include of Aspergillus, Penicillium, Fusarium, Sclerotinia and Ganoderma. The concentration of spores in the air strongly depends on the abundance of their formation during the studied period. This in turn relates to geobotanical region, vegetation, degree of urbanization, climatic conditions, season, current weather, wind force and direction, local microclimate, and many other factors. Changes in humidity affect the concentration of different types of fungal spores. In general they are divided to ‘dry’ (Alternaria, Cladosporium, Puccinia, Ustilago, Melampsora, Epicoccum, Drechslera) and ‘wet’ (Didymella, Fusarium, Ganoderma, Gliocladium, Leptosphaeria, Verticillium). Study of the composition of species and genera are being done using different types of spore samplers, mostly volumetric instruments. Visual identification is based on colony morphology of the fungus and the shape and size of spores. The identification at the species level is possible with molecular tools. Methods based on DNA/RNA amplification are very sensitive and accurate. They allow the identification below the species level, e.g. chemotypes, mating types or isolates with genes or alleles of interest. Aerobiological monitoring is widely used in the epidemiology of human diseases (inhalant allergies) and infections of arable crops (decision support systems for the protection of cultivated plants). Aeromycology is interconnected with such diverse areas as industrial aerobiology, bioterrorism, ecology, climatology or even speleology and cultural heritage.Powietrze jest naturalnym środowiskiem dla zarodników licznych rodzajów i gatunków grzybów. Pomimo niewielkich rozmiarów i znacznego rozproszenia mają one wielki wpływ na zdrowie ludzi i różne kierunki ich działalności, w tym w szczególności na produkcję rolniczą. Badania nad zarodnikami grzybów stanowiącymi część aeroplanktonu są przedmiotem aeromykologii. Niezależnie od szerokości geograficznej i wysokości nad poziomem morza w powietrzu szczególnie często występują grzyby z rodzajów Cladosporium i Alternaria, a ich źródłem jest najczęściej środowisko rolnicze. Innymi często notowanymi rodzajami grzybów, których zarodniki występują w powietrzu są m.in. Aspergillus, Penicillium, Fusarium, Sclerotinia i Ganoderma. Stężenie zarodników w powietrzu jest ściśle uzależnione od obfitości ich tworzenia w danym okresie, co jest pochodną regionu geobotanicznego, szaty roślinnej, stopnia zurbanizowania danej lokalizacji, warunków klimatycznych, pory roku, aktualnej pogody, siły i kierunku wiatru, lokalnego mikroklimatu i wielu innych czynników. Zmiany wilgotności powietrza wpływają na stężenie zarodników różnych rodzajów grzybów, określanych na tej podstawie jako „suche” (Alternaria, Cladosporium, Puccinia, Ustilago, Melampsora, Epicoccum, Drechslera) lub „mokre” (Didymella, Fusarium, Ganoderma, Gliocladium, Leptosphaeria, Verticillium). Badania składu rodzajowego i gatunkowego prowadzone są przy zastosowaniu różnego rodzaju chwytaczy zarodników, a identyfikacja wizualna na podstawie morfologii kolonii grzyba oraz kształtu i wymiarów zarodników uzupełniana jest obecnie przez wyjątkowo czułe metody detekcji molekularnej, specyficzne względem rodzajów, gatunków, chemotypów, a nawet składu genów i kompozycji poszczególnych alleli. Monitoring aerobiologiczny znajduje bezpośrednie wykorzystanie w epidemiologii chorób ludzi (alergologia) i roślin uprawnych (systemy wspierania decyzji w ochronie roślin uprawnych). Badania z zakresu aeromykologii znajdują zastosowanie w tak różnych kierunkach jak aerobiologia przemysłowa, bioterroryzm, ekologia, dziedzictwo kulturowe, klimatologia lub speleologia

The ESCAPE project : Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) and ALADIN (Aire Limitee Adaptation Dynamique Developpement International); and COSMO-EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU-GPU arrangements

A weather forecast model accuracy analysis and ECMWF enhancement proposal by neural network

This paper presents a neural network approach for weather forecast improvement. Predicted parameters, such as air temperature or precipitation, play a crucial role not only in the transportation sector but they also influence people's everyday activities. Numerical weather models require real measured data for the correct forecast run. This data is obtained from automatic weather stations by intelligent sensors. Sensor data collection and its processing is a necessity for finding the optimal weather conditions estimation. The European Centre for Medium-Range Weather Forecasts (ECMWF) model serves as the main base for medium-range predictions among the European countries. This model is capable of providing forecast up to 10 days with horizontal resolution of 9 km. Although ECMWF is currently the global weather system with the highest horizontal resolution, this resolution is still two times worse than the one offered by limited area (regional) numeric models (e.g., ALADIN that is used in many European and north African countries). They use global forecasting model and sensor-based weather monitoring network as the input parameters (global atmospheric situation at regional model geographic boundaries, description of atmospheric condition in numerical form), and because the analysed area is much smaller (typically one country), computing power allows them to use even higher resolution for key meteorological parameters prediction. However, the forecast data obtained from regional models are available only for a specific country, and end-users cannot find them all in one place. Furthermore, not all members provide open access to these data. Since the ECMWF model is commercial, several web services offer it free of charge. Additionally, because this model delivers forecast prediction for the whole of Europe (and for the whole world, too), this attitude is more user-friendly and attractive for potential customers. Therefore, the proposed novel hybrid method based on machine learning is capable of increasing ECMWF forecast outputs accuracy to the same level as limited area models provide, and it can deliver a more accurate forecast in real-time.Web of Science1923art. no. 514

Development of Grid e-Infrastructure in South-Eastern Europe

Over the period of 6 years and three phases, the SEE-GRID programme has established a strong regional human network in the area of distributed scientific computing and has set up a powerful regional Grid infrastructure. It attracted a number of user communities and applications from diverse fields from countries throughout the South-Eastern Europe. From the infrastructure point view, the first project phase has established a pilot Grid infrastructure with more than 20 resource centers in 11 countries. During the subsequent two phases of the project, the infrastructure has grown to currently 55 resource centers with more than 6600 CPUs and 750 TBs of disk storage, distributed in 16 participating countries. Inclusion of new resource centers to the existing infrastructure, as well as a support to new user communities, has demanded setup of regionally distributed core services, development of new monitoring and operational tools, and close collaboration of all partner institution in managing such a complex infrastructure. In this paper we give an overview of the development and current status of SEE-GRID regional infrastructure and describe its transition to the NGI-based Grid model in EGI, with the strong SEE regional collaboration.Comment: 22 pages, 12 figures, 4 table

Prediction Space Weather Using an Asymmetric Cone Model for Halo CMEs

Halo coronal mass ejections (HCMEs) are responsible of the most severe geomagnetic storms. A prediction of their geoeffectiveness and travel time to Earth's vicinity is crucial to forecast space weather. Unfortunately coronagraphic observations are subjected to projection effects and do not provide true characteristics of CMEs. Recently, Michalek (2006, {\it Solar Phys.}, {\bf237}, 101) developed an asymmetric cone model to obtain the space speed, width and source location of HCMEs. We applied this technique to obtain the parameters of all front-sided HCMEs observed by the SOHO/LASCO experiment during a period from the beginning of 2001 until the end of 2002 (solar cycle 23). These parameters were applied for the space weather forecast. Our study determined that the space speeds are strongly correlated with the travel times of HCMEs within Earth's vicinity and with the magnitudes related to geomagnetic disturbances

COST 733 - WG4: Applications of weather type classification

The main objective of the COST Action 733 is to achieve a general numerical method for assessing, comparing and classifying typical weather situations in the European regions. To accomplish this goal, different workgroups are established, each with their specific aims: WG1: Existing methods and applications (finished); WG2: Implementation and development of weather types classification methods; WG3: Comparison of selected weather types classifications; WG4: Testing methods for various applications. The main task of Workgroup 4 (WG4) in COST 733 implies the testing of the selected weather type methods for various classifications. In more detail, WG4 focuses on the following topics:• Selection of dedicated applications (using results from WG1), • Performance of the selected applications using available weather types provided by WG2, • Intercomparison of the application results as a results of different methods • Final assessment of the results and uncertainties, • Presentation and release of results to the other WGs and external interested • Recommend specifications for a new (common) method WG2 Introduction In order to address these specific aims, various applications are selected and WG4 is divided in subgroups accordingly: 1.Air quality 2. Hydrology (& Climatological mapping) 3. Forest fires 4. Climate change and variability 5. Risks and hazards Simultaneously, the special attention is paid to the several wide topics concerning some other COST Actions such as: phenology (COST725), biometeorology (COST730), agriculture (COST 734) and mesoscale modelling and air pollution (COST728). Sub-groups are established to find advantages and disadvantages of different classification methods for different applications. Focus is given to data requirements, spatial and temporal scale, domain area, specifi

CLIMATE CHANGE AND INTERNATIONAL TOURISM: A SIMULATION STUDY

The literature on tourism and climate change lacks an analysis of the global changes in tourism demand. Here a simulation model of international tourism is presented that fills that gap. The current pattern of international tourist flows is modelled using 1995 data on departures and arrivals for 207 countries. Using this basic model the impact on arrivals and departures through changes in population, per capita income and climate change are analysed. In the medium to long term, tourism will grow, however the growth from climate change is smaller than for population and income changes.Tourism demand, climate change, global model

High-resolution fire danger forecast for Poland based on the Weather Research and Forecasting Model

Due to climate change and associated longer and more frequent droughts, the risk of forest fires increases. To address this, the Institute of Meteorology and Water Management implemented a system for forecasting fire weather in Poland. The Fire Weather Index (FWI) system, developed in Canada, has been adapted to work with meteorological fields derived from the high-resolution (2.5 km) Weather Research and Forecasting (WRF) model. Forecasts are made with 24- and 48-h lead times. The purpose of this work is to present the validation of the implemented system. First, the results of the WRF model were validated using in situ observations from ∼70 synoptic stations. Second, we used the correlation method and Eastaugh\u27s percentile analysis to assess the quality of the FWI index. The data covered the 2019 fire season and were analysed for the whole forest area in Poland. Based on the presented results, it can be concluded that the FWI index (calculated based on the WRF model) has a very high predictive ability of fire risk. However, the results vary by region, distance from human habitats, and size of fire

Machine learning in weather prediction and climate analyses : applications and perspectives

In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research - photovoltaic and wind energy, atmospheric physics and processes; in climate research - parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting

A Probabilistic Approach to the Drag-Based Model

The forecast of the time of arrival of a coronal mass ejection (CME) to Earth is of critical importance for our high-technology society and for any future manned exploration of the Solar System. As critical as the forecast accuracy is the knowledge of its precision, i.e. the error associated to the estimate. We propose a statistical approach for the computation of the time of arrival using the drag-based model by introducing the probability distributions, rather than exact values, as input parameters, thus allowing the evaluation of the uncertainty on the forecast. We test this approach using a set of CMEs whose transit times are known, and obtain extremely promising results: the average value of the absolute differences between measure and forecast is 9.1h, and half of these residuals are within the estimated errors. These results suggest that this approach deserves further investigation. We are working to realize a real-time implementation which ingests the outputs of automated CME tracking algorithms as inputs to create a database of events useful for a further validation of the approach.Comment: 18 pages, 4 figure