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Background: The Australian Fire Danger Rating System program (AFDRS) has built a new fire danger rating system for Australia. A live trial of the system's Research Prototype (AFDRSRP), based on fire behaviour thresholds, was run and evaluated between October 2017 and March 2018. Aims: Live trial results are critically analysed, and knowledge gaps and recommendations for future work discussed. Methods: Australian bushfire experts assessed wildfires and prescribed burns across a range of vegetation types and weather conditions. Forecast fire danger ratings calculated using: (1) AFDRSRP; and (2) Forest Fire Danger Index (FFDI) and Grassland Fire Danger Index (GFDI) were compared against ratings derived by expert opinion for each evaluation fire (n = 336). Key results: Overall performance of AFDRSRP was superior to the FFDI/GFDI system (56 vs 43% correct), with a tendency to over-predict rather than under-predict fire potential. AFDRSRP also demonstrated its value to assess fire danger in fuel types not conforming to current grassland or forest models; e.g. for fuels that were grouped to use mallee-heath, spinifex and shrubland fire spread models. Conclusions: The AFDRSRP live trial was successful, outperforming the existing operational fire danger system. Implications: Identified improvements would further enhance AFDRSRP performance, ensuring readiness for operational implementation. A Research Prototype of the Australian Fire Danger Rating System (AFDRSRP) was designed and tested in a live trial. Fire danger ratings derived from the AFDRSRP and FFDI/GFDI systems were compared to expert-derived ratings for 336 fires. In this paper the results of the live trial are presented and discussed. This article belongs to the Collection Australian Fire Danger Rating System. [ABSTRACT FROM AUTHOR]

Background: Development of the Australian Fire Danger Rating System began in 2017 with a project aimed at demonstrating the feasibility of a new fire danger rating system through a Research Prototype (AFDRSRP) that accounted for variability in Australian vegetation types, was nationally applicable, modular and open to continuous improvement. Aims: In this manuscript, we identify and define transition points and categories for the AFDRSRP. We discuss user responses to the categorisation during a live trial evaluation of the AFDRSRP and reflect on limitations and potential improvements. Methods: A review of available literature, broad consultation with stakeholders and reanalysis of fire impact data were used to determine suitable thresholds for categorising fire danger within the AFDRSRP. Key results: Fire danger categories within the AFDRSRP reflect transitions in fire behaviour that result in application of different fire management strategies or are associated with variation in serious consequences and impacts. Conclusions: The AFDRSRP incorporated the best available science, supported by a well-defined framework for categorising and defining fire danger making it suitable for application across Australian fire jurisdictions and range of fuel types. Implications: The framework allows fire managers to assess the accuracy and appropriateness of forecasted fire danger. We identify and present defined categories and thresholds for the Australian Fire Danger Rating System: Research Prototype based on reflecting transitions in fire behaviour that result in application of different fire management strategies or associated with variation in potential for serious consequences and impacts. This article belongs to the Collection Australian Fire Danger Rating System. [ABSTRACT FROM AUTHOR]

Background: Fire danger rating systems are used daily across Australia to support fire management operations and communications to the general public regarding potential fire danger. Aims: In this paper, we introduce the Australian Fire Danger Rating System (AFDRS), providing a short historical account of fire danger rating in Australia as well as the requirements for an improved forecast system. Methods: The AFDRS combines nationally consistent, spatially explicit fuel information with forecast weather and advanced fire behaviour models and knowledge to produce locally relevant ratings of fire behaviour potential. Key results: A well-defined framework is essential for categorising and defining fire danger based on operational response, the potential for impact and observable characteristics of fire incidents. The AFDRS is modular, supporting continuous and incremental improvements and allowing upgrades to components in response to new science. Conclusions: The AFDRS provides a new method to estimate fire danger based on the best available fire behaviour models, leading to potentially significant improvements in the way fire danger is calculated, forecast and interpreted. Implications: The Australian Fire Danger Rating System was implemented in 2022, the most significant change to fire danger forecasting in Australia in more than 50 years. We introduce the Australian Fire Danger Rating System (AFDRS), providing a history of fire danger rating in Australia and requirement for advancements. We describe development, design principles and the supporting framework of the AFDRS as an introduction to subsequent papers in this series. This article belongs to the Collection Australian Fire Danger Rating System. [ABSTRACT FROM AUTHOR]

The European Space Agency's Aeolus mission, launched in August 2018, provides the first global horizontal line‐of‐sight wind profile measurements. Previous studies have shown that Aeolus winds in global forecast systems improve the overall forecast skill, especially in the upper tropospheric tropics and in other data‐sparse regions. In this study, we use a series of observing system experiments with the latest version of the reprocessed Aeolus wind product (2B11) to better characterize the locations and drivers of improved skill from Aeolus with Environment and Climate Change Canada's Global Deterministic Prediction System. Observing system experiments that test the impact of Aeolus winds and the impact of all operational wind observations are carried out, covering the period summer 2019 and winter 2019–2020. Assimilation of operational winds improves the tropospheric wind forecast over the Tropics by a reduction of 8% in the forecast error, and adding the Aeolus winds to the assimilations results in an extra 0.7–0.9%. Aeolus wind impacts are improvements are 0.7–0.9% for the Arctic, and 0.4–0.6% over the Northern and Southern Hemisphere extratropics. The scale dependence of these impacts is investigated using spatial spectra (spherical harmonic decomposition). The improvement is quantified using the difference of the 250 hPa kinetic energy forecast error spectra between experiments. The operational winds largely improve the forecast of planetary scale to intermediate scale for spherical wave numbers between 1 and 20 in the short‐range forecasts. The operational wind impact decreases as the forecast lead time increases. On the other hand, the impact of Aeolus is mostly seen in the intermediate to large scale range with a peak around spherical wave number 8. The Aeolus‐related improvement around this wave number increases with forecast lead time. This analysis suggests that Aeolus winds provide estimates of the wind state that are valuable and complementary to that provided from current operational winds. [ABSTRACT FROM AUTHOR]

Reducing the use of pesticides in agricultural systems is a prerequisite for sustainable agriculture and, therefore, knowledge on the factors that influence the regional insect pest densities is necessary. Based on multi-site and multi-annual observations of the cabbage stem weevil [Ceutorhynchus pallidactylus (Marsham, 1802)] in winter oilseed rape (Brassica napus Linnaeus) and the corresponding meteorological measurements, a statistical relationship for forecasting the abundance was derived. The model explains 84% of the variation of the data set. The remaining 16% might be explained by the landscape effects and agricultural practices, such as crop protection. Based on the statistical relationship between the mean winter air temperature and the abundance of the cabbage stem weevil in the winter oilseed rape, risk maps were derived as a forecast tool for practical farming.

Copyright of Aeronautical Science Bulletins / Repüléstudományi Közlemények is the property of National University of Public Service and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper presents approach used for the development of the forecasting system of extreme hydro-meteorological events in the region of the Sea of Azov. Due to numerous dangerous extreme events that occurred in the beginning of XXI century the issue of creation such system has become very relevant and important. The forecasting system, named EX-MARE, was started developing in 2014 as a complex of mathematical models. For each type of hydro-meteorological events, the modeling component was designed. The EX-MARE system is based on a scenario approach implied the consideration a variety of possible futures taking into account the existing uncertainty. Accurate extreme events estimation requires automated monitoring systems and longterm database application. In the paper, the detail description of the system components and the data sources is examined. Three case studies about the sea surges, flash flood and ice conditions researches demonstrate the application of the EX-MARE system and the benefits of its using. Further development of the EX-MARE system assumes adding data on exposure and vulnerability to perform the risk assessment, as well as focusing on multi-hazards exploring methodology.

Reducing the use of pesticides in agricultural systems is a prerequisite for sustainable agriculture and, therefore, knowledge on the factors that influence the regional insect pest densities is necessary. Based on multi-site and multi-annual observations of the cabbage stem weevil [Ceutorhynchus pallidactylus (Marsham, 1802)] in winter oilseed rape (Brassica napus Linnaeus) and the corresponding meteorological measurements, a statistical relationship for forecasting the abundance was derived. The model explains 84% of the variation of the data set. The remaining 16% might be explained by the landscape effects and agricultural practices, such as crop protection. Based on the statistical relationship between the mean winter air temperature and the abundance of the cabbage stem weevil in the winter oilseed rape, risk maps were derived as a forecast tool for practical farming. [ABSTRACT FROM AUTHOR]

The efficient system of state strategic management provides a foundation for competitive economy. State governance of the Russian Federation includes a monitoring system, which ensures successful implementation of strategic plans at all levels. Monitoring covers analysis of the situation and building short-term forecasts of the development. The article presents hybrid model and software tools for short-term forecasting of social and economic indices in Russia. The system of hybrid forecast models embraces a complex of regressive models and extending totality of models, including artificial neuron networks, solution trees, etc. Regressive models consist of regressive equation systems describing behavior of forecast indices of Russian economy in the national accounts system. Functioning of the equation system is led by fixed expert-scenario conditions. Those indices, whose quantitative forecasts cannot be built by regressive models, will be forecast by the module of intellectual models. The principle system advantages are its openness and adaptability. It can be introduced in situational regional and municipal centers. An important direction in the education process is the development of advanced methods of teaching based on analyzing and modeling economic situations. The authors propose to apply their forecast system in the Situational Center of the Russian Plekhanov University of Economics to train students and conduct research work.

RESUMO - A pinta preta causada por Alternaria spp. é uma das principais doenças do tomateiro ocorrendo em cultivos de tomate na microrregião de Joaçaba a qual é responsável por mais de 57% da produção do Estado de Santa Catarina. Para o controle desta doença, são aplicados fungicidas semanalmente, o que implica em aumento de custos de produção. O objetivo deste trabalho foi comparar a aplicação preventiva semanal de fungicida com o sistema de previsão TomCast. Este sistema utiliza a duração do molhamento foliar e a temperatura média neste período para calcular a severidade diária da doença (VSD). As pulverizações baseadas no calendário semanal iniciaram na mesma semana do transplante enquanto com o previsor Tom Cast iniciaram quando o valor de VSD acumulado atingiu a limiar estabelecido (10, 15, 20, 25 e 30 VSD). Em cada um dos anos estudados, a pulverização de acordo com o Tom Cast resultou no controle de doença e produção comparável ao calendário semanal, mas retardou o início da pulverização em 26 a 46 dias e também reduziu o número de pulverizações em até 84%.

The El Niño–Southern Oscillation (ENSO) can cause climate anomalies on a global scale, and further affect human life and activities in coastal zones. Therefore, its forecast is of great significance for early disaster warning and coastal management. However, the frequent occurrence of central Pacific (CP) El Niño events increases the diversity and complexity of ENSO, severely reducing its prediction efficiency. In this study, an extended ensemble coupled data assimilation–forecast system was employed to investigate the prediction of different types of El Niño events, including eastern Pacific (EP) and CP events. The extended system was based on the fifth-generation Lamont–Doherty Earth Observation (LDEO5) model, in which an advanced ensemble Kalman filter was used to construct a multisource data assimilation system, and a stochastic optimal method was used to measure the influence of atmospheric stochastic processes on model prediction errors. The extended system was used to predict two types of El Niño events that occurred between January 1950 and December 2018. The results showed that the extended system was generally able to predict EP events with a higher accuracy than CP events for all lead times. The extended system successfully predicted the mature phase of EP events up to 12 months in advance but could only predict the mature phase of CP events up to 6 months in advance. The extended system was also able to depict the evolution of both EP and CP events, although the sea surface temperature anomalies were underestimated. The extended system not only provides a useful platform for improving ENSO prediction accuracies in association with El Niño diversity but also provides an important tool for disaster early warning and coastal management. • The advanced EnKF and SO methods are employed to extend the LDEO5 model. • The extended system successfully identifies the different flavors of El Niño in prediction. • Prediction errors are reduced over the equatorial central and eastern Pacific. • Accurately predicting the flavor and intensity of ENSO is important for disaster early warning and coastal management. [ABSTRACT FROM AUTHOR]

Copyright of Revista Perspectiva Empresarial is the property of Fundacion Universitaria Ceipa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

O controle da requeima da batata requer aplicação freqüente de fungicidas, o que encarece a produção, impactando de modo desnecessário o ambiente. A utilização de modelos de previsão dessa doença permitiria reduzir as aplicações sem afetar a produção. Neste trabalho, objetivou-se avaliar os modelos "Blitecast e Prophy" como referência para o controle da requeima por fungicidas. Os experimentos foram conduzidos na primavera de 2004 e no outono de 2005, em Santa Maria, RS. Os dados meteorológicos foram medidos no centro da área experimental, a 0,10 e a 1,50 m acima da superfície do solo. Utilizaram-se diferentes valores de severidade (VS) acumulada, calculada pelos modelos "Blitecast" (VS= 18, 24, 30, 36 e 42) e "Prophy" (VS= 15, 20, 25, 30 e 35) que se constituíram os tratamentos, adicionando-se o tratamento com aplicação semanal e a testemunha, sem aplicação. O delineamento foi inteiramente casualizado com quatro repetições, sendo cada parcela composta de quatro fileiras de plantas com 5 m de comprimento. Avaliou-se a severidade da requeima por parcela a cada três a cinco dias. Verificou-se que o uso do modelo "Blitecast" com 18 valores de severidade acumulados, incrementou, em pelo menos, 42,6% a produtividade de tubérculos comerciais em relação às áreas sem aplicação de fungicida e reduziu o número de aplicações em 25% nos períodos úmidos e, em 70% nos períodos secos, em relação às aplicações semanais. A eficiência de controle da requeima foi similar à obtida com aplicações semanais de fungicida nos tratamentos Bli18 e Pro15. O uso do modelo "Prophy" requer maior número de aplicações do que o "Blitecast" e não resultou em melhor controle.The control potato late blight needs a great number of fungicide sprayings. These increase the costs of cropping and cause undesirable environmental impacts. The use of forecast systems to predict disease has the potential of reducing fungicide applications without reducing yield. The objective of this study was to evaluate the performance of Blitecast and Prophy systems as a reference model for predicting late blight potato and support decision of spray fungicides. Experiments were carried out during Spring 2004 and Autumn 2005, in Santa Maria, RS, Brazil. Meteorological data were measured in the center of the experimental area at 0.10 and 1.50 m above soil surface. Different accumulated severity values (VS) were calculated with 'Blitecast' (VS = 18, 24, 30, 36 and 42) and 'Prophy' (VS = 15, 20, 25, 30 and 35) forecast systems. These values were used to perform. Two additional treatments were the weekly sprays and without fungicides. The experimental design was a completely randomized, with four replications. Each plot had four rows plants with 5 m length. Late blight severity was evaluated in each three to five days. The fungicide spraying schedule based on Blitecast system with 18 accumulated severity values increased tuber yield at least 42.6 % compared to the control without fungicides treatment. It also reduced the number of sprayings by 25% during wet periods and 70% during dry periods compared to weekly sprayings. The efficiency of controlling late blight was similar to the weekly sprayings treatment in the treatments Bli18 e Pro15. The Prophy model predicted higher number of fungicide sprayings than the Blitecast system and did not improve disease control.

A system was designed for monitoring current soil moisture and predicting soil moisture changes in the next 1-7 days in Liaoning province. In this system, data of soil volumetric water content from 54 automatic observation stations and weather forecast data from Central Meteorological Observatory in Liaoning were used. The soil moisture data of 8 soil layers was included, and they were different among different stations. The parameters of stations like longitude, latitude, altitude, etc were obtained from the observation stations. The soil parameters like field capacity, soil bulk density, wilting humidity were obtained by measuring soil samples when the stations were established. The next 7 days weather forecast data from the National Meteorological Center of CMA included daily maximum temperature, minimum temperature, precipitation, and cloudiness. A regression model of the sunshine and cloudiness was established based on the information of 54 stations during 1981 to 2010. All the data above were input into the system for monitoring and forecasting the soil moisture. The system was developed by VC++ and Fortran based on soil water dynamic and CERES-MAIZE model. The model simulated the runoff process, infiltration process, evapotranspiration process and root water uptake process to obtain the soil moisture data of 8 layers. The system was used to predict the next 1-7 days soil moisture from June to August 2015 in order to verify the accuracy of the forecast. Results showed that when the absolute error between forecast and measured relative soil moisture was less than 3%, the highest accuracy was 52.9% for the next 1-d forecast. When the difference was less than 5%, the highest accuracy was 70.3%. When the absolute error was less than 10%, the accuracy was 78.5% for the 7 d forecast. The highest accuracy was 91.1% for the next 1-d forecast. The prediction accuracy decreased with increasing forecast date. In addition, drought level was predicted using the data of June-August, 2015. According to the threshold of relative soil moisture, 5 type of drought grade included severe drought, moderate drought, light drought, suitable, saturation with relative soil moisture less than 40%, 40% to 50%, 50% to 60%, 60% to 90%, and larger than and equal to 90%. The lowest value of drought level accuracy was 70.1%, and the highest value of drought level accuracy was 81.9%. Liaoning province suffered varying degrees of drought in the summer of 2015, which had impacted growth of maize and other field crops. The system played an important supporting role in agriculture meteorological service, as proved by its application in July 24. The study provides an effective system for improving the quality and efficiency in agriculture meteorological service. [ABSTRACT FROM AUTHOR]