Journal articles on the topic 'Precipitation forecasting Tasmania Mathematical models'

Statistical and mathematical models of forecasting are of paramount importance for the understanding and study of databases, especially when applied to data of climatological variables, which enables the atmospheric study of a city or region, enabling greater management of the anthropic activities and actions that suffer the direct or indirect influence of meteorological parameters, such as precipitation and temperature. Therefore, this article aimed to analyze the behavior of monthly time series of Average Minimum Temperature, Average Maximum Temperature, Average Compensated Temperature, and Total Precipitation in Belém (Pará, Brazil) on data provided by INMET, for the production and application forecasting models. A 30-year time series was considered for the four variables, from January 1990 to December 2020. The Box and Jenkins methodology was used to determine the statistical models, and during their applications, models of the SARIMA and Holt-Winters class were estimated. For the selection of the models, analyzes of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Autocorrelation Correlogram (ACF), and Partial Autocorrelation (PACF) and tests such as Ljung-Box and Shapiro-Wilk were performed, in addition to Mean Square Error (NDE) and Absolute Percent Error Mean (MPAE) to find the best accuracy in the predictions. It was possible to find three SARIMA models: (0,1,2) (1,1,0) [12], (1,1,1) (0,0,1) [12], (0,1,2) (1,1,0) [12]; and a Holt-Winters model with additive seasonality. Thus, we found forecasts close to the real data for the four-time series worked from the SARIMA and Holt-Winters models, which indicates the feasibility of its applicability in the study of weather forecasting in the city of Belém. However, it is necessary to apply other possible statistical models, which may present more accurate forecasts.

Numerical weather prediction (NWP) has long been a difficult task for meteorologists. Atmospheric dynamics is extremely complicated to model, and chaos theory teaches us that the mathematical equations used to predict the weather are sensitive to initial conditions; that is, slightly perturbed initial conditions could yield very different forecasts. Over the years, meteorologists have developed a number of different mathematical models for atmospheric dynamics, each making slightly different assumptions and simplifications, and hence each yielding different forecasts. It has been noted that each model has its strengths and weaknesses forecasting in different situations, and hence to improve performance, scientists now use an ensemble forecast consisting of different models and running those models with different initial conditions. This ensemble method uses statistical post-processing; usually linear regression. Recently, machine learning techniques have started to be applied to NWP. Studies of neural networks, logistic regression, and genetic algorithms have shown improvements over standard linear regression for precipitation prediction. Gagne et al proposed using multiple machine learning techniques to improve precipitation forecasting. They used Breiman’s random forest technique, which had previously been applied to other areas of meteorology. Performance was verified using Next Generation Weather Radar (NEXRAD) data. Instead of using an ensemble forecast, it discusses the usage of techniques pertaining to machine learning to improve the precipitation forecast. This paper is to present an approach for mapping of precipitation data. The project attempts to arrive at a machine learning method which is optimal and data driven for predicting precipitation levels that aids farmers thereby aiming to provide benefits to the agricultural domain.

Gradual global climate change poses new challenges to the mathematical sciences, which are related to forecasting of meteorological conditions, preparing the infrastructure for possible rains, storms, droughts, and other climatic disasters. One of the most common approaches is synthetic regression-probability models, which use the spatio-temporal probability density functions of precipitation level. This approach is applied to the statistics of precipitation in the Kharkiv region, which shows the tendency to a gradual increase in air temperature, high indices of basic water stress, indices of drought and riverside flood threats. Open data on temperature distributions and precipitation were processed using various probability statistics. It is shown that the lognormal distribution most accurately describes the measurement data and allows making more accurate prognoses. Estimates of drought and flood probabilities in Kharkiv region under different scenarios of climate change dynamics have been carried out. The results of the study can be used for management of water resources on urban territories at global climate warming.

Abstract In this study, a novel method for forecasting the flood risk in a tropical country is proposed, called CIHAM-UC-FFR. The method is based on the rainfall–runoff process. The CIHAM-UC-FFR method consists of three stages: (1) calibration and validation for the effective precipitation model, called CIHAM-UC-EP model, (2) calibration of forecasting models for components of the CIHAM-UC-EP model, (3) proposed model for forecasting of gridded flood risk called CIHAM-UC-FR. The CIHAM-UC-EP model has a mathematical structure derived from a conceptual model obtained by applying the principle of mass conservation combined with the adapted principle of Fick's law. The CIHAM-UC-FR model is a stochastic equation based on the exceedance probability of the forecast effective precipitation. Various scenarios are shown for a future time where the flood risk is progressively decreased as the expected life parameter of the hydraulic structure is increased.

Abstract Weather forecasting skill has been improved over recent years owing to advances in the representation of physical processes by numerical weather prediction (NWP) models, observational systems, data assimilation and postprocessing, new computational capability, and effective communications and training. There is an area that has received less attention so far but can bring significant improvement to weather forecasting—the calibration of NWP models, a process in which model parameters are tuned using certain mathematical methods to minimize the difference between predictions and observations. Model calibration of the NWP models is difficult because 1) there are a formidable number of model parameters and meteorological variables to tune, and 2) a typical NWP model is very expensive to run, and conventional model calibration methods require many model runs (up to tens of thousands) or cannot handle the high dimensionality of NWP models. This study demonstrates that a newly developed automatic model calibration platform can overcome these difficulties and improve weather forecasting through parameter optimization. We illustrate how this is done with a case study involving 5-day weather forecasting during the summer monsoon in the greater Beijing region using the Weather Research and Forecasting Model. The keys to automatic model calibration are to use global sensitivity analysis to screen out the most important parameters influencing model performance and to employ surrogate models to reduce the need for a large number of model runs. Through several optimization and validation studies, we have shown that automatic model calibration can improve precipitation and temperature forecasting significantly according to a number of performance measures.

The paper describes the mathematical formulation and numerical method of the TSUNM3 high-resolution mesoscale meteorological model being developed at Tomsk State University. The model is nonhydrostatic and includes three-dimensional nonstationary equations of hydrothermodynamics of the atmospheric boundary layer with parameterization of turbulence, moisture microphysics, long-wave and short-wave (solar) radiation, and advective and latent heat flows in the atmosphere and at the boundary of its interaction with the underlying surface. The numerical algorithm is constructed using structured grids with uniform spacing in horizontal directions and condensing to the Earth surface in the vertical direction. When approximating the differential formulation of the problem, the finite volume method with the second order approximation in the spatial variables is used. Explicit-implicit approximations in time (Adams–Bashforth and Crank–Nicolson) are used to achieve second-order accuracy in time. The paper presents results of numerical forecasting of the main meteorological parameters of the atmosphere (temperature, humidity, wind speed and direction) and precipitation in different seasons in the Siberian region. The models were tested with the help of observations obtained using the Volna-4M sodar, MTR-5 temperature profile meter, and Meteo-2 ultrasonic weather stations of the Atmosfera Collective Use Center. The improved TSUNM3 model is shown to adequately reflect the precipitation time and intensity. However, in some cases, the times of its beginning and end do not always coincide, the difference can reach several hours. The precipitation phase state is reflected reliably. Over 70% of precipitation cases are confirmed by numerical calculations. The model satisfactorily predicts temperature and humidity characteristics. The quality of the precipitation forecast model is comparable to the modern mesoscale models, such as the Weather Research and Forecasting (WRF) model.

In this work, 8 meteorological variables were modeled in the Yabú station, Cuba, for which the daily database of this meteorological station was used, where the meteorological variables were taken into account are: extreme temperatures, extreme humidity and its average value, precipitation, wind force and cloudiness corresponding to the period 1977 to 2021. A linear mathematical model was obtained using the Objective Regressive Regression (ORR) methodology for each variable, which explains its behavior according to these variables, 15, 13, 10 and 8 years in advance. The calculation of the mean error with respect to the persistence forecast in temperatures, wind strength and cloudiness, as well as the persistence model was better with respect to humidity, this allows having valuable long-term information of the weather in a locality, which results in better decision making in the different aspects of the economy and society that are impacted by the weather forecast. It is concluded that these models allow long-term weather forecasting, opening a new possibility for forecasting, so that weather chaos can be overcome if this way of forecasting is used; moreover, it is the first time that an ORR model is applied to weather forecasting processes for a specific day so many years in advance.

The article examines models and proposes a methodical approach to highway maintenance in the winter period, taking into account world experience. It was determined that the assessment of winter road maintenance measures performed in different countries takes different forms according to: quality indicator or standard; cost accounting and analysis; organized management system; standards for inspection and monitoring of works; measurement of conformity of service provision; analysis of complaints from users of the road network; methods of forecasting and prevention of certain winter phenomena. It is proposed to use the WSI (FHWA) index, which is calculated on the basis of the average value of daily snowfall and the recorded minimum and maximum temperature on average for the season, to assess the severity of the impact of weather on winter maintenance. It is recommended that the results of precipitation forecasting and the level of the WSI index be used as a basis for determining the operational level of service, which forms a set of potential measures for winter road maintenance. It is proposed to justify the level of service based on models of winter maintenance and forecasting measures that allow to form a methodical approach to highway maintenance. A methodological approach has been developed, which is based on the use of a mathematical model of road maintenance in the winter period, which is a function of minimizing the accumulated indicators of the quality of winter maintenance of road elements depending on their weight for maintenance.

AbstractAtmospheric moisture tracking models are used to identify and quantify sources and sinks of water in the atmospheric branch of the hydrologic cycle. These models are primarily used to investigate the origin of moisture resulting in precipitation for particular regions around the globe. Moisture tracking models vary widely in their level of complexity, depending on the number of physical processes represented. Complex models are comprehensive in their physical representation, but computationally much more expensive than simple models, which only focus on specific physical processes and use simplifying assumptions. We present the mathematical derivation of the new two-layer dynamical recycling model (2L-DRM), a simple analytical moisture tracking model that relaxes the vertically integrated formulation of the original one-layer DRM. By comparing the simple DRM to a very complex moisture tracking model that uses water vapor tracers embedded within the Weather Research and Forecasting regional climate model (WRF-WVT) for the North American monsoon region, we pinpoint the absence of vertical wind shear as the main deficiency in the simple DRM. When comparing both simple models (DRM and 2L-DRM) to the WRF-WVT (which we treat as “truth”), the 2L-DRM better captures the spatial extent, the net amount, and the temporal variability of precipitation that originates from oceanic and local terrestrial sources. The 2L-DRM is well suited to study the large-scale climatological sources of moisture, and for these applications, performs on par with the much more complex and computationally demanding WRF-WVT model.

Наводяться дані про залежність розвитку шкід-ливих поколінь ґронової листовійки на винограднихнасадженнях рівнинно-степового Криму від абіо-тичних чинників: середньодобової температуриповітря, суми опадів, відносної вологості повітря,а також площі листової поверхні куща винограду.На основі цих даних розроблені математичні мо-делі прогнозу розвитку фітофага, що дадуть змо-гу оптимізувати кратність і своєчасність захис-них заходів у боротьбі з зазначеним шкідником наконкретному сорті винограду. Provides information on the intent of the development ofmalicious generations Lobesia botrana of the leaf rolleron vine plantations of plain-steppe Crimea from abioticfactors: the average daily air temperature, amount of precipitation,relative air humidity, as well as the area of leafsurface bush of grapes. On the basis of these datadeveloped mathematical models of forecasting of thedevelopment of the phytophage, that allows to optimizethe frequency and timeliness of protective measures inthe fight against this pest on a particular cultivar ofgrapes.

Abstract Seasonal climate prediction, in general, can achieve excellent results with a multimodel system. A relevant calibration of individual models and an optimal combination of individual models are the key elements leading to this success. However, this commonly used approach appears to be insufficient to remove the intermodel systematic errors (IMSE), which represent similar error properties in individual models after their calibration. A new postprocessing method is proposed to correct the IMSE and to increase the prediction skill. The first step consists of carrying out a diagnosis on the calibrated errors before constructing the multimodel ensemble. In contrast to previous studies, the calibrated errors here are treated directly as the investigation target, and temporal correlation coefficients between the calibrated errors and other meteorological variables are calculated. In the second stage, mathematical and statistical tools are applied in an effort to forecast the IMSE in individual models. Then, the IMSE are removed from the calibrated results and the new corrected data are used to construct the multimodel ensemble. The hindcast of the European Union–funded Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) multimodel system is used to test the method. The simulated Southern Oscillation index is used to diagnose and to correct the calibrated errors of the simulated precipitation. The prediction qualities of the corrected data are assessed and compared with those of the uncorrected dataset. The results show that it is feasible to improve seasonal precipitation prediction by forecasting and correcting the IMSE. This improvement is visible not only for the individual models, but also for the multimodel ensemble.

The article is dedicated to PC modeling of the maximum water runoff of the Kuban rivers flowing to the Krasnodar reservoir after the storm rains. A mathematical model of the «precipitation-runoff» type – DWAT was used. This model is recommended by the World Meteorological Organization for use on rivers with flash floods, based on river survey digital elevation models and storm rain forecasts. A possibility of using proposed approach, the estimated possibility of using DWAT for short-term forecasting of flood inflow to the Krasnodar reservoir based on storm precipitation at predictor meteorological stations, which is tentatively assessed based on the analysis of the dependence of the main elements of floods on the storm rain characteristics. In addition to the digital relief model, the model uses georeferenced layers of data on vegetation, land use types and soils of the catchment area. In general, the article shows the process of entering and processing data into the model. The obtained final simulation results, expressed by the forecast hydrograph, are compared with the observed true values. The forecasted flood graphs correspond suffi ciently to the observed ones and in general the ma ximum water discharges of the forecasted floods obtained using the model under study for previous precipitation are for the most part slightly higher than the observed ones, which is partly explained by the spatial data due to map resolution used. The use of more detailed source data and map resolution may improve the final result.

The Thurnham radar is a prototype of a potential operational C-Band dual-polarisation weather radar designed specifically for the measurement of rainfall. It is also designed to increase the radar coverage over London when operating as a conventional C-Band radar as a direct consequence of the Lewes floods of October 2000. Dual-polarisation processing is expected to provide improved estimation of rainfall rates, especially at higher intensities, in terms of clutter removal, attenuation correction and rainfall estimation. In this study, three hydrological models with different mathematical structures were selected to evaluate the impact that dual-polarisation technology could have on operational hydrology and recommendations provided on the further development of the dual-polarisation algorithms in the short term. The preliminary appraisal was focused on the Upper Medway Catchment (south of London, UK) using different precipitation inputs, including raingauge measurements, radar rainfall estimates from single-polarised algorithms (cartesian format) and five different dual-polarisation algorithms (polar format). The influence of the different rainfall inputs on the various hydrological models were compared using a extreme flood event to provide an initial evaluation of the performance of the Thurnham radar. Recommendations for applying dual-polarisation radar to real-time flood forecasting are discussed in detail.

One of the most important indicators characterizing the operating conditions of the fields is the water cut. It negatively affects the way of mining and the possibility of effective use of modern mining technologies. The high water cut of the cover and ore-hosting rocks often serves as the main reason for a decrease in their stability and the manifestation of negative processes (quicksand properties of rocks, caving), as a result of which the safety of miners deteriorates and high-performance mining methods become inapplicable, which generally leads to a decrease in the technical and economic indicators of the open pit. Thus, the study of the regularities of the formation of water inflows in the “Severny” open pit, based on the use of factual material and the development of a sound forecasting methodology are relevant. Purpose of the study. Analyze and assess the mininggeological and mining-technical conditions of the open pit “Severny” using mathematical models. Research methodology. The work uses a modeling method to predict water inflow and calculate the drainage of a quarry field in difficult mining conditions. Devices simulating natural mining conditions were used: a device for electrohydrodynamic analogies (EGDA). Research results: An analogy has been drawn between the phenomenon of laminar steady-state filtration of water in rocks and the passage of current in an electrically conductive medium in the form of electrically conductive paper, less often in liquid electrolytes. The EGDA device was used to determine the water inflows into the quarry, taking into account the factors that complicate the calculations of groundwater filtration (for example, taking into account the movement of the walls of the quarry, the operation of drainage devices, the infiltration of atmospheric precipitation, the movement of water in neighboring aquifers of different permeability), modeling of objects with a complex configuration boundary contours. Conclusion. The modeling method has been improved, which makes it possible to substantiate the use of a specific mining method under certain hydrogeological conditions and to select the optimal drainage scheme to increase the operational reliability of the open pit. The conducted research, observation, study of the existing dam showed the technical feasibility of further development of the deposit with minimization of financial costs. In order to ensure the safety of mining operations, it is envisaged to conduct constant and systemic mining and technical monitoring of mining operations and study the state of the water protection dam and pit walls.

Abstract. The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic important-sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e., the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Abstract. The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic importance sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e. the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Total column ozone (TCO) describes the amount of ozone in the entire atmosphere. Many scholars have used the lower resolution data to study TCO in different regions, but new phenomena can be discovered using high-precision and high-resolution TCO data. This paper used the long time, high accuracy, and high-resolution MSR2 dataset (2000–2019) to analyze the spatial and temporal variation characteristics of TCO over the Yangtze River Delta Urban Agglomeration to explore the relationship between the TCO and meteorological and socio-economic factors. The correlations between the TCO and climatic factors and the driving forces of meteorological and socio-economic factors on the spatial and temporal variability of TCO were also analyzed, and different mathematical models were constructed to fit the TCO for the past 20 years and predict the future trend of the TCO. The results show the following. (1) The TCO over the study area exhibited a quasi-latitudinal distribution, following a slight downtrend during 2000–2019 (0.01 ± 0.18 DU per year) and achieved its maximum in 2010 and minimum in 2019; throughout the year, an inverted V-shaped cycle characterizes the monthly variability of TCO; TCO was significantly higher in spring than in summer and autumn than winter. (2) Precipitation and the absorbed aerosol index (AAI) had critical effects on the spatial distribution of TCO, but meteorological factors were weakly correlated with the annual variation of TCO subject to the game interactions between different external driving factors. The monthly changes in the TCO were not in synergy with that of other meteorological factors, but with a significant hysteresis effect by 3–5 months. Socio-economic factors had a significant influence on TCO over the study area. (3) The Fourier function model can well describe the history and future trend of the annual TCO over the study area. The TCO over the study area shows a fluctuating upward trend (0.27 ± 1.35 DU per year) over the next 11 years. This study enriches the theoretical and technical system of ozone research, and its results can provide the necessary theoretical basis for ozone simulation and forecasting.

Development of a plant disease like Phoma blight (Phoma sojicola) of soybean ]Glycine max (L.) Merr.] in time and space is a variable, largely depending on environmental factors like temperature, precipitation and humidity. Considering importance of this disease, understanding its dynamics via mathematical and statistical models will help in disease forecasting and prevention of yield losses. To serve this objective, present study on epidemiology of Phoma leaf blight of soybean was carried out under natural epiphytotic conditions on 2 soybean cultivars (Shalimar soybean 1 and Kashmir local) at research farm of Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu and Kashmir during rainy (kharif) season 2019 and 2020. Weather factors significantly influenced disease development irrespective of crop cultivar as revealed by correlation matrix between weekly disease score and weather of preceding one and preceding three weeks. Disease intensity was found positively correlated with RH and rains while as the correlation with temperature was negative. It followed similar trend with all the three sets of weather parameter as weather of preceding one week and that of 3rd and preceding three weeks have contributed to the extent of 57, 50 and 51%, respectively. The study further reveals that optimum temperature for all the events of pathogenesis besides inoculum dispersal in this case lies below 25oC while as the optimum relative humidity must be above 90% and this all is made possible when it rains at least once a week in the summer.

Whistler-mode waves in the inner magnetosphere cause electron precipitation in the atmosphere through the physical process of pitch-angle diffusion. The computation of pitch-angle diffusion relies on quasi-linear theory and becomes time-consuming as soon as it is performed at high temporal resolution from satellite measurements of ambient wave and plasma properties. Such an effort is nevertheless required to capture accurately the variability and complexity of atmospheric electron precipitation, which are involved in various Earth’s ionosphere-magnetosphere coupled problems. In this work, we build a global machine-learning model of event-driven pitch-angle diffusion coefficients for storm conditions based on the data of a variety of storms observed by the NASA Van Allen Probes. We first proceed step-by-step by testing 8 nonparametric machine learning methods. With them, we derive machine learning based models of event-driven diffusion coefficients for the storm of March 2013 associated with high-speed streams. We define 3 diagnostics that allow highlighting of the properties of the selected model and selection of the best method. Three methods are retained for their accuracy/efficiency: spline interpolation, the radial basis method, and neural networks (DNN), the latter being selected for the second step of the study. We then use event-driven diffusion coefficients computed from 32 high-speed stream storms in order to build for the first time a statistical event-driven diffusion coefficient that is embedded within the retained DNN model. We achieve a global mean event-driven model in which we introduce a two-parameter dependence, with both the Kp-index and time kept as in an epoch analysis following the storm evolution. The DNN model does not entail any issue to reproduce quite perfectly its target, i.e., averaged diffusion coefficients, with rare exception in the Landau resonance region. The DNN mean model is then used to analyze how mean diffusion coefficients behave compared with individual ones. We find a poor performance of any mean models compared with individual events, with mean diffusion coefficients computing the general trend at best, due to their large variability. The DNN-based model allows simple and fast data exploration of pitch-angle diffusion among its multiple variables. We finally discuss how to conduct uncertainty quantification of Fokker-Planck simulations of storm conditions for space weather nowcasting and forecasting.

Abstract: This study has selected a vortex tracking algorithm scheme for simulating the activity of tropical cyclone in the Vietnam East Sea by CCAM model. The results show that the CCAM model is able to simulate well the large scale in each month through a reasonable description of the movement rules of the tropical cyclone in the study area. Then, this vortex tracking algorithm scheme was applied to test the seasonal forecast with the outputs of the CCAM model with a resolution of 20km for September 2018 and October 2018. The obtaining results are forecasted quite closely in terms of both quantity and high potential occurrence areas of the tropical cyclone when compared with reality. In particular, for October 2018, although the activity area of ​​the tropical cyclone - YUTU is significantly different from the multi-year average activity position, the seasonal forecast results are obtained from the 120 members of the CCAM model captured this difference. 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Проблема и цель. Овес и озимая рожь – популярные и экономически выгодные зерновые культуры, часто возделываемые в севооборотах. В мире производится этих культур свыше30 млн т в год. В нашей стране они возделываются на площади 3500 млн га, но на территории Рязанской области – на небольших площадях, хотя потенциал у овса и озимой ржи высокий и при соблюдении технологии их возделывания, внедрении инновационных приемов и стремлении производителей к цифровизации сельского хозяйства можно получать высокие стабильные урожаи. Преимущество данных культур заключается в отличной отзывчивости на вносимые удобрительные средства. Таким образом, учитывая ценность культур, проведение исследований производства овса и озимой ржи в Рязанской области и долгосрочное прогнозирование урожайности культур в севообороте на основе расчетов имитационной модели агроэкосистемы является актуальным и своевременным. Методология. На основе разработанной программы исследований приняты общеизвестные методы исследований, начиная с теоретических и заканчивая экспериментальными с использованием платформенных решений и цифровых технологий, доступных его резидентам в интернет-пространстве IТ-технологии. Результаты. На овес и озимую рожь приходится незначительная посевная площадь в регионе – 1,5 и 0,3 % соответственно, к тому же прослеживается тенденция к сокращению посевных площадей с 6,9 % до 4,6 % от всех площадей овса и с 15,9 % до 7,9 % от всех площадей ржи в ЦФО. Авторами использована имитационная модель АМПРА и портативная автоматическая метеостанция. Прогноз урожайности овса и озимой ржи осуществлялся по математической схеме. Предсказание ГТК производилось с помощью параболического тренда с учетом цикличности солнечной активности, установленной по временному ряду температур. При построении зависимостей соблюдались условия: объем теоретической информации по урожайности культур намного больше, чем настраиваемых параметров; проверка адекватности моделей осуществлялась с использованием независимого материала. Критерием выбора структуры модели являлась минимизация отклонений расчетных Yт и экспериментальных Yп данных. Анализ полученных моделей хорошо согласует прогноз урожайности овса и озимой ржи, ГТК (тепло и осадки) и качественные характеристики почв с экспериментальными данными. Средние отклонения Yт и Yп составляли 2,1-2,8 %. Авторами разработано органическое удобрение на основе отходов животноводства, поданы 2 заявки на изобретение (№№ регистрации 2021136608 и 2021136640, авторы И.И. Садовая, О.А. Захарова, О.В. Черкасов, Ф.А. Мусаев, М.И. Голубенко, Д.Е. Кучер, Ю.В. Ломова, Е.Н. Коняев), которое позволит повысить урожайность культур в севообороте, сохранить и восполнить плодородие почвы. Расчетные показатели предоставили данные о возможности роста урожайности культур в севообороте до 50 %. Заключение. Полученные результаты исследований при статистической обработке теоретических и экспериментальных данных урожайности овса и озимой ржи в севообороте с использованием компьютерной программы Statistika 10, имитационной модели Ампра, метеоэлементов, регистрируемых автоматической метеостанцией, установили устойчивый рост показателя при внедрении инновационных приемов в технологии возделывания культур. Так, внесение научно-обоснованной дозы органического удобрения на основе отходов животноводства с учетом почвенных и биологических особенностей позволит повысить расчетную урожайность сельскохозяйственных культур в севообороте до 50 %. Problem and purpose. Oats and winter rye are popular and economically viable crops, often cultivated in crop rotations. The world produces over 30 million tons of these crops per year. In our country, they are cultivated on an area of 3,500 million hectares, but on the territory of Ryazan region they are grown on small areas, although the potential for oats and winter rye is high, and if the technology of their cultivation is followed, the introduction of innovative techniques and the desire of producers to digitalize agriculture, one can obtain high stable yields. The advantage of these crops is their excellent response to applied fertilizers. Thus, taking into account the value of crops, conducting research on the production of oats and winter rye in Ryazan region and long-term forecasting of crop yields in crop rotation based on calculations of the agroecosystem simulation model is relevant and well-timed. Methodology. On the basis of the developed research program, well-known research methods have been adopted, starting from theoretical and ending with experimental ones using platform solutions and digital technologies available to its residents in the Internet. Results. Oats and winter rye account for an insignificant sown area in the region - 1.5 and 0.3 %, respectively. In addition, there is a tendency to reduce sown areas from 6.9 % to 4.6 % of all oat areas and from 15.9 % to 7.9 % of all rye areas in the Central Federal District. The authors used the AMPRA simulation model and a portable automatic weather station. The yield forecast for oats and winter rye was carried out according to a mathematical scheme. The HTI was predicted using a parabolic trend, taking into account the cyclicity of solar activity, established by the time series of temperature. When plotting dependencies, the following conditions were observed: the amount of theoretical information on crop yields was much greater than adjustable parameters and the adequacy of the models was checked using independent material. The criterion for choosing the structure of the model was to minimize deviations of calculated Yt and experimental Yp data. The analysis of the obtained models agrees well with the yield forecast for oats and winter rye, HTI (heat and precipitation), and qualitative characteristics of soils with experimental data. The average deviations of Yt and Yp were 2.1-2.8 %. The authors have developed an organic fertilizer based on animal waste, 2 applications for the invention have been registered (registration No. 2021136608 and 2021136640, authors I.I. Sadovaya, O.A. Zakharova, O.V. Cherkasov, F.A. Golubenko, D.E. Kucher, Yu.V. Lomova, E.N. Konyaev), which will increase crop yields, preserve and replenish soil fertility. The calculated indicators provided data on the possibility of increasing crop yields in crop rotation up to 50 %. Conclusion. The results of studies obtained during statistical processing of theoretical and experimental data on the yield of oats and winter rye in crop rotation using computer program Statistika 10, the Ampra simulation model, meteorological elements recorded by an automatic weather station, established a steady increase in the indicator with the introduction of innovative techniques in crop cultivation technologies. Thus, the introduction of a science-based dose of organic fertilizer based on animal waste, taking into account soil and biological characteristics, will increase the estimated yield of crops in the crop rotation up to 50 %.