ПУТИ УПРАВЛЕНИЯ ФИЗИОЛОГИЧЕСКИМИ ПРОЦЕССАМИ И РЕГУЛИРОВАНИЕ ГИДРОТЕРМИЧЕСКОГО РЕЖИМА АГРОЦЕНОЗОВ

2020 ◽  
pp. 15
Author(s):  
N.N. Dubenok ◽  
A.V. Mayer ◽  
S.V. Borodychev
Keyword(s):  
Weather Conditions ◽  
Physiological Process ◽  
Weather Data ◽  
Irrigation Regime ◽  
Hydrothermal Regime ◽  
Regions Of Russia

На основе анализа многолетних метеоданных в южных регионах России, где преобладает жаркий и сухой климат, предложена универсальная многофункциональная система комбинированного орошения, применение которой позволит управлять физиологическим процессом агроценозов, поддерживать поливной режим и в зависимости от погодных условий регулировать гидротермический режим сельскохозяйственных, плодовых и ягодных культур.Based on the analysis of long-term weather data, in the southern regions of Russia, where the hot and dry climate prevails, a universal multifunctional system of combined irrigation is proposed, the use of which will allow controlling the physiological process of agrocenoses, maintaining the irrigation regime and depending on weather conditions, regulating the hydrothermal regime of agricultural, fruit and berry cultures.

scholarly journals Streamflow and weather conditions of seven small coastal watersheds, British Columbia, Canada, 2013–2019

2021 ◽  
Author(s):  
Maartje C. Korver ◽  
Emily Haughton ◽  
William C. Floyd ◽  
Ian J. W. Giesbrecht
Keyword(s):  
Spatial Data ◽  
Weather Conditions ◽  
Scientific Basis ◽  
Weather Data ◽  
Storm Events ◽  
Small Watersheds ◽  
Coastal Watersheds ◽  
Complete Dataset ◽  
Data Gap

Abstract. Hydrometeorological observations of small watersheds of the northeast Pacific coastal temperate rainforest (NPCTR) of North America are important to understand land to ocean ecological connections and to provide the scientific basis for regional environmental management decisions. The Hakai Institute operates a densely networked and long-term hydrometeorological monitoring observatory, that fills a spatial data gap in the remote and sparsely gauged outer coast of the NPCTR. Here we present the first five water years (October 2013–October 2019) of hourly streamflow and weather data from seven small (< 13 km2), coastal watersheds. Average yearly rainfall was 3267 mm, resulting in 2317 mm of runoff and 0.1087 km3 of freshwater exports from all seven watersheds per year. However, rainfall and runoff were highly variable depending on location and elevation. The seven watersheds have rainfall-dominated (pluvial) streamflow regimes, streamflow responses are rapid and most water exports are driven by high-intensity fall and winter storm events. Measuring rainfall and streamflow in remote and topographically complex rainforest environments is challenging, hence advanced and novel automated measurement methods were used. These methods, specifically for stream flow measurement allowed us to quantify uncertainty and identify key sources of error, which varied by gauging location. Links to the complete dataset, watershed delineations with metrics, and calculation scripts can be found in Sect. 6 and 7.

scholarly journals Weather risk management in marine survey campaigns for the offshore investment projects in the Polish Exclusive Economic Zone

2021 ◽  
Author(s):  
Maria Kubacka ◽  
Maciej Matczak ◽  
Maciej Kałas ◽  
Lucjan Gajewski ◽  
Marcin Burchacz
Keyword(s):  
Wind Farms ◽  
Weather Conditions ◽  
Offshore Wind ◽  
Weather Data ◽  
Offshore Wind Farms ◽  
Investment Projects ◽  
Gas Drilling ◽  
The Baltic ◽  
Weather Risk Management

AbstractWeather is a crucial factor and the most unpredictable of all the factors determining success or failure of any offshore activity, such as investments in seabottom grid connectors (gas, energy or communication), oil & gas drilling facilities development as well as erection of offshore wind farms. Weather conditions cannot be foreseen accurately over a time horizon longer than a few days, and so arranging a realistic work schedule for such an enterprise poses a great challenge. This paper identifies and analyzes the greatest risks associated with weather conditions at sea. The importance and impact of weather on the project implementation were assessed and mitigating measures were proposed. As part of the work, a review of scientific literature was conducted, while the core conclusions were reached using the information-gathering techniques and a documentation review of the offshore projects implemented in cooperation with the Maritime Institute. The authors based their analysis on experience from survey campaigns conducted in the Baltic Sea in the areas of the investments planned for implementation. The analysis of risks associated with weather conditions is based on the statistical weather data obtained using the WAM4 model.The research reveals that it is impossible to create an accurate survey schedule for long-term offshore projects, however, using statistics for each individual hydrodynamic parameter can, to some extent, facilitate the creation of survey schedules for maritime projects.

scholarly journals The Influence of Weather Conditions on the Optimal Setting of Photovoltaic Thermal Hybrid Solar Collectors—A Case Study

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4612
Author(s):  
Ryszard Myhan ◽  
Karolina Szturo ◽  
Monika Panfil ◽  
Zbigniew Szwejkowski
Keyword(s):  
Elevation Angle ◽  
Apparent Movement ◽  
Weather Conditions ◽  
Weather Data ◽  
Solar Collectors ◽  
Optimization Task ◽  
Radiation Properties ◽  
Optimal Setting

The potential absorption of solar energy in photovoltaic thermal (PVT) hybrid solar collectors at different tilt angles was compared in the present study. The optimal tilt angles were tested in three variants: during 1 day, 1 year and a period of 30 years. Simulations were performed based on actual weather data for 30 years, including average hourly total radiation, insolation and air temperature. The apparent movement of the Sun across the sky, solar radiation properties, and the electrical and thermal efficiency of a PVT collector were also taken into account in the simulation model. The optimal orientation of the absorber surface was determined by solving an optimization task. The results of the study indicate that in the long-term perspective, the collector’s performance is maximized when the absorber is positioned toward the south at an elevation angle of 34.1°.

Accuracy of Weather Data in Long-Term Pavement Performance Program Database

2000 ◽  
Vol 1699 (1) ◽  
pp. 151-159 ◽  
Author(s):  
Chung-Lung Wu ◽  
Gonzalo R. Rada ◽  
Aramis Lopez ◽  
Yingwu Fang
Keyword(s):  
Weather Conditions ◽  
Test Site ◽  
Climatic Conditions ◽  
Weighting Scheme ◽  
Pavement Performance ◽  
Weather Data ◽  
Climatic Data ◽  
National Climatic Data Center ◽  
Weather Stations

To provide accurate climatic data for pavements under the Long-Term Pavement Performance (LTPP) Program, a climatic database was developed in 1992 and subsequently revised and expanded in 1998. In the development of this database, up to five nearby weather stations were selected for each test site. Pertinent weather data for the selected weather stations were obtained from the U.S. National Climatic Data Center and the Canadian Climatic Center. With a 1/ R2 weighting scheme, site-specific climatic data were derived from the nearby weather station data. The derived data were referred to as “virtual”weather data. To evaluate the effect of environmental factors on pavement performance and design, automated weather stations (AWS) were installed at LTPP Specific Pavement Study Projects 1, 2, and 8 to collect on-site weather data. Since the virtual weather data were developed for all LTPP test sites and will be used for future pavement performance studies, it is essential that the derived virtual data be accurate and representative of the actual onsite climatic conditions. The availability of the AWS weather data has provided an opportunity to evaluate whether virtual weather data can be used to represent on-site weather conditions. Daily temperature data and monthly temperature and precipitation data were used in this experiment. On the basis of the comparisons made between the virtual and onsite measured (AWS) data, it appears that climatic data derived from nearby weather stations using the 1/R2 weighting scheme estimate the actual weather data reasonably well and thus can be used to represent on-site weather conditions in pavement research and design.

Methodology for weather data collection published by meteorological stations on the Internet

2020 ◽  
Author(s):  
Э. Рекашус ◽  
Е. Закабунина ◽  
В. Цейко
Keyword(s):  
Data Collection ◽  
Weather Conditions ◽  
Weather Data ◽  
The Internet ◽  
Climate Effect ◽  
Resistant Varieties ◽  
Meteorological Observations ◽  
Weather Observation ◽  
Analyze Data

Многолетние наблюдения учёных показывают, что продуктивность агрофитоценозов в той или иной степени зависит от погодных условий местности, где они формируются. В связи с этим в научных исследованиях оценка агрометеорологических условий произрастания сельскохозяйственных культур при обосновании полученных экспериментальных данных является общепринятой. Цели проведения данной оценки разнообразны. Например, она проводится для определения влияния метеоусловий и тенденций изменения климата на сезонную и многолетнюю динамику развития вредных организмов. Результаты метеонаблюдений важны для выявления степени устойчивости новых сортов к абиотическим стрессам, а также для выяснения, какой из факторов погоды в большей степени влияет на составляющие продукционного процесса той или иной культуры. Однако в силу разных причин экспериментальные участки не всегда имеют метеорологические площадки для наблюдений за погодой. В этом случае исследователи обращаются к метеосводкам ближайших государственных метеостанций. В настоящее время данная информация находится в открытом доступе в сети Интернет, и имеются технические возможности её сбора и обработки. В связи с этим цель статьи познакомить исследователей, чьи опытные поля не оборудованы собственными метеостанциями, с методикой получения из сети Интернет данной информации. В статье приведён порядок работы с архивом данных о погоде на метеостанциях, который находится в открытом доступе в сети Интернет. Статья представляет интерес для учёных-исследователей, чьи полевые опыты не оборудованы площадками наблюдения за погодой, но при этом есть необходимость в характеристике метеоусловий для обоснования полученных экспериментальных данных. Long-term observations showed that to a certain degree productivity of farm phytocenoses depend on climate of their cultivation area. Therefore, scientists use standard evaluation of weather conditions to analyze data on crop growth and behaviour. Such evaluation has various purposes. For example, it is conducted to determine the climate effect on seasonal and long-term dynamics of pest spread. Meteorological observations are crucial when selecting stress-resistant varieties or identifying environmental factors influencing crop performance. For various reasons trial locations do not always have meteorological sites for weather observation. In this case scientists collect such data from nearest state meteorological stations. There are several ways to collect and process this information which is publicly available on the Internet nowadays. This article presents the weather data collection methodology from the Internet to be used by researches lacking respective equipment on their trial fields. The article describes the procedure for archive analysis of online weather data from meteorological stations. It is of interest for the Institutions that have no weather observation sites but need to characterize weather conditions to justify the obtained experimental data.

scholarly journals Generation of a Test Reference Year for Alūksne, Latvia

2015 ◽  
Vol 33 (1) ◽  
pp. 46-54 ◽  
Author(s):  
Mārtiņš Ruduks ◽  
Arturs Lešinskis
Keyword(s):  
Selection Process ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Building Energy ◽  
Weather Data ◽  
Building Performance ◽  
Climate Data ◽  
Meteorological Observations ◽  
Energy Simulations

Abstract Precise and reliable meteorological data are necessary for building performance analysis. Since meteorological conditions vary significantly from year to year, there is a need to create a test reference year (TRY), to represent the long-term weather conditions over a year. In this paper two different TRY data models were generated and compared: TRY and TRY-2. Both models where created by analysing every 3-hour weather data for a 30-year period (1984–2013) in Alūksne, Latvia, provided by the Latvian Environment Geology and Meteorology Centre (LEGMC). TRY model was generated according to standard LVS EN ISO 15927-4, but to create second model - TRY-2, 30 year average data were applied. The generated TRY contains typical months from a number of different years. The data gathered from TRY and TRY-2 models where compared with the climate data from the Latvian Cabinet of Ministers regulation No. 379, Regulations Regarding Latvian Building Code LBN 003-01. Average monthly temperature values in LBN 003-01 were lower than the TRY and TRY-2 values. The results of this study may be used in building energy simulations and heating-cooling load calculations for selected region. TRY selection process should include the most recent meteorological observations and should be periodically renewed to reflect the long-term climate change.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

Evaluation of the CERES-Rice Model for Precision Nitrogen Management for Rice in Northeast China

2017 ◽  
Vol 8 (2) ◽  
pp. 328-332
Author(s):  
J. Zhang ◽  
Y. Miao ◽  
W.D. Batchelor
Keyword(s):  
Northeast China ◽  
Weather Data ◽  
Cool Season ◽  
Use Efficiency ◽  
Optimum N Rate ◽  
Management Recommendations ◽  
N Management ◽  
N Rates ◽  
N Use

Over-application of nitrogen (N) in rice (Oryza sativaL.) production in China is common, leading to low N use efficiency (NUE) and high environmental risks. The objective of this work was to evaluate the ability of the CERES-Rice crop growth model to simulate N response in the cool climate of Northeast China, with the long term goal of using the model to develop optimum N management recommendations. Nitrogen experiments were conducted from 2011–2015 in Jiansanjiang, Heilongjiang Province in Northeast China. The CERES-Rice model was calibrated for 2014 and 2015 and evaluated for 2011 and 2013 experiments. Overall, the model gave good estimations of yield across N rates for the calibration years (R2=0.89) and evaluation years (R2=0.73). The calibrated model was then run using weather data from 2001–2015 for 20 different N rates to determine the N rate that maximized the long term marginal net return (MNR) for different N prices. The model results indicated that the optimum mean N rate was 120–130 kg N ha–1, but that the simulated optimum N rate varied each year, ranging from 100 to 200 kg N ha–1. Results of this study indicated that the CERES-Rice model was able to simulate cool season rice growth and provide estimates of optimum regional N rates that were consistent with field observations for the area.

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