scholarly journals VARIABILIDADE TEMPORAL E MAPEAMENTO DOS DADOS CLIMÁTICOS DE BOTUCATU–SP

Irriga ◽  
2010 ◽  
Vol 15 (2) ◽  
pp. 131-139 ◽  
Author(s):  
Anderson Antonio da Conceição Sartori ◽  
Alessandra Fagioli da Silva ◽  
Clovis Manoel Carvalho Ramos ◽  
Célia Regina Lopes Zimback
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Temporal Variability ◽  
Temporal Distribution ◽  
Meteorological Station ◽  
Climate Variables ◽  
Climate Data ◽  
Temporal Dependence ◽  
Classical Statistics ◽  
Made In

O trabalho teve objetivo estudar a variabilidade temporal da temperatura do ar, precipitação pluviométrica e umidade relativa do ar na cidade de Botucatu-SP, Brasil, utilizando técnicas geoestatísticas. Os dados de precipitação pluviométrica, temperatura do ar e umidade relativa do ar utilizados no presente estudo são provenientes da Estação Meteorológica da Fazenda Lageado, da Faculdade de Ciências Agronômicas-UNESP. As observações foram realizadas no período de 1988 a 2007, referem-se ao total mensal de precipitação pluvial expressa em altura de lâmina d'água (mm), médias mensais de temperatura em ºC e umidade relativa em %. Os dados foram avaliados por meio da estatística clássica e geoestatística. As variáveis climáticas tiveram sua dependência verificada por variogramas, apresentando dependência temporal maior que 76%. A série temporal de umidade relativa do ar foi a que apresentou maior alcance (8,67 meses) e, conseqüentemente, maior estabilidade climática. O conhecimento da distribuição temporal das variáveis climáticas é importante para o estudo e realização do zoneamento agroclimático, bem como para o dimensionamento do sistema de irrigação das culturas.   UNITERMOS: geoestatística, mapeamento e krigagem     SARTORI, A. A. C.; SILVA, A. F.; RAMOS, C. M. C; ZIMBACK, C. R. L. TEMPORAL VARIABILITY AND CLIMATE DATA MAPS OF BOTUCATU-SP     2 ABSTRACT    The objective of this research to study the temporal variability of air temperature, rainfall and relative humidity at Botucatu-SP, Brazil, using geostatistics techniques. The data of rainfall, air temperature and relative humidity used in this study were obtamed from the Meteorological Station of the Agricultural Sciences College. The observations were made in the period from 1988 to 2007 and refer to the total monthly rainfall expressed in water depth (mm), average monthly of temperature in °C and relative humidity in %. The data were evaluated by means of classical statistics and geostatistics. The climatic variables were their dependence verified by variogramas, presenting temporal dependence greater than 76%. The temporal series of relative humidity presented the greatest value (8.67 months) and, consequently, more stability climate. Knowledge of the temporal distribution of climate variables is important for the study and realization of agroclimatic zoning and for design measurement of rrigation systems.   KEYWORDS: geostatistics, mapping and kriging  

scholarly journals Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Sudarat Chadsuthi ◽  
Sopon Iamsirithaworn ◽  
Wannapong Triampo ◽  
Charin Modchang
Keyword(s):  
Relative Humidity ◽  
Moving Average ◽  
Arima Model ◽  
Southern Region ◽  
Climate Variables ◽  
Climate Data ◽  
Average Temperature ◽  
Minimum Relative Humidity ◽  
Average Maximum ◽  
Influenza Transmission

Influenza is a worldwide respiratory infectious disease that easily spreads from one person to another. Previous research has found that the influenza transmission process is often associated with climate variables. In this study, we used autocorrelation and partial autocorrelation plots to determine the appropriate autoregressive integrated moving average (ARIMA) model for influenza transmission in the central and southern regions of Thailand. The relationships between reported influenza cases and the climate data, such as the amount of rainfall, average temperature, average maximum relative humidity, average minimum relative humidity, and average relative humidity, were evaluated using cross-correlation function. Based on the available data of suspected influenza cases and climate variables, the most appropriate ARIMA(X) model for each region was obtained. We found that the average temperature correlated with influenza cases in both central and southern regions, but average minimum relative humidity played an important role only in the southern region. The ARIMAX model that includes the average temperature with a 4-month lag and the minimum relative humidity with a 2-month lag is the appropriate model for the central region, whereas including the minimum relative humidity with a 4-month lag results in the best model for the southern region.

POSSIBLE CHANGES OF CLIMATE CONDITIONS IN UKRAINE TO THE MIDDLE OF THE XXI CENTURY

2020 ◽  
pp. 94-100
Author(s):  
L.V. Malytska ◽  
V. O Balabukh
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Climate Model ◽  
Temporal Distribution ◽  
Climatic Parameters ◽  
Average Wind Speed ◽  
Climate Conditions ◽  
A1b Scenario

In Ukraine, as in the world, substantial climatic changes have happened throughout past decades. It is a fact that they are manifested in changing of parameters of the thermal regime, regimes of wind and humidity. It is expected that they will be observed also in future that will lead to aggravation of negative effects and risks due to climate change. That determines the relevance of the problem of forecasting such changes in future both globally and regionally. After all, knowledge of climate’s behavior in future is very important in the development of strategies, program and measures to adapt to climate change. The article is devoted to assessing spatio-temporal distribution main climatic indicators (air temperature, wind speed and relative humidity) in Ukraine, their variability and the probable values to the middle of the 21st century (2021-2050). Projection of changes in meteorological conditions was made for A1B scenario of SRES family using data of the regional climate model REMO and data from the hydrometeorological observation network of Ukraine (175 stations). Estimated data obtained from the European FP-6 ENSEMBLES project with a resolution of 25 km. For spatial distribution (mapping) we used open-source Geographic Information System QGIS, type of geographic coordinate system for project is WGS84. In the middle of the XXI century, if A1B scenario is released, it is expected a significant changes of climatic parameters regarding the 1981-2010 climatic norm: air temperature is rise by 1,5 °C, average wind speed is decrease by 5-8%, relative humidity in winter probably drop by 2%, but in summer it rises by 1,5%. The unidirectionality of the changes is characteristic only of air temperature, for wind speed and relative humidity the changes are in different directions. The intensity of changes is also not uniform across the country for all climatic parameters, has its regional and seasonal features. Statistical likelihood for most of highlighted changes for all climatic parameters is 66 % and more, the air temperature change is virtually certain (p-level <0.001).

scholarly journals Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus

2017 ◽  
Vol 41 (3) ◽  
pp. 225-246 ◽  
Author(s):  
Elizabeth Buechler ◽  
Simon Pallin ◽  
Philip Boudreaux ◽  
Michaela Stockdale
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Indoor Air ◽  
Air Conditioning ◽  
Residential Buildings ◽  
The United States ◽  
Climate Data ◽  
Indoor Climate ◽  
Oak Ridge ◽  
Simulation Engine

The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, heating, ventilation, and air-conditioning system performance, and occupant comfort. Therefore, indoor climate data are generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies are influenced by weather, occupant behavior, and internal loads and are generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The purpose of this study was to probabilistically model homes with the simulation engine EnergyPlus to generate indoor climate data that are widely applicable to residential buildings. Monte Carlo methods were used to perform 840,000 simulations on the Oak Ridge National Laboratory supercomputer (Titan) that accounted for stochastic variation in internal loads, air tightness, home size, and thermostat set points. The Effective Moisture Penetration Depth model was used to consider the effects of moisture buffering. The effects of location and building type on indoor climate were analyzed by evaluating six building types and 14 locations across the United States. The average monthly net indoor moisture supply values were calculated for each climate zone, and the distributions of indoor air temperature and relative humidity conditions were compared with ASHRAE 160 and EN 15026 design conditions. The indoor climate data will be incorporated into an online database tool to aid the building community in designing effective heating, ventilation, and air-conditioning systems and moisture durable building envelopes.

scholarly journals Using ERA-Interim Reanalysis output for creating datasets of energy-relevant climate variables

2017 ◽  
Author(s):  
Philip D. Jones ◽  
Colin Harpham ◽  
Alberto Troccoli ◽  
Benoit Gschwind ◽  
Thierry Ranchin ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Climate Variables ◽  
Climate Data ◽  
Near Surface ◽  
Bias Adjustment ◽  
Dewpoint Temperature ◽  
Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim Reanalysis is presented. A number of different bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting those calculated from ERA-Interim to those based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed and relative humidity, available at either 3 or 6 h timescales over the period 1979-2014. This dataset is available to anyone through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S), and can be accessed at present from (ftp://ecem.climate.copernicus.eu). The benefit of performing bias-adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against observations.

scholarly journals WegenerNet high-resolution weather and climate data from 2007 to 2020

2021 ◽  
Vol 13 (3) ◽  
pp. 1307-1334
Author(s):  
Jürgen Fuchsberger ◽  
Gottfried Kirchengast ◽  
Thomas Kabas
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Spatial Density ◽  
Wind Gust ◽  
Climate Data ◽  
Climate Change Research ◽  
Near Surface ◽  
Temporal Sampling ◽  
Weather And Climate ◽  
Data Level

Abstract. This paper describes the latest reprocessed data record (version 7.1) over 2007 to 2020 from the WegenerNet climate station networks, which since 2007 have been providing measurements with very high spatial and temporal resolution of hydrometeorological variables for two regions in the state of Styria, southeastern Austria: (1) the WegenerNet Feldbach Region, in the Alpine forelands of southeastern Styria, which extends over an area of about 22 km × 16 km and comprises 155 meteorological stations placed on a tightly spaced grid with an average spatial density of 1 station per ∼ 2 km2 and a temporal sampling of 5 min, and (2) the WegenerNet Johnsbachtal, which is a smaller “sister network” of the WegenerNet Feldbach Region in the mountainous Alpine region of upper Styria that extends over an area of about 16 km × 17 km and comprises 13 meteorological stations and 1 hydrographic station at altitudes ranging from below 600 m to over 2100 m and with a temporal sampling of 10 min. These networks operate on a long-term basis and continuously provide quality-controlled station time series for a multitude of hydrometeorological near-surface and surface variables, including air temperature, relative humidity, precipitation, wind speed and direction, wind gust speed and direction, soil moisture, soil temperature, and others like pressure and radiation variables at a few reference stations. In addition, gridded data are available at a resolution of 200 m × 200 m for air temperature, relative humidity, precipitation, and heat index for the Feldbach region and at a resolution of 100 m × 100 m for the wind parameters for both regions. Here we describe this dataset (the most recent reprocessing version 7.1) in terms of the measurement site and station characteristics as well as the data processing, from raw data (level 0) via quality-controlled basic station data (level 1) to weather and climate data products (level 2). In order to showcase the practical utility of the data, we also include two illustrative example applications, briefly summarize and refer to scientific uses in a range of previous studies, and briefly inform about the most recent WegenerNet advancements in 2020 towards a 3D open-air laboratory for climate change research. The dataset is published as part of the University of Graz Wegener Center's WegenerNet data repository under the DOI https://doi.org/10.25364/WEGC/WPS7.1:2021.1 (Fuchsberger et al., 2021) and is continuously extended.

scholarly journals Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

2017 ◽  
Vol 9 (2) ◽  
pp. 471-495 ◽  
Author(s):  
Philip D. Jones ◽  
Colin Harpham ◽  
Alberto Troccoli ◽  
Benoit Gschwind ◽  
Thierry Ranchin ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Climate Variables ◽  
Climate Data ◽  
Near Surface ◽  
Bias Adjustment ◽  
Dewpoint Temperature ◽  
Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979–2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

scholarly journals UMA INTRODUÇÃO AO CONFORTO TERMO-AMBIENTAL DO COLÉGIO ESTADUAL SANTA GEMMA GALGANI, CURITIBA, PARANÁ, BRASIL

FLORESTA ◽  
2014 ◽  
Vol 45 (2) ◽  
pp. 409 ◽  
Author(s):  
Daniela Biondi ◽  
Angeline Martini ◽  
Everaldo Marques de Lima Neto
Keyword(s):  
Relative Humidity ◽  
Thermal Comfort ◽  
Air Temperature ◽  
Comfort Index ◽  
State College ◽  
Outdoor Area ◽  
Different Seasons ◽  
Pet Index ◽  
Outer Area ◽  
Made In

O objetivo desta pesquisa foi avaliar as condições de conforto térmico do Colégio Estadual Santa Gemma Galgani, Curitiba, PR comparando ambientes internos e externos em diferentes estações do ano. A análise do conforto térmico foi realizada através do índice PET. As coletas foram feitas no outono, inverno e primavera de 2011, em dois dias e em dois ambientes distintos. Em cada dia de coleta, foi instalado um equipamento dentro da sala de aula e outro na área externa (pátio), caracterizada como: ambiente 1 - área externa com mais de 90% de impermeabilização e menos árvores; e ambiente 2 - área externa com menos de 30% de impermeabilização e mais árvores. A área do colégio possui 7.634,91 m2, sendo 36,56% de áreas permeáveis e 60,44% de impermeáveis. Nas três estações do ano, a temperatura do ar foi mais alta no pátio, com exceção do outono para o ambiente 1 e da primavera para o ambiente 2. Houve diferenças significativas nas condições de conforto entre os ambientes 1 e 2, indicando que as áreas são distintas pela permeabilidade e quantidade de vegetação. Conclui-se que, no geral, as salas de aula do colégio nas estações do outono, inverno e primavera apresentaram conforto térmico na maioria do período observado.Palavras-chave: Índice PET; Termômetro de Globo; temperatura do ar; umidade relativa do ar. AbstractAn introduction to thermal-environmental comfort state college St. Gemma Galgani, Curitiba, Parana, Brazil. The aim of this study was to evaluate the thermal comfort of the State College Santa Gemma Galgani, Curitiba, PR comparing internal and external environments in different seasons. The analysis of thermal comfort index was performed using PET. Collections were made in the fall, winter and spring of 2011 in two days and in two different environments. Every day a collect was installed equipment within the classroom and another in the outer area (outdoor), characterized as an environment 1 - outdoor area with more than 90% of waterproofing and fewer trees; and the environment 2 - external area under 30% waterproofing and more trees. The area of the college has 7634.91 m2 and 36.56% to 60.44% permeable areas and waterproof. In three seasons, the air temperature was higher in the courtyard, with the exception of autumn to the environment and a spring for the environment 2. There were significant differences in terms of comfort between locations 1 and 2, indicating that the areas are distinct permeability and the amount of vegetation. It is concluded that, overall, the classrooms of the college stations in the fall, winter and spring had thermal comfort for most of the observed period.Keywords: PET index; Globe Thermometer; air temperature; relative humidity.

scholarly journals Application of Support Vector Regression to the Prediction of the Long-Term Impacts of Climate Change on the Moisture Performance of Wood Frame and Massive Timber Walls

Buildings ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 188
Author(s):  
Naman Bansal ◽  
Maurice Defo ◽  
Michael A. Lacasse
Keyword(s):  
Relative Humidity ◽  
Full Range ◽  
Support Vector ◽  
Data Series ◽  
Climate Variables ◽  
Climate Data ◽  
Moisture Performance ◽  
Wood Frame ◽  
Massive Timber

The objective of this study was to explore the potential of a machine learning algorithm, the Support Vector Machine Regression (SVR), to forecast long-term hygrothermal responses and the moisture performance of light wood frame and massive timber walls. Hygrothermal simulations were performed using a 31-year long series of climate data in three cities across Canada. Then, the first 5 years of the series were used in each case to train the model, which was then used to forecast the hygrothermal responses (temperature and relative humidity) and moisture performance indicator (mold growth index) for the remaining years of the series. The location of interest was the exterior layer of the OSB and cross-laminated timber in the case of the wood frame wall and massive timber wall, respectively. A sliding window approach was used to incorporate the dependence of the hygrothermal response on the past climatic conditions, which allowed SVR to capture time, implicitly. The variable selection was performed using the Least Absolute Shrinkage and Selection Operator, which revealed wind-driven rain, relative humidity, temperature, and direct radiation as the most contributing climate variables. The results show that SVR can be effectively used to forecast hygrothermal responses and moisture performance on a long climate data series for most of the cases studied. In some cases, discrepancies were observed due to the lack of capturing the full range of variability of climate variables during the first 5 years.

scholarly journals WegenerNet high-resolution weather and climate data 2007 to 2019

2020 ◽  
Author(s):  
Jürgen Fuchsberger ◽  
Gottfried Kirchengast ◽  
Thomas Kabas
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Heat Index ◽  
Spatial Density ◽  
Wind Gust ◽  
Climate Data ◽  
Near Surface ◽  
Temporal Sampling ◽  
Weather And Climate ◽  
Data Level

Abstract. This paper describes the latest reprocessed data record (version 7.1) over 2007 to 2019 from the WegenerNet climate station networks, which since 2007 provide measurements with very high spatial and temporal resolution of hydrometeorological variables for two regions in the state of Styria, southeastern Austria: 1) the WegenerNet Feldbach Region, in the Alpine forelands of southeastern Styria, which extends over an area of about 22 km × 16 km and comprises 155 meteorological stations placed on a tightly spaced grid, with an average spatial density of one station per ∼2 km2 and a temporal sampling of 5 min; and 2) the WegenerNet Johnsbachtal, which is a smaller sister network of the WegenerNet Feldbach Region in the mountainous Alpine region of upper Styria that extends over an area of about 16 km ×17 km and comprises 13 meteorological stations and one hydrographic station, at altitudes ranging from below 600 m to over 2100 m and with a temporal sampling of 10 min. These networks operate on a long-term basis and continuously provide quality-controlled station time series for a multitude of hydrometeorological near-surface and surface variables, including air temperature, relative humidity, precipitation, wind speed and direction, wind gust speed and direction, soil moisture, soil temperature, and others like pressure and radiation variables at a few reference stations. In addition, gridded data are available at a resolution of 200 m × 200 m for air temperature, relative humidity, precipitation and heat index, for the Feldbach Region, and at a resolution of 100 m × 100 m for the wind parameters for both regions. Here we describe this dataset (the most recent reprocessing version 7.1), in terms of the measurement site and station characteristics as well as the data processing from raw data (level 0) via quality-controlled basic station data (level 1) to weather and climate data products (level 2). In order to showcase the practical utility of the data we also include two illustrative example applications and briefly summarize and refer to scientific uses in a range of previous studies. The dataset is published as part of the University of Graz Wegener Center's WegenerNet data repository under the DOI https://doi.org/10.25364/WEGC/WPS7.1:2020.1 (Fuchsberger et al., 2020) and is continuously extended.

scholarly journals IMPACT OF VENTILATIONS IN ELECTRONIC DEVICE SHIELD ON MICRO-CLIMATE DATA ACQUIRED IN A TROPICAL GREENHOUSE

2021 ◽  
pp. 397-404
Author(s):  
Irfan Ardiansah ◽  
Nurpilihan Bafdal ◽  
Awang Bono ◽  
Edy Suryadi ◽  
Ramadhoni Husnuzhan
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Humidity Sensor ◽  
Electronic Device ◽  
Electronic Devices ◽  
P Value ◽  
Climate Data ◽  
Alpha Level ◽  
Recorded Data ◽  
One Way Anova

The greenhouse which is a building used to manipulate the micro-climate is an essential building for plant growth. Greenhouses have one or more devices that are used to monitor their internal environments against changes in micro-climate. The problem is that some devices are metal-based devices and plastics that can be deformed, such as electronic devices, one of which is a micro-climate monitoring device, so a shield that can protect the device but does not interfere with the sensor readings is needed. The purpose of this study was to make and test a plastic-based container called Duradus Junction Box, which has six removable ventilation openings to measure the micro-climate data. This study uses five Duradus Junction Boxes with different numbers of ventilation openings, a micro-controller connected to the air temperature and relative humidity sensor, and a MicroSD module to record all micro-climate data, all devices being then tested simultaneously for 30 days. Statistically, after using One Way ANOVA, this study found that micro-climate measurements result for actual devices data can be considered similar because the P-value for temperature (0.886) and relative humidity (0.917) is greater than alpha level of 0.05. However, when reading the recorded data for both parameters, it can be seen that micro-climate data inside all shields are slightly higher than actual microclimate data ranging from 1 to 2oC for air temperature and 1 to 3% for air relative humidity.

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