Determination of Step Check Quality Control Thresholds on Air Temperature Data at South Tangerang Climatological Station

All data, including air temperature data, must be verified by conducting quality control using the step check method. Step check quality control is carried out by looking at the difference of a parameter in a certain period compared to the threshold value that was already determined. Therefore before carrying out step check quality control, it is necessary to determine the ceiling and floor boundaries of the difference in air temperature data every hour. The data used in this study are hourly air temperature data and hourly present weather data from weather observations at the South Tangerang Climatological Station during 2016 - 2020. In determining the threshold for air temperature step check quality control, the air temperature data is paired with weather condition data to obtain a threshold value according to rain and no rain conditions. The threshold conducted in this study is based on a check for unusual climatological values, where the limits for an unusual and impossible jump in hourly air temperature changes are determined based on a certain percentage of the data distribution. This study uses percentile analysis to determine the threshold, where 5% in the lower and upper part of the data distribution are used as the threshold. The results show various thresholds every hour. The increase in temperature dominates the changes of hourly air temperature in no-rain conditions. The highest threshold for temperature increase occurs at 00.00 – 01.00 UTC at 3.2°C and continues to decrease over time. The highest threshold for temperature decrease occurs at 09.00 UTC - 10.00 UTC at 2.2°C. In rain conditions, the increase in temperature can still occur. However, the decrease in temperature mainly occurs. The highest threshold for temperature increase during rainy conditions is 1.8°C at 01.00 - 02.00 UTC, while the highest threshold for the temperature decrease is 5.8°C at 06.00 UTC – 07.00 UTC. With these results, observers can first carry out quality control with the Step Check method before filling in the data into the system database. Thus, any suspect data either from reading errors or tool errors can be minimized and finally produce a valid dataset.

Numerical Climatic Analysis of Soybean Development in Sowing Dates in Humid Subtropical Climate

The objective of this study was to determine the mean duration and the interannual variability of phenological subperiods and total soybean development cycle for 11 sowing dates in the humid subtropical climate conditions of the state of Rio Grande do Sul. Daily meteorological data were used from 1971 to 2017 obtained from the Pelotas agroclimatological station and from 1968 to 2017 from the main climatological station of Santa Maria. The soybean development simulation was performed considering three sets of cultivars of relative maturity groups between 5.9-6.8, 6.9-7.3 and 7.4-8.0, with intervals between the sowing dates of approximately 10 days, comprising September, 21 to December, 31. The data of phenological subperiods duration and total development cycle were subjected to the exploratory analysis BoxPlot, analysis of variance and mean comparison by the Scott-Knott test, with 5% of probability. The development cycle duration is greater in Pelotas than in Santa Maria. There was a decrease in soybean cycle duration from the first to the last sowing date for both locations. The R1-R5 subperiod duration is decreasing from October to December due to photoperiod reduction.

Utilization of MODIS Surface Reflectance to Generate Air Temperature Information in East Java - Indonesia

Ambient air temperature is main variable in climatological and hydrological analysis, however limited number of meteorological stations in Indonesia was becoming a problem to provide air temperature data for large areas. The objective of this study is to generate air temparature using relationship of land surface temperature and vegetation index. A total of 6 climatological station and 84 MODIS Images for three years (2015 to 2017) were used for the analysis. Research methods include: image georeferencing, band extraction from modis, derivation of NDVI, gererating ambient air temperature, calibrating using local meteorological station, and image interpretation. Results show that the accuracy of MODIS Surface Reflectance product to generate ambient air temperature in East Java at any periods is 86,37%. So MODIS Surface Reflectance product can be used as alternative solution to generate ambient air temperature.

Influence of Enso to Variability of Rain Chain at Tropical Cyclon in Southern East Nusa Tenggara

The Region of East Nusa Tenggara province which is geographically situated around Equator is an area immediately adjacent to place where the growth of tropical cyclones. This research to determine the variability of tropical cyclones that have happened, how the influence of ENSO on the variability of tropical cyclones, and how the impact of tropical cyclones on rainfall in East Nusa Tenggara. In this study, the rainfall data used is represented by 8 stations observations in the region of NTT, namely Kupang, Rote, Sabu, Waingapu, Ruteng, Maumere, Larantuka, and Alor, with period of 19 years ie the assessment year 1996-2014. The data was obtained from Era Interim ECMWF. Tropical cyclone data was obtained from JTWC-Japan and TCWC-Australia, covering a maximum sustained wind data, position, and lifetime cyclone. Rainfall data obtained from Climatological Station of Lasiana-Kupang. Results of the analysis showed that during the study period the total incidence of tropical cyclones in the south of East Nusa Tenggara many as 113 cases, with an average of 6 events cyclones per year and were distributed mainly in the winter between November and April. The results also show that ENSO influence on tropical cyclone variability indirectly to the parameter of tropical cyclones.

Diversity and seasonality of horse flies (Diptera: Tabanidae) in Uruguay

Horse flies (Diptera: Tabanidae) are hematophagous insects that cause direct and indirect losses in livestock production and are important vectors of pathogens. The aim of this study was to determine the diversity and seasonality of horse fly species at an experimental farm in Tacuarembó and the diversity of species in different departments of Uruguay. For 20 months, systematic collections were performed using Nzi and Malaise traps in two different environments at the experimental farm. Temperature, humidity and rainfall were recorded using a local climatological station. In addition, nonsystematic collections were made at farms located in the departments of Paysandú, Tacuarembó and Colonia. A total of 3,666 horse flies were collected, allowing the identification of 16 species. Three species were recorded for the first time in Uruguay: Dasybasis ornatissima (Brèthes), Dasybasis missionum (Macquart), and Tabanus aff. platensis Brèthes. A species that had not been previously taxonomically described was identified (Tabanus sp.1). In the systematic captures, the most abundant species were Tabanus campestris Brèthes, T. aff. platensis and D. missionum, representing 77.6% of the collected specimens. The environment was an important factor related to the abundance of horse flies, as well as the mean temperature. The horse fly season in Tacuarembó started in September and ended in May, with three evident peaks, the most important one during summer. No horse flies were caught during winter. Variations in the prevalence of species in the different departments were observed, indicating the need to carry out new sampling efforts in different areas.

DESEMPENHO DE MÉTODOS DE CÁLCULO DO COEFICIENTE DE TANQUE PARA ESTIMATIVA DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA

DESEMPENHO DE MÉTODOS DE CÁLCULO DO COEFICIENTE DE TANQUE PARA ESTIMATIVA DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA ALAN DELON ANDRADE1, WEZER LISMAR MIRANDA2, LUIZ GONSAGA DE CARVALHO3, PAULO HENRIQUE FERNANDES FIGUEIREDO1, THALINE BIGHI SILVEIRA DA SILVA4 1Graduando em Engenharia Agrícola, Departamento de Engenharia, Universidade Federal de Lavras – Lavras, MG. Fone: (35)8834-3582. E-mail: [email protected]; [email protected] Agrícola, Doutorando em Recursos Hídricos em Sistemas Agrícolas, Departamento de Engenharia, Universidade Federal de Lavras – Lavras, MG. E-mail: [email protected] Agrícola, Prof. Doutor, Departamento de Engenharia, Universidade Federal de Lavras – Lavras, MG. E-mail: [email protected] em Engenharia Florestal, Departamento de Ciências Florestais, Universidade Federal de Lavras – Lavras, MG. E-mail: [email protected] 1 RESUMO Há diferentes processos aplicáveis ao adequado manejo da irrigação e dentre esses os que utilizam a estimativa da evapotranspiração de referência (ETo) como parâmetro. Assim, com este trabalho objetivou-se avaliar diferentes métodos de determinação do coeficiente de tanque (Kp) utilizado na estimativa da ETo com base em dados de evaporação do Tanque Classe “A” (TCA). Os dados meteorológicos foram coletados diariamente entre 1/1/2004 e 31/12/2013. A estimativa de ETo padrão foi realizada pelo método de Penman-Monteith-FAO (PM-FAO). Os métodos utilizados para a obtenção do Kp foram: Allen et al. (1998), Cuenca (1989), Snyder (1992), Pereira et al. (1995) e Doorenbos e Pruitt (1977). O desempenho dessas metodologias foi avaliado pela comparação entre a estimativa de ETo obtida pelo método do TCA com os diferentes valores de Kp em relação à ETo estimada pelo método de PM-FAO. Na escala mensal todas as metodologias avaliadas apresentaram um desempenho satisfatório. Para a avaliação em escala diária o melhor desempenho foi da metodologia proposta por Pereira et al. (1995).Palavras-chave: coeficiente de tanque, manejo de irrigação, agrometeorologia, Penman-Monteith-FAO ANDRADE, A. D.; MIRANDA, W. L.; CARVALHO, L. G. DE; FIGUEIREDO, P. H. F.; SILVA, T. B. S. DAPERFORMANCE OF METHODS FOR CALCULATING THE PAN COEFFICIENT FOR ESTIMATING REFERENCE EVAPOTRANSPIRATION 2 ABSTRACT There are different processes applicable to the suitable irrigation management, and among them there are those which use the reference evapotranspiration (ETo) estimation as a parameter. This research aimed to evaluate different methods for determining pan coefficient (Kp), used to estimate ETo, based on evaporation data of Class "A" pan (TCA). Daily meteorological data were collected at Principal Climatological Station, which is located at the Federal University of Lavras. The period of time analyzed was from 01/01/2004 to 12/31/2013. The standard ETo estimation was performed using Penman-Monteith-FAO (FAO-PM) method. The methods utilized to obtain Kp were: Allen et al. (1998), Cuenca (1989), Snyder (1992), Pereira et al. (1995), and Doorenbos & Pruitt (1977). The performance of these methods was evaluated by comparing the estimated ETo obtained by the PM-FAO method with ETo estimated by the TCA method with different Kp values. In the monthly scale all tested methodologies showed satisfactory performance. For the evaluation in daily scale, the best performance was the methodology proposed by Pereira et al. (1995).Keywords: pan coefficient, irrigation management, agrometeorology, Penman-Monteith-FAO

Avaliação de modelos matemáticos para estimativa da erosividade da chuva na região de São Mateus-ES

<p>Objetivou-se estimar a erosividade da chuva, mediante seis modelos matemáticos, de regressão linear avaliando entre estes, qual é mais indicado para as condições climáticas da região de São Mateus-ES. Os dados pluviométricos foram obtidos junto à Agência Nacional das Águas-ANA, sendo de 1947 a 2014 para Itauninhas, de 1971 a 2014 para Barra Nova, de 1981 a 2014 para São João da Cachoeira Grande e de 1993 a 2014 para Boca da Vala. Para estimar a erosividade da chuva, a partir da precipitação anual e do coeficiente de chuva, foram utilizadas diferentes equações utilizadas em outros estados com aplicação ao estado do Espírito Santo ou ajustadas para o próprio estado. Para os modelos matemáticos (II) e (I), os valores médios foram de 6.541,2 MJ ha^-1 mm h^-1 ano^-1 a 936,357 MJ ha^-1 mm h^-1 ano^-1 (Itauninhas), de 6.995,855 MJ ha^-1 mm h^-1 ano^-1 a 1.420,296 MJ ha^-1 mm h^-1 ano^-1 (Barra Nova), de 6.297,272 MJ ha^-1 mm h^-1 ano^-1 a 1.014,815 MJ ha^-1 mm h^-1 ano^-1 (São João da Cachoeira Grande) e de 5.427,659 MJ ha^-1 mm h^-1 ano^-1 a 1.626,489 MJ ha^-1 mm h^-1 ano^-1 (Boca da Vala). Para os municípios de Barra Nova e Boca da Vala a erosividade da chuva foi estimada pela equação EI₃₀ = 6,4492*pi – 391,63 com distribuição leptocúrtica. Para as outras duas localidades, a distribuição foi platicúrtica. A estação climatológica com o maior valor de erosividade média da chuva foi Barra Nova, enquanto Boca da Vala apresentou a menor erosividade, considerando apenas a estimativa da erosividade da chuva pelo modelo matemático II. Os maiores e menores valores de erosividade da chuva foram obtidos com os modelos matemáticos I e II. Para estimar a erosividade da chuva, nas condições climáticos da região de São Mateus-ES, o modelo matemático mais adequado é o II.</p><p align="center"><strong><em>Evaluation of mathematical models to estimate rainfall erosivity in the region of São Mateus-ES</em></strong></p><p><strong>Abstract</strong><strong>: </strong>This study aimed to estimate the rainfall erosivity by six mathematical models, linear regression, and evaluate these, which is more suitable for the climatic conditions of São Mateus-ES region. The rainfall data were obtained from the National Water Agency-ANA, and 1947-2014 for Itauninhas, 1971-2014 to Barra Nova, 1981-2014 for São João da Cachoeira Grande and 1993-2014 for Boca da Vala. To estimate the rainfall erosivity, from the annual precipitation and rainfall coefficient were used different equations used in other states with application to the state of the Holy Spirit or adjusted to the state itself. For mathematical models (II) and (I), the average values were 6541.2 MJ ha^-1 mm h^-1 year^-1 to 936.357 MJ ha^-1 mm h^-1 year^-1 (Itauninhas) of 6995.855 MJ mm ha^-1 h^-1 year^-1 to 1420.296 MJ ha^-1 mm h^-1 year^-1 (Barra nova), to 6297.272 MJ ha^-1 mm h^-1 year^-1 and 1014.815 MJ mm ha^-1 h^-1 year^-1 (São João da Cachoeira Grande) and 5427.659 MJ ha^-1 mm h^-1 year^-1 to 1626.489 MJ ha^-1 mm h^-1 year^-1 (Boca da Vala). For the municipalities of Barra Nova and Boca da Vala the rainfall erosivity was estimated by EI₃₀ = 6.4492*pi - 391.63 with leptokurtic distribution. For the other two locations, the distribution was platykurtic. The climatological station with the highest amount of average rainfall erosivity was Barra Nova, while Boca da Vala had the lowest erosivity, considering only an estimated rainfall erosivity by the mathematical model II. The highest and lowest values erosivity of the rain were obtained with the mathematical models I and II. To estimate the rainfall erosivity in the climatic conditions of São Mateus-ES region, the most suitable mathematical model is II.</p>

The course, stratification and possibility of simulating relative air humidity in winter wheat stand

The aim of this study was: (i) long-term (2010, 2011 and 2013) evaluation of the relative air humidity in the winter wheat canopy, (ii) finding of relationships between relative air humidity in canopy and computed or measured meteorological values (precipitation totals, evapotranspiration, moisture balance, specific air humidity, volume soil moisture, % of available soil water content, value of soil water potential), (iii) testing of simulation of daily relative air humidity, based on selected meteorological values and potential evapotranspiration (FAO Penman-Monteith method) and actual evapotranspiration, (iv) testing of simulation of relative air humidity hourly values in the wheat canopy, (v) evaluation of dependence between relative air humidity and leaf wetness. The measurement was performed at the experimental field station of Mendel University in Žabčice (South Moravia, the Czech Republic). Data recording for wheat canopy was conducted by means of a meteostation equipped with digital air humidity and air temperature sensors positioned in the ground, effective height of the stand and in 2 m above the ground. The main vegetation period of wheat was divided into three stages to evaluate differences in various growing phases of wheat. The data from nearby standard climatological stations and from agrometeorological station in Žabčice were used for establishment of relationships between relative air humidity in winter wheat canopy and surrounding environment by correlation and regression analysis. Relative air humidity above 90% occurred substantially longer on the ground and at the effective height of the stand in comparison with the height of 2 m. By means of regression analysis we determined that the limit of 90% was reached in the canopy when at the climatological station it was just 60 to 90% for ground level and 70 to 90% for effective height, especially during the night. Slight dependence between measured or computed meteorological variables and relative air humidity in winter wheat canopy was found (r= 0.23 − 0.56 for precipitation totals,r= 0.27 − 0.57 for % of available soil water capacity, etc.). The simulation of hourly values of relative air humidity in wheat canopy is partially possible just when using the data of relative air humidity from the relevant standard climatological station.

A simple model describes development of early peaks in oomycete zoospore inoculum detected in southern UK outdoors horticultural reservoirs

The numbers of water-borne oomycete propagules in outdoor reservoirs used in horticultural nurseries within the UK are investigated in this study. Water samples were recovered from 11 different horticultural nurseries in the southern UK during Jan–May in 2 “cool” years (2010 and 2013; winter temperatures 2.0 and 0.4°C below UK Met Office 30 year winter average, respectively) and 2 “warm” years (2008 and 2012; winter temperatures 1.2 and 0.9°C above UK Met Office 30 year winter average, respectively). Samples were analyzed for total number of oomycete colony forming units (CFU), predominantly members of the families Saprolegniaceae and Pythiaceae, and these were combined to give monthly mean counts. The numbers of CFU were investigated with respect to prevailing climate in the region: mean monthly air temperatures calculated by using daily observations from the nearest climatological station. The investigations show that the number of CFU during spring can be explained by a linear first-order equation and a statistically significant <em>r</em>² value of 0.66 with the simple relationship: [<em>CFU</em>] = <em>a</em>(<em>T</em> − <em>T</em><span><em>_b</em></span>) − <em>b</em>, where <em>a</em> is the rate of inoculum development with temperature <em>T</em>, and <em>b</em> is the baseload population at temperatures below <em>T</em><span><em>_b</em></span>. Despite the majority of oomycete CFU detected being non-phytopathogenic members of the Saprolegniaceae, total oomycete CFU counts are still of considerable value as indicators of irrigation water treatment efficacy and cleanliness of storage tanks. The presence/absence of <em>Pythium</em> spp. was also determined for all samples tested, and <em>Pythium</em> CFU were found to be present in the majority, the exceptions all being particularly cold months (January and February 2010, and January 2008). A simple scenario study (+2 deg C) suggests that abundance of water-borne oomycetes during spring could be affected by increased temperatures due to climate change.

The Inference on the Results of Atmospheric Environment Prediction by the Different Cloud Data

In order to provide reference for using the different cloud data in environmental prediction, based on AERMOD model, the standard cloud scenario and the reference cloud scenarios were constructed by using the 2010 reference climatological station-observed data and general weather station-observed data, and the d index and relative error on pollutant prediction concentration between the reference cloud scenarios and the standard cloud scenario were analyzed. The results show that the 4 or 3 observational cloud data of the project location, or less than 50km or more than 50km to the project location can be used in atmospheric environment prediction.