Modelling parametrization to estimate atmospheric long wave radiation in the Northern Mato Grosso, Brazil

The measures of Atmospheric Long Wave radiation are onerous, which brings the necessity to use alternative methods. Thus, the main aim of this paper was to test and parameterize some models that exist in the literature to estimate atmospheric long wave. The data were collected at Fazenda São Nicolau (2002 - 2003), located in Northwestern of Mato Grosso State. Data were processed hourly, monthly, and seasonal (dry and wet) besides clear and partly cloudy days on the average. The models of Swinbank, Idso Jackson, Idso, Prata and Duarte. were applied. The performance of the models was based on the mean error, square root of mean square error, absolute mean error, Pearson's coefficient and Willmott's coefficient. All models had presented high errors and low Peason’s and Willmott coefficients. After parameterizing, all models reduced their errors and increased Pearson and Willmott’s coefficient. The models of Idso and Swinbank had presented better and worse performance, respectively. It was not observed an increment on the performance of the model when classified according to cloudiness and seasonality. The Idso’s model had presented the lowest errors among the models. The model that had presented worst performance for any tested situation was Swinbank.

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IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Sensors ◽

10.3390/s21051631 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1631

Author(s):

Bruno Guilherme Martini ◽

Gilson Augusto Helfer ◽

Jorge Luis Victória Barbosa ◽

Regina Célia Espinosa Modolo ◽

Marcio Rosa da Silva ◽

...

Keyword(s):

Technology Acceptance ◽

Mean Square Error ◽

Ease Of Use ◽

Coefficient Of Determination ◽

Square Root ◽

Mean Square ◽

Use Context ◽

The Mean ◽

Acceptance Model ◽

The Internet Of Things

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

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Evaluating the Effects of Façade Greening on Human Bioclimate in a Complex Urban Environment

Advances in Meteorology ◽

10.1155/2015/747259 ◽

2015 ◽

Vol 2015 ◽

pp. 1-15 ◽

Cited By ~ 43

Author(s):

Britta Jänicke ◽

Fred Meier ◽

Marie-Therese Hoelscher ◽

Dieter Scherer

Keyword(s):

Heat Stress ◽

Short Wave ◽

Wave Radiation ◽

Short Wave Radiation ◽

Long Wave ◽

Building Facades ◽

Urban Heat ◽

The Mean ◽

Human Bioclimate ◽

Radiant Temperature

The evaluation of the effectiveness of countermeasures for a reduction of urban heat stress, such as façade greening, is challenging due to lacking transferability of results from one location to another. Furthermore, complex variables such as the mean radiant temperature(Tmrt)are necessary to assess outdoor human bioclimate. We observedTmrtin front of a building façade in Berlin, Germany, which is half-greened while the other part is bare.Tmrtwas reduced (mean 2 K) in front of the greened compared to the bare façade. To overcome observational shortcomings, we applied the microscale models ENVI-met, RayMan, and SOLWEIG. We evaluated these models based on observations. Our results show thatTmrt(MD = −1.93 K) and downward short-wave radiation (MD = 14.39 W/m2) were sufficiently simulated in contrast to upward short-wave and long-wave radiation. Finally, we compare the simulated reduction ofTmrtwith the observed one in front of the façade greening, showing that the models were not able to simulate the effects of façade greening with the applied settings. Our results reveal that façade greening contributes only slightly to a reduction of heat stress in front of building façades.

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Comparing plastic syringes and disposable columns to extract n-alkanes in forage and concentrate feeds

Ciência Rural ◽

10.1590/s0103-84782010005000128 ◽

2010 ◽

Vol 40 (8) ◽

pp. 1844-1847 ◽

Cited By ~ 1

Author(s):

Dimas Estrasulas de Oliveira ◽

Luis Orlindo Tedeschi

Keyword(s):

Gas Chromatography ◽

Linear Regression ◽

Mean Square Error ◽

Carbon Chain ◽

Carbon Chain Length ◽

Square Root ◽

Mean Square ◽

Alternative Material ◽

The Mean ◽

Disposable Plastic

Saturated aliphatic hydrocarbons (n-alkanes) were extracted from feed, orts, and bovine fecal samples using disposable, plastic 5mL-syringes as an alternative material to disposable columns, which are normally used in the liquid-solid extraction phase of n-alkanes. For both methods, the n-alkane extracts (carbon chain length between 31 and 36 atoms) were identified using gas chromatography. The linear regression between methods were: 1) feces: column Alkane=2.63+0.92×syringeAlkane [r²=0.94, square root of the mean square error (RMSE)=13.7mg kg-1, n=30] from which the intercept and the slope did not simultaneously differ from zero and unity (P>0.05), respectively; 2) feeds: column Alkane=0.36+1.12×syringeAlkane (r²=0.85, RMSE=1.9mg kg-1, n=21) from which the intercept and the slope did not simultaneously differ from zero and unity (P>0.05), respectively; 3) orts: column Alkane=0.49+0.92×syringeAlkane (r²=0.98, RMSE=1.2mg kg-1, n=15) from which the intercept and the slope did not simultaneously differ from zero and unity (P>0.05), respectively. Materials with low concentration of n-alkanes may affect the values obtained in both methods. These results suggested that disposable plastic syringes might be a viable alternative to columns thus, reducing analytical costs.

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The Influence of Cloudiness on The Net Radiation Balance of A Snow Surface with High Albedo

Journal of Glaciology ◽

10.1017/s0022143000023388 ◽

1974 ◽

Vol 13 (67) ◽

pp. 73-84 ◽

Cited By ~ 84

Author(s):

W. Ambach

Keyword(s):

Greenland Ice Sheet ◽

Short Wave ◽

Mean Value ◽

Wave Radiation ◽

Radiation Balance ◽

Net Radiation ◽

Long Wave ◽

Energy Excess ◽

Overcast Sky ◽

Long Wave Radiation

The short-wave and long-wave radiant fluxes measured in the accumulation area of the Greenland ice sheet during a mid-summer period are discussed with respect to their dependence on cloudiness. At a cloudiness of 10/10, a mean value of 270 J/cm2 d is obtained for the daily totals of net radiation balance, whereas a mean value of only 75 J/cm2 d is observed at 0/10. The energy excess of the net radiation balance with overcast sky is due to the significant influence of the incoming long-wave radiation and the high albedo of the surface (average of 84%). High values of net radiation balance are therefore correlated with high values of long-wave radiation balance and low values of short-wave radiation balance.

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Diagnosis of satellite-derived outgoing long wave radiation in relation to rainfall in India

Meteorology and Atmospheric Physics ◽

10.1007/bf01027472 ◽

1991 ◽

Vol 45 (1-2) ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

M. A. Haque ◽

M. Lal

Keyword(s):

Wave Radiation ◽

Outgoing Long Wave Radiation ◽

Long Wave ◽

Long Wave Radiation

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ABSORPTION OF LONG-WAVE RADIATION IN THE ATMOSPHERE

Radiative Heat Exchange in the Atmosphere ◽

10.1016/b978-0-08-010107-1.50007-7 ◽

1965 ◽

pp. 62-155

Author(s):

K.YA. KONDRAT'YEV

Keyword(s):

Wave Radiation ◽

Long Wave ◽

Long Wave Radiation

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Prediction of Radiation Frost Using Support Vector Machines Based on Micrometeorological Data

Applied Sciences ◽

10.3390/app10010283 ◽

2019 ◽

Vol 10 (1) ◽

pp. 283

Author(s):

Yongzong Lu ◽

Yongguang Hu ◽

Pingping Li ◽

Kyaw Tha Paw U ◽

Richard L. Snyder

Keyword(s):

Yangtze River ◽

Prediction Accuracy ◽

Short Wave ◽

Yangtze River Delta ◽

Wave Radiation ◽

Support Vector ◽

Short Wave Radiation ◽

Long Wave ◽

Vector Machines ◽

Long Wave Radiation

Radiation frost happens frequently in the Yangtze River Delta region, which causes high economic loss in agriculture industry. It occurs because of heat losses from the atmosphere, plant and soil in the form of radiant energy, which is strongly associated with the micrometeorological characteristics. Multidimensional and nonlinear micrometeorological data enhances the difficulty in predicting the radiation frost. Support vector machines (SVMs), a type of algorithms, can be supervised learning which widely be employed for classification or regression problems in research of precision agriculture. This paper is the first attempt of using SVMs to build prediction models for radiation frost. Thirty-two kinds of micrometeorological parameters, such as daily mean temperature at six heights (Tmean0.5, Tmean1.5, Tmean2.0, Tmean3.0, Tmean4.5 and Tmean6.0), daily maximum and minimum temperatures at six heights (Tmax0.5, Tmax1.5, Tmax2.0, Tmax3.0, Tmax4.5 and Tmax6.0, and Tmin0.5, Tmin1.5, Tmin2.0, Tmin3.0, Tmin4.5 and Tmin6.0), daily mean relative humidity at six heights (RH0.5, RH1.5, RH2.0, RH3.0, RH4.5 and RH6.0), net radiation (Rn), downward short-wave radiation (Rsd), downward long-wave radiation (Rld), upward long-wave radiation (Rlu), upward short-wave radiation (Rsu), soil temperature (Tsoil) and soil heat flux (G) and daily average wind speed (u) were collected from November 2016 to July 2018. Six combinations inputs were used as the basis dataset for testing and training. Three types of kernel functions, such as linear kernel, radial basis function kernel and polynomial kernel function were used to develop the SVMs models. Five-fold cross validation was conducted for model fitting on training dataset to alleviate over-fitting and make prediction results more reliable. The results showed that an SVM with the radial basis function kernel (SVM-BRF) model with all the 32 micrometeorological data obtained high prediction accuracy in training and testing sets. When the single type of data (temperature, humidity and radiation data) was used for the SVM without any functions, prediction accuracy was better than that with functions. The SVM-BRF model had the best prediction accuracy when using the multidimensional and nonlinear micrometeorological data. Considering the complexity level of the model and the accuracy of prediction, micrometeorological data at the canopy height with the SVM-BRF model has been recommended for radiation frost prediction in Yangtze River Delta and probably could be applied in elsewhere with the similar terrains and micro-climates.

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