scholarly journals Reference crop evapotranspiration in distinct agricultural regions of Southern Brazil: a comparison of improved empirical models

Author(s):  
Maicon Sérgio Nascimento dos Santos ◽  
Isac Aires de Castro ◽  
Carolina Elisa Demaman Oro ◽  
Giovani Leone Zabot ◽  
Marcus Vinícius Tres

The FAO56 Penman-Monteith model is globally accepted for the accurate determination of reference evapotranspiration (ETo). However, a lack of appropriate data encouraged the improved model’s approach to estimate ETo. This study compared the performance of 10 empirical models of ETo estimation (Penman, Priestley & Taylor, Tanner & Pelton, Makkink, Jensen & Haise, Hargreaves & Samani, Camargo, Benevides & Lopes, Turc, and Linacre) contrasted with the FAO56 model in two regions in Southern Brazil. Data were collected from automatic stations of the Brazilian National Institute of Meteorology (INMET) from December 21, 2019, to February 28, 2021. The determination coefficient (R²), mean square error (nRMSE), mean bias error (MBE), Willmott index (d), and Pearson’s correlation coefficient (r), clustering, and Principal Component Analysis (PCA) were performed. For the different regions, the radiation-based model proposed by Penman was the best alternative for estimating ETo. The model showed the most appropriated values for R2 (0.9015) and r (0.9494). The clustering and PCA analyses indicated the interrelations of the meteorological data and the combination of the models according to the parameters used for the determination of ETo.

2014 ◽  
Vol 5 (1) ◽  
pp. 669-680
Author(s):  
Susan G. Lakkis ◽  
Mario Lavorato ◽  
Pablo O. Canziani

Six existing models and one proposed approach for estimating global solar radiation were tested in Buenos Aires using commonly measured meteorological data as temperature and sunshine hours covering the years 2010-2013. Statistical predictors as mean bias error, root mean square, mean percentage error, slope and regression coefficients were used as validation criteria. The variability explained (R2), slope and MPE indicated that the higher precision could be excepted when sunshine hours are used as predictor. The new proposed approach explained almost 99% of the RG variability with deviation of less than ± 0.1 MJm-2day-1 and with the MPE smallest value below 1 %. The well known Ångström-Prescott methods, first and third order, was also found to perform for the measured data with high accuracy (R2=0.97-0.99) but with slightly higher MBE values (0.17-0.18 MJm-2day-1). The results pointed out that the third order Ångström type correlation did not improve the estimation accuracy of solar radiation given the highest range of deviation and mean percentage error obtained.  Where the sunshine hours were not available, the formulae including temperature data might be considered as an alternative although the methods displayed larger deviation and tended to overestimate the solar radiation behavior.


2019 ◽  
Vol 111 ◽  
pp. 06040
Author(s):  
Min Hee Chung

In the overseas market, power generation and energy service companies have been engaged in the business of providing personalized trading services for the production of electric power through the Internet platform. This is, so that the electric power sharing system between individuals is being developed through the Internet platform. The prediction of insolation is essential for the prediction of power generation for photovoltaic systems. In this study, we present a prediction model for insolation from data observed at the Meteorological Administration. We also present basic data for the development of the insolation prediction model through meteorological parameters provided in future weather forecasts. The prediction model presented is for five years of observation of weather data in the Seoul area. The proposed model was trained by using the feed-forward neural networks, taking into account the daily climatic elements. To validate the reliability of the model, the root mean square error (RMSE), mean bias error (MBE), and mean absolute error (MAE) were used for estimation. The results of this study can be used to predict the solar power generation system and to provide basic information for trading generated output by photovoltaic systems.


2016 ◽  
Author(s):  
Xiaoming Wang ◽  
Kefei Zhang ◽  
Suqin Wu ◽  
Changyong He ◽  
Yingyan Cheng ◽  
...  

Abstract. Surface pressure is a vital meteorological variable for the accurate determination of precipitable water vapor (PWV) using Global Navigation Satellite Systems (GNSS). The lack of pressure observations is a big issue for the study of climate using historical GNSS observations, which is a relatively new area of GNSS applications in climatology. Hence the use of the surface pressure derived from either an empirical model (e.g. Global Pressure and Temperature 2 wet, GPT2w) or a global atmospheric reanalysis (e.g. ERA-Interim) becomes an important alternative solution. In this study, pressure derived from these two methods is compared against the pressure observed at 108 global GNSS stations for the period 2000–2013. Results show that a good accuracy is achieved from the GPT2w-derived pressure in the latitude band of −30 to 30° and the average value of Root-Mean-Square (RMS) errors across all the stations in this region is 2.4 mb. Correspondingly, an error of 5.6 mm and 1.0 mm in its resultant zenith hydrostatic delay (ZHD) and PWV is expected. In addition, GPT2w-derived pressure usually has a larger error in the cold season due to large diurnal ranges, which is not considered in the GPT2w model. The average value of the RMS errors of the ERA-Interim-derived pressure across all the 108 stations is 1.1 mb, which will lead to an equivalent error of 2.5 mm and 0.4 mm in its resultant ZHD and PWV respectively. Our research also indicates that the ERA-Interim-derived pressure has the potential to be used as a useful meteorological data source to obtain high accuracy PWV on a global scale for climate studies and the GPT2w-derived pressure can be potentially used for climatology as well although it may be only suitable for the tropical regions.


2019 ◽  
Vol 89 (23-24) ◽  
pp. 4875-4883 ◽  
Author(s):  
Jing Huang ◽  
Chongwen Yu

The rapid and accurate determination of flax fiber composition is necessary for its application, but until now it has mainly been tested by the wet chemical method, which is time-consuming and not environmentally friendly. In this paper, near-infrared (NIR) spectroscopy was studied to determinate the main composition of flax, in which 43 flax samples were tested according to the traditional Chinese wet chemical component test standard. Five sets of spectra were generated to show the characteristic of each sample; in total 215 spectra sets were collected using a Fourier transform near-infrared spectrometer. The methods of partial least squares (PLS) and principal component regression (PCR) were used to establish the relationships between the data from the chemical and NIR methods. PLS proved to be a better quantitative method than PCR, based on the value of the coefficient of multiple determination for calibration ( Rc2) and prediction ( Rp2), the ratio of performance to standard deviate (RPD) and the root mean square error of prediction (RMSEP). With the best pretreatment method, the spectral range of 10,000–4000 cm–1yielded a better predictive result than the full range, with Rc2of 0.968, Rp2of 0.955, RMSEP of 1.060%, RPD of 4.641 for cellulose and Rc2of 0.958, Rp2of 0.906, RMSEP of 0.678%, RPD of 3.305 for hemicellulose, while the spectral range 6900–5600 cm–1yielded a better predictive result with Rc2of 0.936, Rp2of 0.769, RMSEP of 0.455%, and RPD of 2.366 for lignin. The study shows that NIR models can provide a simple and fast way to analyze flax fiber composition, which is also beneficial to evaluate its quality.


2021 ◽  
Author(s):  
Pascal Hedelt ◽  
MariLiza Koukouli ◽  
Konstantinos Michaelidis ◽  
Taylor Isabelle ◽  
Dimitris Balis ◽  
...  

<p>Precise knowledge of the location and height of the volcanic sulfur dioxide (SO<sub>2</sub>) plume is essential for accurate determination of SO<sub>2</sub> emitted by volcanic eruptions, however so far not available in operational near-real time UV satellite retrievals. The FP_ILM algorithm (Full-Physics Inverse Learning Machine) enables for the first time to extract the SO<sub>2</sub> layer height information in a matter of seconds for current UV satellites and is thus applicable in NRT environments.</p><p>The FP_ILM combines a principal component analysis (PCA) and a neural network approach (NN) to extract the information about the volcanic SO<sub>2</sub> layer height from high-resolution UV satellite backscatter measurements. So far, UV based SO<sub>2 </sub>layer height retrieval algorithms were very time-consuming and therefore not suitable for near-real-time applications like aviation control, although the SO<sub>2</sub> LH is essential for accurate determination of SO<sub>2</sub> emitted by volcanic eruptions.</p><p>In this presentation, we will present the latest FP_ILM algorithm improvements and show results of recent volcanic eruptions.</p><p>The SO<sub>2</sub> layer height product for Sentinel-5p/TROPOMI is developed in the framework of the SO<sub>2</sub> Layer Height (S5P+I: SO<sub>2</sub> LH) project, which is part of ESA Sentinel-5p+ Innovation project (S5P+I). The S5P+I project aims to develop novel scientific and operational products to exploit the potential of the S5P/TROPOMI capabilities. The S5P+I: SO<sub>2</sub> LH project is dedicated to the generation of an SO<sub>2</sub> LH product and its extensive verification with collocated ground- and space-born measurements.</p>


1987 ◽  
Vol 109 (1) ◽  
pp. 9-14 ◽  
Author(s):  
F. C. Hooper ◽  
A. P. Brunger ◽  
C. S. Chan

A model, previously proposed, describing the sky radiance as a continuous function, has been calibrated from 11,000 individual measurements made in scans taken across springtime skies in Toronto using a narrow field of view radiometer. The model reproduces the measured sky radiance with a mean bias error under five percent and a root mean square error only slightly larger than the standard deviation of the measurements. The model is applied to the calculation of the ratio of the clear sky diffuse irradiance on a slope to that on a horizontal surface. Charts are presented for the direct determination of the expected values of these ratios for surfaces at three tilts and at any azimuth.


2019 ◽  
pp. 1094-1104
Author(s):  
Lucas da Costa Santos ◽  
Guilherme Henrique Terra Cruz ◽  
Frank Freire Capuchinho ◽  
Jeffersom Vieira José ◽  
Elton Fialho dos Reis

Evapotranspiration can be sufficiently estimated when meteorological data are available to implement robust models such as Penman-Monteith (PM). However, due to data scarcity, alternative approaches are necessary. In this context, this study aims to compare the reference evapotranspiration (ETo) obtained from the PM standard method with eight empirical equations to identify the simplest method that can be alternative to the reference method (Penman Monteith method) for ten places in state of Goiás (located in west-central Brazil, Brazilian Savanna). To estimate the ETo, air temperature and relative humidity air, wind speed, sunshine and solar radiation data, which were obtained from the data platform National Institute of Meteorology and the Meteorological and Hydrological System of the State of Goiás, were used. For comparison of empirical methods with PM standard method, we used the following statistical indicators: slope and intercept coefficients (β0 and β1) of regressions equations, the coefficient of determination (r²), Pearson's correlation (r), mean bias error (MBE), root mean square error (RMSE) concordance index refined (dr) and performance index (Pi). Our results indicated that the Turc method is the best option for the state of Goiás when meteorological data are not suffeciently available to use the standard PM method. On the other hand, the method of Romanenko did not present acceptable performance in nine of the ten studied localities. Therefore, its use is advised only in the municipality of the Itumbiara. Among evaluated methods the Hargreaves-Samani method is the best alternative, when there is only air temperature data.


2020 ◽  
Vol 6 (1) ◽  
pp. 16-24
Author(s):  
U. Joshi ◽  
K.N. Poudyal ◽  
I.B. Karki ◽  
N.P. Chapagain

The accurate knowledge of solar energy potential is essential for agricultural scientists, energy engineers, architects and hydrologists for relevant applications in concerned fields. It is cleanest and freely available renewable energy measured using CMP6 Pyranometer. However, it is quite challenging to acquire accurate solar radiation data in different locations of Nepal because of the high cost of instruments and maintenances. In these circumstances, it is essential to select an appropriate empirical model to predict global solar radiation for the use of future at low land, Nepalgunj (28.102°N, 81.668°E and alt. 165 masl) for the year 2011-2012. In this paper, six different empirical models have been used based on regression technique, provided the meteorological data. The empirical constants (a = 0.61, b = 0.05, c = -0.0012 and d = -0.017) are obtained to predict Global solar radiation. The values of statistical tools such as mean percentage error, mean bias error, root mean square error, and coefficient of determination obtained for Abdalla model are 1.99%, 0.003 MJ/m2/day, 2.04 MJ/m2/day and 0.74 respectively. Using the error analysis, it is concluded that the Abdalla model is better than others. So the empirical constants of this model are utilized to predict the global solar radiation to the similar geographical sites of Nepal for the years to come and it can be used to estimate the missing data of solar radiation for the respective sites.


2019 ◽  
Vol 7 (2) ◽  
pp. 48
Author(s):  
Davidson O. Akpootu ◽  
Bello I. Tijjani ◽  
Usman M. Gana

The performances of sunshine, temperature and multivariate models for the estimation of global solar radiation for Sokoto (Latitude 13.020N, Longitude 05.250E and 350.8 m asl) located in the Sahelian region in Nigeria were evaluated using measured monthly average daily global solar radiation, maximum and minimum temperatures, sunshine hours, rainfall, wind speed, cloud cover and relative humidity meteorological data during the period of thirty one years (1980-2010). The comparison assessment of the models was carried out using statistical indices of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA). For the sunshine based models, a total of ten (10) models were developed, nine (9) existing and one author’s sunshine based model. For the temperature based models, a total of four (4) models were developed, three (3) existing and one author’s temperature based model. The results of the existing and newly developed author’s sunshine and temperature based models were compared and the best empirical model was identified and recommended. The results indicated that the author’s quadratic sunshine based model involving the latitude and the exponent temperature based models are found more suitable for global solar radiation estimation in Sokoto. The evaluated existing Ångström type sunshine based model for the location was compared with those available in literature from other studies and was found more suitable for estimating global solar radiation. Comparing the most suitable sunshine and temperature based models revealed that the temperature based models is more appropriate in the location. The developed multivariate regression models are found suitable as evaluation depends on the available combination of the meteorological parameters based on two to six variable correlations. The recommended models are found suitable for estimating global solar radiation in Sokoto and regions with similar climatic information with higher accuracy and climatic variability.   


2010 ◽  
Vol 7 (1) ◽  
pp. 221-267 ◽  
Author(s):  
W. Terink ◽  
R. T. W. L. Hurkmans ◽  
P. J. J. F. Torfs ◽  
R. Uijlenhoet

Abstract. In many climate impact studies hydrological models are forced with meteorological data without an attempt to assess the quality of these data. The objective of this study is to compare downscaled ERA15 (ECMWF-reanalysis data) precipitation and temperature with observed precipitation and temperature and apply a bias correction to these forcing variables. Precipitation is corrected by fitting the mean and coefficient of variation (CV) of the observations. Temperature is corrected by fitting the mean and standard deviation of the observations. It appears that the uncorrected ERA15 is too warm and too wet for most of the Rhine basin. The bias correction leads to satisfactory results, precipitation and temperature differences decreased significantly, although there are a few years for which the correction of precipitation is less satisfying. Corrections were largest during summer for both precipitation and temperature, and for September and October for precipitation only. Besides the statistics the correction method was intended to correct for, it is also found to improve the correlations for the fraction of wet days and lag-1 autocorrelations between ERA15 and the observations. For the validation period temperature is corrected very well, but for precipitation the RMSE of the daily difference between modeled and observed precipitation has increased for the corrected situation. When taking random years for calibration, and the remaining years for validation, the spread in the mean bias error (MBE) becomes larger for the corrected precipitation during validation, but the overal average MBE has decreased.


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