Estimating metabolic costs for homeotherms from weather data and morphology: an example using calidridine sandpipers

1997 ◽  
Vol 75 (1) ◽  
pp. 94-101 ◽  
Author(s):  
Ralph V. Cartar ◽  
R. I. Guy Morrison

It is often desirable to estimate the metabolic costs incurred by homeothermic organisms of differing morphology living in different real or hypothetical environmental conditions. To address this problem, we describe a method, based on previously published empirical allometric and heat-transfer equations, that allows a rough estimate to be made of the daily maintenance metabolic costs (i.e., basal and thermoregulatory costs) incurred by a bird in a simple cold two-dimensional environment. The model uses widely available weather variables (temperature, wind speed, and global solar radiation), morphological variables (body mass, height of body's centre of gravity, diameter of torso), and a habitat variable (height of vegetation). We apply the model to weather data from the Canadian Arctic to predict daily metabolic costs for two calidridine sandpiper species (Calidris canutus and C. minutilla) during the summer. The model is extremely sensitive to error in the slope and intercept of the allometric equation predicting conductance from body mass, but is generally robust to other model parameters. Using ambient temperature (Ta) in place of operative temperature (Te) has only a minor (3.5%) effect on predicted metabolic costs, so, given that Te is difficult to estimate, we recommend this substitution (at least for arctic latitudes, where solar radiation is of reduced importance). The model predicts metabolic rates similar to those obtained from an equation based on a heated taxidermic mount for C. canutus, thereby providing some measure of validation. The model can easily be modified to predict metabolic costs for other groups of birds or mammals.


Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 524
Author(s):  
Jihui Yuan ◽  
Kazuo Emura ◽  
Craig Farnham

The Typical meteorological year (TMY) database is often used to calculate air-conditioning loads, and it directly affects the building energy savings design. Among four kinds of TMY databases in China—including Chinese Typical Year Weather (CTYW), International Weather for Energy Calculations (IWEC), Solar Wind Energy Resource Assessment (SWERA) and Chinese Standard Weather Data (CSWD)—only CSWD is measures solar radiation, and it is most used in China. However, the solar radiation of CSWD is a measured daily value, and its hourly value is separated by models. It is found that the cloud ratio (diffuse solar radiation divided by global solar radiation) of CSWD is not realistic in months of May, June and July while compared to the other sets of TMY databases. In order to obtain a more accurate cloud ratio of CSWD for air-conditioning load calculation, this study aims to propose a method of refining the cloud ratio of CSWD in Shanghai, China, using observed solar radiation and the Perez model which is a separation model of high accuracy. In addition, the impact of cloud ratio on air-conditioning load has also been discussed in this paper. It is shown that the cloud ratio can yield a significant impact on the air conditioning load.



2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Haixiang Zang ◽  
Qingshan Xu ◽  
Pengwei Du ◽  
Katsuhiro Ichiyanagi

A modified typical meteorological year (TMY) method is proposed for generating TMY from practical measured weather data. A total of eleven weather indices and novel assigned weighting factors are applied in the processing of forming the TMY database. TMYs of 35 cities in China are generated based on the latest and accurate measured weather data (dry bulb temperature, relative humidity, wind velocity, atmospheric pressure, and daily global solar radiation) in the period of 1994–2010. The TMY data and typical solar radiation data are also investigated and analyzed in this paper, which are important in the utilizations of solar energy systems.



Solar Energy ◽  
2006 ◽  
Author(s):  
Yousuke Kawashima ◽  
Osamu Kawanami ◽  
Itsurou Honda

A simulation of solar hydrogen generation with solar modules and PEM cells in consideration of the solar module temperature for one year was carried out using our measured weather data. The optimal combination of the number of PEM sheets and solar modules was determined and hydrogen conversion efficiency was estimated. Solar module temperature was predicted from the measured data of global solar radiation, ambient temperature, and wind velocity. The current-voltage (I-V) curves of a solar module in arbitrary states were calculated from the (I-V) curves in the reference states using conversion equations (JIS C8913).



2018 ◽  
Vol 7 (2.21) ◽  
pp. 88
Author(s):  
S Shanmuga Priya ◽  
Lisa Maria Ubbenjans ◽  
I Thirunavukkarasu

Measurement of global solar radiation is particularly required for proper design of solar energy conversion systems. This study investigates the use of software tools like neural networks and fuzzy inference systems for modelling so as to predict global solar radiation using different input parameters based on available weather data. Advantages include simplicity, speed and efficiency, to make short term predictions of global solar radiation at different locations in India, Germany and United Kingdom. It helps in estimation of effectiveness of the applied model which matches solar radiation and other meteorological parameters which are in a non-linear relationship. Bayesian Inference algorithm is used for the current study in estimation of global solar radiation.  



2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
R. C. Srivastava ◽  
Harsha Pandey

The amount of solar energy that reaches the earth in one hour is sufficient to supply the world's energy needs for one year. Harvesting this energy efficiently is a huge challenge. In most countries including India, the number of observing stations is inadequate. Therefore, it is essential that some reliable mathematical models be developed to estimate the solar radiation for places where measurements are not carried out and for places where measurement records are not available. In this paper, Angstrom-Prescott model parameters are estimated for seven different sites in India, and a correlation is developed for India, which is found to be a good fit. Also a correlation is developed for predicting the solar radiation using only sunshine hour data.



2013 ◽  
Vol 29 ◽  
pp. 48-57
Author(s):  
Khem N. Poudyal ◽  
Binod K. Bhattarai ◽  
Balkrishna Sapkota ◽  
Berit Kjeldstad

The RadEst 3.00 verson software estimates daily global solar radiation at low altitude plain area using meteorological parameters precipitation, maximum and minimum temperatures and solar radiation of Simara (Lat.27.15°N, Lon.84.98°E, and Alt.137m). Radiation is calculated as the product of the atmospheric transmissivity of radiation times radiation outside the earth atmosphere. The model parameters are fitted in two years data by iterative procedures. An accurate knowledge of solar radiation distribution in each particular geographysical location is crucial for the promotion of solar energy technology. The values estimated by the models are compared with measured radiation data. The performance of the model was evaluated using the statistical tools such as root mean square error (RMSE), mean bias error (MBE), Coefficient of Residual Mass (CRM) and coefficient of determination (r2). The empirical solar radiation models that showed better results using BC, CD, and DB and among them Modular DCBB is the best model for this location The finding coefficients of different models can be utilized for the estimation of solar radiation at the similar meteorological sites of Nepal. DOI: http://dx.doi.org/10.3126/jncs.v29i0.9237Journal of Nepal Chemical SocietyVol. 29, 2012Page: 48-57Uploaded date : 12/3/2013



2017 ◽  
Vol 12 (1) ◽  
pp. 199-209
Author(s):  
Bed Raj KC ◽  
Shekhar Gurung

The RadEst 3.00 version software estimates daily total solar radiation at low land area using meteorological parameters such as precipitation, temperatures and solar radiation of Nepalgunj (Lat.28.05°N, Lon.81.62°E, and Alt.150m). Radiation is calculated as the product of the atmospheric transmissivity of radiation and radiation outside earth atmosphere. The model parameters are fitted in two years data. An accurate knowledge of solar radiation distribution in each particular geographical location is crucial for the promotion of solar active and passive energy technology. The values estimated by the models are compared with measured solar radiation data. The performance of the model was evaluated using root mean square error (RMSE), mean bias error (MBE), Coefficient of Residual Mass (CRM) and coefficient of determination (R2). The RadEst 3.0 software which showed the better results using BC, CD, DB and DCBB, among them the DCBB model is the best model for this site. The values of RMSE, MBE, CRM and R2are 5.20, 3.98, 0.00 and 0.47 respectively. The finding coefficients of different models can be utilized for the estimation of solar radiation at the similar meteorological sites of Nepal.Journal of the Institute of Engineering, 2016, 12(1): 199-209



2012 ◽  
Vol 51 (5) ◽  
pp. 972-985 ◽  
Author(s):  
Prem Woli ◽  
Joel O. Paz

AbstractGlobal solar radiation Rg is an important input for crop models to simulate crop responses. Because the scarcity of long and continuous records of Rg is a serious limitation in many countries, Rg is estimated using models. For crop-model application, empirical Rg models that use commonly measured meteorological variables, such as temperature and precipitation, are generally preferred. Although a large number of models of this kind exist, few have been evaluated for conditions in the United States. This study evaluated the performances of 16 empirical, temperature- and/or precipitation-based Rg models for the southeastern United States. By taking into account spatial distribution and data availability, 30 locations in the region were selected and their daily weather data spanning eight years obtained. One-half of the data was used for calibrating the models, and the other half was used for evaluation. For each model, location-specific parameter values were estimated through regressions. Models were evaluated for each location using the root-mean-square error and the modeling efficiency as goodness-of-fit measures. Among the models that use temperature or precipitation as the input variable, the Mavromatis model showed the best performance. The piecewise linear regression–based Wu et al. model (WP) performed best not only among the models that use both temperature and precipitation but also among the 16 models evaluated, mainly because it has separate relationships for low and high radiation levels. The modeling efficiency of WP was from ~5% to more than 100% greater than those of the other models, depending on models and locations.



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