Solar Radiation Measurements Research Articles

LCV-Pluronic F-127 dosimeter for UV light dose distribution measurements

This work reports on a radiochromic dosimeter for dose distribution measurement after irradiation with UV light. The dosimeter is composed of a 25 % copolymer of poly(ethylene oxide)-block-poly(propylene oxide)-block-poly(ethylene oxide) (Pluronic F-127) that forms a physical gel matrix. The matrix is infused with leuco crystal violet (LCV, 2 mM), 4-(1,1,3,3-tetramethylbutyl)phenyl-polyethylene glycol (Triton X-100, 4 mM) and trichloroacetic acid (TCAA, 17 mM). This dosimeter was exposed to UVA, UVB and UVC radiation and the radiochromic reactions were followed with reflectance spectrophotometry and Raman spectroscopy. Exposition of the dosimeter to all UV light subranges caused LCV to convert to CV molecules absorbing visible light and affecting the dosimeter that changes colour to blue. The intensity of colour was related to absorbed UV dose. The dosimeter is the least sensitive to irradiation with UVA and the most sensitive to irradiation with UVB. The main dose-response characteristics of the dosimeter were obtained: threshold dose, linear and dynamic dose range, dose sensitivity as well as the stability over time after irradiation and stability of recorded dose distribution. Overall, the dosimeter coupled with reflectance spectrophotometry is effective in measuring doses in 1D, whereas using 3D optical scanning techniques should expand its application to 3D dosimetry. This work also provides an approach to using the LCV-Pluronic F-127 dosimeter for measurements of 2D dose distribution with a flat-bed optical scanner as a readout method. LCV-Pluronic F-127 may also be useful for solar radiation measurements.

Erythemal Solar Irradiance, UVER, and UV Index from Ground-Based Data in Central Spain

The study shows an analysis of a 7-year data set measuring Ultraviolet-B (UVB) irradiance values and ultraviolet index TABLEUVI) values derived from ground-based broadband irradiance measurements, satellite-derived total ozone, and UVB solar irradiance recorded in Valladolid (Central Spain). Ultraviolet-B (UVB) solar irradiance measurements in the range (280–315 nm) carried out during the period 2013–2019 at a continental Mediterranean solar station, located in Valladolid (Spain), were analyzed. UVB data recorded using a YES UVB-1 pyranometer were used to estimate erythemal irradiance, ultraviolet erythemal irradiance (UVER), UVI, cumulative dose, and sun protection. Hourly UVER data in January (minimum values) and June (maximum values) were analyzed as an average year for the measurement station. Differences between UVI values at solar noon and the maximum daily value were minimal. It was found that on certain summer days, maximum daily UVI and SED (cumulative daily dose) could be over 12 and 60, respectively. The cumulative dose on the horizontal surface was calculated at the station for different skin types. It was observed that over 45% of the annual dose is received in summer, about 30% in spring, over 15% in autumn, and less than 10% in winter. In addition, the relationship between the maximum daily UVI and the annual accumulated dose in SEDs was studied to provide information on sun protection under low UVI conditions.

Aerosol solar radiative forcing near the Taklimakan Desert based on radiative transfer and regional meteorological simulations during the Dust Aerosol Observation-Kashi campaign

Abstract. The Taklimakan Desert is a main and continuous source of Asian dust particles causing significant direct radiative effects, which are commonly quantified by the aerosol solar radiative forcing (ASRF). To improve the accuracy of estimates of dust ASRF, the Dust Aerosol Observation-Kashi (DAO-K) campaign was carried out near the Taklimakan Desert in April 2019. The objective of the DAO-K campaign is to provide crucial parameters needed for the calculation of ASRF, such as dust optical and microphysical properties, vertical distribution, and surface albedo. The ASRF was calculated using radiative transfer (RT) simulations based on the observed aerosol parameters, additionally considering the measured atmospheric profiles and diurnal variations of surface albedo. As a result, daily average values of ASRF of −19 W m−2 at the top of the atmosphere and −36 W m−2 at the bottom of the atmosphere were derived from the simulations conducted during the DAO-K campaign. Furthermore, the Weather Research and Forecasting model with Chemistry (WRF-Chem), with assimilation of measurements of the aerosol optical depth and particulate matter (PM) mass concentrations of particles with aerodynamic diameter smaller than 2.5 µm (PM2.5) and 10 µm (PM10), is employed to estimate the dust ASRF for comparison. The results of the ASRF simulations (RT and WRF-Chem) were evaluated using ground-based downward solar irradiance measurements, which have confirmed that the RT simulations are in good agreement with simultaneous observations, whereas the WRF-Chem estimations reveal obvious discrepancies with the solar irradiance measurements.

Season-dependent uncertainties in Baseline Surface Radiation Network (BSRN) solar radiation data

A novel radiation calibration method (Qiu et al., 2014) is improved using two new calibration selection criteria for quality control and is then used to analyze the uncertainties in global, direct and scattering solar irradiance (GSI, DSI and SSI, respectively) measurements at Izana, a Baseline Surface Radiation Network (BSRN) site. A total of 476 days of accepted calibration results from 2011 to 2014 are employed. The results reveal season-dependent uncertainties in the GSI, DSI and SSI measurements. The GSI, DSI and SSI measurements are probably lower by 3% ~ 6% in the summer and higher by 1% ~ 4% in the winter. The temperature dependency of solar radiometers is the most likely cause for this result. The calibration results are validated through two types of comparisons. The first is a comparison between aerosol optical thickness (AOT) retrievals from the vertical and horizontal DSI and AOT retrievals from the Aerosol Robotic Network (AERONET). The second is a comparison between in situ radiation measurements and model simulations. Higher GSI and DSI measurements in the summer result in systematically larger AOT retrievals, and they are also responsible for evidently lower measurements relative to model simulations. The winter case shows evidence to the contrary. Differences between in situ radiation measurements and model simulations also indicate the seasonal dependence of the measurement uncertainties. These results are highly significant for understanding the uncertainties in historical solar radiation data.

Feasibility study of miniature near-infrared spectrometer for the measurement of solar irradiance within Arctic snow-cover sea ice

The extremely low temperature, high humidity and limited power supply pose considerable challenges when using spectrometers within the Arctic sea ice. The feasibility of using a miniature low-power near-infrared spectrometer module to measure solar radiation in Arctic sea ice environments was investigated in this study. Temperature and integration time dependences of the spectrometer module were examined over the entire target operating range of −50°C to 30°C, well below the specified operating range of this spectrometer. Using these observations, a dark output prediction model was developed to represent dark output as a function of temperature and integration time. Temperature-induced biases in the saturation output and linear operating range of the spectrometer were also determined. Temperature and integration time dependences of the signal output were evaluated. Two signal output correction models were developed and compared, to convert the signal output at any temperature within the operating temperature range and integration time to that measured at the reference temperature and integration time. The overall performance of the spectrometer was evaluated by integrating it into a refined fiber optic spectrometry system and measuring solar irradiance distribution in the ice cover with thickness of 1.85 m in the Arctic during the 9th Chinese National Arctic Research Expedition. The general shape of the measured solar irradiance above the snow surface agreed well with that measured by other commercial oceanographic spectroradiometers. The measured optical properties of the sea ice were generally comparable to those of similar ice measured using other instruments. This approach provides a general framework for assessing the feasibility of using spectrometers for applications in cold environments.

Investigation of Compression and Reconstruction Methods for Solar Radiation Spectra above, within, and below the Sea Ice in Polar Environments

AbstractTo reduce the amount of data transmitted, while retaining essential information on the original signal after reconstruction, this study investigated three different compression and reconstruction methods for the solar irradiance signals at different levels of the Arctic sea ice. To achieve this, characteristics of solar irradiance at different depths of the Arctic sea ice were analyzed; the solar irradiance profile was measured using a fiber optic spectrometer system at several ice packs from 11 to 25 August 2018, during the ninth Chinese National Arctic Research Expedition. Based on these measurements, three potential methods—fast Fourier transform (FFT), discrete wavelet transform (DWT), and Prony’s method—were selected and applied for the compression and reconstruction of a group of 12 measurements from one profile. The results indicated that the FFT method was generally superior to the DWT method considering the corrected reconstruction rate Rc, under the same compression ratio C. The FFT method had the highest Rc for signals with higher roughness, whereas Prony’s method was more suitable for smoother signals. To ensure that Rc was at least 80% and C was as large as possible, the compression and reconstruction method proposed in this study included the FFT method (C = 6.4) for the signals rougher than 1.5 and Prony’s method employing the matrix pencil method (C = 8) for the smoother signals. The proposed method was successfully verified using an independent group of solar irradiance measurements from another profile in the Arctic sea ice.

Hourly solar irradiance prediction using deep BiLSTM network

Accurate measurement of solar irradiance is of great significance in many applications, such as climatology, energy and engineering. Deep learning models have achieved good results in solar irradiance prediction for a single site. However, most studies take meteorological parameters as the model inputs and the irradiance values as the model outputs. Because different regions have different climates, only considering the relationship between meteorological parameters and irradiance has limitations. This paper presents a novel scheme for forecasting irradiance. The method considers the hourly irradiance prediction model to be the superposition of two parts: a daily average irradiance prediction model and the irradiance amplitude prediction model. Two submodels were constructed by using deep bidirectional long short-term memory (BiLSTM) network. For the task of irradiance prediction for 25 stations located in the United States, which are located in five different climates, the proposed method performs best for 21 stations (84%) in terms of the root mean square error, 18 stations (72%) in terms of the mean absolute error, and 17 stations (68%) in terms of the coefficient of determination. Moreover, the method adopted in this study displays a stronger irradiance prediction ability than the traditional methods for 80% of the climates included in the experiment.

INTRODUCTION OF CLOUDS IN DART MODEL

Abstract. Clouds cover around two thirds of the Earth’s surface. Most of them are thick enough to influence the radiative budget of our planet: they increase the top of atmosphere (TOA) exitance and they alter the bottom of atmosphere (BOA) direct and diffuse irradiance. However, most radiative transfer models dedicated to Earth surfaces, such as DART (Discrete Anisotropic Radiative Transfer), simulate only cloudless atmospheres. We recently introduced clouds in DART in order to improve the modelling of weather for remote sensing simulations. In this implementation, clouds were characterized with user specified optical properties and vertical distribution. They were modelled as layered one-dimensional medium that coexists with gases and aerosols. The atmospheric radiative transfer modelling relies on the discrete ordinate method already in DART. In addition, an iterative inversion procedure was designed to test this improvement with field measurements during two cloudy days at Lamasquère meteorological station (France). Specifically, it derives time-series of atmosphere parameters from time-series of BOA solar irradiance measurements. These inversed atmospheric parameters were used to simulate total and diffuse BOA irradiance in PAR (Photosynthetically Active Radiation) domain. The comparison of time-series of measured and DART simulated PAR irradiance lead to very encouraging results (mean relative error ∼8% for total irradiance and ∼20% for diffuse irradiance). It stresses the potential of DART to accurately simulate irradiance in cloudy days.

In-orbit Earth reflectance validation of TROPOMI on board the Sentinel-5 Precursor satellite

Abstract. The goal of the study described in this paper is to determine the accuracy of the radiometric calibration of the TROPOMI instrument in flight, using its Earth radiance and solar irradiance measurements, from which the Earth reflectance is determined. The Earth reflectances are compared to radiative transfer calculations. We restrict ourselves to clear-sky observations as these are less difficult to model than observations containing clouds and/or aerosols. The limiting factor in the radiative transfer calculations is then the knowledge of the surface reflectance. We use OMI and SCIAMACHY surface Lambertian-equivalent reflectivity (LER) information to model the reflectivity of the Earth's surface. This Lambertian, nondirectional description of the surface reflection contribution results in a relatively large source of uncertainty in the calculations. These errors can be reduced significantly by filtering out geometries for which we know that surface LER is a poor approximation of the real surface reflectivity. This filtering is done by comparing the OMI/SCIAMACHY surface LER information to MODIS surface BRDF information. We report calibration accuracies and errors for 21 selected wavelength bands between 328 and 2314 nm, located in TROPOMI spectral bands 3–7. All wavelength bands show good linear response to the intensity of the radiation and negligible offset problems. Reflectances in spectral bands 5 and 6 (wavelength bands 670 to 772 nm) have good absolute agreement with the simulations, showing calibration errors on the order of 0.01 or 0 %–3 %. Trends over the mission lifetime, due to instrument degradation, are studied and found to be negligible at these wavelengths. Reflectances in bands 3 and 4 (wavelength bands 328 to 494 nm), on the other hand, are found to be affected by serious calibration errors, on the order of 0.004–0.02 and ranging between 6 % and 10 %, depending on the wavelength. The TROPOMI requirements (of 2 % maximal deviation) are not met in this case. Trends due to instrument degradation are also found, being strongest for the 328 nm wavelength band and almost absent for the 494 nm wavelength band. The validation results obtained for TROPOMI spectral band 7 show behaviour that we cannot fully explain. As a result, these results call for more research and different methods to study the calibration of the reflectance. It seems plausible, though, that the reflectance for this particular band is underestimated by about 6 %. A table is provided containing the final results for all 21 selected wavelength bands.

Air Temperature Forecasting Using Machine Learning Techniques: A Review

Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined.

Long-term calibration models to estimate ozone concentrations with a metal oxide sensor

Ozone (O3) is a potent oxidant associated with adverse health effects. Low-cost O3 sensors, such as metal oxide (MO) sensors, can complement regulatory O3 measurements and enhance the spatiotemporal resolution of measurements. However, the quality of MO sensor data remains a challenge. The University of Utah has a network of low-cost air quality sensors (called AirU) that primarily measures PM2.5 concentrations around the Salt Lake City valley (Utah, U.S.). The AirU package also contains a low-cost MO sensor ($8) that measures oxidizing/reducing species. These MO sensors exhibited excellent laboratory response to O3 although they exhibited some intra-sensor variability. Field performance was evaluated by placing eight AirUs at two Division of Air Quality (DAQ) monitoring stations with O3 federal equivalence methods for one year to develop long-term multiple linear regression (MLR) and artificial neural network (ANN) calibration models to predict O3 concentrations. Six sensors served as train/test sets. The remaining two sensors served as a holdout set to evaluate the applicability of the new calibration models in predicting O3 concentrations for other sensors of the same type. A rigorous variable selection method was also performed by least absolute shrinkage and selection operator (LASSO), MLR and ANN models. The variable selection indicated that the AirU’s MO oxidizing species and temperature measurements and DAQ’s solar radiation measurements were the most important variables. The MLR calibration model exhibited moderate performance (R2 = 0.491), and the ANN exhibited good performance (R2 = 0.767) for the holdout set. We also evaluated the performance of the MLR and ANN models in predicting O3 for five months after the calibration period and the results showed moderate correlations (R2s of 0.427 and 0.567, respectively). These low-cost MO sensors combined with a long-term ANN calibration model can complement reference measurements to understand geospatial and temporal differences in O3 levels.

Optical and Radiative Characteristics of Cirrus from the Data of Three-Year Lidar and Actinometric Research at Tomsk State University

Results of experimental studies of the optical and geometric characteristics of high-level clouds over Tomsk combined with actinometric measurements of solar radiation are presented. The diurnal and three-year variations of the characteristics of clouds including those with anomalous backscattering are presented. In contrast to previous studies, the data presented here have been obtained using the parallel accumulation of one-electron pulses during registration of lidar returns.

UTCI field measurements in an urban park in Florence (Italy)

Abstract The aim of this study is to evaluate human thermal comfort in different green area settings in the city of Florence by using the Universal Thermal Climate Index (UTCI). Field measurements of air temperature, solar radiation, relative humidity, wind speed and black globe thermometer were collected during hot summer days in various parts of Cascine Park, the biggest urban park in Florence (Italy). UTCI was evaluated over different surfaces (asphalt, gravel and grass) completely exposed to the sun or shaded by a large lime tree (Tilia × europaea). The results showed strong differences in UTCI values depending on the exposure to tree shade, while no significant difference was found among ground-cover materials when all surfaces were equally exposed to solar radiation. Future studies are needed to investigate the microclimatic effects of different tree species on UTCI.

Daily spectral ocean surface albedo (OSA) parameterization in case of clearness index (Kt) and phytoplankton variability in Malacca Strait

In this study, we examined the albedo's dependence on clearness index (Kt) based on measured global radiation data and phytoplankton variability influence represented by chlorophyll (a) concentration at the upper layers over the Malacca Strait. Half-hourly global solar radiation (5°26′53″ N, 100°11′36″ E) measurement over the period from January to October 2017 was analyzed to investigate the correlation between clearness index and ocean surface albedo (OSA) in case of phytoplankton influence in Malacca Strait coastal waters. Three days (January 29, February 26 and March 22, 2017) with high irradiation values were selected for daily OSA variability study. The parameterization includes bio-optical analytical modelling based on apparent optical properties and photosynthetically active range (PAR) estimation. Daily timescale analysis of spectral albedo in Malacca Strait enables detail examination for the chlorophyll maximum absorption wavelength (λ = 440 nm) parameterization. OSA diurnal variability was estimated using Jin et al. scheme, which is based on the separation of albedo components (direct and diffuse). Hourly changes in radiation (including Kt) and solar position properties provide valuable information to investigate the pathways of daily spectral albedo in case of small concentrations of chlorophyll (a) and clearness index parameterization.

A novel method for fast sky conditions identification from global solar radiation measurements

Sky conditions, and the corresponding luminance and radiance distribution patterns are essential to daylighting, energy and thermal environmental studies. The CIE Standard Skies define the overcast, partly cloudy, and clear sky conditions intuitively by rigorous luminance distributions, which are, however, usually determined by sophisticated and uncommon measurements (e.g. radiation in multiple vertical directions). This study proposes a simple approach to identifying the hourly sky conditions from the global horizontal irradiance (EHG) that is most widely accessible in weather stations with basic radiation measurement facilities. The sophisticated measurements are, therefore, avoided for simple applications. The partly cloudy sky, especially, is identified by the notable disparity of its EHG measurement from the theoretical cloudless condition. The proposed approach interprets the ISO/CIE Standard Sky conditions and their diffuse luminance and radiance distributions successfully. From the datasets at two sites with different climates, the approach correctly identifies 7% more sky conditions than a previous work when the direct beam and diffuse radiation measurements were not accessible. The model can thus be essential to solar energy studies for places with ground-based solar radiation measurements only.

Energy and Environmental Comparison between a Concrete Wall with and without a Living Green Wall: A Case Study in Mexicali, Mexico

In cities with dry arid climate, air conditioning (AC) equipment is necessary for thermal comfort in indoor spaces. The use of this equipment generates an increase in electricity consumption and an increment in CO₂ emissions to the environment; thus, one way to mitigate these negative effects is the Living Green Wall (LGW). The objective of this research is to assess the decrease in thermal gain, energy benefits, and estimate the greenhouse gas (GHG) emissions that are not emitted by the use of the LGW. Measurements of heat flux, solar radiation, and temperatures were made on a concrete wall and another with an LGW in a west-facing building in the city of Mexicali, Mexico. The results indicate that it is possible to reduce 49% of the heat flow through the wall, which reduces the thermal load 102,212 Btu/h to the indoor space, implying the additional work of 8.53 tons of AC. This excess equals 985.6 kWh of electrical energy and generates a total of 697 kg of CO₂ emissions during the warm season. It is concluded that shading with an LWG becomes a very influential element to mitigate the heat fluxes towards the indoor spaces.

Techno-economic performance comparison of crystalline and thin film PV panels under varying meteorological conditions: A high solar resource southern hemisphere case

Photovoltaic panel technologies have evolved considerably over a limited period. The most popular PV panel technologies can be divided into two main groups, the first being crystalline technologies and second, thin film technologies. This investigation compares the financial performance of three different photovoltaic (PV) panel technologies, namely, monocrystalline, polycrystalline and thin film copper indium gallium selenide (CIGS), based on measurements from a test facility and for varying meteorological conditions. The yield measurements of the panels from the site over a two-year period are used to develop the techno-economic indicator performance, for a proposed commercial installation. Measurements of solar irradiation and temperature are incorporated into a regression model for yield sensitivity analysis, which in turn is used to investigative the sensitivity of financial performance.It was seen that CIGS panels delivered on average 11.6% more yield when compared to monocrystalline technologies per kWp, with sustained outputs even during relatively colder periods. The improved financial performance of CIGS panels over monocrystalline panels was seen via a 24% increase in discounted return on investment, 7.8% reduction in payback period and 21% improvement in net present value. The regression model indicates that CIGS panel yields are less sensitive to lower solar irradiation and more sensitive towards higher temperatures. Overall variation of financial indicators for a range of yield outputs, is lowest for CIGS panels compared to both types of crystalline panels. CIGS panels show lower variability in financial returns particularly in conditions with higher temperatures that makes the results valid in geographical locations with higher temperatures and higher solar irradiation. The findings are useful for multiple stakeholders within the PV industry who have an interest in sub-Saharan Africa and the southern hemisphere, where such investigations are limited though solar resource is abundant.

Analysis of the Potential for Renewable Utilization in Kosovo Power Sector

The reduction in greenhouse gas emissions and the decarbonization of the power sector through the utilization of available renewable technologies are challenging issues that Kosovo has to tackle right now, in order to fight the high pollution caused by a coal-based power system. Around 91.43% of installed capacities for electricity generation in Kosovo are based on coal-fired power plants. The aim of this paper is to show the potential for renewable utilization, using data measurements of wind, solar irradiation, biomass, and average water flows at different area locations to identify their utilization potential. Furthermore, a review on the currently available and future renewable energy projects integrated into the electricity sector is presented. A 54% carbon dioxide emission reduction potential was estimated in the power sector when considering maximum utilization potential of biomass, wind, solar renewable energies compared to a referent scenario. The results obtained from this review have shown the pathways for identifying the potential utilization of renewable as well as the actual and planned use of renewable implemented projects into the Kosovo Power Sector.

Estimation of the impact of differences in the CH4 absorption line parameters on the accuracy of methane atmospheric total column retrievals from ground-based FTIR spectra

The information in spectroscopic databases is routinely updated, therefore it is necessary to evaluate on a regular basis how efficient the improvements in spectroscopic data are with respect to the atmospheric remote sensing problems. One of such problems is the methane total column retrieval from the ground-based FTIR measurements of the direct solar radiation. The CH4 line parameters contained in various modern spectroscopic databases demonstrate some differences. In the present study, the impact of these differences on the atmospheric methane retrievals is estimated. The ground-based FTIR measurements at the St.Petersburg and Kourovka observational stations (Russian Federation) are taken as the basis for the study. The retrieval algorithms conform to the specifications worked out by the IRWG NDACC and TCCON networks. The databases HITRAN2001, HITRAN2008, HITRAN2012, HITRAN2016, GEISA, ATM12, ATM16, ATM19 and the GOSAT2014 line list are used as the input for the forward modeling and for the retrievals of the CH4 total column. Different criteria for quality assessment of fitting of measured and simulated transmittance spectra are considered. It has been shown that the variability of the methane total column values retrieved using absorption line parameters from different spectroscopic databases reaches 1.7%. The obtained results confirmed that for mid-infrared range the current IRWG NDACC recommendation to use HITRAN2001 is still valid: the HITRAN2001 data provide better CH4 retrievals than the newer spectroscopic databases, with the exception of one database (ATM19) yielding slightly different results but comparable to HITRAN2001. The best retrieval results in the near-infrared region are observed when the ATM, HITRAN2008 and GEISA2011 databases are used. The lowest value of RMS corresponded to the calculations which used the ATM19 line list.

Performance improvement of empirical models for estimation of global solar radiation in India: A k-fold cross-validation approach

In this work, global solar radiation is estimated based on sunshine duration. Solar radiation measurements have been collected from the Indian Meteorological Department (Pune, India) for the period 1986–2000. 25 model forms were selected from the literature which correlates the clearness index with sunshine duration. The coefficients of models are extracted from the data. k-fold cross-validation is then employed to improve the performance of the models. Data is split into k-groups with each group containing the same amount of data. (k-1) groups are utilized for the development of models and the rest one group is utilized for the testing of models performance. The procedure is repeated k-times and those coefficients are selected which produces the least error. Models are evaluated and compared with the help of statistical errors. Further, the statistical errors were scaled and Global Performance Indicator (GPI) was evaluated. Using GPI, models were given rank in order of suitability of estimates produced. The GPI value lies between −9.4269 and 0.4695. It has been observed that the quartic model (M-04) exhibited the best performance with MBE = 0.0259, RMSE = 1.7927, MPE = 0.1495, RRMSE = 0.0352, erMAX = 0.9072, MARE = 0.0812, MAE = 1.3843, U95 = 6.3100, t-stats = 0.2397 and R = 0.8597 resulting in the highest GPI amongst all the models which were proposed.