Extraterrestrial Radiation Research Articles

Simulation of Daily Diffuse Solar Radiation Based on Three Machine Learning Models

Solar radiation is an important parameter in the fields of computer modeling, engineering technology and energy development. This paper evaluated the ability of three machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Multivariate Adaptive Regression Splines (MARS), to estimate the daily diffuse solar radiation (Rd). The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters (including mean average temperature (Ta), theoretical sunshine duration (N), actual sunshine duration (n), daily average air relative humidity (RH), and extra-terrestrial solar radiation (Ra)). And their estimation accuracies were subjected to comparative analysis. The three models were first trained using meteorological data from 1966 to 2000. Then, the 2001-2015 data was used to test the trained machine learning model. The results show that the XGBoost had better accuracy than the other two models in coefficient of determination (R2 ), root mean square error (RMSE), mean bias error (MBE) and normalized root mean square error (NRMSE). The MARS performed better in the training phase than the testing phase, but became less accurate in the testing phase, with the R2 value falling by 2.7-16.9% on average. By contrast, the R2 values of SVM and XGBoost increased by 2.9-12.2% and 1.9-14.3%, respectively. Despite trailing slightly behind the SVM at the Beijing station, the XGBoost showed good performance at the rest of the stations in the two phases. In the training phase, the accuracy growth is small but observable. In addition, the XGBoost had a slightly lower RMSE than the SVM, a signal of its edge in stability. Therefore, the three machine learning models can estimate the daily Rd based on local inputs and the XGBoost stands out for its excellent performance and stability.

Comparative Analysis of the Performance of Six Models for the Estimation of Global Solar Radiation for Katsina, Nigeria

Solar radiation data is an essential pre-requisite for the designing, sizing and performing evaluation of any solar energy conversion system in any part of the globe, even though solar radiation data are not readily available for many location of many developing countries such as Nigeria, hence the needs to rely on empirical models. There are many developed models across the globe for the estimation of solar radiation, to identify the optimum performing model for locations such as Katsina requires the comparism between the various models. In this study, performance evaluation of six selected models for the estimation of global solar radiation was carried out for Katsina location, Nigeria. The models were formed from different combinations of some meteorological parameters (sunshine hours, relative humidity and temperatures) obtained for a period of ten years (2006-2015) from Nigerian Meteorological Agency (NIMET). Monthly average extraterrestrial global solar radiation was evaluated for the locations. The data was analysed to find the empirical constants for all the selected models in the locations. Estimated values of global solar radiation was obtained from the six selected models. The estimated values were then compared using statistical parameters (mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE) and coefficient of determinant (R²). Model 6 was found to be the optimum model for Katsina because it fitted the measured data most for each month of year based on various statistical parameters used for the analysis.

Scale problem: Influence of grid spacing of digital elevation model on computed slope and shielded extra-terrestrial solar radiation

Solar radiation is the primary energy source that drives many of Earth’s physical and biological processes and determines the patterns of climate and productivity on the surface of the Earth. A fundamental proportion of solar radiation is composed of shielded extraterrestrial solar radiation (SESR), which can be computed using the slope and aspect derived from a digital elevation model (DEM). The objective of this paper is to determine the influence of the grid spacing of the DEM (the influence of the scale of the DEM) on the errors of slope, aspect and SESR. This paper puts forward the concepts of slope representation error, aspect representation error, and SESR representation error and then studies the relations among these errors and the grid spacing of DEMs. We find that when the grid spacing of a DEM becomes coarser, the average SESR increases; the increase in SESR is dominated by the grid cells of the DEM with a negative slope representation error, whereas SESR generally decreases in the grid cells with a positive slope representation error. Although the grid spacing varies, the distribution of the percentages of positive SESR representation errors on the slope, which is classified into 11 slope intervals, is independent of the grid spacing; this distribution is concentrated across some slope intervals. Moreover, the average absolute value and mean square error of the SESR representation error are closely related to those of the slope representation error and the aspect representation error. The findings in this study may be useful for predicting and reducing the errors in SESR measurements and may help to avoid mistakes in future research and in practical applications in which SESR is the data of interest or plays a vital role in an analysis.

Global Solar Radiation Models in Minas Gerais, Southeastern Brazil

Accurate and complete global solar radiation (Hs) data at a specific region are crucial for regional climate assessment, crop growth modeling, and all operations that use solar energy. However, in the Minas Gerais state, Southeastern Brazil (SEB), the number of weather stations that measure global solar radiation is scarce, and when it is available, it presents gaps in the time series. An attractive alternative to solve the data gap problem is to estimate global solar radiation using empirical models. In this study, thirteen models based on maximum and minimum air temperatures, precipitation, sunshine duration, and extraterrestrial solar radiation were compared in the daily estimation ofHs.Data from 10 weather stations, from 1999 to 2017, located in Minas Gerais were used. Also, cluster analysis was used to group the localities (weather stations) with similar patterns of model performance, climatic classification (Köppen–Geiger and Thornthwaite), and seasonal data variability, considering minimum and maximum air temperatures, precipitation, sunshine duration, and global solar radiation. Although it is apparently simple, studies on this subject are scarce and the few existing ones in Minas Gerais have flaws, which justifies this study. The models were evaluated by root mean square error (RMSE), mean absolute percentage error (MAPE), mean bias error (BIAS), Willmott’s index of agreement (d), and performance index (c-index). Models based on sunshine duration, such as those proposed by Ertekin and Yaldiz and by Newland, showed the best performance (averagec-index = 0.71). Models based on temperature and precipitation showed the worst results (averagec-index = 0.41). Cluster analysis showed that there is a similar pattern between the performance of the models, climatic classification, and seasonal variability of data among the localities of Minas Gerais. In general, models that presented extremely poor performance were formed with weather stations located in the dry zone, but with different climate classification, and models that presented very good (and good) performance were composed by weather stations located in the humid zone (dry subhumid) with the same climate classification and similar seasonal variability. Furthermore, the models based on temperature have a tendency to overestimate radiation values below 10 MJ·m−2 day−1and to underestimate values higher than 25 MJ·m−2 day−1. This point is a limitation of the model for estimating global solar radiation below and above these levels, showing the influence of atmospheric systems and atmospheric attenuation mechanisms of global solar radiation.

Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction

Accurate estimation of pan evaporation (Ep) is of great significance to the development of agricultural irrigation systems and agricultural water resources management. The purpose of this study was to explore the applicability of coupling extreme learning machine (ELM) model with two new meta-heuristic algorithms, i.e. whale optimization algorithm (WOA) and flower pollination algorithm (FPA) for monthly Ep prediction. To achieve this goal, two hybrid models of WOAELM and FPAELM were developed for predicting monthly Ep in the Poyang Lake Basin of Southern China as a case study. Their performances were further compared with the differential evolution algorithm-optimized ELM (DEELM), improved M5 model tree (M5P) and artificial neural networks (ANN) models. Monthly climatic parameters, including maximum and minimum temperature (Tmax and Tmin), sunshine duration (n), relative humidity (RH), wind speed (U) and Ep from four weather stations in the basin from 2001 to 2015 were collected, those of which during 2001–2010 were used for model training and those during 2011–2015 for model testing. The obtained results showed that the hybrid FPAELM model exhibited the highest prediction accuracy at all the four stations, followed by the hybrid WOAELM model, both of which were superior to the other traditional models. Heuristic algorithms, especially FPA, are highly recommended for improving performance of standalone machine learning models. Compared with the combination of multi-meteorological elements, the combination of Tmax, Tmin and extraterrestrial solar radiation achieved higher but still satisfactory prediction accuracy, with the absolute error less than 0.1 mm d−1 averaged over the four stations. Tmax, Tmin and extraterrestrial solar radiation were thus suggested to be used for monthly Ep estimation in this area considering the convenience of data acquisition.

A new perspective on the sunshine duration variability

The sunshine duration data is analysed here from a new perspective, different from the traditional ones. The study is focused on pairs of periods of shining sun (called clear periods) followed by periods with the sun covered by clouds (called dark periods). Their statistical and sequential properties are illustrated by using results obtained from satellite observations and ground measurements at Timisoara (Romania). Solar irradiance time series measured at ground are converted into a database consisting of pairs of clear/dark periods. These pairs are associated with three cloud classes derived from satellite observations: C1 (the sky is cloud-free), C2 (cloudy sky) and C3 (sky with semi-transparent or fractional clouds). The study was conducted by using tools from survival analysis. This is a novelty in sunshine duration analysis. Since information is missing during the night, the clear/dark period correlation was studied considering a censoring procedure (for all nine combinations of cloud types at the beginning of dark and clear intervals). Cox regression has been used to study the influence of covariates (such as extraterrestrial solar irradiance and sun elevation angle) on sunshine duration distribution. Results show that increase of extraterrestrial solar radiation tends to increase the risk of stopping any clear or dark interval, i.e. it tends to increase the variability in solar irradiance. Regardless of cloud class, stochastically shorter intervals are found for increasing sun elevation angle.

Comparison of daily diffuse radiation models in regions of China without solar radiation measurement

Considering diffuse solar radiation is essential for thermal environment construction and solar energy applications. Due to lack of solar radiation measurement in China, developing appropriate models for data estimation is becoming more and more important. In areas that lack solar radiation measurement, using estimated global solar radiation values or extraterrestrial solar radiation is a key problem. In this study, data obtained between 2000 and 2017 from 17 stations were selected to develop and verify 18 diffuse solar radiation models and three global solar radiation models. The models were evaluated using the mean bias error, mean absolute bias error, root mean squared error, and Nash-Sutcliffe efficiency coefficient. The results showed that the diffuse fraction models based on measured and calculated global solar radiation performed better than diffuse coefficient models. Models containing both clearness index and relative sunshine duration and four parameters (clearness index, relative sunshine duration, mean air temperature and relative humidity) obtained the best performance among all of models. Choosing a precise and suitable global radiation model is essential for estimating daily diffuse radiation in areas without global radiation measurement, especially in areas with low clearness index. A simplified model based only on clearness index can be applied to calculate daily diffuse radiation in Northern China. In areas with lower clearness index and those in Southern China, global radiation models that include multiple parameters (clearness index, temperature difference, and atmospheric pressure) are the optimal options.

Coupling a Bat Algorithm with XGBoost to Estimate Reference Evapotranspiration in the Arid and Semiarid Regions of China

Accurate estimation of reference evapotranspiration (ETo) is key to agricultural irrigation scheduling and water resources management in arid and semiarid areas. This study evaluates the capability of coupling a Bat algorithm with the XGBoost method (i.e., the BAXGB model) for estimating monthly ETo in the arid and semiarid regions of China. Meteorological data from three stations (Datong, Yinchuan, and Taiyuan) during 1991–2015 were used to build the BAXGB model, the multivariate adaptive regression splines (MARS), and the gaussian process regression (GPR) model. Six input combinations with different sets of meteorological parameters were applied for model training and testing, which included mean air temperature (Tmean), maximum air temperature (Tmax), minimum air temperature (Tmin), wind speed (U), relative humidity (RH), and solar radiation (Rs) or extraterrestrial radiation (Ra, MJ m−2·d−1). The results indicated that BAXGB models (RMSE = 0.114–0.412 mm·d−1, MAE = 0.087–0.302 mm·d−1, and R2 = 0.937–0.996) were more accurate than either MARS (RMSE = 0.146–0.512 mm·d−1, MAE = 0.112–0.37 mm·d−1, and R2 = 0.935–0.994) or GPR (RMSE = 0.289–0.714 mm·d−1, MAE = 0.197–0.564 mm·d−1, and R2 = 0.817–0.980) model for estimating ETo. Findings of this study would be helpful for agricultural irrigation scheduling in the arid and semiarid regions and may be used as reference in other regions where accurate models for improving local water management are needed.

Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data

Accurate estimation of reference evapotranspiration (ETo) is required in many fields, e.g. irrigation scheduling design, agricultural water management, crop growth modeling and drought assessment. Nevertheless, reliable estimation of ETo is difficult when lack of complete or long-term meteorological data at the target station. This study evaluated the efficiency of a new tree-based soft computing model, Light Gradient Boosting Machine (LightGBM), for estimating daily ET0 using limited local (target-station) and external (cross-station) meteorological data from 49 weather stations in humid subtropical region of China, including 16 in Jiangxi Province and other 33 in the region. The performance of LightGBM was compared with the tree-based M5 Model Tree (M5Tree) and Random Forests (RF) as well as four empirical models (Hargreaves-Samani, Tabari, Makkink and Trabert). Eight input combinations of daily meteorological data including maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (Hr), wind speed at 2 m height (U2), extraterrestrial solar radiation (Ra) and global solar radiation (Rs) calculated from sunshine duration (n) for the period 2001–2015 were used to test the models. The results showed that LightGBM was superior to M5Tree and RF in local applications under all input combinations during testing, with average root mean square error (RMSE) of 0.08–0.58 mm d−1, 0.11–0.62 mm d−1and 0.13–0.60 mm d−1, respectively. M5Tree performed slightly better than RF under input combinations 6–8, whereas RF outperformed M5Tree under the other input combinations. However, all three soft computing models produced much better daily ETo estimates than the corresponding empirical models with the same input variables. Rs was the most influential meteorological variable for daily ETo estimation in this region, followed by Tmax and Tmin, Hr and finally U2. In external applications, LightGBM also generally performed better than the RF, M5Tree and empirical models. Soft computing models developed with meteorological data from Station 57894, having the most similar climatic characteristics to the other stations, gave satisfactory ETo estimates for the 15 cross stations in Jiangxi Province, even for the other 33 stations across the humid subtropical region of China. LightGBM was proved to be efficient and exhibit good generalization capability in both local and external applications, which was thus recommended as an alternative model for daily ETo estimation.

Comparison of BP, PSO-BP and statistical models for predicting daily global solar radiation in arid Northwest China

Accurate prediction of global solar radiation (Rs) is important for understanding meteorological and hydrological processes, as well as the utilization of solar energy and development of clean production. In order to improve the accuracy and universality of daily Rs prediction in arid Northwest China, back-propagation neural network (BP) and BP optimized by the particle swarm optimization algorithm (PSO-BP) along with six statistical models (Ångström-Prescott, Bristow-Campbell, Swartman-Ogunlade, Sebaii, Chen and Abdalla) were adopted and compared with measured Rs data from eight representative meteorological stations across four sub-climatic zones, including the temperate continental arid zone, temperate continental high temperature-arid zone, plateau continental semi-arid zone and temperate monsoon semi-arid zone. The results showed that PSO-BP models (coefficient of determination, R2, 0.7649–0.9678) were more accurate than BP models (R2, 0.7215–0.9632) and statistical models (R2, 0.5630–0.9445) for the daily Rs prediction in the four sub-zones of arid Northwest China. The PSO-BP1 and BP1 models (with sunshine duration, maximum and minimum temperature, relative humidity and extraterrestrial radiation as inputs), PSO-BP2 and BP2 (with sunshine duration, maximum and minimum temperature and extraterrestrial radiation as inputs) performed better than the other models, with R2, mean absolute error, root mean square error, relative root mean square error and Nash-Sutcliffe coefficient ranging 0.9228–0.9678, 1.5546–1.6309 MJ·m−2·d−1, 2.0054–1.7579 MJ·m−2·d−1, 0.1517–0.1329 and 0.9017–0.9604, respectively, among which the PSO-BP1 model provided the most accurate results. Sunshine-based models (R2, 0.7533–0.9678) were generally superior to temperature-based models (R2, 0.5630–0.8492), which indicated that sunshine duration was more influential for daily Rs prediction than temperature in this area. Overall, the PSO-BP model exhibits the best generalization capability and is recommended for more accurate daily Rs prediction in arid Northwest China.

Estimation of daily, weekly and monthly global solar radiation using ANNs and a long data set: a case study of Fortaleza, in Brazilian Northeast region

A 14-year-long data set containing daily values of meteorological variables was used to train three artificial neural networks (ANNs) for daily, weekly averaged and monthly averaged global solar radiation prediction for Fortaleza, in the Brazilian Northeast region. Local climate is semiarid coastal. Day of the year, maximum temperature, minimum temperature, irradiance, precipitation, cloudiness, extraterrestrial radiation, relative humidity, evaporation and wind speed were adopted as predictors. The ANNs were developed by an in-house code and trained with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. Besides the lack of explicit predictors able to model El Niño and La Niña phenomena, which have strong influence on local weather, the accuracy of the predictions was considered excellent according to its values of normalized root-mean-square error (nRMSE) and good relative to mean absolute percentage error (MAPE) values. Both error metrics presented the smallest values for the monthly case study.

The local intrinsic curvature of wavefronts allows to detect optical vortices.

We describe a method for effectively distinguishing the radiation endowed with optical angular momentum, also known as optical vortex, from ordinary light. We show that by detecting the inversion of the transverse intrinsic curvature sign (ITICS) an optical vortex can be locally recognized. The method is effective under conditions of huge importance for the exploitation of optical vortices, such as the far field of the source and access to a small fraction of the wavefront only. The validity of the method has been verified with table-top experiments with visible light, and the results show that a measurement performed over a transverse distance smaller than 4% of the beam diameter distinguishes a vortex from a Gaussian beam with a significance of 93.4%. New perspectives are considered for the characterization of vortices, with potential impact on the detection of extra-terrestrial radiation as well as on broadcast communication techniques.

Estimating the global solar radiation in Putrajaya using the Angstrom-Prescott model

Global solar radiation (GSR) studies are essential in enhancing the use of solar PV systems especially for Building Integrated PV system (BIPV). Various studies on GSR had been carried out in Malaysia including Penang, Kuala Lumpur and Kota Bharu at the earlier stage. Afterward, data from the Malaysian Meteorological Department (MET) and the Malaysian National University were used to estimate the monthly average daily global radiation for various locations in Malaysia including Kuala Lumpur, Kuala Lumpur, Petaling Jaya, Bandar Baru Bangi, Kota Baru, Senai, Bayan Lepas, Kuching and Kota Kinabalu. GSR in Putrajaya, a location which has identified to implement Malaysian Building Integrated Photovoltaic (MBIPV) projects has yet to be estimated. The conventional method to obtain the amount of GSR at a location is to install a measuring device like a pyranometer at the site to observe and document its day-to-day recordings. However, this is a time-consuming and costly practice. The models used to estimate the GSR are usually based on astronomical factors, geometrical factors, physical factors and meteorological factors. There are numerous models used to predict GSR which can be categorized based on sunshine, cloud, temperature and other meteorological parameters. Additionally, the global solar energy models can be grouped by its modelling techniques such as linear, nonlinear, artificial intelligence and fuzzy logic modelling techniques. The reviews discovered that the models used to estimate GSR were mostly correlated to sunshine duration (SD), air temperature, relative humidity and geographical parameters like latitude, longitude and altitude. In this paper, the GSR in Putrajaya is estimated from the hourly solar radiation and daily sunshine hours data from the Malaysian Meteorological Department. The data, along with the calculated monthly average daily extraterrestrial radiation on a horizontal surface and monthly average maximum possible daily sunshine is used to plot a scatter graph that is linearly fitted. The gradient and vertical axis cut-off from the linear fit is used to obtain the coefficients in the Angstrom-Prescott (A-P) model. Subsequently, the coefficients were used to estimate the GSR in Putrajaya. In line with the government’s initiative, this study will provide an estimation of the GSR in Putrajaya where the finding will be beneficial to the stakeholders, policy maker, developer and investor and energy authority. The average monthly GSR was obtained for nine out of twelve months with highest at 21.18 MJ/m2 in March and lowest at 18.34 MJ/m2 in June.

Estimating impact of weather factors on wheat yields by using panel model approach — The case of Serbia

In Serbia irrigation is not widely utilized to reduce water scarcity in crop production. Therefore, wheat yields largely depend on weather factors. Over the past two decades, there has been recorded a significant change in weather conditions in Serbia. Such change produces concerns about Serbia’s food security and exports since wheat is among the most important agricultural products. In this paper authors analyze and quantify the impact of weather factors on the achieved wheat yields, using a set of panel data on selected Serbian municipalities in fourteen years (2000–2013). The multidimensional regression was conducted as a sort of quasi-experiment, combining data on achieved yields in selected municipalities, with data on weather factors: temperature, precipitation, extraterrestrial radiation, and evapotranspiration. Utilizing the Hargreaves method of determining reference evapotranspiration, average daily water deficit was computed as a single representative indicator of weather conditions. Testing was conducted on four predefined sub-periods within the vegetation season of wheat, and the impact of average daily water deficit on wheat yields was estimated for each of these sub-periods. Results show a robust, statistically significant impact of change in average daily water deficit on decreased wheat yields. Growth of water deficit by 0.1 mm, in the period November 15th to April 1st results with 175 kg/ha lower yields, while in the period April 1st to May 15th results in 45 kg/ha lower yields. Impact shows to be conditional on the altitude, rapidly losing on intensity and significance above 100 m.

Comparison between Regenerator Model and the Instantaneous Direct Solar Radiation for Port Harcourt

This paper compares the result generated by the Regenerator technique and the Ideriah’s method for instantaneous solar radiation. It considered the generation of the actual power input from the regenerator. The actual power input to the photovoltaic panel was calculated from a power model derived from the regenerator’s standard power that is nominally provided, regenerator material and reflectivity of the material. The amount of visible radiation absorbable by photovoltaic plates was further derived in the model and a value of 0.386Pi was obtained. The Ideriah’s model was used to calculate global solar radiation from extraterrestrial solar radiation. It utilized the concept of transmittance to arrive at the amount of insolation received locally (i.e. at the point of interest). It also calculated the amount of insolation at optical thicknesses (Air mass values) representing top of the hour from 5.0am to 7.0pm. Comparison & result showed that the regenerator model gave 84% to 97% correctness or an approximated average correctness of 94% when compared to the Ideriah’s model. Chi-square was used to test the relationship between the results. Chi-square results showed a high level of significance in the similarity of the results obtained. Python software was used to simulate the results and graph presentation.

Comparison between Regenerator Model and the Instantaneous Direct Solar Radiation for Port Harcourt

This paper compares the result generated by the Regenerator technique and the Ideriah’s method for instantaneous solar radiation. It considered the generation of the actual power input from the regenerator. The actual power input to the photovoltaic panel was calculated from a power model derived from the regenerator’s standard power that is nominally provided, regenerator material and reflectivity of the material. The amount of visible radiation absorbable by photovoltaic plates was further derived in the model and a value of 0.386Pi was obtained. The Ideriah’s model was used to calculate global solar radiation from extraterrestrial solar radiation. It utilized the concept of transmittance to arrive at the amount of insolation received locally (i.e. at the point of interest). It also calculated the amount of insolation at optical thicknesses (Air mass values) representing top of the hour from 5.0am to 7.0pm. Comparison & result showed that the regenerator model gave 84% to 97% correctness or an approximated average correctness of 94% when compared to the Ideriah’s model. Chi-square was used to test the relationship between the results. Chi-square results showed a high level of significance in the similarity of the results obtained. Python software was used to simulate the results and graph presentation.

Evaporation modelling by heuristic regression approaches using only temperature data

ABSTRACTAccurate estimation of pan evaporation (Epan) is very important in water resources management, irrigation scheduling and water budget of lakes. This study investigates the accuracy of two heuristic regression approaches, multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) in estimating pan evaporation using only temperature data as input. Monthly minimum temperature, maximum temperature and Epan data from three Turkish stations were used, with month number (periodicity information) added as input to see its effect on estimation accuracy. The models were compared with the calibrated Hargreaves-Samani (CHS), Stephens-Stewart (SS) and multiple linear regression methods. Three different train-test splitting strategies (50%–50%, 60%–40% and 75%–25%) were employed for better evaluation of the applied methods. The results show that the MARS method generally estimated monthly Epan with higher accuracy compared to the M5Tree, CHS and SS methods. When extraterrestrial radiation, calculated from Julian date and latitude information, was used as input to the SS instead of solar radiation, satisfactory estimates were obtained. A positive effect on model accuracy was observed when involving periodicity information in inputs and increasing training data length.

Tolerances of Deinococcus geothermalis Biofilms and Planktonic Cells Exposed to Space and Simulated Martian Conditions in Low Earth Orbit for Almost Two Years.

Fossilized biofilms represent one of the oldest known confirmations of life on the Earth. The success of microbes in biofilms results from properties that are inherent in the biofilm, including enhanced interaction, protection, and biodiversity. Given the diversity of microbes that live in biofilms in harsh environments on the Earth, it is logical to hypothesize that, if microbes inhabit other bodies in the Universe, there are also biofilms on those bodies. The Biofilm Organisms Surfing Space experiment was conducted as part of the EXPOSE-R2 mission on the International Space Station. The experiment was an international collaboration designed to perform a comparative study regarding the survival of biofilms versus planktonic cells of various microorganisms, exposed to space and Mars-like conditions. The objective was to determine whether there are lifestyle-dependent differences to cope with the unique mixture of stress factors, including desiccation, temperature oscillations, vacuum, or a Mars-like gas atmosphere and pressure in combination with extraterrestrial or Mars-like ultraviolet (UV) radiation residing during the long-term space mission. In this study, the outcome of the flight and mission ground reference analysis of Deinococcus geothermalis is presented. Cultural tests demonstrated that D. geothermalis remained viable in the desiccated state, being able to survive space and Mars-like conditions and tolerating high extraterrestrial UV radiation for more than 2 years. Culturability decreased, but was better preserved, in the biofilm consortium than in planktonic cells. These results are correlated to differences in genomic integrity after exposure, as visualized by random amplified polymorphic DNA-polymerase chain reaction. Interestingly, cultivation-independent viability markers such as membrane integrity, ATP content, and intracellular esterase activity remained nearly unaffected, indicating that subpopulations of the cells had survived in a viable but nonculturable state. These findings support the hypothesis of long-term survival of microorganisms under the harsh environmental conditions in space and on Mars to a higher degree if exposed as biofilm.

Solar radiation estimation methods using ANN and empirical models

Many empirical equations and methods have been used and proposed in order to estimate the solar radiation (Rs). In this work, empirical equations, such as Hargreaves method, Artificial Neural Networks (ANN) technology and multi-linear regression methods (MLR) were used to estimate solar radiation. The daily meteorological measurements of air temperature, radiation, humidity and wind velocity from the stations of Aristotle University Farm and Amintaio in Northern Greece were used to derive the solar radiation models. The measurements of Rs were used to derive new and to evaluate existing models. Different combinations of input variables were examined in the ANN models, and in the MLR models different variables were used. The results of RMSE criteria of the examined models showed that they are in the same range with many other models describing Rs as summarized in many review articles. The use of extraterrestrial radiation and the square root of daily difference in temperature in the ANN and MLR models improve the accuracy of the results. The results of ANN models in comparison to MLR models using the same input variables are consistent between them.

A cloud identification algorithm over the Arctic for use with AATSR–SLSTR measurements

Abstract. The accurate identification of the presence of cloud in the ground scenes observed by remote-sensing satellites is an end in itself. The lack of knowledge of cloud at high latitudes increases the error and uncertainty in the evaluation and assessment of the changing impact of aerosol and cloud in a warming climate. A prerequisite for the accurate retrieval of aerosol optical thickness (AOT) is the knowledge of the presence of cloud in a ground scene. In our study, observations of the upwelling radiance in the visible (VIS), near infrared (NIR), shortwave infrared (SWIR) and the thermal infrared (TIR), coupled with solar extraterrestrial irradiance, are used to determine the reflectance. We have developed a new cloud identification algorithm for application to the reflectance observations of the Advanced Along-Track Scanning Radiometer (AATSR) on European Space Agency (ESA)-Envisat and Sea and Land Surface Temperature Radiometer (SLSTR) on board the ESA Copernicus Sentinel-3A and -3B. The resultant AATSR–SLSTR cloud identification algorithm (ASCIA) addresses the requirements for the study AOT at high latitudes and utilizes time-series measurements. It is assumed that cloud-free surfaces have unchanged or little changed patterns for a given sampling period, whereas cloudy or partly cloudy scenes show much higher variability in space and time. In this method, the Pearson correlation coefficient (PCC) parameter is used to measure the “stability” of the atmosphere–surface system observed by satellites. The cloud-free surface is classified by analysing the PCC values on the block scale 25×25 km2. Subsequently, the reflection at 3.7 µm is used for accurate cloud identification at scene level: with areas of either 1×1 or 0.5×0.5 km2. The ASCIA data product has been validated by comparison with independent observations, e.g. surface synoptic observations (SYNOP), the data from AErosol RObotic NETwork (AERONET) and the following satellite products: (i) the ESA standard cloud product from AATSR L2 nadir cloud flag; (ii) the product from a method based on a clear-snow spectral shape developed at IUP Bremen (Istomina et al., 2010), which we call ISTO; and (iii) the Moderate Resolution Imaging Spectroradiometer (MODIS) products. In comparison to ground-based SYNOP measurements, we achieved a promising agreement better than 95 % and 83 % within ±2 and ±1 okta respectively. In general, ASCIA shows an improved performance in comparison to other algorithms applied to AATSR measurements for the identification of clouds in a ground scene observed at high latitudes.