Cloudiness Index Research Articles

Adaptive cloudiness index for enhanced photovoltaic energy prediction and management in low-income smart homes using geographic information system

Solar-powered homes can be an optimal solution for the lack of continuous power sources problem in initial low-income communities. However, the challenge of Photovoltaic (PV) uncertainty can make it difficult to coordinate this vital solar energy in real-time. This paper proposes a new, low-cost solution for assessing the uncertainty of photovoltaic power generation in smart home energy management systems. The proposed index, inspired by the well-known clearness index, is an adaptive deterministic indicator that only requires free Geographic Information System (GIS) models and PV power measurement, without the need for expensive high-tech controllers or expert engineers/programmers. The proposed index successfully predicts the daily PV energy with errors of less than 3% for more than 93% of studied days, according to the 2020 measured solar radiation of the studied case in an African developing location, i.e. Cairo. Egypt.

Annual Daily Irradiance Analysis of Clusters in Mexico by Machine Learning Algorithms

The assessment of solar resources involves the utilization of physical or satellite models for the determination of solar radiation on the Earth’s surface. However, a critical aspect of model validation necessitates comparisons against ground-truth measurements obtained from surface radiometers. Given the inherent challenges associated with establishing and maintaining solar radiation measurement networks—characterized by their expense, logistical complexities, limited station availability and the imperative consideration of climatic criteria for siting—countries endowed with substantial climatic diversity face difficulties in station placement. In this investigation, the measurements of annual solar irradiation, from meteorological stations of the National Weather Service in Mexico, were compared in different regions clustered by similarities in altitude, TL Linke, albedo and cloudiness index derived from satellite images; the main objective is to find the best ratio of annual solar irradiation in a set of clusters. Employing machine learning algorithms, this research endeavors to identify the most suitable model for predicting the ratio of annual solar irradiation and to determine the optimal number of clusters. The findings underscore the efficacy of the L-method as a robust technique for regionalization. Notably, the cloudiness index emerges as a pivotal feature, with the Random Forest algorithm yielding superior performance with a R2 score of 0.94, clustering Mexico into 17 regions.

Prediction of solar irradiance with machine learning methods using satellite data

ABSTRACT Solar energy is used in many domestic and industrial areas. To make maximum use of solar energy, it is important to know the irradiance value per unit area. Irradiance measurements are carried out at many stations. In cases where the irradiance value is not measured, empirical models are generally used. With the widespread use of machine learning methods in prediction problems in many fields, it has gained increasing popularity recently. This study aims to predict the solar irradiance of all cities in the Mediterranean region in Turkiye using statistical method and popular machine learning methods. The performances of these models are compared. Ensemble Learning methods (Gradient Boosting, Extreme Gradient Boosting) which are among the popular machine learning methods, and Artificial Neural Networks were used. The data used belong to 8 cities in the Mediterranean Region, Türkiye. Declination angle, wind speed, ambient temperature, relative humidity, and cloudiness index were chosen as input variables. When the results of the models established for each city are examined, it is seen that the models established with machine learning methods are more successful than statistical methods. The best results were obtained from models established with the Extreme Gradient Boosting method (R2 = 0.9993, MAPE = 0.0119).

Improved estimation of hourly direct normal solar irradiation (DNI) using geostationary satellite visible channel images over moderate albedo areas

An accurate knowledge of the direct solar irradiance at normal incidence (DNI) is required to size solar energy systems, specially those using solar concentration technologies. In the absence of measurements, DNI can be estimated over an arbitrary site with a dedicated satellite model or by using satellite-derived global horizontal irradiance (GHI) and a phenomenological diffuse-direct separation model. This second procedure is error-prone, specially under partial cloudiness conditions and low solar elevation angles. A novel and simple semi-empirical satellite-based model to estimate DNI in moderate albedo regions using a physical clear sky model and a cloudiness index obtained from visible satellite imagery is proposed and assessed. An ergodic assumption in which the images are spatially averaged to better represent the hourly time basis is used. Over the target region (Southeastern South America, SESA), the new DNI satellite model outperforms the alternative strategy at the hourly level, with an average uncertainty of 20% (versus 25%) when compared with three measurement station’s data sets located in center Argentina, southern Brazil and Uruguay.

Day-ahead optimal scheduling of smart electric storage heaters: A real quantification of uncertainty factors

Optimized controls are particularly promising for flexible and efficient management of space heating and cooling systems in buildings. However, when controls are based on predictive models, their effectiveness is affected by the reliability of the models used. In this paper we propose a quantification analysis of some of the main uncertainty factors that can be observed in an optimal control really implemented in a building. A day-ahead optimal scheduling was applied to the heating system (composed of smart electric heaters with thermal storage) of a single room in an office building located in Osimo (Italy). The control algorithm is formulated to determine the charging periods of the heaters with the objective of minimizing the withdrawal of energy from the grid. The control takes into account the electricity produced by a photovoltaic plant and must maintain the internal air temperature close to an imposed setpoint.Firstly, the actual application of the control is shown during two selected days. Secondly, the analysis is extended to quantify the impact on the control performance of the prediction uncertainty of the input variables. The variable that has the greatest impact is the weather forecast and, specifically, the cloudiness index, which determines the solar gains. The different moment in time in which the weather forecast is predicted has proved to have a significant impact on the charging periods of the heaters (expected variation ranges from -50% to + 100%) and on the prediction of the indoor air temperature (variations observed up to 40%).

Solar irradiance forecasting models using machine learning techniques and digital twin: A case study with comparison

The ever-increasing demand for energy and power consumption due to population growth, economic expansion, and evolving consumer choices has led to the need for renewable energy sources. Traditional energy sources such as coal, oil, and gas have contributed to global pollution and have adverse effects on human health. As a result, the use of renewable energy for power generation has increased tremendously. One such area of research is solar irradiation prediction, which utilizes Artificial Intelligence and Machine Learning techniques. With the use of real-time predicted data, the digital twins are intended to add value to the organization by identifying and preventing problems, predicting performance, and improving operations. This paper provides an overview of various learning methods used for predicting irradiance and presents a new ensemble solar irradiance forecasting model that combines eight machine learning models to ensure model diversity. The model's most critical factors for predicting irradiance include temperature, cloudiness index, relative humidity, and day of the week. To conduct a comprehensive analysis, the proposed 8-Stacking Regression Cross Validation (8 STR-CV) model was tested using data from three different climatic zones in India. The model's high accuracy scores of 98.8% for Visakhapatnam, 98% for Nagpur, and 97.8% for the mountainous region make it a valuable tool for future prediction in various sectors, including power generation and utilization planning.

ANÁLISE DA CAPACIDADE DE COMPREENSÃO PELO PACIENTE DO TERMO DE CONSENTIMENTO CIRÚRGICO INFORMADO

Introduction: Medical Assistance, Assistance and research, the principles initiated by the Nuremberg Code, were materialized through written documents called Consent Forms. The present study aims to assess the understanding of the consent form in hospitals in the state of São Paulo. Method: Terms that meet the inclusion criteria were retrieved in the analysis with textual readability analysis, and the specific online assessment instrument of the textual readability program was used: Flesch Readability Test; Gulpease Index; Flesch-Kincaid graduation level; Adapted Gunning cloudiness index ; Automated readability index; Coleman-Liau index. Results: The present study presents data between texts of terms with: two at level 11 (high readability); eight are from level 13 to16 (medium readability) and three are from level 17 to18 (low readability). Therefore, the results of the present study indicated that in order to understand the analyzed terms the reader had to be at a univerisity level course ou have graduated from the university. Conclusion: It is recommended that sample be expanded and that they hospitals adopt measures that readability of terms. Keywords: Free and Informed Consent, Understanding, Operative Surgical Procedures

Narrow but robust advantages in two-big-leaf light use efficiency models over big-leaf light use efficiency models at ecosystem level

This study aims to (1) investigate whether two-big-leaf light use efficiency (LUE) models (TL) outperform big-leaf LUE models (BL) by incorporating different gross primary productivity (GPP) responses in sunlit and shaded leaves; (2) explore the robustness of using the leaf area index (LAI), clumping index (Ω) and spherical leaf angle distribution to partition canopies into sunlit and shaded leaves across canopy architectures; (3) identify optimal light response forms in LUE models. To exclude influences of drivers of GPP other than radiation, we collected various formulations of GPP response functions to temperature, vapor pressure deficit, CO2, soil water supply, light intensity and cloudiness index to construct 5600 BLs and 1120 TLs. These models were evaluated at 196 globally-distributed eddy covariance sites from the FLUXNET observational network using the Nash-Sutcliffe model efficiency (NSE), root mean squared error and Bayesian information criterion. Across all sites, the best big-leaf model (BL*; NSE=0.82) was statistically equal to the best TL (TL*; NSE=0.84). However, daily dynamics in GPP under hot and dry conditions were best described using TL* in 17% of sites, highlighting the local importance in separating sunlit and shaded leaves. Across approaches to represent effective LAI, the best approach relies on using normalized difference vegetation index with a spherical or flexible leaf angle distribution across sites rather than satellite LAI and Ω. We also observed similar performance between non-rectangular hyperbola and reciprocal light response functions across TLs. Models degrade when the maximum LUE is not differentiated between sunlit and shaded leaves, but not when light saturation levels are the same. Despite functional differences, the best five TLs agree in a larger contribution of shaded leaf area to total GPP, resulting from higher LAI and LUE. Overall, these results suggest marginal but robust selection of TL compared to BL.

Characteristics and Distribution of Some Radiation Parameters over Nigeria

This study examined the distribution and characteristics of Surface Solar Radiation Downward (SSRD), sunshine duration, cloudiness index, relative humidity, temperature, and wind speed from the archive of the NASA Power project between the years 2010 and 2020 over Nigeria using 37 stations. The datasets were subjected to monthly and seasonal analysis over three climatic zones in Nigeria. The results show that the SSRD of the Sahel is generally higher than that of the Savannah and Guinea Coast. Sunshine duration was found to increase seasonally and from south to north. From the extreme edge of the coastal region in the southern part of Nigeria to its far northern region, the mean sunshine duration hour increases northward from about 2075 hours over the coastal region of the south to about 3525 hours in the north. Relative humidity fluctuates between 70-90%, which peaks in August and declined back to its minimum (70%) in December. Observed over all seasons, relative humidity decreases northward from the far south near the Atlantic Ocean to the far north of Nigeria. This is in opposition to the pattern of 2m temperature observed, that is, the 2 m temperature spatial pattern increases from the south to the north. In terms of sky conditions classification, over Nigeria, the cloudiness index is generally less than 0.45. Over the Guinea Coast cloudiness index is observed to be higher than in Savannah and the Sahel zones in all the months with the lowest index of 0.42 in March and the highest index of 0.45 in September. However, the northeastern part of Nigeria is observed to be majorly cloudier in JJA and SON seasons than the far southern region of the country. However, wind speed is observed to be significantly lower than 2.7 m/s over the Guinea Coast compared to other zones. Relatively high and strong winds were noticed to be widespread over the extreme part of the northeastern and northwestern parts of Nigeria in all the seasons, making the region more suitable for wind energy generation.

Improved Clear Sky Model from In Situ Observations and Spatial Distribution of Aerosol Optical Depth for Satellite-Derived Solar Irradiance over the Korean Peninsula

In solar resource assessment, the climatological environment of the target area is objectively quantified by the cloudiness or clear sky index, which is defined as the ratio of global horizontal irradiance to clear sky solar insolation. The clear sky model calculates incoming solar irradiance on the ground surface considering several atmospheric parameters such as water vapor and aerosol optical depth. This study investigated the importance of aerosol optical depth for deriving clear sky irradiance in radiative transfer models and examined its viability in a universal or community model for public use. The evaluation was conducted based on ground observations at the Korea Institute of Energy Research (KIER) station from January to December 2021. The original simulation was performed using the monthly mean of aerosol optical depth obtained from the Aerosol Robotic Network station; the mean absolute error was 29.9 W m−2. When the daily mean of in situ observations at KIER was incorporated into the clear sky model, the mean absolute error was reduced to 9.7 W m−2. Our results confirm that the clear sky model using gridded datasets of aerosol optical depth is suitable for use as a universal or community model.

An unambiguous cloudiness index for nonwovens

Cloudiness or formation is a concept routinely used in industry to address deviations from homogeneity in nonwovens and papers. Measuring a cloudiness index based on image data is a common task in industrial quality assurance. The two most popular ways of quantifying cloudiness are based on power spectrum or correlation function on the one hand or the Laplacian pyramid on the other hand. Here, we recall the mathematical basis of the first approach comprehensively, derive a cloudiness index, and demonstrate its practical estimation. We prove that the Laplacian pyramid as well as other quantities characterizing cloudiness like the range of interaction and the intensity of small-angle scattering are very closely related to the power spectrum. Finally, we show that the power spectrum can be measured easily by image analysis methods and carries more information than the alternatives.

Evaluation of atmosphere clearness and cloudiness parameters in the southern regions of Ukraine using statistical analysis

Introduction. This paper deals with the specific aspects of insolation of the terrestrial surface in the southern regions of Ukraine, namely the clearness index and diffuse fraction of the atmosphere. The study was based on satellite data of the average daily insolation and total cloudiness in the nodes of the two-degree grid for the domain with coordinates 48°-45° N and 29°-39° E for the period of 1981-2020. The purpose of article. The purpose was to develop statistic models of horizontal surface insolation for various locations of study domain. Main focus was put on special characteristics in conditions of fixed cloudiness. Satellite data for the summer season had been used to evaluate the maximum solar energy potential of Ukraine. Methods. Application statistical analysis and means of cartographic data layout were used in the paper. Results. It was found that with the highest (more than 50%) frequency the total cloud cover can be characterized by the atmosphere clarity corresponding to a clear sky condition. The significance of irradiation of the terrestrial surface with diffuse solar radiation has been observed, with the share of such radiation in the global irradiation (diffuse ratio) being closely inversely related to the clearness index (correlation about -0.97). In turn, both diffuse ratio and clearness index are statistically dependent on the sky clarity, that allowed deriving analytical functions - diffuse ratio and clearness index - of the sky clarity, which appeared to be S- and Z-shaped curves, respectively. Dispersion of the clearness index ( ) and the diffuse fraction ( ) values and the strength of their statistical relationship significantly depend on the sky clarity. The empirical distribution of the two-dimensional random variable ( ; ) well meets the Gaussian distribution, and the obtained dispersion ellipses allowed calculating the confidence intervals of the two-dimensional random variable (clearness index: diffuse fraction) for a given confidence level. The spatial distribution of the clearness index and diffuse fraction of the atmosphere in the southern regions of Ukraine revealed a significant dependence of these indices on the latitude and the type of underlying surface. At the end of the summer a seasonal effect has been observed in the spatial distribution of the clearness index and diffuse fraction, which can be explained by the specific seasonal features of atmospheric circulation, caused by the spreading out of the eastern ridge of the Azores anticyclone and the general situation with blocking developments in the Atlantic-European sector of the Northern Hemisphere. The scientific novelty. Correlation and regression models of special insolation characteristics in conditions of various cloudiness that are represented in this paper are new to Ukraine. Analysis of two-dimensional random value spread (clearness index: cloudiness index) allowed to assess probabilities of integral solar radiation flows. The obtained analytical membership functions for monthly average values of clearness and cloudiness indices depending on the level of sky clarity proved to be applicable for determining respective indices for daily time scale. Practical significance. The obtained results are important for comprehensive assessment of the solar / photovoltaic resources of southern regions of Ukraine. Specifically, analytical dependences have practical values for the purpose of forecasting direct and diffuse solar radiation in various time scales based on publically available global records of solar radiation.

Evaluation of global solar radiation, cloudiness index and sky view factor as potential indicators of Ghana's solar energy resource

The increasing demand of Ghana's renewable energy sector requires a comprehensive evaluation of sky conditions that may be favourable for the efficient harnessing of solar energy resources. However, data for such evaluation are typically scarce due to instrumentation challenges. Empirical estimates using sunshine duration and cloud cover as input data were used to evaluate the solar energy resources’ potential at 22 synoptic stations distributed across the four climatic zones of Ghana (savannah, transition, forest and coastal) for the period 2015 – 2018. Sunshine duration was recorded using the Campbell-Stokes sunshine recorder while the cloud amount was done manually through visual observation. The modified Ångström-Prescot sunshine model was used to estimate the monthly mean global solar radiation. The cloudiness index was then computed as the ratio of the mean cloud cover to the global solar radiation. A modified Steyn-method, based on sunshine duration, was used to estimate the sky view factor. Ordinary kriging was used to interpolate mean quarterly stationary values of the sky condition parameters into spatial distribution over Ghana. Results show that the dry periods (November to April) are characterised by high solar radiations, clear clouds and visible skies as a result of high temperatures, less cloud cover, and low relative humidity, whereas the opposite conditions are observed in the wet periods (May to October). The savannah and coastal zones recorded higher solar radiations with up to 22 MJ m−2 day−1 in the dry season compared to the forest and transition zones. Cloudiness index was above 0.3 and below 0.2 in the wet and dry seasons respectively, for the forest, transition and coastal zones, whereas the index was between 0.15 and 0.3 in the wet season and below 0.1 in the dry season for the savannah zone. Sky view factor was consistently between 0.7 and 0.8 in the dry season, with low values below 0.7 in August for all the climatic zones. Spatial interpolation of the sky condition parameters revealed that the savannah, coastal and eastern transition climatic zones observe high solar radiations, clear and visible skies and are therefore more favourable for harnessing solar energy. These locations may be more appropriate for setting up large-scale solar farms, though dust associated with the Harmattan dry season can be a hinderance.

High-intensity ultrasound processing of baobab fruit pulp: Effect on quality, bioactive compounds, and inhibitory potential on the activity of α-amylase and α-glucosidase

Effect of high-intensity ultrasound (HIU) compared with thermal treatments on baobab fruit pulp (BFP) quality and bioactive properties were investigated. HIU treatments, particularly at intensities of 687.5 W/cm2 for 5 min, and 344 W/cm2 for 15 min significantly (p < 0.05) increased the cloudiness index, ascorbic acid (AA) retention, total phenolic and flavonoid contents, and antioxidant capacity besides a more potent α-amylase and α-glucosidase inhibition relative to thermally treated samples. Moreover, the physicochemical parameters, colour index, and browning index were maintained with HIU besides lower 5-hydroxymethylfurfural values than thermal processing. HPLC analysis revealed that the content of most phenolic compounds was the highest in HIU treatments besides a 235–256% increase in procyanidin C1 compared with control samples. The AA retention following HIU treatments was 87.62–102.86% compared to 30.47–61.90% in thermally treated samples. Our analyses portrayed ultrasound as a feasible alternative to conventional thermal processing of BFP.

Evaluation of clearness index and cloudiness index using measured global solar radiation data: A case study for a tropical climatic region of Nigeria

This study uses a two-year dataset (January 2016-December 2017) on global solar radiation to model the clearness and cloudiness indices at Ile-Ife, Nigeria, a tropical location. Analysis of the daily variations showed that most days at the location are uniform in cloudiness, with scarcely any day either extremely cloudy or extremely clear. On a monthly basis, the clearness index ranged from 0.27 to 0.51, while cloudiness index ranged from 0.44 to 0.72, confirming that the variations of both ratios are influenced by changes in the position of the sun, turbidity and clouds. The seasonal variations of the two parameters were observed to be inverse, so that increases in the clearness index induced by cloudless sky and dry months correspond to decreases in the cloudiness index values and vice versa. The minimum values (0.10 and 0.28) of the clearness index at the location are observed to be higher than the minimum value of 0.05 stipulated for the clearness index in cloudy conditions. Two empirical equations for estimating the maximum clearness index in terms of the average clearness index and the cloudiness index in terms of the clearness index are developed and recommended for other locations with climatological conditions similar to those of the study site. It was furthermore observed from the evaluation of formulated empirical equations that the Hollands and Huget and the Saunier et al. models performed well and improved by about 23.62 and 3.66%, respectively, after calibration, to make the models suitable for their application at other tropical areas.

The efficacy of a dual-axis solar tracking device in tropical climate

The demand for energy and the pressure for reducing environmental impacts is increasing in developing countries, mainly in agricultural areas. The generation of electricity from photovoltaic panels can be economically and environmentally advantageous as a source of renewable energy and the ability to reach remote consumers. The present study aimed to evaluate the performance of a photovoltaic system equipped with a sun-tracking device, comparing to a fixed panel. The test compared two panels of a photovoltaic cell system, one used a rotation module in two-axis, and the other a fixed one (control), for capturing solar energy throughout the day in a tropical region of Brazil. Solar energy data were obtained in the two photovoltaic panels with data continuously recorded six months, with a weather characteristic of high cloudiness and rainfall indexes. The commissioning of the tested photovoltaic panels was done on bright days. Power results indicated that the two-axis tracker system was useful during the test, presenting an increase of 26% when compared to the fixed panel. It was found that when the cloudiness and the rain index are very high, the sun tracking system might not be as efficient as foreseen. Rainfall and cloudiness index are essential factors for determining the feasibility of using a tracker device in tropical regions.

Determination of the Soiling Impact on Photovoltaic Modules at the Coastal Area of the Atacama Desert

With an elevation of 1000 m above sea level, once the coastal mountain range is crossed, the Atacama Desert receives the highest levels of solar radiation in the world. Global horizontal irradiations over 2500 kWh/(m2 year) and a cloudiness index below 3% were determined. However, this index rises to 45% in the coastal area, where the influence of the Pacific Ocean exists with a large presence of marine aerosols. It is on the coastal area that residential photovoltaic (PV) applications are concentrated. This work presents a study of the soiling impact on PV modules at the coastline of Atacama Desert. The current–voltage characteristics of two multicrystalline PV modules exposed to outdoor conditions were compared, while one of them was cleaned daily. Asymptotic behavior was observed in the accumulated surface dust density, over 6 months. This behavior was explained by the fact that as the glass became soiled, the probability of glass-to-particle interaction decreased in favor of a more likely particle-to-particle interaction. The surface dust density was at most 0.17 mg·cm−2 per month. Dust on the module led to current losses in the range of 19% after four months, which in turn produced a reduction of 13.5%rel in efficiency.

Обзор и классификация методов оценки и прогнозирования солнечной радиации

Various methods used to estimate and predict solar radiation are reviewed. Ground-based solar radiation intensity measurements are not carried out everywhere, despite the fact that the need to estimate the gross (theoretical) potential of solar energy in a region arises quite often. In such cases, approximate estimates of solar energy resources are carried out using various models, the main ones of which are considered in this paper. The availability of an adequately estimated local solar energy potential is important not only for rational distribution of funds in designing, but also for predicting the output of a power plant that uses solar energy. A general classification of methods for estimating and predicting solar radiation by the type of used parameters and estimated quantities (physical and statistical models) is presented. Statistical methods include conventional empirical models, in which the monthly average daily sums of solar radiation on a horizontal surface are estimated with dividing them into 4 categories depending on the input parameters involved, and models that evaluate the sums of the solar radiation diffuse component on horizontal (decomposition models and parametric models) and inclined (isotropic and anisotropic models) receiving areas. Models based on data mining methods are also referred to statistical methods for estimating and predicting solar radiation. The suggested statistical models are based on various parameters such as sunshine duration, maximum ambient temperature, relative humidity, pressure, latitude and longitude of the locality, the day of year ordinal number, cloudiness index, average ambient temperature, altitude, average wind velocity, precipitation, etc. The results of modeling the monthly average daily amounts of total solar radiation on a horizontal surface according to the universal empirical models are evaluated and compared with the long-term average data of ground-based measurements on the territory of Kyrgyzstan and with the data from the NASA POWER database.

Meteofactors and admittance of patients with atrial fibrillation for emergency medical care

This study presents the results of a retrospective analysis of the challenges of patients with atrial fibrillation (AF) for ambulance medical care depending on weather conditions and season. Aim. This study aimed to evaluate the effect of meteorological factors on addressing of patients with AF for emergency medical care in the context of seasonal dynamics. Materials. Archival cards of ambulance calls in the winter, spring, and summer 2012 and the corresponding databases of average daily meteorological parameters, day-to-day changes (∆), and pathogenicity indices were used: air temperature (it), atmospheric pressure (iр), humidity (ih), wind speed (iv), cloudiness (in), and general weather pathogenicity index (IWP). Results. In winter, the most significant negative effect is caused by fluctuations in meteo-factors: average daily air temperature and atmospheric pressure, it, ∆p, IWP; in spring were the average daily air temperature and atmospheric pressure, it, i∆t; in summer was i∆t. The response of the population to AF for medical care in the winter (6.7 ± 0.5 person/day) and spring (6.9 ± 0.4 person/day) is higher than that in summer (5.2 ± 0.5 person/day). Conclusion. The maximum number of correlation links between the frequency of ambulance medical care calls and meteorological factors is present in the winter and spring and has gender and season specifics. Seasonality factor affects the treatment of patients with AF for emergency medical care.

Incorporating diffuse radiation into a light use efficiency and evapotranspiration model: An 11-year study in a high latitude deciduous forest

The fraction of diffuse photosynthetic active radiation (PAR) reaching the land surface is one of the biophysical factors regulating carbon and water exchange between terrestrial ecosystems and the atmosphere. This is especially relevant for high latitude ecosystems, where cloudy days are prevalent. Without considering impacts of diffuse PAR, traditional ‘top-down’ models of ecosystem gross primary productivity (GPP) and evapotranspiration (ET), which use satellite remote sensing observations, are biased towards clear sky conditions. This study incorporated a cloudiness index (CI), an index for the fraction of diffuse PAR, into a joint ‘top-down’ model that uses the same set of biophysical constraints to simulate GPP and ET for a high latitude temperate deciduous forest. To quantify the diffuse PAR effects, CI along with other environmental variables derived from an eleven-year eddy covariance data set were used to statistically explore the independent and joint effects of diffuse PAR on GPP, ET, incident light use efficiency (LUE), evaporative fraction (EF) and ecosystem water use efficiency (WUE). The independent and joint effects of CI were compared from global sensitivity analysis of the ‘top-down’ models. Results indicate that for independent effects, CI increased GPP, LUE, ET, EF and WUE. Analysis of joint effects shows that CI mainly interacted with the radiation intercepted in the canopy (PAR, net radiation and leaf area index) to influence GPP, ET and WUE. Moreover, Ta and vapor pressure saturation deficit played a major role for the joint influence of CI on LUE and EF. In the growing season from May to October, variation in CI accounts for 11.9%, 3.0% and 7.8% of the total variation of GPP, ET and transpiration, respectively. As the influence of CI on GPP is larger than that on ET, this leads to an increase in WUE with CI. Joint GPP and ET model results showed that when including CI, the root mean square errors (RMSE) of daily GPP decreased from 1.64 to 1.45gCm−2d−1 (11.7% reduction) and ET from 15.79 to 14.50 Wm−2 (8.2% reduction). Due to the interaction of diffuse PAR with plant canopies, the largest model improvements using CI for GPP and ET occurred during the growing season and for the transpiration component, as suggested by comparisons to sap flow measurements. Furthermore, our study suggests a potential biophysical mechanism, not considered in other studies: under high diffuse PAR conditions, due to the increased longwave emission from clouds, canopy temperature gets higher and enhances GPP and transpiration in this temperature-limited high latitude ecosystem.