scholarly journals Local short-term variability in solar irradiance

2016 ◽  
Vol 16 (10) ◽  
pp. 6365-6379 ◽  
Author(s):  
Gerald M. Lohmann ◽  
Adam H. Monahan ◽  
Detlev Heinemann
Keyword(s):  
Power Systems ◽  
Classification Scheme ◽  
Grid Integration ◽  
Short Term ◽  
Clear Sky ◽  
Smoothing Effects ◽  
Sensor Density ◽  
Sky Conditions ◽  
Sky Type ◽  
Field Variability

Abstract. Characterizing spatiotemporal irradiance variability is important for the successful grid integration of increasing numbers of photovoltaic (PV) power systems. Using 1 Hz data recorded by as many as 99 pyranometers during the HD(CP)2 Observational Prototype Experiment (HOPE), we analyze field variability of clear-sky index k* (i.e., irradiance normalized to clear-sky conditions) and sub-minute k* increments (i.e., changes over specified intervals of time) for distances between tens of meters and about 10 km. By means of a simple classification scheme based on k* statistics, we identify overcast, clear, and mixed sky conditions, and demonstrate that the last of these is the most potentially problematic in terms of short-term PV power fluctuations. Under mixed conditions, the probability of relatively strong k* increments of ±0.5 is approximately twice as high compared to increment statistics computed without conditioning by sky type. Additionally, spatial autocorrelation structures of k* increment fields differ considerably between sky types. While the profiles for overcast and clear skies mostly resemble the predictions of a simple model published by Hoff and Perez (2012), this is not the case for mixed conditions. As a proxy for the smoothing effects of distributed PV, we finally show that spatial averaging mitigates variability in k* less effectively than variability in k* increments, for a spatial sensor density of 2 km−2.

scholarly journals Local short-term variability in solar irradiance

2016 ◽  
Author(s):  
Gerald M. Lohmann ◽  
Adam H. Monahan ◽  
Detlev Heinemann
Keyword(s):  
Power Systems ◽  
Classification Scheme ◽  
Grid Integration ◽  
Short Term ◽  
Smoothing Effects ◽  
Spatio Temporal ◽  
Sensor Density ◽  
Sky Conditions ◽  
Sky Type ◽  
Field Variability

Abstract. Characterizing spatio-temporal irradiance variability is important for the successful grid integration of increasing numbers of photovoltaic (PV) power systems. Using 1 Hz data recorded by as many as 99 pyranometers during the HD(CP)2 Observational Prototype Experiment HOPE, we analyze field variability of clearsky index k∗ (i.e. irradiance normalized to clearsky conditions) and sub-minute k∗ increments (i.e. changes over specified intervals of time) for distances between tens of meters and about ten kilometers. By means of a simple classification scheme based on k∗ statistics, we identify overcast, clear and mixed sky conditions, and demonstrate that the last of these is the most potentially problematic in terms of short-term PV power fluctuations. Under mixed conditions, the probability of relatively strong k∗ increments of ±0.5 is approximately twice as high compared to increment statistics computed without conditioning by sky type. As well, spatial autocorrelation structures of k∗ increment fields differ considerably between sky types. While the profiles for overcast and clear skies mostly resemble the predictions of a simple model published by Hoff and Perez (2012), this is not the case for mixed conditions. As a proxy for the smoothing effects of distributed PV, we finally show that spatial averaging mitigates variability in k∗ less effectively than variability in k∗ increments, for a spatial sensor density of 2 km−2.

scholarly journals Measurements of UV irradiance within the area of one satellite pixel

2008 ◽  
Vol 8 (18) ◽  
pp. 5615-5626 ◽  
Author(s):  
P. Weihs ◽  
M. Blumthaler ◽  
H. E. Rieder ◽  
A. Kreuter ◽  
S. Simic ◽  
...  
Keyword(s):  
Ozone Monitoring Instrument ◽  
Short Term ◽  
Uv Index ◽  
Clear Sky ◽  
Uv Irradiance ◽  
Instantaneous Values ◽  
The Mean ◽  
The Difference ◽  
Sky Conditions ◽  
Better Than

Abstract. A measurement campaign was performed in the region of Vienna and its surroundings from May to July 2007. Within the scope of this campaign erythemal UV was measured at six ground stations within a radius of 30 km. First, the homogeneity of the UV levels within the area of one satellite pixel was studied. Second, the ground UV was compared to ground UV retrieved by the ozone monitoring instrument (OMI) onboard the NASA EOS Aura Spacecraft. During clear-sky conditions the mean bias between erythemal UV measured by the different stations was within the measurement uncertainty of ±5%. Short term fluctuations of UV between the stations were below 3% within a radius of 20 km. For partly cloudy conditions and overcast conditions the discrepancy of instantaneous values between the stations is up to 200% or even higher. If averages of the UV index over longer time periods are compared the difference between the stations decreases strongly. The agreement is better than 20% within a distance of 10 km between the stations for 3 h averages. The comparison with OMI UV showed for clear-sky conditions higher satellite retrieved UV values by, on the average, approximately 15%. The ratio of OMI to ground measured UV lies between 0.9 and 1.5. and strongly depends on the aerosol optical depth. For partly cloudy and overcast conditions the OMI derived surface UV estimates show larger deviation from the ground-based reference data, and even bigger systematic positive bias. Here the ratio OMI to ground data lies between 0.5 and 4.5. The average difference between OMI and ground measurements is +24 to +37% for partly cloudy conditions and more than +50% for overcast conditions.

scholarly journals Effects of temporal averaging on short-term irradiance variability under mixed sky conditions

2017 ◽  
Author(s):  
Gerald M. Lohmann ◽  
Adam H. Monahan
Keyword(s):  
Power Systems ◽  
Single Point ◽  
Time Averaging ◽  
Short Term ◽  
Systematic Analysis ◽  
Electrical Grid ◽  
Surface Irradiance ◽  
Temporal Averaging ◽  
Averaging Time ◽  
Sky Conditions

Abstract. Characterizations of short-term variability in solar radiation are required to successfully integrate large numbers of photovoltaic power systems into the electrical grid. Previous studies have used ground-based irradiance observations with a range of different temporal resolutions, and a systematic analysis of the effects of temporal averaging on the representation of variability is lacking. Using high-resolution surface irradiance data with original temporal resolutions between 0.01 s and 1 s from six different locations in the Northern Hemisphere, we characterize the changes in representation of temporal variability resulting from time averaging. In this analysis, we condition all data to states of mixed skies, which are the most potentially problematic in terms of local PV power volatility. Statistics of clear-sky index k* and its increments Δk*τ (i.e., normalized surface irradiance and changes therein over specified intervals of time) are considered separately. Our results indicate that a temporal averaging time scale of around 1 s marks a transition in representing single-point irradiance variability, such that longer averages result in substantial underestimates of variability. Higher-resolution data increase the complexity of data management and quality control without appreciably improving the representation of variability. The results do not show any substantial discrepancies between locations or seasons.

scholarly journals Cloud and aerosol classification for 2 1/2 years of MAX-DOAS observations in Wuxi (China) and comparison to independent data sets

2015 ◽  
Vol 8 (5) ◽  
pp. 4653-4709 ◽  
Author(s):  
Y. Wang ◽  
M. Penning de Vries ◽  
P. H. Xie ◽  
S. Beirle ◽  
S. Dörner ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Optical Absorption Spectroscopy ◽  
Data Sets ◽  
Original Algorithm ◽  
Clear Sky ◽  
Cloud Parameters ◽  
Cloud Classification ◽  
Aerosol Classification ◽  
Cloud Optical Thickness ◽  
Sky Conditions

Abstract. Multi-Axis-Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of trace gases can be strongly influenced by clouds and aerosols. Thus it is important to identify clouds and characterise their properties. In a recent study Wagner et al. (2014) developed a cloud classification scheme based on the MAX-DOAS measurements themselves with which different "sky conditions" (e.g. clear sky, continuous clouds, broken clouds) can be distinguished. Here we apply this scheme to long term MAX-DOAS measurements from 2011 to 2013 in Wuxi, China (31.57° N, 120.31° E). The original algorithm has been modified, in particular in order to account for smaller solar zenith angles (SZA). Instrumental degradation is accounted for to avoid artificial trends of the cloud classification. We compared the results of the MAX-DOAS cloud classification scheme to several independent measurements: aerosol optical depth from a nearby AERONET station and from MODIS, visibility derived from a visibility meter; and various cloud parameters from different satellite instruments (MODIS, OMI, and GOME-2). The most important findings from these comparisons are: (1) most cases characterized as clear sky with low or high aerosol load were associated with the respective AOD ranges obtained by AERONET and MODIS, (2) the observed dependences of MAX-DOAS results on cloud optical thickness and effective cloud fraction from satellite indicate that the cloud classification scheme is sensitive to cloud (optical) properties, (3) separation of cloudy scenes by cloud pressure shows that the MAX-DOAS cloud classification scheme is also capable of detecting high clouds, (4) some clear sky conditions, especially with high aerosol load, classified from MAX-DOAS observations corresponding to the optically thin and low clouds derived by satellite observations probably indicate that the satellite cloud products contain valuable information on aerosols.

scholarly journals Determination of luminance distribution under tropical sky conditions

Vestnik MGSU ◽  
2019 ◽  
pp. 1096-1105
Author(s):  
Nguyen Thi Khanh Phuong
Keyword(s):  
Great Influence ◽  
Research Problem ◽  
Natural Illumination ◽  
The Real ◽  
Clear Sky ◽  
Luminance Distribution ◽  
High Solar Radiation ◽  
Sky Conditions ◽  
Sky Type ◽  
Overcast Sky

Introduction. Natural illumination calculations depend on the sky luminance distribution. The most often used diagram of sky luminance in handbooks and guidelines is the luminance distribution in the cloudy sky proposed by Moon and Spencer. This concept actually includes the tropical areas of Vietnam, where the overcast sky and clear sky does not typically occur. To improve the calculation of natural illumination, it is necessary to determine the luminance distribution in the real sky. Materials and methods. In solving the research problem, the real sky types for Hanoi were identified using the 15 international standard sky types with their descriptions by lighting climate, which is provided using the method by R. Kittler. The descriptions are derived from the data on diffuse horizontal illumination Dv, extraterrestrial horizontal illumination Ev and light turbidity coefficient Tv. For a specific sky type, the standard parameters were selected for calculating the luminance distribution of the real sky. Results. The obtained results show that the typical sky type of Hanoi is the partly cloudy sky, no gradation towards zenith, with slight bleaching towards the Sun (type VI). The sky types from October to December are partly cloudy with the obscured Sun (type IX) and partly cloudy with the more luminant circumsolar area (type X). The study shows that the state of cloud coverage has a great influence on the level of diffuse horizontal illumination and luminance distribution under tropical sky conditions. Conclusions. It is revealed that the typical sky type for Hanoi is neither overcast nor clear sky. A typical sky with statistic dominance of cirrus and stratus clouds under effect of high solar radiation of Vietnamese tropical climate gives a high level of diffuse horizontal illuminance. The results show that the difference in luminance distribution between the CIE standard overcast sky and Kittler’s intermediate sky can be resolved at the angles of sky point elevation above horizon γ is higher than 50° with the relative errors below 10 %. In other words, the luminance distribution β of the considered sky type is significant for a system of side natural illumination.

scholarly journals Effects of temporal averaging on short-term irradiance variability under mixed sky conditions

2018 ◽  
Vol 11 (5) ◽  
pp. 3131-3144 ◽  
Author(s):  
Gerald M. Lohmann ◽  
Adam H. Monahan
Keyword(s):  
Power Systems ◽  
Single Point ◽  
Time Averaging ◽  
Short Term ◽  
Systematic Analysis ◽  
Electrical Grid ◽  
Surface Irradiance ◽  
Temporal Averaging ◽  
Averaging Time ◽  
Sky Conditions

Abstract. Characterizations of short-term variability in solar radiation are required to successfully integrate large numbers of photovoltaic power systems into the electrical grid. Previous studies have used ground-based irradiance observations with a range of different temporal resolutions and a systematic analysis of the effects of temporal averaging on the representation of variability is lacking. Using high-resolution surface irradiance data with original temporal resolutions between 0.01 and 1 s from six different locations in the Northern Hemisphere, we characterize the changes in representation of temporal variability resulting from time averaging. In this analysis, we condition all data to states of mixed skies, which are the most potentially problematic in terms of local PV power volatility. Statistics of clear-sky index k* and its increments Δk*τ (i.e., normalized surface irradiance and changes therein over specified intervals of time) are considered separately. Our results indicate that a temporal averaging time scale of around 1 s marks a transition in representing single-point irradiance variability, such that longer averages result in substantial underestimates of variability. Higher-resolution data increase the complexity of data management and quality control without appreciably improving the representation of variability. The results do not show any substantial discrepancies between locations or seasons.

scholarly journals The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images

2020 ◽  
Vol 12 (21) ◽  
pp. 3671
Author(s):  
Junxia Jiang ◽  
Qingquan Lv ◽  
Xiaoqing Gao
Keyword(s):  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Solar Irradiation ◽  
Short Term ◽  
Clear Sky ◽  
Forecasting Models ◽  
Forecasting Performance ◽  
Sky Imager ◽  
Short Term Forecasting ◽  
Sky Conditions

Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time.

Evaluation of net shortwave radiation over China with a regional climate model

2020 ◽  
Vol 80 (2) ◽  
pp. 147-163
Author(s):  
X Liu ◽  
Y Kang ◽  
Q Liu ◽  
Z Guo ◽  
Y Chen ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Radiant Energy ◽  
Energy System ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Monsoon Region ◽  
Clear Sky ◽  
Sky Conditions

The regional climate model RegCM version 4.6, developed by the European Centre for Medium-Range Weather Forecasts Reanalysis, was used to simulate the radiation budget over China. Clouds and the Earth’s Radiant Energy System (CERES) satellite data were utilized to evaluate the simulation results based on 4 radiative components: net shortwave (NSW) radiation at the surface of the earth and top of the atmosphere (TOA) under all-sky and clear-sky conditions. The performance of the model for low-value areas of NSW was superior to that for high-value areas. NSW at the surface and TOA under all-sky conditions was significantly underestimated; the spatial distribution of the bias was negative in the north and positive in the south, bounded by 25°N for the annual and seasonal averaged difference maps. Compared with the all-sky condition, the simulation effect under clear-sky conditions was significantly better, which indicates that the cloud fraction is the key factor affecting the accuracy of the simulation. In particular, the bias of the TOA NSW under the clear-sky condition was <±10 W m-2 in the eastern areas. The performance of the model was better over the eastern monsoon region in winter and autumn for surface NSW under clear-sky conditions, which may be related to different levels of air pollution during each season. Among the 3 areas, the regional average biases overall were largest (negative) over the Qinghai-Tibet alpine region and smallest over the eastern monsoon region.

Efficient downscaling of satellite oceanographic data with convolutional neural networks

2021 ◽  
Vol 12 (3) ◽  
pp. 46-47
Author(s):  
Nikita Saxena
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Sea Surface ◽  
Clear Sky ◽  
Oceanographic Data ◽  
Computationally Expensive ◽  
Sky Conditions ◽  
Data Inference

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.

Sign in / Sign up

Export Citation Format

Share Document