Shortwave surface radiation budget network for observing small-scale cloud inhomogeneity fields

Madhavan, Bomidi Lakshmi; Kalisch, John; Macke, Andreas

doi:10.5194/amtd-8-2555-2015

Cited by 6 publications

(7 citation statements)

References 29 publications

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“…Based on the maintenance protocol, data are provided with quality flags ("good", "okay, but sometimes spurious", "bad or ignore completely" or "missing or no observations"). Madhavan et al (2015) give a description of the pyranometer network within the HOPE campaign, details of the hardware, quality flags and an investigation of measurement uncertainties.…”

Section: Irradiance Sensor Networkmentioning

confidence: 99%

“…We decided to use only measurements which were flagged by the data provider as "good". As stations were maintained once a week and quality flags were given for the whole week, data gaps most of the time cover a whole week (Madhavan et al, 2015). As a consequence, a reduced subset of 50 stations with at least 70 % of the maximal possible number of measurements available was used when comparing performance for different stations (Sect.…”

Section: Data Selectionmentioning

confidence: 99%

“…The imager was part of the LACROS supersite within the HOPE measurement campaign; see Madhavan et al (2015) for the location and details. The Canon digital CCD camera equipped with a Raynox fisheye lens realized a field of view of 183 • .…”

Section: Sky Imagermentioning

confidence: 99%

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Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

et al. 2016

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Abstract. Clouds are the dominant source of small-scale variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the worldwide electric power supply increases the need for accurate solar radiation forecasts.In this work, we present results of a very short term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A 2-month data set with images from one sky imager and high-resolution GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series into different cloud scenarios.Overall, the sky-imager-based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depends strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1-2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability, which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

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Section: Irradiance Sensor Networkmentioning

confidence: 99%

Section: Data Selectionmentioning

confidence: 99%

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Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

et al. 2016

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“…This is because several satellite-based data are error-prone due to the lack of accuracy in sunrise and sunset times [57]. Furthermore, the refraction effect at a SZA of 85 • or less is negligible between the apparent SZA and the real SZA but produces significant errors at sunrise or sunset [58].…”

Section: Discussionmentioning

confidence: 99%

Retrieval of Reflected Shortwave Radiation at the Top of the Atmosphere Using Himawari-8/AHI Data

et al. 2018

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This study developed a retrieval algorithm for reflected shortwave radiation at the top of the atmosphere (RSR). This algorithm is based on Himawari-8/AHI (Advanced Himawari Imager) whose sensor characteristics and observation area are similar to the next-generation Geostationary Korea Multi-Purpose Satellite/Advanced Meteorological Imager (GK-2A/AMI). This algorithm converts the radiance into reflectance for six shortwave channels and retrieves the RSR with a regression coefficient look-up-table according to geometry of the solar-viewing (solar zenith angle, viewing zenith angle, and relative azimuth angle) and atmospheric conditions (surface type and absence/presence of clouds), and removed sun glint with high uncertainty. The regression coefficients were calculated using numerical experiments from the radiative transfer model (SBDART), and ridge regression for broadband albedo at the top of the atmosphere (TOA albedo) and narrowband reflectance considering anisotropy. The retrieved RSR were validated using Terra, Aqua, and S-NPP/CERES data on the 15th day of every month from July 2015 to February 2017. The coefficient of determination (R 2 ) between AHI and CERES for scene analysis was higher than 0.867 and the Bias and root mean square error (RMSE) were −21.34-5.52 and 51.74-59.28 Wm −2 . The R 2 , Bias, and RMSE for the all cases were 0.903, −2.34, and 52.12 Wm −2 , respectively.

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“…a. Data selection Pyranometers data are from the HOPE measurements campaign 13 and refer to the period from 2 April to 24 July 2013. The setup included 99 openfield installations with silicon photodiode pyranometers distributed over an area of approximately 48 km 2 .…”

Section: Pyranometers Measurements and Processingmentioning

confidence: 99%

Solar irradiance estimations for modeling the variability of photovoltaic generation and assessing violations of grid constraints: A comparison between satellite and pyranometers measurements with load flow simulations

Sossan

Scolari

Gupta

et al. 2019

Journal of Renewable and Sustainable Energy

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Global horizontal irradiance (GHI) is typically used to model the potential of distributed photovoltaic (PV) generation. On the one hand, satellite estimations are non-pervasive and already available from commercial providers, but they have a limited spatiotemporal resolution. On the other hand, local estimations, e.g., from pyranometers, sky-cameras and monitored PV plants, capture local irradiance patterns and dynamics, but they require insitu monitoring infrastructure and upgrading the asset of electrical operators. Considering that in most power systems, PV generation is typically the aggregated contribution of many distributed plants, are local GHI estimations necessary to characterize the variability of the power flow at the grid connection point (GCP) and detect violations of the limits of voltages and line currents accurately? To reply, we consider GHI measurements from a dense network of pyranometers (used to model the ground truth GHI potential), satellite estimations for the same area, and information about a medium and low voltage distribution system. We perform load flows at different levels of installed PV capacity and compare the nodal voltages, line currents, and the power at the GCP when the irradiance is from pyranometers and when from satellite estimations, deriving conclusions on the necessity, or not, of highly spatiotemporally resolved irradiance estimations.

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Shortwave surface radiation budget network for observing small-scale cloud inhomogeneity fields

Cited by 6 publications

References 29 publications

Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

Retrieval of Reflected Shortwave Radiation at the Top of the Atmosphere Using Himawari-8/AHI Data

Solar irradiance estimations for modeling the variability of photovoltaic generation and assessing violations of grid constraints: A comparison between satellite and pyranometers measurements with load flow simulations

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