Special Issue "Remote Sensing of Energy Meteorology"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmosphere Remote Sensing".

Deadline for manuscript submissions: 30 April 2020.

Special Issue Editors

Dr. Andreas Kazantzidis
E-Mail Website
Guest Editor
Associate Professor, Laboratory of Atmospheric Physics, Physics Department, University of Patras, 26500 Patras, Greece
Tel. +302610997549
Interests: surface solar irradiance resource and forecasting; aerosol and cloud optical properties; radiative transfer modelling; satellite observations; all-sky imagers and short-term forecasting; geostatistical algorithms for mapping applications; surface radiation monitoring
Prof. Sven-Erik Gryning
E-Mail
Guest Editor
Department of Wind Energy, Technical University of Denmark, Risø Campus Frederiksborgvej 399, Building 125, 4000 Roskilde, Denmark
Tel. +45 28841323
Interests: boundary-layer meteorology; wind profile within and above the surface boundary-layer; wind energy; wind Lidar; ceilometer; boundary-layer height

Special Issue Information

Dear Colleagues,

The renewable energy sector has seen explosive growth over the last decade, allowing solar/wind energy farms to become more efficient and to be manufactured at lower costs. However, all areas do not have the same energy potential; thus, the need to further understand and efficiently use available solar/wind resources is a key motivation for research. 

During the last decade, remote sensing observations have been widely used in solar/wind energy applications.

Concerning solar energy, satellite-derived surface solar irradiance measurements are necessary for high-resolution solar resource assessment as well as short-term forecasting. Sky imagers are widely used to deal with the high spatial and temporal variability of clouds as well as to deal with the challenging task of solar resource assessment and forecasting in very short spatial and time scales.

Wind energy today is a mature and commercially competitive technique for energy generation. However, wind resources are highly variable in time and space. This has spurred the development and application of remote sensing techniques for wind energy applications. The leading remote sensing techniques are Lidars (wind Lidars and ceilometers), radars, and satellites.

The proposed Special Issue aims to incorporate contributions from both industry and academia that address all aspects of remote sensing of energy meteorology, from scientific fundamentals to practical energy industry needs. In addition, this issue will highlight the latest scientific/technological developments in this field of study.

Dr. Andreas Kazantzidis
Prof. Sven-Erik Gryning
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Solar resource and forecasting
  • Solar energy remote sensing
  • Clouds and aerosols
  • Lidar, radar and satellite techniques (with an emphasis on wind and boundary-layer height)
  • Wind energy and wind power meteorology (for resource assessment)
  • Atmospheric turbulence and gusts (for wind turbine load calculations)
  • Wake measurements and modelling (individual wind turbines and between wind farms)
  • Use of remote sensing for wind energy forecasts

Published Papers (4 papers)

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Research

Open AccessArticle
The Seamless Solar Radiation (SESORA) Forecast for Solar Surface Irradiance—Method and Validation
Remote Sens. 2019, 11(21), 2576; https://doi.org/10.3390/rs11212576 - 02 Nov 2019
Abstract
Due to the integration of fluctuating weather-dependent energy sources into the grid, the importance of weather and power forecasts grows constantly. This paper describes the implementation of a short-term forecast of solar surface irradiance named SESORA (seamless solar radiation). It is based on [...] Read more.
Due to the integration of fluctuating weather-dependent energy sources into the grid, the importance of weather and power forecasts grows constantly. This paper describes the implementation of a short-term forecast of solar surface irradiance named SESORA (seamless solar radiation). It is based on the the optical flow of effective cloud albedo and available for Germany and parts of Europe. After the clouds are shifted by applying cloud motion vectors, solar radiation is calculated with SPECMAGIC NOW (Spectrally Resolved Mesoscale Atmospheric Global Irradiance Code), which computes the global irradiation spectrally resolved from satellite imagery. Due to the high spatial and temporal resolution of satellite measurements, solar radiation can be forecasted from 15 min up to 4 h or more with a spatial resolution of 0.05 . An extensive validation of this short-term forecast is presented in this study containing two different validations based on either area or stations. The results are very promising as the mean RMSE (Root Mean Square Error) of this study equals 59 W/m 2 (absolute bias = 42 W/m 2 ) after 15 min, reaches its maximum of 142 W/m 2 (absolute bias = 97 W/m 2 ) after 165 min, and slowly decreases after that due to the setting of the sun. After a brief description of the method itself and the method of the validation the results will be presented and discussed. Full article
(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)
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Open AccessArticle
Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer
Remote Sens. 2019, 11(19), 2247; https://doi.org/10.3390/rs11192247 - 26 Sep 2019
Abstract
Accurate prediction of wind turbine wakes is important for more efficient design and operation of wind parks. Volumetric wake measurements of nacelle-mounted Doppler lidars are used to characterize the wake of a full-scale wind turbine and to validate an analytical wake model that [...] Read more.
Accurate prediction of wind turbine wakes is important for more efficient design and operation of wind parks. Volumetric wake measurements of nacelle-mounted Doppler lidars are used to characterize the wake of a full-scale wind turbine and to validate an analytical wake model that incorporates the effect of wind veer. Both, measurements and model prediction, show an elliptical and tilted spanwise cross-section of the wake in the presence of wind veer. The error between model and measurements is reduced compared to a model without the effect of wind veer. The characterization of the downwind velocity deficit development and wake growth is robust. The wake tilt angle can only be determined for elliptical wakes. Full article
(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)
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Open AccessArticle
Atmospheric Transmittance Model Validation for CSP Tower Plants
Remote Sens. 2019, 11(9), 1083; https://doi.org/10.3390/rs11091083 - 07 May 2019
Cited by 1
Abstract
In yield analysis and plant design of concentrated solar power (CSP) tower plants, increased uncertainties are caused by the mostly unknown solar attenuation between the concentrating heliostat field and the receiver on top of the tower. This attenuation is caused mainly by aerosol [...] Read more.
In yield analysis and plant design of concentrated solar power (CSP) tower plants, increased uncertainties are caused by the mostly unknown solar attenuation between the concentrating heliostat field and the receiver on top of the tower. This attenuation is caused mainly by aerosol particles and water vapor. Various on-site measurement methods of atmospheric extinction in solar tower plants have been developed during recent years, but during resource assessment for distinct tower plant projects in-situ measurement data sets are typically not available. To overcome this lack of information, a transmittance model (TM) has been previously developed and enhanced by the authors to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity and barometric pressure measurements. Previously the model was only tested at one site. In this manuscript, the enhanced TM is validated for three sites (CIEMAT’s Plataforma Solar de Almería (PSA), Spain, Missour, Morocco (MIS) and Zagora, Morocco (ZAG)). As the strongest assumption in the TM is the vertical aerosol particle profile, three different approaches to describe the vertical profile are tested in the TM. One approach assumes a homogeneous aerosol profile up to 1 kilometer above ground, the second approach is based on LIVAS profiles obtained from Lidar measurements and the third approach uses boundary layer height (BLH) data of the European Centre for Medium-Range Weather Forecasts (ECMWF). The derived broadband transmittance for a slant range of 1 km ( T 1 k m ) time series is compared with a reference data set of on-site absorption- and broadband corrected T 1 k m derived from meteorological optical range (MOR) measurements for the temporal period between January 2015 and November 2017. The absolute mean bias error (MBE) for the TM’s T 1 k m using the three different aerosol profiles lies below 5% except for ZAG and one profile assumption. The MBE is close to 0 for PSA and MIS assuming a homogeneous extinction coefficient up to 1 km above ground. The root mean square error (RMSE) is around 5–6% for PSA and ZAG and around 7–8% for MIS. The TM performs better during summer months, during which more data points have been evaluated. This validation proves the applicability of the transmittance model for resource assessment at various sites. It enables the identification of a clear site with high T 1 k m with a high accuracy and provides an estimation of the T 1 k m for hazy sites. Thus it facilitates the decision if on-site extinction measurements are necessary. The model can be used to improve the accuracy of yield analysis of tower plants and allows the site adapted design. Full article
(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)
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Open AccessArticle
Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance Nowcasts
Remote Sens. 2019, 11(9), 1059; https://doi.org/10.3390/rs11091059 - 05 May 2019
Abstract
The incoming downward shortwave solar irradiance is harvested to an increasing extent by solar power plants. However, the variable nature of this energy source poses an operational challenge for solar power plants and electrical grids. Intra hour solar irradiance nowcasts with a high [...] Read more.
The incoming downward shortwave solar irradiance is harvested to an increasing extent by solar power plants. However, the variable nature of this energy source poses an operational challenge for solar power plants and electrical grids. Intra hour solar irradiance nowcasts with a high temporal and spatial resolution could be used to tackle this challenge. All sky imager (ASI) based nowcasting systems fulfill the requirements in terms of temporal and spatial resolution. However, ASI nowcasts can only be used if the required accuracies for applications in solar power plants and electrical grids are fulfilled. Scalar error metrics, such as mean absolute deviation, root mean square deviation, and skill score are commonly used to estimate the accuracy of nowcasting systems. However, these overall error metrics represented by a single number per metric are neither suitable to determine the real time accuracy of a nowcasting system in the actual weather situation, nor suitable to describe any spatially resolved nowcast accuracy. The performance of ASI-based nowcasting systems is strongly related to the prevailing weather conditions. Depending on weather conditions, large discrepancies between the overall and current system uncertainties are conceivable. Furthermore, the nowcast accuracy varies strongly within the irradiance map as higher errors may occur at transient zones close to cloud shadow edges. In this paper, we present a novel approach for the spatially resolved real-time uncertainty specification of ASI-based nowcasting systems. The current irradiance conditions are classified in one of eight distinct temporal direct normal irradiance (DNI) variability classes. For each class and lead-time, an upper and lower uncertainty value is derived from historical data, which describes a coverage probability of 68.3%. This database of uncertainty values is based on deviations of the irradiance maps, compared to three reference pyrheliometers in Tabernas, Andalucia over two years (2016 and 2017). Increased uncertainties due to transient effects are considered by detecting transient zones close to cloud shadow edges within the DNI map. The width of the transient zones is estimated by the current average cloud height, cloud speed, lead-time, and Sun position. The final spatially resolved uncertainties are validated with three reference pyrheliometers, using a data set consisting of the entire year 2018. Furthermore, we developed a procedure based on the DNI temporal variability classes to estimate the expected average uncertainties of the nowcasting system at any geographical location. The novel method can also be applied for global tilted or horizontal irradiance and is assumed to improve the applicability of the ASI nowcasts. Full article
(This article belongs to the Special Issue Remote Sensing of Energy Meteorology)
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