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

2016 ◽  
Vol 16 (5) ◽  
pp. 3399-3412 ◽  
Author(s):  
Thomas Schmidt ◽  
John Kalisch ◽  
Elke Lorenz ◽  
Detlev Heinemann
Keyword(s):  
Solar Radiation ◽  
Forecast Error ◽  
Forecast Skill ◽  
Small Scale ◽  
Surface Solar Radiation ◽  
Data Set ◽  
Forecast Performance ◽  
Electric Power Supply ◽  
Sky Imager ◽  
Spatio Temporal

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.

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

2015 ◽  
Vol 15 (19) ◽  
pp. 26997-27039 ◽  
Author(s):  
T. Schmidt ◽  
J. Kalisch ◽  
E. Lorenz ◽  
D. Heinemann
Keyword(s):  
Solar Radiation ◽  
Forecast Error ◽  
Forecast Skill ◽  
Surface Solar Radiation ◽  
Forecast Performance ◽  
Electric Power Supply ◽  
Step Model ◽  
Sky Imager ◽  
Spatio Temporal ◽  
Analysis Error

Abstract. Clouds are the dominant source of variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the world-wide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a shortest-term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A two month dataset with images from one sky imager and high resolutive 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 in different cloud scenarios. Overall, the sky imager based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depend 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.

scholarly journals Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data

2020 ◽  
Author(s):  
Mieke Kuschnerus ◽  
Roderik Lindenbergh ◽  
Sander Vos
Keyword(s):  
Time Series ◽  
Laser Scanning ◽  
Clustering Algorithm ◽  
Coastal Areas ◽  
Small Scale ◽  
Temporal Data ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Deformation Processes ◽  
Spatio Temporal

Abstract. Sandy coasts are constantly changing environments governed by complex interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and support analysis of geomorphological deformation processes. This novel technique delivers 3D representations of a part of the coast at hourly temporal and centimetre spatial resolution and allows to observe small scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatio-temporal data set is needed. In order to allow data mining in an automated way, we extract time series in elevation or range and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well known clustering algorithms, k-means, agglomerative clustering and DBSCAN, and identify areas that undergo similar evolution during one month. We test if they fulfil our criteria for a suitable clustering algorithm on our exemplary data set. The three clustering methods are applied to time series of 30 epochs (during one month) extracted from a data set of daily scans covering a part of the coast at Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused for example by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatio-temporal data set for predominant deformation patterns with the associated regions, where they occur.

Evaluating Forecast Performance and Sensitivity to the GSI EnKF Data Assimilation Configuration for the 28 – 29 May 2017 Mesoscale Convective System Case

2020 ◽  
Author(s):  
Jonathan Labriola ◽  
Youngsun Jung ◽  
Chengsi Liu ◽  
Ming Xue
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Forecast Skill ◽  
Convective System ◽  
Observation Error ◽  
Forecast Performance ◽  
Radar Observations ◽  
Data Thinning

AbstractIn an effort to improve radar data assimilation configurations for potential operational implementation, GSI EnKF data assimilation experiments based on the operational system employed by the Center for Analysis and Prediction of Storms (CAPS) realtime Spring Forecast Experiments are performed. These experiments are followed by 6-hour forecasts for an MCS on 28 – 29 May 2017. Configurations examined include data thinning, covariance localization radii and inflation, observation error settings, and data assimilation frequency for radar observations.The results show experiments that assimilate radar observations more frequently (i.e., 5 – 10 minutes) are initially better at suppressing spurious convection. However, assimilating observations every 5 minutes causes spurious convection to become more widespread with time, and modestly degrades forecast skill through the remainder of the forecast window. Ensembles that assimilate more observations with less thinning of data or use a larger horizontal covariance localization radius for radar data predict fewer spurious storms and better predict the location of observed storms. Optimized data thinning and horizontal covariance localization radii have positive impacts on forecast skill during the first forecast hour that are quickly lost due to the growth of forecast error. Forecast skill is less sensitive to the ensemble spread inflation factors and observation errors tested during this study. These results provide guidance towards optimizing the configuration of the GSI EnKF system. Among DA the configurations tested, the one employed by the CAPS Spring Forecast Experiment produces the most skilled forecasts while remaining computationally efficient for realtime use.

scholarly journals Yearly changes in surface solar radiation in New Caledonia

2015 ◽  
Vol 12 (1) ◽  
pp. 1-4 ◽  
Author(s):  
P. Blanc ◽  
C. Coulaud ◽  
L. Wald
Keyword(s):  
Solar Radiation ◽  
New Caledonia ◽  
Solar Irradiation ◽  
Data Sets ◽  
Climate Data ◽  
Surface Solar Radiation ◽  
Data Set ◽  
The Mean ◽  
Energy Plants

Abstract. New Caledonia experiences a decrease in surface solar irradiation since 2004. It is of order of 4% of the mean yearly irradiation over the 10 years period: 2004–2013, and amounts to −9 W m−2. The preeminent roles of the changes in cloud cover and to a lesser extent, those in aerosol optical depth on the decrease in yearly irradiation are evidenced. The study highlights the role of data sets offering a worldwide coverage in understanding changes in solar radiation and planning large solar energy plants such as the ICOADS (International Comprehensive Ocean-Atmosphere Data Set) of the NOAA and MACC (Monitoring Atmosphere Composition and Climate) data sets combined with the McClear model.

scholarly journals Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables

2015 ◽  
Vol 19 (9) ◽  
pp. 3969-3990 ◽  
Author(s):  
F. Hoss ◽  
P. S. Fischbeck
Keyword(s):  
Quantile Regression ◽  
High Reliability ◽  
Forecast Error ◽  
Skill Score ◽  
Water Levels ◽  
Training Data ◽  
Forecast Errors ◽  
Data Set ◽  
Forecast Performance ◽  
Independent Variables

Abstract. This study applies quantile regression (QR) to predict exceedance probabilities of various water levels, including flood stages, with combinations of deterministic forecasts, past forecast errors and rates of water level rise as independent variables. A computationally cheap technique to estimate forecast uncertainty is valuable, because many national flood forecasting services, such as the National Weather Service (NWS), only publish deterministic single-valued forecasts. The study uses data from the 82 river gauges, for which the NWS' North Central River Forecast Center issues forecasts daily. Archived forecasts for lead times of up to 6 days from 2001 to 2013 were analyzed. Besides the forecast itself, this study uses the rate of rise of the river stage in the last 24 and 48 h and the forecast error 24 and 48 h ago as predictors in QR configurations. When compared to just using the forecast as an independent variable, adding the latter four predictors significantly improved the forecasts, as measured by the Brier skill score and the continuous ranked probability score. Mainly, the resolution increases, as the forecast-only QR configuration already delivered high reliability. Combining the forecast with the other four predictors results in a much less favorable performance. Lastly, the forecast performance does not strongly depend on the size of the training data set but on the year, the river gauge, lead time and event threshold that are being forecast. We find that each event threshold requires a separate configuration or at least calibration.

scholarly journals A 16-year dataset (2000–2015) of high-resolution (3 hour, 10 km) global surface solar radiation

2019 ◽  
Author(s):  
Wenjun Tang ◽  
Kun Yang ◽  
Jun Qin ◽  
Xin Li ◽  
Xiaolei Niu
Keyword(s):  
High Resolution ◽  
Solar Radiation ◽  
Radiant Energy ◽  
Reanalysis Data ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Surface Solar Radiation ◽  
Data Set ◽  
Global Surface

Abstract. The recent release of the International Satellite Cloud Climatology Project (ISCCP) HXG cloud products and new ERA5 reanalysis data enabled us to produce a global surface solar radiation (SSR) dataset: a 16-year (2000–2015) high-resolution (3 h, 10 km) global SSR dataset with an improved physical parameterization scheme. The main inputs were cloud optical depth from ISCCP-HXG cloud products, the water vapor, surface pressure and ozone from ERA5 reanalysis data, and albedo and aerosol from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The estimated SSR data was evaluated against surface observations measured at 42 stations of the Baseline Surface Radiation Network (BSRN) and 90 radiation stations of the China Meteorological Administration (CMA). Validation against the BSRN data indicated that the mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (R) for the instantaneous SSR estimate at 10 km scale were −11.5 W m−2, 113.5 W m−2, and 0.92, respectively. The error was clearly reduced when the data were upscaled to 90 km; RMSE decreased to 93.4 W m−2 and R increased to 0.95. For daily SSR estimates at 90 km scale, the MBE, RMSE and R at the BSRN were −5.8 W m−2, 33.1 W m−2 and 0.95, respectively. These error metrics at the CMA radiation stations were 2.1 W m−2, 26.9 W m−2 and 0.95, respectively. Comparisons with other global satellite radiation products indicated that our SSR estimates were generally better than those of the ISCCP flux dataset (ISCCP-FD), the global energy and water cycle experiment surface radiation budget (GEWEX-SRB), and the Earth's Radiant Energy System (CERES). Our SSR dataset will contribute to the land-surface process simulations and the photovoltaic applications in the future. The data set is available at https://doi.org/10.11888/Meteoro.tpdc.270112 (Tang, 2019).

scholarly journals Satellite-based trends of solar radiation and cloud parameters in Europe

2018 ◽  
Vol 15 ◽  
pp. 31-37 ◽  
Author(s):  
Uwe Pfeifroth ◽  
Jedrzej S. Bojanowski ◽  
Nicolas Clerbaux ◽  
Veronica Manara ◽  
Arturo Sanchez-Lorenzo ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Water Cycle ◽  
Parameter Analysis ◽  
Positive Trend ◽  
Climate Data ◽  
High Quality ◽  
Surface Solar Radiation ◽  
Cloud Parameters ◽  
Common Framework ◽  
Spatio Temporal

Abstract. Solar radiation is the main driver of the Earth's climate. Measuring solar radiation and analysing its interaction with clouds are essential for the understanding of the climate system. The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates satellite-based, high-quality climate data records, with a focus on the energy balance and water cycle. Here, multiple of these data records are analyzed in a common framework to assess the consistency in trends and spatio-temporal variability of surface solar radiation, top-of-atmosphere reflected solar radiation and cloud fraction. This multi-parameter analysis focuses on Europe and covers the time period from 1992 to 2015. A high correlation between these three variables has been found over Europe. An overall consistency of the climate data records reveals an increase of surface solar radiation and a decrease in top-of-atmosphere reflected radiation. In addition, those trends are confirmed by negative trends in cloud cover. This consistency documents the high quality and stability of the CM SAF climate data records, which are mostly derived independently from each other. The results of this study indicate that one of the main reasons for the positive trend in surface solar radiation since the 1990's is a decrease in cloud coverage even if an aerosol contribution cannot be completely ruled out.

scholarly journals Long-term series and trends in surface solar radiation in Athens, Greece

2018 ◽  
Vol 18 (4) ◽  
pp. 2395-2411 ◽  
Author(s):  
Stelios Kazadzis ◽  
Dimitra Founda ◽  
Basil E. Psiloglou ◽  
Harry Kambezidis ◽  
Nickolaos Mihalopoulos ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Lower Limit ◽  
Sunshine Duration ◽  
Mediterranean Area ◽  
Linear Change ◽  
Surface Solar Radiation ◽  
Data Set ◽  
Ssr Analysis ◽  
The Difference

Abstract. We present a long-term series of surface solar radiation (SSR) from the city of Athens, Greece. SSR measurements were performed from 1954 to 2012, and before that (1900–1953) sunshine duration (SD) records were used in order to reconstruct monthly SSR. Analysis of the whole data set (1900–2012) mainly showed very small (0.02 %) changes in SSR from 1900 to 1953, including a maximum decrease of −2.9 % decade−1 in SSR during the 1910 to 1940 period, assuming a linear change. For the dimming period 1955–1980, −2 % decade−1 was observed that matches various European long-term SSR-measurement-related studies. This percentage in Athens is in the lower limit, compared to other studies in the Mediterranean area. For the brightening period 1980–2012 we calculated +1.5 % decade−1, which is also in the lower limit of the reported positive changes in SSR around Europe. Comparing the 30-year periods 1954–1983 and 1983–2012, we found a difference of 4.5 %. However, measurements of the first 30-year period are associated with higher uncertainties than those of the second period, especially when looking at year-to-year changes. The difference between the two periods was observed for all seasons except winter. Analyzing SSR calculations of all-sky and clear-sky (cloudless) conditions/days, we report that most of the observed changes in SSR after 1954 can be attributed partly to cloudiness and mostly to aerosol load changes.

SARAH-3 – a new satellite-based Climate Data Record of Surface Solar Radiation parameters

2021 ◽  
Author(s):  
Uwe Pfeifroth ◽  
Jaqueline Drücke ◽  
Jörg Trentmann ◽  
Rainer Hollmann
Keyword(s):  
Solar Radiation ◽  
Sunshine Duration ◽  
Global Radiation ◽  
Surface Radiation ◽  
Climate Data ◽  
Regional Variability ◽  
Surface Solar Radiation ◽  
Data Set ◽  
Data Record ◽  
Climate Data Record

<p class="western"><span lang="en-US">The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates and distributes high quality long-term climate data records (CDR) of energy and water cycle parameters, which are freely available.</span></p> <p class="western"><span lang="en-US">In 2022, a new version of the “Surface Solar Radiation data set – Heliosat” will be released: SARAH-3. As the previous editions, the SARAH-3 climate data record is based on satellite observations from the first and second METEOSAT generations and provides various surface radiation parameters, including global radiation, direct radiation, sunshine duration, photosynthetic active radiation and others. SARAH-3 covers the time period 1983 to 2020 and offers 30-minute instantaneous data as well as daily and monthly means on a regular 0.05° x 0.05° lon/lat grid.</span></p> <p class="western" align="left"><span lang="en-US">In this presentation, an overview of the SARAH climate data record and their applications will be given. A focus will be on the SARAH-3 developments and validation with surface reference observations. Further, SARAH-3 will be used for a first analysis of the climate variability and potential trends of global radiation in Europe during the last decades. </span><span lang="en-US">The data record reveals that there is an increasing trend of surface solar radiation in Europe during the last decades, which is superimposed by decadal and regional variability.</span></p>

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