scholarly journals Evaluation of ECMWF model to predict daily and monthly solar radiation over Indonesia region

2021 ◽  
Vol 893 (1) ◽  
pp. 012074
Author(s):  
Y Sianturi ◽  
A Sopaheluwakan ◽  
K A Sartika
Keyword(s):  
Solar Radiation ◽  
Large Scale ◽  
Interpolation Method ◽  
Model Performance ◽  
Mapping Method ◽  
Future Application ◽  
Model Output ◽  
Quantile Mapping ◽  
Surface Solar Radiation ◽  
Operational Activity

Abstract Solar radiation forecast is a pivotal information needed in the operational activity of large-scale solar energy production. In this study, the reliability of SSRD (surface solar radiation downward) forecast from the 51 ensemble members in the ECMWF (European Centre for Medium Range Forecast) long-range forecast to predict daily and monthly radiation in 5 climatological stations in Indonesia is evaluated. The global horizontal irradiance (GHI) data from the solar radiation observation network from January 2018 – December 2020 are used in the quantitative evaluation of the SSRD forecast. Post-processing methods are applied to the model output, namely the bilinear interpolation method and the empirical quantile mapping to reduce consistent biases in the model output. The evaluation was carried out for different cloud covers based on the calculation of clearness index (k_t). The cloud condition affects the performance of the model, where the highest correlation value is achieved during sunny days (0.18 – 0.65) and the lowest correlation happens in overcast days (0.05 – 0.35). Models also tend to underestimate radiation when the sky is clear and overestimate it in cloudy days, based on negative MBE values during clear days (-0.47 kWh/m2 – -1.29 kWh/m2). The spatial averaging method did not necessarily improve the accuracy of the forecast, but the empirical quantile mapping method provides better accuracy, which is indicated by a values (mean error ratio) lower than 1 in most stations. Information about the influence of cloud cover on model performance can be used in future application of the model output and the bias correction process carried out in this study can be applied to reduce bias in the model.

scholarly journals Is global dimming and brightening in Japan limited to urban areas?

2016 ◽  
Author(s):  
Katsumasa Tanaka ◽  
Atsumu Ohmura ◽  
Doris Folini ◽  
Martin Wild ◽  
Nozomu Ohkawara
Keyword(s):  
Solar Radiation ◽  
Rural Areas ◽  
Urban Areas ◽  
Large Scale ◽  
Temporal Trends ◽  
Surface Radiation ◽  
Surface Solar Radiation ◽  
Decadal Time Scale ◽  
Global Dimming ◽  
Urban And Rural Areas

Abstract. Observations worldwide indicate secular trends of all-sky surface solar radiation on decadal time scale, termed global dimming and brightening. Accordingly, the observed surface radiation in Japan generally shows a strong decline till the end of the 1980s and then a recovery toward around 2000. Because a substantial number of measurement stations are located within or proximate to populated areas, one may speculate that the observed trends are strongly influenced by local air pollution and are thus not of large-scale significance. This hypothesis poses a serious question as to what regional extent the global dimming and brightening are significant: Are the global dimming and brightening truly global phenomena, or regional or even only local? Our study focused on 14 meteorological observatories that measured all-sky surface solar radiation, zenith transmittance, and maximum transmittance. On the basis of municipality population time series, historical land use maps, recent satellite images, and actual site visits, we concluded that eight stations had been significantly influenced by urbanization, with the remaining six stations being left pristine. Between the urban and rural areas, no marked differences were identified in the temporal trends of the aforementioned meteorological parameters. Our finding suggests that global dimming and brightening in Japan occurred on a large scale, independently of urbanization.

scholarly journals Evaluation of Multi-Reanalysis Solar Radiation Products Using Global Surface Observations

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 42 ◽  
Author(s):  
Xiaomin Peng ◽  
Jiangfeng She ◽  
Shuhua Zhang ◽  
Junzhong Tan ◽  
Yang Li
Keyword(s):  
Solar Radiation ◽  
Large Scale ◽  
Spatial Scales ◽  
Global Solar Radiation ◽  
Hydrological Process ◽  
China Meteorological Administration ◽  
Surface Solar Radiation ◽  
Climate Zones ◽  
Tropical Regions ◽  
Spatial Coverage

Solar radiation incident at the Earth’s surface is an essential driver of the energy exchange between the atmosphere and the surface and is also an important input variable in the research on the surface eco-hydrological process. The reanalysis solar radiation dataset is characterized by a long time series and wide spatial coverage and is used in the research of large-scale eco-hydrological processes. Due to certain errors in their production process of the reanalysis of solar radiation products, reanalysis products should be evaluated before application. In this study, three global solar-radiation reanalysis products (ERA-Interim; JRA-55; and NCEP-DOE) in different temporal scales and climate zones were evaluated using surface solar-radiation observations from the National Meteorological Information Center of the China Meteorological Administration (CMA, Beijing, China) and the Global Energy Balance Archive (GEBA, Zürich, Switzerland) from 2000 to 2009. All reanalysis products (ERA-Interim; JRA-55; and NCEP-DOE) overestimated with an annual bias of 14.86 W/m2, 22.61 W/m2, and 31.85 W/m2; monthly bias of 15.17 W/m2, 21.29 W/m2, and 36.91 W/m2; and seasonal bias of 15.08 W/m2, 21.21 W/m2, and 36.69 W/m2, respectively. In different Köppen climate zones, the annual solar radiation of ERA-Interim performed best in cold regions with a bias of 10.30 W/m2 and absolute relative error (ARE) of 8.98%. However, JRA-55 and NCEP-DOE showed the best performance in tropical regions with a bias of 20.08 W/m2 and −0.12 W/m2, and ARE of 11.00% and 9.68%, respectively. Overall, through the evaluations across different temporal and spatial scales, the rank of the three reanalysis products in order was the ERA-Interim, JRA-55, and NCEP-DOE. In addition, based on the evaluation, we analyzed the relationship between the error (ARE) of the reanalysis products and cloud cover, aerosol, and water vapor, which significantly influences solar radiation and we found that cloud was the main cause for errors in the three solar radiation reanalysis products. The above can provide a reference for the application and downscaling of the three solar radiation reanalysis products.

scholarly journals Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies

2016 ◽  
Vol 20 (2) ◽  
pp. 685-696 ◽  
Author(s):  
E. P. Maurer ◽  
D. L. Ficklin ◽  
W. Wang
Keyword(s):  
Spatial Scale ◽  
Statistical Downscaling ◽  
Bias Correction ◽  
Large Scale ◽  
Climate Model ◽  
Model Output ◽  
Quantile Mapping ◽  
Impact Studies ◽  
Mapping Bias ◽  
Climate Model Output

Abstract. Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generally effective method for accommodating systematic biases in large-scale model output is quantile mapping, which has been applied to many variables and shown to reduce biases on average, even in the presence of non-stationarity. Quantile-mapping bias correction has been applied at spatial scales ranging from hundreds of kilometers to individual points, such as weather station locations. Since water resources and other models used to simulate climate impacts are sensitive to biases in input meteorology, there is a motivation to apply bias correction at a scale fine enough that the downscaled data closely resemble historically observed data, though past work has identified undesirable consequences to applying quantile mapping at too fine a scale. This study explores the role of the spatial scale at which the quantile-mapping bias correction is applied, in the context of estimating high and low daily streamflows across the western United States. We vary the spatial scale at which quantile-mapping bias correction is performed from 2° ( ∼  200 km) to 1∕8° ( ∼  12 km) within a statistical downscaling procedure, and use the downscaled daily precipitation and temperature to drive a hydrology model. We find that little additional benefit is obtained, and some skill is degraded, when using quantile mapping at scales finer than approximately 0.5° ( ∼  50 km). This can provide guidance to those applying the quantile-mapping bias correction method for hydrologic impacts analysis.

scholarly journals Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies

2015 ◽  
Vol 12 (10) ◽  
pp. 10893-10920 ◽  
Author(s):  
E. P. Maurer ◽  
D. L. Ficklin ◽  
W. Wang
Keyword(s):  
Spatial Scale ◽  
Statistical Downscaling ◽  
Bias Correction ◽  
Large Scale ◽  
Climate Model ◽  
Model Output ◽  
Quantile Mapping ◽  
Impact Studies ◽  
Mapping Bias ◽  
Climate Model Output

Abstract. Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generally effective method for accommodating systematic biases in large-scale model output is quantile mapping, which has been applied to many variables and shown to reduce biases on average, even in the presence of non-stationarity. Quantile mapping bias correction has been applied at spatial scales ranging from areas of hundreds of kilometers to individual points, such as weather station locations. Since water resources and other models used to simulate climate impacts are sensitive to biases in input meteorology, there is a motivation to apply bias correction at a scale fine enough that the downscaled data closely resembles historically observed data, though past work has identified undesirable consequences to applying quantile mapping at too fine a scale. This study explores the role of the spatial scale at which the quantile-mapping bias correction is applied, in the context of estimating high and low daily streamflows across the Western United States. We vary the spatial scale at which quantile mapping bias correction is performed from 2° (∼ 200 km) to 1/8° (∼ 12 km) within a statistical downscaling procedure, and use the downscaled daily precipitation and temperature to drive a hydrology model. We find that little additional benefit is obtained, and some skill is degraded, when using quantile mapping at scales finer than approximately 0.5° (∼ 50 km). This can provide guidance to those applying the quantile mapping bias correction method for hydrologic impacts analysis.

scholarly journals Is global dimming and brightening in Japan limited to urban areas?

2016 ◽  
Vol 16 (21) ◽  
pp. 13969-14001 ◽  
Author(s):  
Katsumasa Tanaka ◽  
Atsumu Ohmura ◽  
Doris Folini ◽  
Martin Wild ◽  
Nozomu Ohkawara
Keyword(s):  
Solar Radiation ◽  
Rural Areas ◽  
Urban Areas ◽  
Large Scale ◽  
Temporal Trends ◽  
Surface Radiation ◽  
Surface Solar Radiation ◽  
Decadal Time Scale ◽  
Global Dimming ◽  
Urban And Rural Areas

Abstract. Worldwide observations indicate secular trends of all-sky surface solar radiation on a decadal time scale, termed global dimming and brightening. Accordingly, the observed surface radiation in Japan generally shows a strong decline until the end of the 1980s and then a recovery until around 2000. Because a substantial number of measurement stations are located within or close to populated areas, one may speculate that the observed trends are strongly influenced by local air pollution and are thus not of large-scale significance. This hypothesis poses a serious question as to what regional extent the global dimming and brightening are significant: are the global dimming and brightening truly global phenomena, or regional, or even only local? Our study focused on 14 meteorological observatories that measured all-sky surface solar radiation, zenith transmittance, and maximum transmittance. On the basis of municipality population time series, historical land use maps, recent satellite images, and actual site visits, we concluded that eight stations have been significantly influenced by urbanization, with the remaining six stations being left pristine. Between the urban and rural areas, no marked differences were identified in the temporal trends of the aforementioned meteorological parameters. Our findings suggest that global dimming and brightening in Japan occurred on a large scale, independently of urbanization.

scholarly journals A Digital Framework to Predict the Sunshine Requirements of Landscape Plants

2021 ◽  
Vol 11 (5) ◽  
pp. 2098
Author(s):  
Heyi Wei ◽  
Wenhua Jiang ◽  
Xuejun Liu ◽  
Bo Huang
Keyword(s):  
Solar Radiation ◽  
Plant Species ◽  
Large Scale ◽  
Normal Growth ◽  
Adaptive Selection ◽  
Landscape Plant ◽  
Landscape Plants ◽  
The Stability ◽  
Stability And Accuracy ◽  
The City

Knowledge of the sunshine requirements of landscape plants is important information for the adaptive selection and configuration of plants for urban greening, and is also a basic attribute of plant databases. In the existing studies, the light compensation point (LCP) and light saturation point (LSP) have been commonly used to indicate the shade tolerance for a specific plant; however, these values are difficult to adopt in practice because the landscape architect does not always know what range of solar radiation is the best for maintaining plant health, i.e., normal growth and reproduction. In this paper, to bridge the gap, we present a novel digital framework to predict the sunshine requirements of landscape plants. First, the research introduces the proposed framework, which is composed of a black-box model, solar radiation simulation, and a health standard system for plants. Then, the data fitting between solar radiation and plant growth response is used to obtain the value of solar radiation at different health levels. Finally, we adopt the LI-6400XT Portable Photosynthetic System (Li-Cor Inc., Lincoln, NE, USA) to verify the stability and accuracy of the digital framework through 15 landscape plant species of a residential area in the city of Wuhan, China, and also compared and analyzed the results of other researchers on the same plant species. The results show that the digital framework can robustly obtain the values of the healthy, sub-healthy, and unhealthy levels for the 15 landscape plant species. The purpose of this study is to provide an efficient forecasting tool for large-scale surveys of plant sunshine requirements. The proposed framework will be beneficial for the adaptive selection and configuration of urban plants and will facilitate the construction of landscape plant databases in future studies.

scholarly journals Process-Based Model Prediction of Coastal Dune Erosion through Parametric Calibration

2021 ◽  
Vol 9 (6) ◽  
pp. 635
Author(s):  
Hyeok Jin ◽  
Kideok Do ◽  
Sungwon Shin ◽  
Daniel Cox
Keyword(s):  
Large Scale ◽  
Numerical Models ◽  
Model Performance ◽  
Coastal Dunes ◽  
Wave Transformation ◽  
Storm Event ◽  
Face Model ◽  
Dune Erosion ◽  
Simulation Performance ◽  
Sand Bar

Coastal dunes are important morphological features for both ecosystems and coastal hazard mitigation. Because understanding and predicting dune erosion phenomena is very important, various numerical models have been developed to improve the accuracy. In the present study, a process-based model (XBeachX) was tested and calibrated to improve the accuracy of the simulation of dune erosion from a storm event by adjusting the coefficients in the model and comparing it with the large-scale experimental data. The breaker slope coefficient was calibrated to predict cross-shore wave transformation more accurately. To improve the prediction of the dune erosion profile, the coefficients related to skewness and asymmetry were adjusted. Moreover, the bermslope coefficient was calibrated to improve the simulation performance of the bermslope near the dune face. Model performance was assessed based on the model-data comparisons. The calibrated XBeachX successfully predicted wave transformation and dune erosion phenomena. In addition, the results obtained from other two similar experiments on dune erosion with the same calibrated set matched well with the observed wave and profile data. However, the prediction of underwater sand bar evolution remains a challenge.

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