scholarly journals On the spatial representativeness of point station measurements for comparison with WRF estimates

2021 ◽  
Author(s):  
Antonio Serrano ◽  
Guadalupe Sánchez-Hernández ◽  
Julio A. H. Escobar ◽  
María Luisa Cancillo
Keyword(s):  
Solar Radiation ◽  
Fossil Fuels ◽  
Spatial Scales ◽  
Model Performance ◽  
Wrf Model ◽  
Short Term ◽  
Single Station ◽  
The Difference ◽  
Clear Cases ◽  
Short Term Variability

<p>Solar energy proves to be an interesting alternative to conventional sources based on the burning of fossil fuels. However, it shows a high short-term variability that makes its integration into the electricity mix difficult. To facilitate this integration, reliable short- and medium-term forecasts become highly necessary. To respond to this demand, solar radiation forecasting models have emerged. Among them, Weather Research and Forecasting (WRF) has become particularly promising and has shown good performance at different temporal and spatial scales. The performance of these models is usually assessed by comparing their estimates with point measurements at selected stations. This comparison is hampered by the difference in spatial dimensions between the model estimates (representative of a given area) and the station (point) measurements. This difference introduces a certain error in the forecast, mainly related to the short-scale variability of cloudiness. Despite being essential to understand model validation, this issue has not been sufficiently investigated. In this framework, the present study analyzes the effect of the spatial representativeness of point measurements when used to validate model estimates. For this purpose, a specific one-month measurement campaign was conducted, deploying seven pyranometers in the vicinity of the city of Badajoz, Spain. To ensure their intercomparability, all pyranometers were calibrated with respect to a reference pyranometer previously calibrated by the World Radiation Center in Davos, Switzerland. Solar radiation was measured at a 1-minute basis to record the short-term variability due to cloudiness. Two series were constructed with these data, one corresponding to a selected station and the other to the average of the seven stations. These series of measurements were compared with the estimates provided by the WRF model for the same period and location. A configuration with two nested domains of 27 km and 9 km was used. Model performance showed better agreement when averaging was used instead of individual measurements, with RMSE improving from 89 W/m² to 77 W/m². Cloudy cases contributed the most to the differences between station measurements and model estimates, showing an RMSE greater than 100 W/m2, more than three times higher than the RMSE for clear cases (about 33 W/m2). The difference between the stations and the model for cloudy cases is reduced from 125 W/m2 to 107 W/m2 when averaged measurements are considered instead of single station measurements. This study contributes to the understanding of the representativeness of point station measurements for validation and comparison with WRF estimates. Acknowledgments. This work is partially funded by FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación of Spain through project RTI 2018-097332-B-C22, and by Junta de Extremadura-FEDER through project GR18097.</p>

scholarly journals Kalman filter model, as a tool for short-term forecasting of solar potential: case of the Dakar site

2018 ◽  
Vol 57 ◽  
pp. 01004
Author(s):  
A. Mbaye ◽  
J. Ndong ◽  
M.L. NDiaye ◽  
M. Sylla ◽  
M.C. Aidara ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Solar Radiation ◽  
Mean Squared Error ◽  
Model Performance ◽  
Global Solar Radiation ◽  
Plant Production ◽  
Bias Error ◽  
Short Term ◽  
Filter Model ◽  
Short Term Forecasting

The prediction of solar potential is an important step toward the evaluation of PV plant production for the best energy planning. In this study, the discrete Kalman filter model was implemented for short-term solar resource forecasting one the Dakar site in Senegal. The model input parameters are constituted at a time t of the air temperature, the relative humidity and the global solar radiation. The expected output at time t+T is the global solar radiation. The model performance is evaluated with the square root of the normalized mean squared error (NRMSE), the absolute mean of the normalized error (NMAE), the average bias error (NMBE). The model Validation is carried out by means of the data measured within the Polytechnic Higher School of Dakar for one year. The simulation results following the 20 minute horizon show a good correlation between the prediction and the measurement with an NRMSE of 4.8%, an NMAE of 0.27% and an NMBE of 0.04%. This model could contribute to help photovoltaic based energy providers to better plan the production of solar photovoltaic plants in Sahelian environments.

scholarly journals A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

2018 ◽  
Author(s):  
Isabel Urbich ◽  
Jörg Bendix ◽  
Richard Müller
Keyword(s):  
Solar Radiation ◽  
Optical Flow ◽  
Spatial Scales ◽  
Surface Radiation ◽  
Estimation Methods ◽  
Short Term ◽  
Short Term Forecast ◽  
Cloud Albedo ◽  
Optical Flow Method ◽  
Term Forecast

The increasing use of renewable energies as a source of electricity has led to a fundamental transition of the power supply system. The integration of fluctuating weather-dependent energy sources into the grid already has a major impact on the load flows of the grid. As a result, the interest in forecasting wind and solar radiation with a sufficient accuracy over short time horizons grew. In this study the short-term forecast of the effective cloud albedo based on optical flow estimation methods are investigated. The optical flow method utilized here is TV-L1 from the open source library OpenCV. This method uses a multi-scale-approach to capture cloud motions on various spatial scales. After the clouds are displaced the solar surface radiation will be calculated with SPECMAGIC NOW which computes the global irradiation spectrally resolved from satellite imagery. Due to a high temporal and spatial resolution of satellite measurements the effective cloud albedo and thus solar radiation can be forecasted from 5 minutes up to 4 hours with a resolution of 0.05°. In the following there will be a brief description of the method for the short-term forecast of the effective cloud albedo. Subsequently evaluation results will be presented and discussed. Finally an outlook of further developments will be given.

Impact assessment of short-term variability of solar radiation in Rajasthan using SRRA data

2017 ◽  
Vol 78 ◽  
pp. 798-806 ◽  
Author(s):  
Sujit Kumar Tripathy ◽  
Indradip Mitra ◽  
Detlev Heinemann ◽  
Godugunur Giridhar ◽  
S. Gomathinayagam
Keyword(s):  
Solar Radiation ◽  
Impact Assessment ◽  
Short Term ◽  
Short Term Variability

scholarly journals An Object-Oriented Multiscale Verification Scheme

2010 ◽  
Vol 25 (1) ◽  
pp. 79-92 ◽  
Author(s):  
Steven A. Lack ◽  
George L. Limpert ◽  
Neil I. Fox
Keyword(s):  
Multiple Scales ◽  
Spatial Scales ◽  
Model Performance ◽  
Wrf Model ◽  
Object Oriented ◽  
Object Identification ◽  
Minimum Size ◽  
False Alarms ◽  
Multiscale Approach ◽  
Verification Scheme

Abstract Object-oriented verification methodology is becoming more and more common in the evaluation of model performance on high-resolution grids. The research herein describes an advanced version of an object-oriented approach that involves a combination of object identification on multiple scales with Procrustes shape analysis techniques. The multiscale object identification technique relies heavily on a novel Fourier transform approach to associate the signals within convection to different spatial scales. Other features of this new verification scheme include using a weighted cost function that can be user defined for object matching using different criteria, delineating objects that are more linear in character from those that are more cellular, and tagging object matches as hits, misses, or false alarms. Although the scheme contains a multiscale approach for identifying convective objects, standard minimum intensity and minimum size thresholds can be set when desirable. The method was tested as part of a spatial verification intercomparison experiment utilizing a combination of synthetic data and real cases from the Storm Prediction Center (SPC)/NSSL Weather Research and Forecasting (WRF) model Spring Program 2005. The resulting metrics, including error measures from differences in matched objects due to displacement, dilation, rotation, and intensity, from these cases run through this new, robust verification scheme are shown.

Short-term variability of solar radiation

Solar Energy ◽  
2006 ◽  
Vol 80 (5) ◽  
pp. 600-606 ◽  
Author(s):  
Teolan Tomson ◽  
Gunnar Tamm
Keyword(s):  
Solar Radiation ◽  
Short Term ◽  
Short Term Variability

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

scholarly journals THE VALUE OF ROBUST STATISTICAL FORECASTS IN THE COVID-19 PANDEMIC

2021 ◽  
Vol 256 ◽  
pp. 19-43
Author(s):  
Jennifer L. Castle ◽  
Jurgen A. Doornik ◽  
David F. Hendry
Keyword(s):  
Structural Change ◽  
Measurement Errors ◽  
Epidemiological Models ◽  
Short Term ◽  
Data Measurement ◽  
Policy Interventions ◽  
Changing Trends ◽  
Technological Advances ◽  
The Difference ◽  
The Uk

The Covid-19 pandemic has put forecasting under the spotlight, pitting epidemiological models against extrapolative time-series devices. We have been producing real-time short-term forecasts of confirmed cases and deaths using robust statistical models since 20 March 2020. The forecasts are adaptive to abrupt structural change, a major feature of the pandemic data due to data measurement errors, definitional and testing changes, policy interventions, technological advances and rapidly changing trends. The pandemic has also led to abrupt structural change in macroeconomic outcomes. Using the same methods, we forecast aggregate UK unemployment over the pandemic. The forecasts rapidly adapt to the employment policies implemented when the UK entered the first lockdown. The difference between our statistical and theory based forecasts provides a measure of the effect of furlough policies on stabilising unemployment, establishing useful scenarios had furlough policies not been implemented.

Short term wind energy resource prediction using WRF model for a location in western part of Turkey

2021 ◽  
Vol 13 (1) ◽  
pp. 013303
Author(s):  
Elcin Tan ◽  
S. Sibel Mentes ◽  
Emel Unal ◽  
Yurdanur Unal ◽  
Bahtiyar Efe ◽  
...  
Keyword(s):  
Wind Energy ◽  
Energy Resource ◽  
Wrf Model ◽  
Short Term ◽  
Resource Prediction

scholarly journals How Do Subway Signs Affect Pedestrians’ Wayfinding Behavior through Visual Short-Term Memory?

2021 ◽  
Vol 13 (12) ◽  
pp. 6866
Author(s):  
Haoru Li ◽  
Jinliang Xu ◽  
Xiaodong Zhang ◽  
Fangchen Ma
Keyword(s):  
Public Transportation ◽  
Short Term Memory ◽  
Memory Capacity ◽  
Cognitive Test ◽  
Subway Station ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
The Difference ◽  
Types Of Information

Recently, subways have become an important part of public transportation and have developed rapidly in China. In the subway station setting, pedestrians mainly rely on visual short-term memory to obtain information on how to travel. This research aimed to explore the short-term memory capacities and the difference in short-term memory for different information for Chinese passengers regarding subway signs. Previous research has shown that people’s general short-term memory capacity is approximately four objects and that, the more complex the information, the lower people’s memory capacity. However, research on the short-term memory characteristics of pedestrians for subway signs is scarce. Hence, based on the STM theory and using 32 subway signs as stimuli, we recruited 120 subjects to conduct a cognitive test. The results showed that passengers had a different memory accuracy for different types of information in the signs. They were more accurate regarding line number and arrow, followed by location/text information, logos, and orientation. Meanwhile, information type, quantity, and complexity had significant effects on pedestrians’ short-term memory capacity. Finally, according to our results that outline the characteristics of short-term memory for subway signs, we put forward some suggestions for subway signs. The findings will be effective in helping designers and managers improve the quality of subway station services as well as promoting the development of pedestrian traffic in such a setting.

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