Mission
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The Atmospheric Optics Group of Valladolid University (GOA-UVa) is involved in the study of atmospheric components, mainly aerosols, with optical methods. The GOA calibration facility is devoted to radiometric calibration of optical instrumentations such as photometers, and it is part of the AERONET-Europe Central Facility, partially funded by the European Union. As a university group, our researchers carry out educational and training activity (graduate, master and PhD thesis). In this site you can find information about the work of the group, members, research lines, publications, projects, vacancies, etc. |
Latests 5 Publications
2024
1.
Daniel González-Fernández; Roberto Román; David Mateos; Celia Herrero del Barrio; Victoria E. Cachorro; Gustavo Copes; Ricardo Sánchez; Rosa Delia García; Lionel Doppler; Sara Herrero-Anta; Juan Carlos Antuña-Sánchez; África Barreto; Ramiro González; Javier Gatón; Abel Calle; Carlos Toledano; Ángel Frutos
Retrieval of Solar Shortwave Irradiance from All-Sky Camera Images Journal Article
In: Remote Sensing, vol. 16, no. 20, 2024, ISSN: 2072-4292.
@article{rs16203821,
title = {Retrieval of Solar Shortwave Irradiance from All-Sky Camera Images},
author = {Daniel González-Fernández and Roberto Román and David Mateos and Celia Herrero del Barrio and Victoria E. Cachorro and Gustavo Copes and Ricardo Sánchez and Rosa Delia García and Lionel Doppler and Sara Herrero-Anta and Juan Carlos Antuña-Sánchez and África Barreto and Ramiro González and Javier Gatón and Abel Calle and Carlos Toledano and Ángel Frutos},
url = {https://www.mdpi.com/2072-4292/16/20/3821},
doi = {10.3390/rs16203821},
issn = {2072-4292},
year = {2024},
date = {2024-10-14},
urldate = {2024-01-01},
journal = {Remote Sensing},
volume = {16},
number = {20},
abstract = {The present work proposes a new model based on a convolutional neural network (CNN) to retrieve solar shortwave (SW) irradiance via the estimation of the cloud modification factor (CMF) from daytime sky images captured by all-sky cameras; this model is named CNN-CMF. To this end, a total of 237,669 sky images paired with SW irradiance measurements obtained by using pyranometers were selected at the following three sites: Valladolid and Izaña, Spain, and Lindenberg, Germany. This dataset was randomly split into training and testing sets, with the latter excluded from the training model in order to validate it using the same locations. Subsequently, the test dataset was compared with the corresponding SW irradiance measurements obtained by the pyranometers in scatter density plots. The linear fit shows a high determination coefficient (R2) of 0.99. Statistical analyses based on the mean bias error (MBE) values and the standard deviation (SD) of the SW irradiance differences yield results close to ?2% and 9%, respectively. The MBE indicates a slight underestimation of the CNN-CMF model compared to the measurement values. After its validation, model performance was evaluated at the Antarctic station of Marambio (Argentina), a location not used in the training process. A similar comparison between the model-predicted SW irradiance and pyranometer measurements yielded R2=0.95, with an MBE of around 2% and an SD of approximately 26%. Although the precision provided by the SD at the Marambio station is lower, the MBE shows that the model’s accuracy is similar to previous results but with a slight overestimation of the SW irradiance. Finally, the determination coefficient improved to 0.99, and the MBE and SD are about 3% and 11%, respectively, when the CNN-CMF model is used to estimate daily SW irradiation values.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The present work proposes a new model based on a convolutional neural network (CNN) to retrieve solar shortwave (SW) irradiance via the estimation of the cloud modification factor (CMF) from daytime sky images captured by all-sky cameras; this model is named CNN-CMF. To this end, a total of 237,669 sky images paired with SW irradiance measurements obtained by using pyranometers were selected at the following three sites: Valladolid and Izaña, Spain, and Lindenberg, Germany. This dataset was randomly split into training and testing sets, with the latter excluded from the training model in order to validate it using the same locations. Subsequently, the test dataset was compared with the corresponding SW irradiance measurements obtained by the pyranometers in scatter density plots. The linear fit shows a high determination coefficient (R2) of 0.99. Statistical analyses based on the mean bias error (MBE) values and the standard deviation (SD) of the SW irradiance differences yield results close to ?2% and 9%, respectively. The MBE indicates a slight underestimation of the CNN-CMF model compared to the measurement values. After its validation, model performance was evaluated at the Antarctic station of Marambio (Argentina), a location not used in the training process. A similar comparison between the model-predicted SW irradiance and pyranometer measurements yielded R2=0.95, with an MBE of around 2% and an SD of approximately 26%. Although the precision provided by the SD at the Marambio station is lower, the MBE shows that the model’s accuracy is similar to previous results but with a slight overestimation of the SW irradiance. Finally, the determination coefficient improved to 0.99, and the MBE and SD are about 3% and 11%, respectively, when the CNN-CMF model is used to estimate daily SW irradiation values.
2.
Xindan Zhang; Lei Li; Huizheng Che; Oleg Dubovik; Yevgeny Derimian; Brent Holben; Pawan Gupta; Thomas F. Eck; Elena S. Lind; Carlos Toledano; Xiangao Xia; Yu Zheng; Ke Gui; Xiaoye Zhang
Aerosol Components Derived from Global AERONET Measurements by GRASP: A New Value-Added Aerosol Component Global Dataset and Its Application Journal Article
In: Bulletin of the American Meteorological Society, vol. 105, no. 10, pp. E1822 - E1848, 2024.
@article{Zhang2024Aerosol,
title = {Aerosol Components Derived from Global AERONET Measurements by GRASP: A New Value-Added Aerosol Component Global Dataset and Its Application},
author = {Xindan Zhang and Lei Li and Huizheng Che and Oleg Dubovik and Yevgeny Derimian and Brent Holben and Pawan Gupta and Thomas F. Eck and Elena S. Lind and Carlos Toledano and Xiangao Xia and Yu Zheng and Ke Gui and Xiaoye Zhang},
url = {https://journals.ametsoc.org/view/journals/bams/105/10/BAMS-D-23-0260.1.xml},
doi = {10.1175/BAMS-D-23-0260.1},
year = {2024},
date = {2024-10-14},
urldate = {2024-01-01},
journal = {Bulletin of the American Meteorological Society},
volume = {105},
number = {10},
pages = {E1822 - E1848},
publisher = {American Meteorological Society},
address = {Boston MA, USA},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3.
Daniel González-Fernández; Roberto Román; Juan Carlos Antuña-Sánchez; Victoria E. Cachorro; Gustavo Copes; Sara Herrero-Anta; Celia Herrero del Barrio; África Barreto; Ramiro González; Ramón Ramos; Patricia Martín; David Mateos; Carlos Toledano; Abel Calle; Ángel Frutos
A neural network to retrieve cloud cover from all-sky cameras: A case of study over Antarctica Journal Article
In: Quarterly Journal of the Royal Meteorological Society, vol. n/a, no. n/a, 2024.
@article{gonzalez2024CCNeural,
title = {A neural network to retrieve cloud cover from all-sky cameras: A case of study over Antarctica},
author = {Daniel González-Fernández and Roberto Román and Juan Carlos Antuña-Sánchez and Victoria E. Cachorro and Gustavo Copes and Sara Herrero-Anta and Celia Herrero del Barrio and África Barreto and Ramiro González and Ramón Ramos and Patricia Martín and David Mateos and Carlos Toledano and Abel Calle and Ángel Frutos},
url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.4834},
doi = {https://doi.org/10.1002/qj.4834},
year = {2024},
date = {2024-08-28},
journal = {Quarterly Journal of the Royal Meteorological Society},
volume = {n/a},
number = {n/a},
abstract = {Abstract We present a new model based on a convolutional neural network (CNN) to predict daytime cloud cover (CC) from sky images captured by all-sky cameras, which is called CNN-CC. A total of 49,016 daytime sky images, recorded at different Spanish locations (Valladolid, La Palma, and Izaña) from two different all-sky camera types, are manually classified into different CC (oktas) values by trained researchers. Subsequently, the images are randomly split into a training set and a test set to validate the model. The CC values predicted by the CNN-CC model are compared with the observations made by trained people on the test set, which serve as reference. The predicted CC values closely match the reference values within ±$$ ± $$1?oktas in 99% of the cloud-free and overcast cases. Moreover, this percentage is above 93% for the rest of partially cloudy cases. The mean bias error (MBE) and standard deviation (SD) of the differences between the predicted and reference CC values are calculated, resulting in MBE=0.007$$ mathrmMBE=0.007 $$?oktas and SD=0.674$$ mathrmSD=0.674 $$?oktas. The MBE and SD are also represented for different intervals of measured aerosol optical depth and Ångström exponent values, revealing that the performance of the CNN-CC model does not depend on aerosol load or size. Once the model is validated, the CC obtained from a set of images captured every 5?min, from January 2018 to March 2022, at the Antarctic station of Marambio (Argentina) is compared against direct field observations of CC (not from images) taken at this location, which is not used in the training process. As a result, the model slightly underestimates the observations with an MBE of ?$$ - $$0.3?oktas. The retrieved data are analyzed in detail. The monthly and annual CC values are calculated. Overcast conditions are the most frequent, accounting for 46.5% of all observations throughout the year, rising to 64.5% in January. The annual mean CC value at this location is 5.5?oktas, with a standard deviation of approximately 3.1?oktas. A similar analysis is conducted, separating data by hours, but no significant diurnal cycles are observed except for some isolated months.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract We present a new model based on a convolutional neural network (CNN) to predict daytime cloud cover (CC) from sky images captured by all-sky cameras, which is called CNN-CC. A total of 49,016 daytime sky images, recorded at different Spanish locations (Valladolid, La Palma, and Izaña) from two different all-sky camera types, are manually classified into different CC (oktas) values by trained researchers. Subsequently, the images are randomly split into a training set and a test set to validate the model. The CC values predicted by the CNN-CC model are compared with the observations made by trained people on the test set, which serve as reference. The predicted CC values closely match the reference values within ±$$ ± $$1?oktas in 99% of the cloud-free and overcast cases. Moreover, this percentage is above 93% for the rest of partially cloudy cases. The mean bias error (MBE) and standard deviation (SD) of the differences between the predicted and reference CC values are calculated, resulting in MBE=0.007$$ mathrmMBE=0.007 $$?oktas and SD=0.674$$ mathrmSD=0.674 $$?oktas. The MBE and SD are also represented for different intervals of measured aerosol optical depth and Ångström exponent values, revealing that the performance of the CNN-CC model does not depend on aerosol load or size. Once the model is validated, the CC obtained from a set of images captured every 5?min, from January 2018 to March 2022, at the Antarctic station of Marambio (Argentina) is compared against direct field observations of CC (not from images) taken at this location, which is not used in the training process. As a result, the model slightly underestimates the observations with an MBE of ?$$ - $$0.3?oktas. The retrieved data are analyzed in detail. The monthly and annual CC values are calculated. Overcast conditions are the most frequent, accounting for 46.5% of all observations throughout the year, rising to 64.5% in January. The annual mean CC value at this location is 5.5?oktas, with a standard deviation of approximately 3.1?oktas. A similar analysis is conducted, separating data by hours, but no significant diurnal cycles are observed except for some isolated months.
4.
Sandra Graßl; Christoph Ritter; Jonas Wilsch; Richard Herrmann; Lionel Doppler; Roberto Román
From Polar Day to Polar Night: A Comprehensive Sun and Star Photometer Study of Trends in Arctic Aerosol Properties in Ny-Ålesund, Svalbard Journal Article
In: Remote Sensing, vol. 16, no. 19, 2024, ISSN: 2072-4292.
@article{Graßl2024,
title = {From Polar Day to Polar Night: A Comprehensive Sun and Star Photometer Study of Trends in Arctic Aerosol Properties in Ny-Ålesund, Svalbard},
author = {Sandra Graßl and Christoph Ritter and Jonas Wilsch and Richard Herrmann and Lionel Doppler and Roberto Román},
url = {https://www.mdpi.com/2072-4292/16/19/3725},
doi = {10.3390/rs16193725},
issn = {2072-4292},
year = {2024},
date = {2024-07-22},
urldate = {2024-01-01},
journal = {Remote Sensing},
volume = {16},
number = {19},
abstract = {The climate impact of Arctic aerosols, like the Arctic Haze, and their origin are not fully understood. Therefore, long-term aerosol observations in the Arctic are performed. In this study, we present a homogenised data set from a sun and star photometer operated in the European Arctic, in Ny-Ålesund, Svalbard, of the 20 years from 2004–2023. Due to polar day and polar night, it is crucial to use observations of both instruments. Their data is evaluated in the same way and follows the cloud-screening procedure of AERONET. Additionally, an improved method for the calibration of the star photometer is presented. We found out, that autumn and winter are generally more polluted and have larger particles than summer. While the monthly median Aerosol Optical Depth (AOD) decreases in spring, the AOD increases significantly in autumn. A clear signal of large particles during the Arctic Haze can not be distinguished from large aerosols in winter. With autocorrelation analysis, we found that AOD events usually occur with a duration of several hours. We also compared AOD events with large-scale processes, like large-scale oscillation patterns, sea ice, weather conditions, or wildfires in the Northern Hemisphere but did not find one single cause that clearly determines the Arctic AOD. Therefore the observed optical depth is a superposition of different aerosol sources.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The climate impact of Arctic aerosols, like the Arctic Haze, and their origin are not fully understood. Therefore, long-term aerosol observations in the Arctic are performed. In this study, we present a homogenised data set from a sun and star photometer operated in the European Arctic, in Ny-Ålesund, Svalbard, of the 20 years from 2004–2023. Due to polar day and polar night, it is crucial to use observations of both instruments. Their data is evaluated in the same way and follows the cloud-screening procedure of AERONET. Additionally, an improved method for the calibration of the star photometer is presented. We found out, that autumn and winter are generally more polluted and have larger particles than summer. While the monthly median Aerosol Optical Depth (AOD) decreases in spring, the AOD increases significantly in autumn. A clear signal of large particles during the Arctic Haze can not be distinguished from large aerosols in winter. With autocorrelation analysis, we found that AOD events usually occur with a duration of several hours. We also compared AOD events with large-scale processes, like large-scale oscillation patterns, sea ice, weather conditions, or wildfires in the Northern Hemisphere but did not find one single cause that clearly determines the Arctic AOD. Therefore the observed optical depth is a superposition of different aerosol sources.
5.
A. F. Almansa; Á. Barreto; N. Kouremeti; R. González; A. Masoom; C. Toledano; J. Gröbner; R. D. García; Y. González; S. Kazadzis; S. Victori; Ó. Álvarez; F. Maupin; V. Carreño; V. E. Cachorro; E. Cuevas
The Langley ratio method, a new approach for transferring photometer calibration from direct sun measurements Journal Article
In: Atmospheric Measurement Techniques, vol. 17, no. 2, pp. 659–675, 2024.
@article{Almansa2024b,
title = {The Langley ratio method, a new approach for transferring photometer calibration from direct sun measurements},
author = {A. F. Almansa and Á. Barreto and N. Kouremeti and R. González and A. Masoom and C. Toledano and J. Gröbner and R. D. García and Y. González and S. Kazadzis and S. Victori and Ó. Álvarez and F. Maupin and V. Carreño and V. E. Cachorro and E. Cuevas},
url = {https://amt.copernicus.org/articles/17/659/2024/},
doi = {10.5194/amt-17-659-2024},
year = {2024},
date = {2024-01-26},
urldate = {2024-01-01},
journal = {Atmospheric Measurement Techniques},
volume = {17},
number = {2},
pages = {659–675},
keywords = {},
pubstate = {published},
tppubtype = {article}
}