A5081516734- Atmospheric aerosols and clouds
- Urban Heat Island Mitigation
- Solar Radiation and Photovoltaics
- Atmospheric chemistry and aerosols
- Atmospheric and Environmental Gas Dynamics
- Atmospheric Ozone and Climate
- Impact of Light on Environment and Health
- Remote Sensing in Agriculture
Universitat de València
2017-2024
Universitat de Miguel Hernández d'Elx
2024
We propose a methodological approach to provide the accurate and calibrated measurements of sky radiance broadband solar irradiance using High Dynamic Range (HDR) images sky-camera. This is based on detailed instrumental characterization SONA sky-camera in terms image acquisition processing, as well geometric radiometric calibrations. As result, 1 min time resolution database geometrically radiometrically HDR has been created available since February 2020, with daily updates. An extensive...
In this study, we present a methodology to obtain the Aerosol Optical Depth (AOD) and Angstrom Exponent (AE) from images of sky under particular conditions. For this, have applied machine-learning (ML) models radiometric High Dynamic Range (HDR) image database sky. A series variables derived calibrated channels each has been used as input ML. Each variable is proxy principal plane radiance which related with AOD AE in image. Gaussian Process Regressor involved ML model different choices...
All Sky-cameras were originally designed to obtain cloud cover, being their main standard product. However, when the images are properly calibrated, geometrically and radiatively, it is possible accurately determine spectral (three band: RGB) sky radiance, as well broadband diffuse irradiance in any arbitrary plane (Valdelomar et al., 2021). In this work, we present a comprehensive methodology separate radiative contribution of aerosols clouds partially cloudy scenarios using calibrated High...
The aim of this study is to predict the main aerosol properties in atmosphere, Aerosol Optical Depth (AOD) and Angstrom Exponent (AE), with aid machine learning techniques images from an All-Sky camera. Two different have been used work: a random forest (RF) artificial neural network (ANN) target values furnished by AERONET database. HDR camera sited Burjassot (Spain) used. All them taken clear-sky condition (without clouds) depth. Selected come out range 0 0.5 AOD at 500 nm as reference....
All sky-cameras are devices with a very high potential in order to study atmospheric phenomena and were originally designed obtain the cloud cover. However, methods based different approaches produce significant differences results. State-of-art usually offer better performance, thanks computer vision machine learning (ML) techniques, than traditional algorithms on channel ratios using both fixed adaptive thresholds classify pixels of one image as or free. We have developed cover threshold...
A commercial all-sky-camera is employed to derive a whole-sky product of Cloud Optical Depth. The methodology consists in radiative closure combining measurements the blue and red channels with libRadtran 1D monochromatic radiance simulations. Besides, matrix data quality Flags obtained for every COD image. indicate reliability retrieval at each pixel, gives information about method solve ambivalence. In addition, also indicates presence out-of-range radiances respect RT Such are related...
The radiative closure methodologies to obtain Cloud Optical Depth (COD) from Remote Sensing techniques have traditionally relied on one-dimensional (1D) assumptions. These assumptions might be far away the radiation transport over a realistic three-dimensional (3D) atmosphere, especially in cloudy conditions, as natural inhomogeneities of clouds are not conveniently represented and treated 1D models. differences between 3D approaches manifests effects: a) plane-parallel albedo bias and, b)...