arXiv subjects

Kostas Philippopoulos

Publications and source records attributed to Kostas Philippopoulos.

2 recordsLinked to original sources

arXiv2026

Deep Learning Super Resolution for Satellite Cloud Mask Downscaling

A vast amount of optical satellite data is being transmitted to Earth-based servers every day, and more than half of this data is affected by haze or clouds. Additionally, this data suffers from the fundamental trade-off between spatial and temporal resolution, which remains largely unresolved, making the acquisition of continuous high-resolution satellite observations of clouds an ongoing challenge. This work addresses this challenge by proposing two Deep Learning super-resolution methods for the accurate downscaling of SEVIRI cloud mask products, as well as a novel cross-sensor cloud mask dataset called SEVMOD-CM, created by spatially and temporally matching MODIS and SEVIRI satellite observations. The two proposed models are a CNN-based (SpatialCNN) and a GAN-based (SpatialGAN) Neural Network. Trained on the SEVIRI spectral and cloud mask products, the proposed methods predict the corresponding MODIS Cloud masks, achieving a 4x spatial enhancement across sensor domains. Both approaches are evaluated experimentally, and compared against the standard bicubic interpolation upsampling technique. The experimental results demonstrate the value of the proposed models and dataset for the remote sensing community, highlighting the benefits of applying super-resolution techniques to geostationary-derived cloud mask products for applications such as atmospheric monitoring, weather forecasting, disaster risk reduction, solar energy forecasting, and climate research.

cs.CV↗

arXiv2017

Improved Very-short-term Wind Forecasting using Atmospheric Classification

We present a regime-switching vector-autoregressive method for very-short-term wind speed forecasting at multiple locations with regimes based on large-scale meteorological phenomena. Statistical methods short-term wind forecasting out-perform numerical weather prediction for forecast horizons up to a few hours, and the spatio-temporal interdependency between geographically dispersed locations may be exploited to improve forecast skill. Here we show that conditioning spatio-temporal interdependency on `atmospheric modes' can further improve forecast performance. The modes are defined from the atmospheric classification of wind and pressure fields at the surface level, and the geopotential height field at the 500hPa level. The data fields are extracted from the MERRA-2 reanalysis dataset with an hourly temporal resolution over the UK, atmospheric patterns are classified using self-organising maps and then clustered to optimise forecast performance. In a case study based on 6 years of measurements from 23 weather stations in the UK, a set of three atmospheric modes are found to be optimal for forecast performance. The skill in the one- to six-hour-ahead forecasts is improved at all sites compared to persistence and competitive benchmarks. Across the 23 test sites, one-hour-ahead root mean squared error is reduced by between 0.3% and 4.1% compared to the best performing benchmark, and by and average of 1.6% over all sites; the six-hour-ahead accuracy is improved by an average of 3.1%.

stat.AP↗