http://hdl.handle.net/20.500.11765/14266 2026-09-23T22:10:33Z http://hdl.handle.net/20.500.11765/18255 Título : NWCSAF High Resolution Winds (NWCSAF GEO-I HRW) Stereo AMVs over the Atlantic Ocean Autor : García Pereda, Javier; Carr, James L.; Friberg, Mariel D.; Wu, Dong L.; Madani, Houria; Lei, Xuming Resumen : The “stereo height assignment method” developed for NASA and NOAA for GOES-R ABI Atmospheric Motion Vectors (AMVs), a purely geometric method using the parallax observed from different satellites, has been included in the NWCSAF AMV product (NWCSAF GEO-I HRW, High Resolution Winds) as an additional height assignment option for AMVs in the region jointly observed by MTG-I and GOES-East over the Atlantic Ocean. Stereo and the alternative non-stereo height assignment (“Cross Correlation Contribution (CCC)”) are compared with ECMWF ERA5 reanalysis winds and EarthCARE ATLID Mie Attenuated Backscatter (MAB) curtains. For low-level clouds, both methods generally conform well with apparent cloud tops in MAB curtains. For higher clouds, more differences are seen between stereo and non-stereo heights. Compared with ERA5 winds, stereo shows the most improvement above 6 km. However, the stereo method produces 70–90% less AMVs since additional high-quality matches are needed from both FCI and ABI imagery. An updated HRW will be released to NWCSAF users in 2027 as version “NWCSAF GEO-I v2027.” The combined provision of both stereo and non-stereo CCC height assignment methods will enable further studies (already planned) of the AMV best-fit level for different satellite channels and cloud types, heights and depths. 2026-01-01T00:00:00Z http://hdl.handle.net/20.500.11765/18240 Título : EUMETNET SRNWP-EPS EFI/SOT Software: From Research to Operations in AEMET-γSREPS Autor : Montolio Llenas, Joan; Callado Pallarés, Alfons; Gómez Navarro, Juan José Resumen : The EUMETNET Short Range Numerical Weather Prediction - Ensemble Prediction System (SRNWP-EPS) EFI/SOT software is a result from the EUMETNET SRNWP-EPS 2019 to 2023 project. It computes the Extreme Forecast Index (EFI) and Shift of Tails index (SOT) for the different EUMETNET Member States Limited Area Modelling - Ensemble Prediction Systems (LAM-EPS). The software, implemented in Python 3, has been developed from scratch within a multiprocessor Linux environment: the European Centre for Medium-Range Weather Forecasts (ECMWF) ATOS High Performance Computer Facility (HPCF). The software has been developed testing on EFI and SOT indices for a wide variety of meteorological variables such as accumulated precipitation (AccPcp), accumulated snow (AccSnw), 2 m maximum and minimum temperature (T2mMax and T2mMin) and 10 m maximum wind gust (G10m). In order to overcome the computational limitations derived from LAM-EPSs in terms of constructing a large and robust EPS-climatology through an expensive re-forecast with the last LAM-EPS version, the software follows methods from Météo-France that consists of temporal and spatial relaxation techniques that include neighboring data in space and time to enrich the small available dataset used to construct the EPS-climatology. Additionally, the software introduces a land-sea mask that aims at applying physically-based restrictions to the grid points included by the spatial relaxation methods. Specific extreme weather events have been selected to showcase the software with some EUMETNET Member States LAM-EPSs. Moreover, an already operational implementation of the EUMETNET SRNWP-EPS EFI/SOT software is showcased in AEMET-γSREPS, the LAM-EPS developed and operationally run at AEMET, the Spanish Meteorological Agency. The software provides products similar to the ones from the European Centre for Medium-Range Weather Forecasts Ensemble (ECMWF ENS), and are currently available for AEMET forecasting offices. 2026-01-01T00:00:00Z http://hdl.handle.net/20.500.11765/18161 Título : Capturing the extremes: a quasi-comonotonicity-based algorithm for disaggregating daily to hourly rainfall Autor : Correa, Carlos; Hernanz Lázaro, Alfonso; San-Felipe, Iván; Rodríguez Guisado, Esteban Resumen : Disaggregating daily precipitation data into hourly time scales is crucial for hydrological modelling, urban drainage design, and extreme rainfall risk assessment. This study presents Q-CODA, a novel Quasi-COmonotonicity-based Disaggregation Algorithm that leverages the quasi-comonotonic relationship between daily precipitation totals and their sub-daily maxima to generate hourly rainfall sequences consistent with observed extremes. The method combines a Fréchet–Hoeffding upper bound copula to constrain sub-daily maxima with a K-nearest neighbours approach and an iterative adjustment algorithm to ensure consistency with daily totals and multiple sub-daily constraints. Q-CODA is evaluated through a 5-fold cross-validation over 91 meteorological stations across Spain (1996–2024) and compared against state-of-the-art methods, including nearest-neighbour resampling, Poisson cluster models, multiplicative cascades, and deep learning approaches. Results show that Q-CODA consistently outperforms state-of-the-art methods in reproducing extremes. Across stations, median values indicate a 1-D Wasserstein distance of 0.015 compared to 0.021–0.073, and a bias in the 99.9th percentile of −2.8 % versus −29 % to +11 %. Temporal structure is also well preserved, with event duration bias of −2.0 % (vs. −22 % to +13 %) and lag-1 autocorrelation bias of −4.4 % (vs. −37 % to −7.8 %). For intensity-duration-frequency curves, Q-CODA attains a median root mean square error of 1.16 mm h−1 for a 100-year return period, improving upon the 1.62–4.60 mm h−1 range of alternative methods. Additional analyses across other climate regimes, including the Pacific Northwest and Florida (United States), show consistently strong performance, indicating stable and reliable behaviour under varying climatic conditions. Furthermore, a semi-parametric regionalised extension enables application at ungauged locations while maintaining competitive accuracy. Overall, Q-CODA provides a consistent and transferable framework for sub-daily rainfall disaggregation with clear advantages for extreme-value representation and hydrometeorological applications. 2026-01-01T00:00:00Z http://hdl.handle.net/20.500.11765/17980 Título : Pre-training for deep statistical climate downscaling: enhancing consistency and robustness across regional datasets Autor : González-Abad, José; Iturbide, Maialen; Hernanz Lázaro, Alfonso; Gutiérrez, José Manuel Resumen : We explore how deep learning can improve local climate projections by adapting a national model to regional data. By relying on a paradigm called pre-training, we show that models can produce more consistent and physically aligned results, even when data is limited. This helps make future climate projections more reliable and supports better planning at both national and local levels. 2026-01-01T00:00:00Z