The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of the climate crisis are being increasingly felt, the need for accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on meteorological variables using statistical techniques as well as artificial intelligence (AI) for a specific area of interest utilising data from an in situ meteorological station, and to produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location; in our case, in a hotel in the northern area of Crete, Greece. The temporal resolution of the measurements used was 3 h and the forecast horizon considered here was up to 2 days. As prediction techniques, seasonal autoregressive integrated moving average (SARIMA), AI techniques like the long short-term memory (LSTM) neural network and hybrid combinations of the two are used. Multiple meteorological variables are considered as input for the LSTM and hybrid methodologies, like temperature, relative humidity, atmospheric pressure and wind speed, unlike the SARIMA that has a single variable. Variables of interest are divided into those that present seasonality and patterns, such as temperature and humidity, and those that are more stochastic with no known seasonality and patterns, such as wind speed and direction. Two benchmark techniques are used for comparison and quantification of the added predictive ability, namely the climatological forecast and the persistence model, which shows a considerable amount of improvement over the naive prediction methods, especially in the 1-day forecasts. The results indicate that the examined hybrid methodology performs best at temperature and wind speed forecasts, closely followed by the SARIMA, whereas LSTM performs better overall at the humidity forecast, even after the correction of the hybrid to the SARIMA model. Lastly, different hybrid methodologies are discussed and introduced for further improvement of meteorological predictions.

This study investigated advancements in atmospheric forecasting by integrating real-time observational data into the Weather Research and Forecasting (WRF) model through the WRF-Data Assimilation (WRF-DA) framework. By refining atmospheric models, we aimed to improve regional high-resolution wave and hydrodynamic forecasts essential for environmental management. Focused on southern Greece, including Crete, the study applied a 3D-Var assimilation technique within WRF, downscaling forecasting data from the Global Forecast System (GFS) to resolutions of 9 km and 3 km. The results showed a 4.7% improvement in wind speed predictions, with significant gains during forecast hours 26–72, enhancing model accuracy across METAR validation locations. These results underscore the positive impact of the integration of additional observational data on model accuracy. This study also highlights the utility of refined atmospheric models for real-world applications through their use in forcing ocean circulation and wave models and subsequent Digital Twin of the Ocean applications. Two such applications—optimal ship routing to minimize CO₂ emissions and oil spill trajectory forecasting to mitigate marine pollution—demonstrate the practical utility of improved models through what-if scenarios in easily deployable, containerized formats.

The current work focuses on establishing the parameters that influence the wind’s behavior over the Aegean and Ionian Seas and estimating the wind potential in the region based on long-term historic climate data. Combining a downscaling technique performed with the well-founded WRF-ARW computational algorithm and a number of simultaneous meteorological station time series, an attempt is made to investigate how regional changes may affect low-altitude wind speed distribution at hub height (100 m a.s.l.). The provided time-series coastal data span the entire region of interest from north to south. WRF-ARW v.3.9 is utilized to associate the geostrophic wind distribution obtained from long-term Copernicus ERA5 wind data with the localized wind potential over lower altitudes. Evaluation and correlation of the observational data to the predicted wind climate are performed, and the statistical differences that arise are investigated. High-accuracy wind resource potential maps are thus obtained in the region. Also, a few distinctive flow patterns are identified, such as wind speed cut-off regions and very high wind speed distributions, which are presented in specific southern regions of the Aegean Sea.