## 1 Introduction  
In the realm of atmospheric science, wind-pattern forecasting holds a pivotal role in understanding and predicting weather phenomena. The atmosphere is characterized by variables such as temperature and humidity, which influence atmospheric pressure and wind movement. Wind is generated by air flowing from high to low pressure, and its direction is influenced by the Coriolis effect. Local wind patterns are affected by topography, which can cause wind enhancement, deceleration, or changes in direction. Downslope winds are caused by air being forced down a slope, while sea breezes are caused by temperature differences between water and land. These local wind patterns can have significant impacts on wind velocity and direction.  
Numerical weather prediction (NWP) uses physical simulation models to forecast weather. The main stages of NWP are data synthesis, simulation, postprocessing, and data assimilation. Improvements in NWP have come from better measurements and increased processing power. However, long-range and short-range weather forecasting remains challenging due to the chaotic nature of meteorological systems. For competitive sailing, high-resolution NWP models are used for site characterization and short-term forecasting. Site characterization helps sailors prepare for events, while short-term forecasting provides information on wind direction and velocity.  
Sailing is a competitive sport that requires physical fitness, technical skill, and tactical decision-making. Success in sailing depends on accurate wind forecasts, as wind velocity and direction affect boat speed and route selection. High-resolution wind forecasts are particularly important for sailing events, as they can help sailors make informed decisions about their race strategy. NWP models are commonly used for sailing forecasts, but they can be limited in their ability to provide the necessary spatial and temporal resolution. As a result, new prediction methods are being developed to meet the specific requirements of sailing events.  
Machine learning (ML) is a field of computer science that enables computers to learn from data without being explicitly programmed. ML algorithms construct mathematical models based on sample data, allowing them to make predictions or decisions. Deep learning, a type of ML based on artificial neural networks, has gained prominence due to its ability to identify patterns and relationships in complex data. ML techniques are finding increasing applications in natural science, including tasks such as language processing, speech recognition, image analysis, and disease outbreak prediction.  
Accurate wind forecasts are crucial for sailors' strategic and tactical decisions. To enhance wind predictions, the research employs machine learning (ML) techniques, particularly convolutional neural networks (CNNs) and multilayer perceptron (MLP). These models are trained using data from the Weather Research and Forecasting (WRF) atmospheric model and real data from the Israeli Meteorological Service (IMS) to improve wind velocity and direction predictions on Lake Kinneret. The research aims to provide improved wind forecasts for competitive sailing, where accurate weather information can give sailors a competitive advantage.  
In this research, we aimed to improve the temporal prediction of wind velocity and direction at a specific point in Lake Kinneret. We employed an ML model to postprocess the WRF atmospheric model predictions to improve prediction accuracy. The WRF atmospheric model output was used as an input for the training process of the ML model in conjunction with the site observations (provided by the Israeli Meteorological Service, IMS) as the validation set. This methodology was applied to two ML models (CNN and MLP).  
The remainder of this paper is structured as follows: Section 2 describes related background and prior work, Section 3 presents our combined numerical forecast model and ML method, Section 4 showcases the experimental results, and Section 5 summarizes our conclusions.