Weather apps have come a long way in recent years, providing users with detailed weather information that goes beyond just temperature and precipitation. Now, weather apps are using data to offer personalized recommendations for clothing, activities, food and drink, and more. By using a combination of location data, weather data, and user preferences, weather apps are able to offer customized recommendations that improve the user experience.

For example, some weather apps are using machine learning algorithms to analyze user behavior and preferences, and then make recommendations based on that data. This means that if a user always checks the weather before going for a run, the app might start recommending running routes based on the user's location and weather conditions.

Other weather apps are using personalized recommendations to suggest clothing and accessories based on the weather conditions. By analyzing the temperature, humidity, and other factors, these apps can suggest the best type of clothing to wear for the current weather conditions, as well as for the rest of the day.

Overall, personalized recommendations are a powerful tool in weather app design, helping to improve the user experience and make weather information more relevant to individual users.