# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 1 Introduction  
Generally, air temperature forecasts have improved enormously in recent years, but there are still large variations at micro-locations where the differences in air temperatures can be huge. To build more accurate forecast models for such micro-locations, it is necessary to have professional meteorological stations, measurements from drones, satellites, weather balloons and to update the weather models as often as possible with new data, as this is the only way to detect sudden changes in the weather at a particular micro-location and its surrounding area. Unfortunately, there are currently too few meteorological stations to improve the built weather model for air temperature forecasts at each micro-location. This prediction can only be performed at those micro-locations where we have good-quality and frequent meteorological data obtained from professional meteorological stations. The forecasting of various parameters at micro-locations or specific limited geographical areas has been explored in several research studies. For instance, [PERSON] et al. [1] addressed the challenge of predicting fine dust levels at specific micro-locations where observatories are absent, relying on datasets collected from meteorological stations. Their study proposed an innovative strategy that incorporates micro-location data, specifically latitude andlongitude, to enhance prediction accuracy. Similarly, [PERSON] et al. [2] introduced a novel approach that combines hyperlocal weather prediction with anomaly detection using the Internet of Things (IoT) sensor networks and advanced Machine Learning (ML) techniques. IoT sensors are hardware components capable of bridging the physical and digital worlds. They are seamlessly connected to the Internet, enabling automation, predictive maintenance, and data-driven insights. These sensors represent building blocks for establishing modern concepts, like Industry 5.0 [3] and Agriculture 5.0 [4]. By leveraging data from multiple spatially distributed but relatively close locations, their method produces high-resolution weather models capable of predicting short-term localized conditions such as temperature, pressure, and humidity. An interesting perspective is presented by [PERSON] et al. [5], who developed a transformative model employing a mechanism to extrapolate location embeddings from data-rich regions to target locations with limited training data. This approach enables accurate forecasting for previously untrained areas, demonstrating the potential of advanced spatial modeling techniques to address data scarcity. Apart from the work of [PERSON] and [PERSON] [6], we distinguish between short-term quality and reliable weather data obtained from weather stations, as well as long-term dependencies inherited within sensor networks [7; 8]. Nowadays, predicting air temperature is typically based on weather datasets that vary between each other in the spacial resolution of weather data referring to a specific region. Indeed, we distinguish between lower- and higher-resolution weather datasets, where the former include weather data from relatively large regions (e.g., 5.5 km of spacial resolution), while the latter are more detailed (e.g., less than 2.5 km of spacial resolution). On the other hand, the lower-resolution datasets include long-term historical weather data (i.e., in more than a 40 year period) sampled at one hour intervals, while the higher-resolution datasets incorporate short-term history weather data sampled in the last two years at three hour intervals. In line with this, a micro-location refers to the highest resolution dataset including current weather data of the specific location (i.e., 0 km of spacial resolution) that needs to be obtained from a mobile weather station. Let us mention that micro-meteorological elements, like dew and scintillation in the lower layers of the troposphere, are not included in the study explicitly because they are indirectly already included in the prediction model [9].