# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 4 Experiments and Results  
The goal of our experimental work was to show that the Maximus can predict the air temperature at micro-locations using modern ML methods more accurately than by using general weather forecasting models. The prediction was performed using the LSTM NN. In line with this, various experiments were conducted with different LSTM topologies, hyper-parameter optimizations, and evaluation metrics. Three sources of the input weather data were taken into consideration during our experiments: (1) the lower-resolution CERRA dataset for LSTM learning, (2) the higher-resolution ICON-D2 dataset for evaluation and simulation, and (3) real weather data obtained from a mobile meteorological station capturing the current weather conditions at a specific micro-location.  
In this study, the air temperature was predicted for \(T=12\) h in the future using LSTM networks and various SW sizes. These networks are very complex for real applications due  
Figure 7: Maximus prediction of air temperature for a micro-location workflow.  
to a lot of hyper-parameters, various LSTM topologies, and even the quality and quantity of the training data. Indeed, all these issues in the application of the LSTM need to be tuned during the large experimental work.  
The experiments were run on a personal computer with the characteristics as shown in Table 5.  
The measurement site is equipped with a fully automated personal weather station. The station is installed on natural terrain, surrounded by grass and a garden, with the nearest village located several kilometers away and a lack of significant urban infrastructure. This rural setting minimizes anthropogenic influences on the recorded data. The station follows a fixed placement strategy to ensure the consistency and representativeness of atmospheric conditions: sensors are mounted according to manufacturer-recommended heights and clearances to avoid obstructions and localized bias. All sensors are calibrated before deployment using reference instruments, and periodic validation checks are conducted to ensure long-term accuracy. The station is configured for real-time monitoring, automatically recording meteorological parameters and transmitting data to a secure cloud platform at 10 min intervals. This setup enables continuous data availability and supports quality control procedures based on automated range and consistency checks. These details have been added to the revised Methods section to clarify the observational framework.  
In practice, the Maximus proposed methods were implemented as follows: At first, the sensors sent data via cable to the central unit. The central unit has ports open for specific sensors. When connected, it has LCDs, which show if the sensors have successfully connected and recognize them, as well as any errors that appear. The central unit has a 4G-supported SIM card inside (cellular network), which periodically, specifically around every 10 min, connects to the cloud and sends data written to the database for the last 10 min.  
Two experiments were conducted, as follows:  
* Searching for the Maximus optimal air temperature prediction model;
* Comparison of the Maximus optimal air temperature predicted by the indicated optimal prediction model with the reference based on the higher-resolution ICOND2 dataset.  
The experiments are described in detail in the remainder of this paper.