# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 2 Material and Methods
### Weather Datasets  
The rapidly growing number of weather datasets, as well as continuous improvements in technology and public availability, have enabled scientists to dive into the details of sophisticated weather models and their mathematical and physical background. A more universal approach to this issue has been adopted by the Copernicus Climate Change Service (C3S) society, who provide authoritative information about the past, present and future climate in Europe and the rest of the world. The aim of the society is to produce and deliver Regional ReAnalysis (RRA) weather data for Europe. The reanalysis combines model data with observations, into a complete and consistent dataset using the laws of physics. The Copernicus European Regional Reanalysis (CERRA) datasets provide spatially and temporally consistent historical reconstructions of the meteorological variables in the atmosphere and at the surface. CERRA is the generic name for the following three datasets [16]:  
* CERRA: the high-resolution dataset produced at a horizontal resolution of 5.5 km containing atmospheric and surface variables.
* CERRA-EDA: the dataset contains atmospheric and surface variables produced at a horizontal resolution of 11 km by a 10-member ensemble of data assimilation systems.
* CERRA-Land: this dataset contains precipitation and soil variables available at a 5.5 km grid spacing.  
These datasets provide reanalysis data on model levels for Europe from 1984 to the present. Indeed, CERRA is the most suitable one for our experimental study, as it contains the surface variables that we need to integrate into our learning procedure. Conceptually, it belongs to the category of lower-resolution datasets.  
On the other hand, the German National Weather Agency (ger. Deutscher Wetterdiens, DWD) [17] offers access to many climate data from the Climate Data Center (CDC) [18] for further processing. Moreover, the same agency also offers several Numerical Weather Prediction (NWP) models, like [19]: the global ICOsahedral Nonhydrostatic (ICON), and two regional ICON-EU and ICON-D2. ICON defines the domain that covers the global region including most of the Atlantic Ocean, Greenland, and Europe, extending a little bit to the east and south with a horizontal resolution of 13 km. ICON-EU enables weather prediction in regions inside ICON, which serve as the initial conditions to this \"local nest\" model with a horizontal resolution of about 7 km. In this way, it covers Europe and extends to a small area in Africa, and also to the east. For the purpose of creating an higher-resolution weather model, ICON-D2 Regional NWP (RNWP) was created, with a horizontal resolution of about 2.2 km, which provides new 48 h forecasts every 3 h [19]. This high-resolution weather dataset is available for Central Europe, including Slovenia [20].  
Moreover, the ICON-D2 weather dataset has much better horizontal resolution than its predecessors. The details including land use, a digital elevation model, and other terrestrial data are crucial to capture the local weather circulations, including turbulence and local advection. Convective clouds have much better local precision with more detailed orography, and can simulate precipitation more accurately, including thunderstorms which are very local in nature.  
\begin{table}
\begin{tabular}{c c} \hline
**Measuring Characteristics** & **Values** \\ \hline Measurement range & \(-40\)\({}^{\circ}\)C to \(75\)\({}^{\circ}\)C (\(-40\)\({}^{\circ}\)F to \(167\)\({}^{\circ}\)F) \\ Accuracy & \(\pm 0.25\)\({}^{\circ}\)C from \(-40\)\({}^{\circ}\)C to \(0\)\({}^{\circ}\)C (\(\pm 0.45\)\({}^{\circ}\)F from \(-40\)\({}^{\circ}\)F to \(32\)\({}^{\circ}\)F) \\ Resolution & \(0.02\)\({}^{\circ}\)C \\ Operating temperature range & \(-40\)\({}^{\circ}\)C to \(75\)\({}^{\circ}\)C (\(-40\)\({}^{\circ}\)F to \(167\)\({}^{\circ}\)F) \\ \hline \end{tabular}
\end{table}
Table 4: Characteristics of relative humidity sensor [15].