# The Role of Data-Driven Methodologies in Weather Index Insurance
## 4 Methodology
### Data  
#### 4.1.1 Data Sources  
According to [27], the performance of a model can be affected by selecting a \"bad algorithm\" or \"bad data\"; in addition, for most machine learning algorithms, a considerable amount of data is needed for them to work well [27]. For the specific case developed, selection criteria were implemented to build a representative database of the problem. Additionally, some of the selected predictors were proven to be significant in previously developed research, such as the database selected by [PERSON] et al. [24].  
Satellite data: Regarding satellite data, the Google Earth engine was used. It is a cloud-based geospatial tool that allows for the analysis of satellite images. [PERSON] et al. [24] used a criteria to select data sources and to implement a tool that could be used in the context of parametric risk financing tools. These are summarized below:  
1. **Spatial resolution.** A precise spatial resolution that takes into account the different climatic characteristics of the area of interest is necessary to develop accurate parametric hedging products.  
Figure 3: Project M = methodology flowchart.  
2. **Frequency**. The datasets selected should be commensurate with the duration of the event of interest. In the case of floods, which are rapid phenomena, daily frequencies or lower temporalities are required.
3. **Spatial coverage**. Global coverage allows for the extension of the developed approach to areas other than the region of interest.
4. **Temporal coverage**. Since extreme events are infrequent, a spatial coverage of at least 20 years is necessary for correct model calibration.
5. **Latency**. A low latency time (i.e., time delay until the most recent data is obtained) is necessary to develop tools capable of identifying extreme events close to real-time.  
Based on the criteria, and restricting the search to databases available in the Google Earth engine, three precipitation databases and a database with a surface run-off variable, and four soil moisture levels were selected. The use of multiple databases allows researchers to improve the ability of the models in capturing extreme events [24]. The selected datasets for precipitation came from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Global Precipitation Measurement (GPM) (Figures 4-6). The CHIRPS data (Figure 4) shows the most extreme differences in precipitation levels. The central and north regions present lowest values (1000 mm per year). In the other hand, the east and southeast regions present the highest precipitation levels (5000 mm per year). The PERSIANN (Figure 5) and GPM (Figure 6) figures show the same configuration of precipitation values, but with different resolutions.  
On the other hand, for the run-off and soil moisture variable, the ERA5 dataset was used, which is produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The main characteristics of the datasets are shown in Tables 1 and 2.