Errors in station location can arise due to several factors, including inaccuracies in GPS signals, interference caused by multipath or atmospheric conditions, poor satellite geometry, errors with receiver clocks, or even deliberate interference. These variables can result in inaccuracies when calculating the precise position of a GPS receiver.
For location coordinates, noise can contribute to inaccuracies, causing slight variations or shifts in the reported location as shown in Figure 7. In the case of 2-meter surface temperature data, noise can introduce errors or biases into temperature readings, making it difficult to identify accurate temperature patterns and trends. This can impact the reliability of weather forecasts, climate studies, and other applications that rely on precise temperature data.
4.2 Data Reliability and ERA Datasets
Owing to the relatively flat gradient of the locations of study within Frankfurt and the high number of available weather stations there, we managed to obtain surface temperature measurements of relatively high accuracy within the city. In anticipation of the inherently random nature of temperature variations, the simulated noise levels were deemed necessary in accounting for this unpredictability, and indeed we were able to generate correlation values for each location at a given level of noise. However, as we extracted our chosen data solely from the ERA5 dataset, the accuracy of our analysis may have had the opportunity to improve in significant degrees if the dataset’s land-based counterpart, ERA5-Land (Gleixner et al., 2020), was used in conjunction with the data obtained in this study. This would have allowed for the potential identification of any non-surface variables, such as cloud cover, for any potential influence on surface temperature. This also may have potentially mitigated any significant lack of clarity in the data resulting from the coarse resolution of the ERA5 dataset (Gleixner et al., 2020).
We assess the synthetic dataset's precision, dependability, and general robustness to improve the data quality assessment. A range of factors were introduced to evaluate the degree to which the data quality can be enhanced and maintained, including station movements, errors, and noise. The analysis in Figure 3 helps us pinpoint which locations were most sensitive or responsive to the introduction of noise, providing insights into how noise affects the correlation between temperature data and location coordinates for different weather stations. Noise, in this sense, refers to the unpredictable variations in the temperature data. For example, if location 1 has a correlation of 0.9633642, it means there is a strong positive variable (temperature) being measured at location 1 with low noise or random variability in the data.