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  /  5G   /  Bolstering Weather Forecast Model to mitigate the 5G Leakage
Weather Forecasts, 5G networks, 5G radiation, 5G leakage, Inaccurate weather Prediction

Bolstering Weather Forecast Model to mitigate the 5G Leakage

Existing 5G networks and towers are hampering the prediction in the traditional weather forecast models.

The sweltering conversation about weather forecast getting impaired due to the 5G network is hitting every corner of the world. Weather Forecast is a very reliable means to determine the changing climate around the world. Technologies like artificial intelligence, the Internet of Things, and data analytics are contributing to enhanced weather forecast models.

However, the existing 5G networks are hampering the prediction in weather forecasts. A report by the congressional research Service indicates the discrepancies in weather prediction due to 5G networks. The report also points out the disparity in codes and algorithms in weather forecast models due to the 5G network.

Neil Jacobs, the NOAA acting Under Secretary of Ocean and Atmosphere states that the implementation of 5G networks would result in 77% data loss of weather forecasts. Presently the report indicates an accumulated degradation of 30% forecasting data, which ultimately leads to an inaccurate forecast of hurricanes.

Environmental issues have been a peek concern amongst global leaders. Environmentalists and climate change activists have already warned about the several implications due to climate change. The discrepancies due to the 5G network add to the many perils the world has to face due to climate change.

That’s why the researchers at Rutgers University have formulated a model that can limit the complications of weather forecasting due to the radiations of the 5G network. The research paper titled, “Modeling the Impact of 5G Leakage on Weather Prediction” aids in understanding the impact of 5G leakage on the accuracy of data assimilation based weather prediction algorithms by using a first-order propagation model to characterize the effect of the leakage signal on the brightness temperature (atmospheric radiance) and the induced noise temperature at the receiving antenna of the passive sensor (radiometer) on the weather observation satellite. It also characterizes the resulting inaccuracies when using the Weather Research and Forecasting Data Assimilation model (WRFDA) to predict temperature and rainfall.

The paper states that the leakage of energy from the 5G bands into the 23.8 GHz Wave band perturbs the radiance of atmospheric thermal emissions which is measured by the passive sensors on the weather satellites, thereby lowering the validity and precision of weather forecast models.

The paper points out the proximity of the 23.8 Hz Wave band with the n258 band of the 5G network to be the primary reason for the leakage of 5G. Moreover, as the 23.8 GHz band has water vapor measurements in the atmosphere, resulting in the absorption of electromagnetic signals, this band is comparatively more sensitive to absorb radiation as compared to other bands.

Additionally, when 5G equipment is transmitting signals in bands near the 23.8 GHz frequency a passive sensor on a weather satellite measuring water vapor might be affected, resulting in erroneous data which further degrades the accurate weather predictions.

 

Improved Propagation Models for 5G Leakage and Induced Radiance

The researchers state that for a better estimate of the induced radiance due to the leakage of 5G signals on weather satellites, more detailed propagation models need to be used, in which the absorption loss of 5G signals as they pass through the atmosphere is considered. By knowing the absorption and transmittance coefficients of the atmosphere in any given geographical area, 5G signals power at the satellite sensors monitoring the atmosphere’s radiance can be estimated.

 

Spatial Density, Elevation, and Directionality

 

The researcher points out that the aggregate 5G leakage power into the 23.8 GHz waveband depends on the spatial density of the 5G transmitters in a given geographical area. That’s why the elevation of such transmitters from the earth’s surface and their directionality, the density of outdoor base stations, UEs, drones and outdoor IoT devices are needed to be assessed. For this, 5G equipment density distribution models can be evaluated to the aggregate number of devices in a grid of 48 square km, and estimation can be drawn about the total energy leakage in a particular direction near the location of weather satellites.

 

Cross-layering to mitigate 5G Impact on 23.8GHz

The researchers list the cross-layer PHY/antenna approach as the best, to spatially and spectrally mitigate 5G leakage. For spatial mitigation, with the help of techniques such as direct antenna modulation, the results seem promising, whereas for spectral mitigation of the 5G leakage technologies like filters must be enabled for promising outlook.

 

Improved Weather Forecasting Algorithms

The researchers observe that for the enhanced coexistence of 5G systems and reliable weather forecasting, an updated data assimilation model must be used for weather forecasting, which will aid in feasible adaption based on the dynamics of 5G systems in time, space, and frequency.

 

Conclusion

The researchers seem positive that the existing models and solutions will enhance the weather forecasting process and will mitigate the adverse implications due to 5G networks. Since both entities namely the weather forecasting model and 5G network towers are paramount with the diversifying globe, the mentioned approach will mitigate the correlated challenges.