דוקטורנטים מספרים - Yoav Rubin
High Resolution Humidity Measurements from the Cellular Network
Supervisors: Prof. Pinhas Alpert and Prof. Dorita Rostkier-Edelstein
Atmospheric humidity is an integral part of a variety of environmental processes. The water vapor evaporation and condensation cycles play a cardinal role in the earth's energy budget by transferring heat from the surface to the atmosphere and vice-versa; thus, they have a major impact on extreme rainfall events.
Weather forecasting relies heavily on atmospheric numerical weather prediction (NWP) models, the accuracy of which is largely determined by the quality of initial conditions. Humidity, in particular, is vital to the model initialization. Increasing the number of humidity measurements would likely improve rain forecast accuracy and allow us to examine the effects of different land covers on humidity in the local environment, and therefore on the weather.
I’m Yoav Rubin, a PhD student in the Geophysics Department at the Porter School of Environment and Earth Sciences at Tel Aviv University. In my research I am studying and developing an innovative technique for taking humidity measurements in high resolution. This technique is based on the cellular network, and more specifically on Commercial Microwave Links (CMLs), which are widely deployed and cover vast areas - including those that are not covered by meteorological stations. Therefore, CMLs enable us to gauge real-time humidity in high resolution and provide a better assessment of the land cover effect on near-surface humidity.
My work focuses on the significant influence of land cover on atmospheric surface humidity, a variable that can be well quantified using the proposed CMLs approach.
In my Ph.D. I aim to study the effect of different land covers, such as forests, agriculture fields and urban areas, on the near-surface humidity. The main motivation for my research is to get a better assessment of the land cover effect on the humidity field and later to assimilate high-resolution humidity measurements from the CMLs into NWP models.
With proper development, this technique will allow nowcasting and forecasting of extreme weather events on local scales such as floods, heavy rains, and hail. Subsequently, this will enable better and faster decision-making processes in agriculture, aviation, and environmental readiness – which could save lives, make better use of resources, and lead to improved responses.