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Innovative Mini Ultralight Radioprobes to Track Lagrangian Turbulence Fluctuations within Warm Clouds: Electronic
Miryam E Paredes Quintanilla1, Shahbozbek Abdunabiev1, Marco Allegretti1
1Politecnico di Torino, Department of Electronics and Telecommunications (DET), Corso Duca Degli Abruzzi 24, 10129 Torino, Italy.
Sensors (Basel, Switzerland)
|March 6, 2021
Summary
New ultralight radioprobes accurately measure small-scale turbulence within clouds. This advancement improves weather forecasting and climate modeling by providing crucial data on atmospheric turbulence and its impact on cloud formation.
Area of Science:
- Atmospheric Science
- Cloud Physics
- Turbulence Research
Background:
- Cloud dynamics are crucial for weather and climate models but are difficult to study at small scales.
- Understanding turbulence mechanisms within clouds is essential for accurate forecasting.
- Current methods struggle to measure fine-scale turbulence in situ.
Purpose of the Study:
- To develop and validate an experimental method for in situ measurement of small-scale turbulence in clouds.
- To create an in-field cloud Lagrangian dataset using innovative ultralight radioprobes.
- To investigate the influence of turbulence on cloud formation.
Main Methods:
- Design and implementation of compact, lightweight, expendable mini-radioprobes (≈30 cm diameter, ≈20 g).
- Customized mini-radioprobe board (5 cm × 5 cm) with sensors for local fluctuation measurement.
- Passive floating and real-time data transmission to ground stations.
Main Results:
- Laboratory and field experiments demonstrate successful probe performance.
- Probes provide accurate measurements of atmospheric turbulence, validated against external references.
- Data collected offers insights into small-scale turbulence within warm clouds.
Conclusions:
- The developed ultralight radioprobes offer a novel solution for in situ cloud turbulence measurement.
- This technology enhances the monitoring of atmospheric turbulence and its effects on cloud formation.
- The findings contribute to improved weather forecasting and climate modeling capabilities.

