MMWAP - MilliMeter Wave Atmospheric Propagation- advancing microphysical-radiative modeling, statistical characterization and neural-network prediction of cloud attenuation and emission

Abstract

Usual theoretical models for signal propagation and emission in cloudy and rainy atmosphere are available and tested up to 50 GHz (Q band), Microphysical issues arise when considering MMW wavelengths at V and W band due to the resonant Mie scattering effects due to cloud droplets and hydrometeor particles on signal amplitude. The radiative transfer integro-differential equation is considered a general framework where both extinction and multiple scattering can be treated, even though only via numerical solutions in complex cases. Moreover, microphysical-radiative numerical models need to be parameterized and tuned to local climatologicy in order to be representative of measured statistics of tropospheric extinction and emission. Verification of tropospheric signal attenuation and sky noise temperature models and statistical behavior at MMWs is quite cumbersome since it requests space transmitters at W band within dedicated campaigns. The latter are quite costly and rare, even though recently cubesat spacecraft payloads are becomingavailable. From ground, a very promising approach to test W band model-based predictions is the MMW Sun-tracking radiometry using the Sun as natural signal emitter. Ground-based MMW Sun-tracking campaigns have been carried out and-or are running mainly in the US and in Italy, but the acquired data sets have still to be fully exploited for testing the model-based propagation predictions at MMWs.

Document Details

Document Type
DoD Grant Award
Publication Date
Jan 04, 2023
Source ID
FA86552217171

Entities

People

  • Frank S. Marzano

Organizations

  • Air Force Office of Scientific Research
  • Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome
  • United States Air Force

Tags

Readers

  • Atmospheric Remote Sensing.
  • Neural Network Machine Learning.
  • Radar Systems Engineering.

Technology Areas

  • 5G
  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space