An EnKF-based method to produce rainfall maps from simulated satellite-to-ground MW-link signal attenuation

Area of Science:

• Atmospheric Science and Meteorology
• Remote Sensing
• Signal Processing

Background:

• Accurate rainfall measurement is crucial but challenging due to precipitation's high variability, especially in a changing climate.
• Microwave (MW) telecommunication links offer a cost-effective opportunity to measure rainfall by analyzing signal attenuation caused by raindrops.
• Processing these signals presents complexities including rainfall boundaries, melting layers, and non-weather attenuations.

Purpose of the Study:

• To develop and present an Ensemble Kalman Filter (EnKF)-based method for dynamically retrieving rainfall fields.
• To leverage the high sampling rate of MW telecommunication link measurements for improved precipitation data.
• To demonstrate the method's capability in managing probabilistic information and potential link outages.

Main Methods:

• Utilized an Ensemble Kalman Filter (EnKF) approach to process signal attenuation data from microwave telecommunication links.
• Integrated ancillary data from meteorological satellites and models for advection, initial/boundary conditions, and rain height.
• Employed synthetic 3D experiments to test and validate the EnKF method's performance.

Main Results:

• The EnKF-based method successfully reproduced rainfall structures and quantities with accuracy.
• The system effectively managed probabilistic information inherent in the measurements and handled potential link outages.
• The approach demonstrated computational viability for operational implementation across various link geometries.

Conclusions:

• The developed EnKF method provides a robust and computationally efficient way to retrieve rainfall fields using opportunistic microwave link data.
• This technique enhances precipitation monitoring capabilities, offering high temporal and spatial resolution data.
• The method is adaptable and suitable for operational deployment, augmenting existing meteorological observation networks.