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

Measuring rainfall is complex, due to the high temporal and spatial variability of precipitation, especially in a changing climate, but it is of great importance for all the scientific and operational disciplines dealing with rainfall effects on the environment, human activities, and economy. Microwave (MW) telecommunication links carry information on rainfall rates along their path, through signal attenuation caused by raindrops, and can become measurements of opportunity, offering inexpensive chances to augment information without deploying additional infrastructures, at the cost of some smart processing. Processing satellite telecom signals brings some specific complexities related to the effects of rainfall boundaries, melting layer, and nonweather attenuations, but with the potential to provide worldwide precipitation data with high temporal and spatial samplings. These measurements have to be processed according to the probabilistic nature of the information they carry. An ensemble Kalman filter (EnKF)-based method has been developed to dynamically retrieve rainfall fields in gridded domains, which manages such probabilistic information and exploits the high sampling rate of measurements. The paper presents the EnKF method with some representative tests from synthetic 3D experiments. Ancillary data are assumed as from worldwide-available operational meteorological satellites and models, for advection, initial and boundary conditions, and rain height. The method reproduces rainfall structures and quantities in a correct way, and also manages possible link outages. Its results are also computationally viable for operational implementation and applicable to different link observation geometries and characteristics.