Author(s)
Angie Crews and Scott Palo
Abstract
Passive microwave radiometer datasets are critical to numerical weather prediction models (NWP), yet they are also inherently susceptible to RFI due to their measurement of low power natural emissions. In 2019, the ITU reallocated additional frequency bands to 5G applications that are adjacent to those commonly used by microwave radiometers, causing the potential for 5G out-of-band emissions and harmonics to impact microwave radiometer observations. In this work, we analyze on-orbit Advanced Technology Microwave Sounder (ATMS) datasets over urban areas with deployed 5G cellular networks. ATMS data is compared to truth data generated by the Community Radiative Transfer Model (CRTM), a fast radiative transfer model developed by NOAA’s Joint Center for Satellite Data Assimilation (JCSDA). Brightness temperatures are generated in CRTM using the sensor’s spectral response data as well as atmospheric and surface properties from ERA5, a reanalysis dataset generated by ECMWF. We calculate the observation-minus-background (O-B) bias using ATMS on-orbit observations and CRTM ground truth and then statistically analyze the results. Here we show initial results and analysis of on-orbit ATMS data for the Denver metropolitan area.