  The 5G band allocated in the 26 GHz spectrum referred to as 3GPP band n258,
has generated a lot of anxiety and concern in the meteorological data
forecasting community including the National Oceanic and Atmospheric
Administration (NOAA). Unlike traditional spectrum coexistence problems, the
issue here stems from the leakage of n258 band transmissions impacting the
observations of passive sensors (e.g. AMSU-A) operating at 23.8 GHz on weather
satellites used to detect the amount of water vapor in the atmosphere, which in
turn affects weather forecasting and predictions. In this paper, we study the
impact of 5G leakage on the accuracy of data assimilation based weather
prediction algorithms by using a first order propagation model to characterize
the effect of the leakage signal on the brightness temperature (atmospheric
radiance) and the induced noise temperature at the receiving antenna of the
passive sensor (radiometer) on the weather observation satellite. We then
characterize the resulting inaccuracies when using the Weather Research and
Forecasting Data Assimilation model (WRFDA) to predict temperature and
rainfall. For example, the impact of 5G leakage of -20dBW to -15dBW on the
well-known Super Tuesday Tornado Outbreak data set, affects the meteorological
forecasting up to 0.9 mm in precipitation and 1.3 {\deg}C in 2m-temperature. We
outline future directions for both improved modeling of 5G leakage effects as
well as mitigation using cross-layer antenna techniques coupled with resource
allocation.
