Ground-based microwave radiometry is a common tool to estimate profiles of the atmosphere. With a high temporal resolution radiometers have became an alternative to atmospheric sounding like radiosondes. However remote sensing radiometry requires the use of inversion algorithms, where methods like linear-, quadratic-regression or Artificial Neural Network are commonly used. The present study implements a Bayesian inversion technique as alternative to the state-of-the-art retrieval algorithms provided by the radiometer’s manufacturer firmware. The Bayesian inversion provides advantages over other established methods, namely: the use of a-priori suited for the specific climatology under observation, the estimation of the most likely profile along with its uncertainty obtained from the posteriori distribution, and the feasibility to add synergistic observations from other instruments to increase retrieval capabilities. To estimate the uncertainties resulting from the Bayesian and firmware retrieval algorithms, synthetic radiometer data have been created by means of radiative transfer simulations using radiosonde profiles as descriptor of atmospheric states. These synthetic data mimics the instrument’s firmware binary files letting the radiometer to perform retrievals as real measurements. By analyzing the differences from retrieval results relative to the known true profile we assess uncertainty metrics to characterize the algorithms. It has been found that Bayesian inversion reproduces more accurately the profile vertical structure as compared to the firmware, specially for humidity profiles. Absolute errors have been strongly reduced mainly at the lower atmosphere. The study concludes that Bayesian inversion for ground-based atmospheric profiling produces results resembling observations by radiosondes when a suitable a-priori distribution is used.

The Global Precipitation Measurement core satellite (GPM) has been collecting high quality precipitation data since 2014 over the globe with its Dual-frequency Precipitation Radar (DPR; Ku-band and Ka-band). Specificly over Germany, GPM provides data with typically two daily overpasses. Thus providing a unique opportunity to have a satellite based standard for estimation of precipitation in order to compare and evaluate ground-based radar network counterpart products. The German national weather service (DWD, Deutscher Wetterdienst) provides precipitation observations from its operational radar network RADOLAN as a composite products derived from 17 dual-pol C-band radars. The RADOLAN (RY) regular products are Germany-wide composites of precipitation estimates based on a set of precipitation type dependent Z-R relationships derived for liquid hydrometeors applied to radar reflectivity after clutter- and beam blockage-corrections. In this contribution we focus to compare three years of GPM DPR and RADOLAN precipitation products. This allows us to evaluate at which extend these two Near Surface products are consistent when observed from different geometries and obtained by independent instruements and retrieval methods. We quantify the uncertainties when directly comparing the DPR near surface product with RY. It is shown that a direct comparisons might not take into account a set of uncertainties originated from the scans geometry from DPR and RADOLAN, precipitation types, and sampling volumes. Therefore we suggest an adjusted DPR product, which is extracted from the DPR vertical profiles and adapted to fit the specific RY measurement configuration e.g. scans height and beam width. This allows a much more detailed classification of the hydrometoer phases per measuring volume, which we define as non-uniform phase beam filling (NPBF). The NPBF gives information about the ratio of liquid, solid or mixed hydrometeors in a given volume. Orographic, synoptic, microphysical influences as well as NPBF effects are examined and their uncertainties introduced on a direct comparison of satellite with ground-based producs are put into consideration. The adaptation of the DPR precipitation products to the specific scan geometry of the individual ground radars improves the correlation and reduce the RMSE.