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. Thermodynamic profiles are often retrieved from the multi-wavelengthbrightness temperature observations made by microwave radiometers (MWRs)using regression methods (linear, quadratic approaches), artificialintelligence (neural networks), or physical iterative methods. Regressionand neural network methods are tuned to mean conditions derived from aclimatological dataset of thermodynamic profiles collected nearby. Incontrast, physical iterative retrievals use a radiative transfer modelstarting from a climatologically reasonable profile of temperature and watervapor, with the model running iteratively until the derived brightnesstemperatures match those observed by the MWR within a specified uncertainty. In this study, a physical iterative approach is used to retrieve temperatureand humidity profiles from data collected during XPIA (eXperimentalPlanetary boundary layer Instrument Assessment), a field campaign held fromMarch to May 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility.During the campaign, several passive and active remote sensing instrumentsas well as in situ platforms were deployed and evaluated to determine theirsuitability for the verification and validation of meteorological processes.Among the deployed remote sensing instruments were a multi-channel MWR aswell as two radio acoustic sounding systems (RASSs) associated with 915and 449 MHz wind profiling radars. In this study the physical iterative approach is tested with differentobservational inputs: first using data from surface sensors and the MWR indifferent configurations and then including data from the RASS in theretrieval with the MWR data. These temperature retrievals are assessedagainst co-located radiosonde profiles. Results show that the combination of the MWR and RASS observations in the retrieval allows for a more accurate characterization of low-level temperature inversions and that these retrieved temperature profiles match the radiosonde observations better thanthe temperature profiles retrieved from only the MWR in the layer betweenthe surface and 3 km above ground level (a.g.l.). Specifically, in this layerof the atmosphere, both root mean square errors and standard deviations ofthe difference between radiosonde and retrievals that combine MWR and RASSare improved by mostly 10 %–20 % compared to the configuration that does notinclude RASS observations. Pearson correlation coefficients are alsoimproved. A comparison of the temperature physical retrievals to themanufacturer-provided neural network retrievals is provided in Appendix A.

Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations