Title: Temperature and water vapor mixing ratio profiles retrieved from the MWR. Location: Orange Site during the PERDIGAO Field Campaign Lat/Lon: 39.713 degN, -7.736 degE Date updated: 2 March 2018 Contacts: Dave Turner, NOAA (dave.turner@noaa.gov) Petra Klein, Univ Oklahoma (pkklein@ou.edu) Tyler Bell, Univ Oklahoma (tyler.bell@ou.edu) --- Background The AERIoe algorithm (Turner and Loehnert 2014) retrieves profiles of temperature and water vapor mixing ratio, together with cloud properties for a single-layer cloud (i.e., LWP, effective radius), from AERI-observed infrared radiance spectrum. Version 2 of this algorithm (Turner and Blumberg 2018) is able to incorporate other observations into the retrieval, such as microwave radiometer (MWR) radiance observations. The data can be used to characterize the evolution of the planetary boundary layer and boundary layer clouds. The inputs were modified to essentially make this a MWR-only retrieval by not including any radiance observations from the AERI that provide sensitivity to the profile of temperature or humidity. Thus, this retrieval is essentially equivalent to the MWRoe retrieval in Blumberg et al. (2015). Before the retrieval could be performed, the systemmatic error in the MWR's observed radiance data was analyzed. Tyler Bell, a graduate student at the University of Oklahoma, used colocated radiosonde data from the Orange Site as input into the MonoRTM radiative transfer model (which is the forward model used for the microwave radiance portion of AERIoe) to determine bias offsets for all 14 channels of the MWR that is part of CLAMPS-1. These bias values were removed before the AERIoe retrieval was performed. This dataset was collected at the "Orange Site" in Portugal during the PERDIGAO 2017 field campaign. The retrieval was run at 15-minute resolution, but retrievals can be performed at the maximum temporal resolution of the MWR instrument (order 2 min) -- if this higher temporal resolution is desired, please contact Dave Turner. This is a physical-iterative retrieval method. The retrieval of thermodynamic profiles from spectral microwave radiance observations is an ill-posed problem, and thus constraints need to be included in the retrieval algorithm to provide physically plausible results. Here, we use a climatology of 1571 radiosonde profiles collected Lisbon Portugal during the late spring/early summer as our prior information in an optimal estimation framework. As the method uses an optimal estimation framework, a full error covariance matrix of each solution is included in the output file. The 1-sigma uncertainty of each retrieved variable, which is derived from the error covariance matrix, is included for each scientific field and is named "sigma_X", where "X" is the name of the scientific field (e.g., 'temperature'). The information content in the AERI observations, which is in the "dfs" field, on the thermodynamic profiles is primarily concentrated in the lowest 3 km or up to cloud base; the retrieved data should not be used above that level (or used with caution). There is an overall "qc_flag" field that is set when a retrieval should not be trusted. However, the logic that sets this flag didn't work correctly, and it should not be used. Instead, consider all retrievals that have a value for "converged_flag" greater than 0 and less than 9 as valid. If you have any questions, contact Dave Turner. Normally, numerical weather prediction (NWP) model output is used as an additional observation in the retrieval in order to constrain the middle-to-upper troposphere (i.e., above 4 km AGL). However, I did not have NWP output handy, so I used temporally interpolated radiosonde observations from the Orange Site above 4 km for this purpose. --- Version information This is Release_2_7 of the AERIoe algorithm. It was run without significant AERI radiance observations, and thus is essentially identical to the MWRoe retrieval algorithm. --- Details on the MWR The MWR (microwave radiometer) is a hardened, operational 14-channel microwave radiometer that was developed by Radiometer Physics GmbH (RPG). RPG calls this MWR a Humidity and Temperature Profiler (HATPRO; Rose et al. 2005). It has 7 channels that observe downwelling radiance between 22.2 and 31.4 GHz (K-band; water vapor line and window region) and between 52 and 60 GHz (V-band; oxygen absorption lines). The K-band and V-band channels were calibrated by viewing a liquid nitrogen target before PERDIGAO to calibrate an internal noise diode transfer standard. A post-experiment calibration confirmed that the calibration did not drift during the campaign. The MWR used in this field campaign was part of the Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS-1; Wagner et al. 2018), which was developed by the University of Oklahoma and the NOAA National Severe Storms Laboratory. The AERIoe (really MWRoe) retrievals were performed by Dave Turner. --- References Blumberg, W.G., D.D. Turner, U. Loehnert, and S. Castleberry, 2015: Ground-based temperature and humidity profiling using spectral infrared and microwave observations. Part 2: Actual retrieval performance in clear sky and cloudy conditions. J. Appl. Meteor. Clim., 54, 2305-2319, doi:10.1175/JAMC-D-15-0005.1. Rose, T., S. Crewell, U. Löhnert, and C. Simmer, 2005: A network suitable microwave radiometer for operational monitoring of the cloudy atmosphere. Atmos. Res., 75, 183-200, doi:10.1016/j.atmosres.2004.12.005 Turner, D.D., and U. Loehnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based Atmospheric Emitted Radiance Interferometer (AERI). J. Appl. Meteor. Clim., 53, 752-771, doi:10.1175/JAMC-D-13-0126.1. Turner, D.D., and W.G. Blumberg, 2018: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE Selected Topics Appl. Earth Obs. Remote Sens., submitted. Wagner, T.J., P.M. Klein, and D.D. Turner, 2018: A new generation of ground-based mobile platforms for active and passive profiling of the boundary layer. Bull. Amer. Meteor. Soc., submitted --- END