Microwave Radiometer - UND Radiometrics MWR, Rufus - Raw Data

**Overview**

These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.

**Data Details**

All output files are named automatically using the following format:

yyyy-mm-dd_hh-mm-ss_xxx.csv,

where yyyy is the year when the file was started, mm is the month of the year, dd is the day of the month, hh is the hour of the day, mm is the minute of the hour, ss is the second of the minute, and xxx defines the output file type as follows:

  - xxx=lv0 level0 file  
  - xxx=lv1 level1 file  
  - xxx=lv2 level2 file  

All output files contain a sequential record number in the first field, starting with the number 1. All output files contain a date/time stamp in the second field of all records that contain time-dependent data.

lv0 files contain raw, unprocessed data in engineering units. lv0
files contain 100 percent of the information needed to reprocess the raw data with alternative calibration information or algorithms.

lv1 files contain real-time brightness temperatures (TB) for each channel specified in the configuration file. Real-time level1 files are produced from contemporaneous level0 data and calibration information in the configuration file.

lv2 files contain records of real-time retrievals of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) profiles. The retrievals are produced using the contemporaneous level1 data and the neural network files specified in the configuration file.

**Data Quality**

**NOAA/PSD: Wasco OR and Troutdale OR**

Microwave radiometers (MWRs) must be calibrated periodically, both for the K-band and V-band. The calibration is needed to convert measured voltages/counts into brightness temperatures (TB). Two types of calibrations are possible: the liquid nitrogen (LN2), or cold target one, and tipping curve calibration (TCC). All microwave channels (K-band and V-band) can be calibrated using LN2 as a cold absolute standard. The disadvantage of the LN2 calibration is that it requires several people onsite to perform. Conversely, the advantage of a TCC is that it can be performed remotely. However, a successful TCC requires a non-optically thick atmosphere at the frequency at stake. At approximately sea level, only K-band channels are transparent enough to be calibrated via this method. For this reason, trips to perform LN2 calibrations are scheduled approximately every six months. Also, after the LN2 calibrations have been performed, radiosonde were launched for sanity checks and will be used to test the calibrations' accuracy.

TCC calibrations also have been scheduled to occur remotely (more often than LN2 calibrations, approximately 1-2 months). This schedule for LN2 and TCC calibrations should ensure the quality and reliability of data collected with the MWRs because it depends on the instrument's thermal stability, noise level, and calibration accuracy (Solheim et al. 1998a). 

MWRs retrieve vertical profiles of atmospheric variables using historic radiosondes and a regression method or neural network (Solheim et al. 1998a, 1998b; Ware et al. 2003). The algorithm, based on a radiative transfer model (Rosenkranz 1998), was trained for all WFIP2-deployed MWRs by the Radiometrics staff on a multi-year radiosonde climatology from the sites' proximity.

All MWRs are equipped with surface observations of temperature, pressure, and relative humidity, which also were calibrated prior to the WFIP2 campaign. These surface observations are important because they serve as a boundary condition for the neural network approach. One quality control (QC) approach involves monitoring the good functioning of the surface sensor (comparing to collocated surface measurements from other met stations) and identifying periods of possible malfunctions. If this happens, the retrieved atmospheric profiles most likely would not be accurate, However, the level0 files (where the TB are saved) will be post-reprocessed (using software from Radiometrics) with corrected values of surface observations to re-retrieve the atmospheric profiles saved in the level2 files.

**CU: Condon OR**

Same as for the NOAA/PSD MWRs.

**UND: Rufus, OR**

Same as for the NOAA/PSD MWRs.

However, for this MWR, it was not possible to perform the LN2 calibration at the time of deployment (November 2015). Still, it is possible to post-reprocess the level0 files (where the TB are saved) with the information from the LN2 calibration performed in May 2016 to re-retrieve the atmospheric profiles saved in the level2 files from November 2015 to May 2016. This will be performed by the University of Notre Dame (UND) team members.

**IMPORTANT**: Due to the failure of the infrared temperature sensor (IRT) sensor on December 09, 2016, data do not have reliable liquid water profiles and cloud information since December 09, 2016. The IRT failure does **NOT** affect temperature, relative humidity, and water vapor profiles. 

End date for the UND MWR at Rufus, OR: 26 January 2017.

**Uncertainty**

Bianco L., K. Friedrich, J. Wilczak, D. Hazen, D. Wolfe, R. Delgado, S. Oncley, and J. K. Lundquist, 2016: Assessing the accuracy of microwave radiometers and radio acoustic sounding systems for wind energy applications, submitted to AMT.

Tags: 00, a2e, atmosphere-to-electrons, eere, microwave-radiometer, mwr, raw-data, und-radiometrics-mwr-rufus, weto, wfip2, wind, wind-forecast-improvement-project-2, z02
Organization: Department of Energy