**Overview** Wind Profiler with Radio Acoustic Sounding System (RASS) data. **Data Quality** Automatic quality control (QC) followed by visual manual QC.

**Overview** **Winds** A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities are remotely sensed along all beam directions and combined to derive the horizontal wind field over the radar. These data typically are sampled and averaged hourly and usually have 6-m and/or 100-m vertical resolutions up to 4 km for the 915 MHz and 8 km for the 449 MHz systems. **Temperature** To measure atmospheric temperature, a radio acoustic sound system (RASS) is used in conjunction with the wind profile. These data typically are sampled and averaged for five minutes each hour and have a 60-m vertical resolution up to 1.5 km for the 915 MHz and 60-m up to 3.5k m for the 449 MHz. **Data Quality** Various quality control (QC) algorithms developed over the years process data in real time on the radar software layer. These algorithms, which run in real time, act on time-series, spectra, moment, and consensus data layers that are persisted in various forms. For a detailed description, refer to the attached QC document: *915 and 449 MHz Radar Wind Profilers and RASS QC*.

**Overview** **Winds** A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities are remotely sensed along all beam directions and combined to derive the horizontal wind field over the radar. These data typically are sampled and averaged hourly and usually have 6-m and/or 100-m vertical resolutions up to 4 km for the 915 MHz and 8 km for the 449 MHz systems. **Temperature** To measure atmospheric temperature, a radio acoustic sound system (RASS) is used in conjunction with the wind profile. These data typically are sampled and averaged for five minutes each hour and have a 60-m vertical resolution up to 1.5 km for the 915 MHz and 60-m up to 3.5k m for the 449 MHz. **Data Quality** Various quality control (QC) algorithms developed over the years process data in real time on the radar software layer. These algorithms, which run in real time, act on time-series, spectra, moment, and consensus data layers that are persisted in various forms. For a detailed description, refer to the attached QC document: *915 and 449 MHz Radar Wind Profilers and RASS QC*.

**Overview** Wind Profiler with Radio Acoustic Sounding System (RASS) data. **Data Quality** Automatic quality control (QC) followed by visual manual QC.

**Overview** **Winds** A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities are remotely sensed along all beam directions and combined to derive the horizontal wind field over the radar. These data typically are sampled and averaged hourly and usually have 6-m and/or 100-m vertical resolutions up to 4 km for the 915 MHz and 8 km for the 449 MHz systems. **Temperature** To measure atmospheric temperature, a radio acoustic sound system (RASS) is used in conjunction with the wind profile. These data typically are sampled and averaged for five minutes each hour and have a 60-m vertical resolution up to 1.5 km for the 915 MHz and 60-m up to 3.5k m for the 449 MHz. **Data Details** Spectra data are stored in two daily files, a header (file names contain "H") and a data (file names contain "D") file. The (H)eader files are made up of binary data records containing information about the operational parameters of the profiler, while (D)ata files, also composed of binary data records, contain the spectra data collected by the profiler, i.e. spectral values for each spectral bin for every range gate. **Data Quality** Various quality control (QC) algorithms developed over the years process data in real time on the radar software layer. These algorithms, which run in real time, act on time-series, spectra, moment, and consensus data layers that are persisted in various forms. For a detailed description, refer to the attached QC document: *915 and 449 MHz Radar Wind Profilers and RASS QC*. **Uncertainty** The uncertainty is defined by the spacing of the spectral bin.

**Overview** Wind Profiler with Radio Acoustic Sounding System (RASS) data. **Data Quality** Automatic quality control (QC) followed by visual manual QC.

**Overview** Wind Profiler with Radio Acoustic Sounding System (RASS) data. **Data Quality** Automatic quality control (QC) followed by visual manual QC.

**Overview** **Winds.** A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities are remotely sensed along all beam directions and are combined to derive the horizontal wind field over the radar. These data typically are sampled and averaged hourly and usually have 6-m and/or 100-m vertical resolutions up to 4 km for the 915 MHz and 8 km for the 449 MHz systems. **Temperature.** To measure atmospheric temperature, a radio acoustic sounding system (RASS) is used in conjunction with the wind profile. These data typically are sampled and averaged for five minutes each hour and have a 60-m vertical resolution up to 1.5 km for the 915 MHz and 60 m up to 3.5 km for the 449 MHz. **Moments and Spectra.** The raw spectra and moments data are available for all dwells along each beam and are stored in daily files. For each day, there are files labeled "header" and "data." These files are generated by the radar data acquisition system (LAP-XM) and are encoded in a proprietary binary format. Values of spectral density at each Doppler velocity (FFT point), as well as the radial velocity, signal-to-noise ratio, and spectra width for the selected signal peak are included in these files. Attached zip files, *449mhz-spectra-data-extraction.zip* and *449mhz-moment-data-extraction.zip*, include executables to unpack the spectra, (GetSpectra32.exe) and moments (GetMomSp32.exe), respectively. Documentation on usage and output file formats also are included in the zip files. **Data Details** Note, the b0 data is identical to 00 data but a netcdf extraction of the b0 data was also created for the duration of the WFIP2 campaign. **Data Quality** Various quality control (QC) algorithms developed over the years process data in real time on the radar software layer. These algorithms, which run in real time, act on time-series, spectra, moment, and consensus data layers that are persisted in different forms.

**Overview** These profiling lidar datasets collect profiles of wind speed and wind direction from nominally 40 m above the surface to 220 m above the surface, depending on visibility. **Data Quality** Only data points with CNR -22 dB are included in these 2-min averaged files.

**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