The Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS dataset consists of brightness temperature measurements collected by the Advanced Microwave Precipitation Radiometer (AMPR) onboard the NASA ER-2 high-altitude research aircraft. AMPR provides multi-frequency microwave imagery, with high spatial and temporal resolution for deriving cloud, precipitation, water vapor and surface properties. These measurements were taken during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Data files are available from January 18, 2020 through February 28, 2022 in netCDF-4 format.

The Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS dataset consists of brightness temperature measurements collected by the Advanced Microwave Precipitation Radiometer (AMPR) onboard the NASA ER-2 high-altitude research aircraft. AMPR provides multi-frequency microwave imagery, with high spatial and temporal resolution for deriving cloud, precipitation, water vapor, and surface properties. These measurements were taken during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Data files are available from December 16, 2019, through March 2, 2023, in netCDF-4 format.

The SBU Micro Rain Radar 2 (MRR-2) IMPACTS dataset consists of reflectivity, Doppler velocity, signal-to-noise ratio, spectral width, droplet size, Liquid Water Content, melting layer, drop size distribution, rain attenuation, rain rate, and radial velocity data collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2022). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. Both the MRR-2 and the MRR-PRO instruments were used to collect data for this dataset. The dataset files are available from January 1 through February 28, 2020 in netCDF-3 and netCDF-4/CF formats.

The SBU Parsivel IMPACTS dataset consists of precipitation data collected by the Parsivel disdrometer in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Parsivel disdrometer data include particle size distribution, fall speed, radar reflectivity, and precipitation rate. The dataset files are available in netCDF-3 format from January 1, 2020, through March 2, 2023.

**Overview** Reviewed dataset that also includes post-reprocessed level1 and level2 data files from November 2015 to May 2016 (refer to "Additional Information"). Monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km. **Data Details** A detailed overview of the mwr data already is associated to mwr.z03 and the raw dataset mwr.z02.00. Because it was not possible to perform the LN2 calibration at the time of the deployment (November 2015), the University of Notre Dame team has post-reprocessed the level0 files (where the brightness temperatures are saved) with information from the LN2 calibration performed in May 2016 to re-retrieve the atmospheric profiles saved in the Level 2 files from November 2015 to May 2016. Therefore, the present dataset contains the post-reprocessed level1 and level2 data files from November 2015 to May 2016. After May 05, 2016, no post-reprocessing was needed. **IMPORTANT 1**: Due to the IRT sensor failure 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. **IMPORTANT 2**: Because of a bug in the Radiometrics reprocessing software, the first two profiles in each of the post-reprocessed Level 2 data files from November 2015 to May 2016 should be discarded. A new version of the data will be provided if and when Radiometrics provides a new version of the software. **Data Quality** Same as for mwr.z03. **Uncertainty** Same as for mwr.z03. **Constraints** Same as for mwr.z03.

**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

The SBU Mobile Sounding IMPACTS dataset consists of mobile sounding profiles collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Mobile sounding profiles were obtained about every three hours during snow events by the Stony Brook University (SBU). The sounding measures temperature, humidity, height, and horizontal wind direction and speed in the atmosphere. Atmospheric pressure is calculated from GPS height. Data files are available from January 18, 2020 through February 27, 2020 in netCDF-3 format.

The SBU Ka-band Scanning Polarimetric Radar (KASPR) IMPACTS dataset consists of polarimetric radar data collected by the Stony Brook University (SBU) Ka-band Scanning Polarimetric Radar (KASPR) during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2022). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. KASPR provided detailed observations of cloud and precipitation microphysics, specifically ice and snow processes. These data include reflectivity, mean velocity, spectrum width, linear depolarization ratio, differential reflectivity, differential phase, specific differential phase, co-polarized correlation coefficient, and signal-to-noise ratio. The dataset files are available from January 6, 2020 through February 26, 2020 in netCDF-4 format.

The SBU Parsivel IMPACTS dataset consists of precipitation data collected by the Parsivel disdrometer in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2022). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Parsivel disdrometer data include particle size distribution, fall speed, radar reflectivity and precipitation rate. The dataset files are available in netCDF-3 format from January 1 through February 27, 2020.

**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