This dataset contains over 14,000 hours of regional radar mosaics over the northeast US from 600+ winter storm days between 1996-2023. Winter storm days are defined when at least 2 out of 15 surface stations in the northeast US (see attached map and .csv) produced at least 1 inch of snow over the 24 hour period. Sequences of these mosaics aid in analyzing the precipitation area and the structures within winter storms. Radar reflectivity data is combined from the first, lowest (0.5 degree) elevation angle from 12 NEXRAD WSR-88D radars in the northeast US (see attached). The scans occur every 5-10 minutes from each radar depending on the radar scan settings. The time label of the regional map is based on the scan time central radar, KOKX (Upton, NY). Scans from other radars in the region are used for that time as long as they are within 8 minutes of the KOKX scan. The polar radar data from each radar is interpolated to a regional 1202 km x 1202 km Cartesian grid with 2 km grid spacing covering 35.73-46.8 degN and 66.36-81.85 degW. Where the radar domains overlap, we take the highest reflectivity value. For dates after dual-polarization integration (2012 onwards), files contain the correlation coefficient (RHO_HV) field and a binary field that can be used to “image mute” the reflectivity which reduces the visual prominence of melting and mixed precipitation commonly mistaken for heavy snow. Image muting is applied where radar reflectivity is ≥ 20 dBZ and RHO_HV is ≤ 0.97. This product is different from other widely used radar mosaics such as the MRMS produced by NOAA since it does not interpolate to a constant altitude and thus preserves the finer scale details in the reflectivity field. Because the data used to create these mosaics are not interpolated to a constant altitude, the altitude varies over the region (altitudes of radar scan used at each grid point are provided as a field for each data file). This data set is specifically designed to analyze fine-scale structures in winter storms.

Description of the data and file structure

Part 1 contains files pre-dual polarization integration (1996-2012)
Part 2 contains files post-dual polarization integration (2012-2023)

Files are zipped by year (i.e. 1996.zip, etc.). Part 1 contains 17 .zip files and part 2 contains 12 .zip files. expected_numer_of_files.xlsx lists the number of expected files in each .zip file.

File type: The files in this dataset are CF-1.8 compliant netCDF4 files.

Naming convention:

• Files are named with the following convention: YYYYMMDD_hhmmss_stitched.nc.
• For post dual-pol dates, files with available correlation coefficient data, and thus an image muted field, files are named as follows: YYYYMMDD_hhmmss_stitched_muted.nc

Combining data from multiple radars: See section 2.1 of Tomkins et al. (2022) for full details on how the data is combined from multiple radars.

Description of variables in each file:

• ref_max: Maximum reflectivity at each grid point
• alt_max: Altitude of max. reflectivity
• rho_max: Correlation coefficient corresponding to max. reflectivity (only available for post-dual pol dates)
• muted_mask: Boolean field, True where image muted (only available for post-dual pol dates)

Radars used in the Northeast US regional mosaics: KBGM, KBOX, KBUF, KCCX, KCXX, KDIX, KDOX, KENX, KGYX, KLWX, KOKX, KTYX (see map.png)

Central radar: KOKX

Regional map bounds: N: 46.8 deg, S: 35.73 deg, E: -66.36 deg, W: -81.85 deg (see map.png)

Sharing/Access information

Data was derived from the following sources:

• NOAA Next Generation Radar (NEXRAD) Level 2 Base Data accessed via Amazon Web Services (AWS)

Code/Software

These files were created using Python 3.6 and the open-source Py-ART python package. We added a function to image mute radar objects, beginning with Py-ART version 1.11.8. An example of how to use the function is provided here.

Software to create the composites and other supplemental material can be found in the part 1 repository. Instructions to modify the software to process WSR-88D data from other regions are included.

Files can be read with open-source software packages such as ncdf4 in R and netCDF4 and Py-ART in Python. Examples for reading the files in Python is included below:

With netCDF4:

import netCDF4

data = netCDF4.Dataset('YYYYMMDD_stitched.nc')
print(data)

With Py-ART:

import pyart

grid = pyart.io.read_grid('YYYYMMDD_stitched.nc')