Add MRMS datasets with ~2-minute time step. Likely two datasets, one for each variables at 0.01 and 0.005 degree resolution.
Running list of variables
Quality Control & Diagnostics (0.01°)
- RadarQualityIndex
- ReflectivityQC
Hydrology & Precipitation (0.01°)
- PrecipRate
- RadarOnly_QPE_01H
- PrecipFlag
- FLASH_Ratio_FFG_MAX
Severe Weather (0.01°)
- MESH
- NLDN_CG_005min_AvgDensity
- NLDN_CG_030min_AvgDensity
Severe Weather: Tornado & Wind (0.005°)
- RotationTrack30min
- RotationTrackML30min
- RotationTrack60min
- RotationTrackML60min
- AzShear_0-2km
- AzShear_3-6km
Implementation
Most likely as a virtual icechunk dataset (#513) using a Gzip -> Gribberish decode pipeline pointing to underlying NODD MRMS files on S3.
Open questions
- The AzShear variables have irregular timestamps. They are on average 2-minutely, but actually have +/- a handfull of seconds in their timestamps. Do we snap these to a regular 2 minute grid or do we put them in their own dataset each? If we do snap them, we could provide a coordinate array along the time dimension with the exact issuance timestamp.
Add MRMS datasets with ~2-minute time step. Likely two datasets, one for each variables at 0.01 and 0.005 degree resolution.
Running list of variables
Quality Control & Diagnostics (0.01°)
Hydrology & Precipitation (0.01°)
Severe Weather (0.01°)
Severe Weather: Tornado & Wind (0.005°)
Implementation
Most likely as a virtual icechunk dataset (#513) using a Gzip -> Gribberish decode pipeline pointing to underlying NODD MRMS files on S3.
Open questions