Many products are derived from the 3-D reflectivity field. These products often involve the use of numerical prediction model data.
TIP: Zooming in far enough allows the display of individual pixel/gate values. (Example)
Composite reflectivity derived from mosaic3D
This composite reflectivity has been edited with a neural network quality controlled algorithm (QCNN; Lakshmanan 2007). The unit of this field is dBZ.
Lakshmanan, V., A. Fritz, T. Smith, K. Hondl, G. J. Stumpf, 2007: An automated technique to quality control radar reflectivity data. J. Appl. Meteor., 46, 288-305.
The composite reflectivity is the maximum reflectivity in each grid column in the 3-D mosaic. This cref has not been quality control edited to remove non-precipitating echoes. The unit of this field is dBZ.
Maximum Composite Reflectivity over the previous hour.
Composite reflectivity with Canadian radars. Note the Canadian radars are at C-Band and they may not have thorough quality control.
The hybrid scan reflectivity is a 2-D mosaic of single radar hybrid scan reflectivity fields. The single radar hybrid scan reflectivity is obtained by searching each grid column in the single radar 3D reflectivity Cartesian grid, from bottom to top, until the first non-missing reflectivity value is identified. The reflectivity value is then recorded as the single radar hybrid scan reflectivity (in dBZ) at the given grid cell, and the height of the grid cell is recorded as the single radar hybrid scan height.
The Seamless Hybrid Scan Reflectivity at any range/azimuth bin with partial blockages of 10 to 50% is a weighted mean of reflectivities from the PPS-type hybrid scan and from the next tilt above. See here.
The VPR correction uses an average VPR and adjusts the radar data form aloft to the surface. See Zhang, J., Y. Qi, 2010: A Real-Time Algorithm for the Correction of Brightband Effects in Radar-Derived QPE. J. Hydrometeor, 11, 1157–1171.
This variable is the height of the grid level where the column maximum reflectivity (composite reflectivity) is found. The unit is km above MSL.
This field is the height of the lowest non-missing single radar hybrid scan reflectivities at each MRMS grid cell. The unit of Height Hybrid Scan Reflectivity is meters AGL (above ground level).
This is the height above ground for each azimuth and range bin above ground level.
This is the height above ground for each azimuth and range bin above mean sea level.
The Radar Quality Index is an indicator of radar quality with respect to beam height and the 0C height. Data closest to the radar (ground) and below the 0C height have the highest quality, i.e., values close to 1. Data at far ranges and above 0C have the lowest values.
Zhang, J., Y. Qi, C. Langston, B. Kaney, 2011: Radar Quality Index (RQI) - a combined measure for beam blockage and VRR effects in a national network. Weather Radar and Hydrology
The echo top height is obtained by searching each grid column in the 3D reflectivity mosaic grid, from top to bottom, until the first reflectivity value greater than 18 dBZ is found. The height of the grid level where the reflectivity value was found is recorded as the ETP18 value for the given grid cell. The unit of the echo top is km above MSL.
The VIL product is derived based on the procedures described in Greene and Clarke (1972).
Greene, D. R. and R. A. Clark, 1972: Vertically Integrated Liquid Water-A New Analysis Tool. Mon. Wea. Rev., 100, 548-552.
The Vertically Integrated Liquid Density product is derived based on the procedures described in Amburn and Wolf (1997).
Amburn, S. A. and P. L.Wolf, 1997: VIL Density as a Hail Indicator. Wea. Forecasting, 12, 473-478.
The precipitation flag indicates the surface precipitation type and the radar observation representativeness at each grid point. The representativeness of radar observations with respect to surface precipitation estimation is highly correlated to the height of the radar observation and its proximity to the bright band layer (BBL) and to the surface. The top and bottom heights of the BBL are identified from the vertical profile of reflectivity (if it exists) at each radar site, and then objectively analyzed onto the MRMS grid using the RUC 0C height as the background.
Currently, each grid column in the 3-D mosaic grid is analyzed and classified.
A grid column is classified as being convective if one of the following criteria is satisfied:
Using a VPR to determine if a grid column is tropical (reference Xu et al., 2007, .doc, 1.2 MB):
Temperature soundings are obtained from the RAP 20 km model analysis.
The 2-D surface precipitation phase indicates the precipitation state (frozen or liquid) at each grid cell.
This indicates if a grid point is convective or stratiform.
Objective analysis of the Tropical Identification (TRID) precipitation type. Values range from zero to one. THis field was used to test reduce potential overestimation by using the tropical Z-R far away from a radar identified as tropical. PWR is currently not used.
Indicates severity of hail according to Witt, A., M. D. Eilts, G. J. Stumpf, J. T. Johnson, E. D. Mitchell, K. W. Thomas, 1998: An Enhanced Hail Detection Algorithm for the WSR-88D. Wea. Forecasting, 13, 286–303.).
Probability of severe hail according to Witt, A., M. D. Eilts, G. J. Stumpf, J. T. Johnson, E. D. Mitchell, K. W. Thomas, 1998: An Enhanced Hail Detection Algorithm for the WSR-88D. Wea. Forecasting, 13, 286–303.).
Maximum expected hail size according to Witt, A., M. D. Eilts, G. J. Stumpf, J. T. Johnson, E. D. Mitchell, K. W. Thomas, 1998: An Enhanced Hail Detection Algorithm for the WSR-88D. Wea. Forecasting, 13, 286–303.).
Within the mosaic tools, there is the ability to select showing either one product or the difference between any two products with the same units.