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Surface Precipitation Rate (SPR)

Surface Precipitation Rate (SPR)

Short Description

Uses the Seamless Hybrid Scan Reflectivity (SHSR) mosaic and Surface Precipitation Type (SPT) field to compute instantaneous rain rates in mm/hr. Different Z-R relationships are assigned based on the SPT field, to provide the most accurate SPR field.



Primary Users


Input Sources

Seamless Hybrid Scan Reflectivity (SHSR), Surface Precipitation Type (SPT)


Spatial resolution: 1 km x 1 km

Temporal resolution: 2 minutes

Product Creation

Using the Surface Precipitation Type (SPT) field, each grid point is classified as one of seven categories. Based on the category, an accompanying reflectivity-to-rainfall (Z-R) relationship is assigned which most accurately explains the amount of rainfall to expect based on the radar echo. The reflectivity data used in these Z-R relationships is from the Seamless Hybrid Scan Reflectivity (SHSR) mosaic product. It should be emphasized that the current SPR does NOT include dual-pol variables.

Technical Details

 The Z-R equations, along with their respective upper limits, are described for the non-tropical categories in the table below:

Currently, both the Warm and Cool Stratiform types use the same Z-R relationship criteria, as denoted in the table. Eventually, MRMS plans to utilize dual-pol Z-R relationships to define SPRs. Therefore, the categories are labeled separately right now in order to prepare for their differing dual-pol calculations in later versions.

Defining Z-R relationships for the tropical rainfall categories is more complicated because it is dependent on location, as well as a special weighting system that incorporates the Probability of Warm Rain (POWR) product. A table and summary are below:

Figure 1. Map of the County Warning Areas with respect to the transition zone for the tropical Z-R selections.

  • If the grid cell is west of 102°W:
    • The tropical Z-R is used...Z = 250*R1.2 (capped at 147.4 mm/hr, or 50 dBZ)
    • This is regardless of the tropical classification.
  • If the grid cell is east of 100°W:
    • …and POWR < 0.5
      • Tropical/convective...use the convective Z-R (Z = 300*R1.4)
      • Tropical/stratiform...use the warm stratiform Z-R (Z = 200*R1.6)
    • …and POWR > 0.7
      • The tropical Z-R is used...Z = 250*R1.2
      • This is regardless of the tropical classification.
    • …and POWR is within [0.5, 0.7]
      • Use the equation below to create a weighted combination of the tropical Z-R and either the warm stratiform or convective Z-R, depending on the SPT classification:




Rtrop uses the tropical Z-R relationship.

Rx uses either the warm stratiform or convective Z-R relationship, depending on the classification.

α, wtrop are all scaling functions based on POWR.

p1 and p2 are POWR thresholds of 0.5 and 0.7, respectively.

p3 and p4 are POWR thresholds of 0.75 and 1.0, respectively. These values are only used in extreme tropical environments (e.g. landfall hurricanes) to increase rain rates beyond what the tropical Z-R would normally allow.

αmax is the maximum value that α is allowed to assume for a given month and its highest values coincide with the North Atlantic tropical cyclone frequency climatology; αmax values are shown in Figure 1.

f is the spatial variability factor, which is used to reduce the influence that αmax has throughout the country. Figure 3 shows the distribution of f.

  • If the grid cell is in the transition zone of 100 and 102°W:
    • A linear ramping of the two methods is done to ensure the continuity of the precipitation rate field is preserved.

Figure 2. Maximum values for the alpha multiplier, αmax, by month of the year.

Figure 3. Map of the spatial variability factor f. In areas where f = 0.0, alpha has no influence (and therefore, no multiplier applied to the rain rates). In areas where f = 1.0, alpha is applied to its fullest (i.e. rain rates will be increased).


Grams, Heather M., Jian Zhang, and Kimberly L. Elmore. "Automated Identification of Enhanced Rainfall Rates Using the Near-Storm Environment for Radar Precipitation Estimates." Journal of Hydrometeorology 2014 (2014).

Zhang, J., K. Howard, S. Vasiloff, C. Langston, B. Kaney, Y. Qi, L. Tang, H. Grams, D. Kitzmiller, J. Levit, 2014: Initial Operating Capabilities of Quantitative Precipitation Estimation in the Multi-Radar Multi-Sensor System. 28th Conf. on Hydrology, Amer. Meteor. Soc.

Other MRMS product documentation: Seamless Hybrid Scan Reflectivity, Surface Precipitation Type, Probability Of Warm Rain, QPE - Radar Only