# Gauge Influence Index - Gauge Influence Index - Warning Decision Training Division (WDTD)

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## Products Guide

# Gauge Influence Index

## Short Description

The Gauge Influence Index (GII) displays the spatial influence of the hourly rain gauges that are applied to the Q3 Local Gauge Bias Corrected Radar Precipitation Accumulation (Q3GC_SHSR) product. The GII shows the interpolation weighting function calculated for the local gauge bias correction on the MRMS grid.## Subproducts

None## Primary Users

NWS WFOs, NWS RFCs

## Input Sources

QC Rain Gauges, 1-HR QPE Radar-Only## Resolution

Spatial Resolution: 1km x 1km

Temporal Resolution: 1 hour

## Product Creation

The Gauge Influence Index (GII) displays the spatial influence of the hourly rain gauges that are applied to the Q3 Local Gauge Bias Corrected Radar Precipitation Accumulation (Q3GC_SHSR) product. The GII shows the interpolation weighting function calculated for the local gauge bias correction on the MRMS grid. The Q3GC_SHSR product first calculates a bias between the quality controlled (QC) rain gauges and the 1-hour Q3 radar-only QPE at each gauge site. The bias is then interpolated onto the two-dimensional MRMS grid using an inverse distance weighting (IDW) scheme (please reference the Q3 Local Gauge Bias Corrected Radar Precipitation Accumulation technical document for more details).

In the IDW scheme, a weight parameter (w) is calculated for each rain gauge (k) and MRMS grid cell (i) pair using the following equation:

In this equation, d is the distance between the rain gauge and the pixel, b is a unitless exponent, and D is the specific radius of influence for each rain gauge. The details regarding this equation and how each variable is calculated are presented in the Q3 Local Gauge Bias Corrected Radar Precipitation Accumulation technical document. The summation of weights given to all gauges that impact a given MRMS grid cell is defined as the Gauge Influence Index (GII) value at the grid cell.

## Technical Details

## References

Zhang, J., K. Howard, C. Langston, B. Kaney, Y. Qi, L. Tang, H. Grams, Y. Wang, S. Cocks, S. Martinaitis, A. Arthur, K. Cooper, J. Brogden, and D. Kitzmiller, 2016: Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities. Bull. Amer. Meteor. Soc., 97, 621–638.