Gauge Influence Index - Warning Decision Training Division (WDTD)
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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
Latest Update: MRMS Version 11.5
Accessible on: MRMS Development site
NOTE: In the MRMS version 11.5 update, the latency of the gauge influence index (GII) products was reduced from ~1.5 hour to 1hr. In addition to the reduced latency, the number of input hourly gauge observations was increased. These benefits are a result of the input gauge data feed switch from Hydrometeorological Automated Data System (HADS) only to a combination of HADS plus Meteorological Assimilation Data Ingest System (MADIS).
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.
Applications
The GII product shows the spatial distribution of the gauge influence in the Q3 Local Gauge Bias Corrected Radar Precipitation Accumulations.
Example Images
Figure 1 shows an example Q3 Radar hourly QPE (Figure1a) that contained underestimation errors due to significant heavy rain during the Hurricane Matthew (7-9 Oct. 2016) that was not captured by the tropical Z-R relationship (Z=250R1.2) with an enhance factor α of ~1.45 (Zhang et al. 2016) applied in the Q3 radar only QPE. The underestimation was reduced (Figure 1b) after the local gauge bias correction. The associated GII field (Figure 1c) shows the spatial distribution of the gauge impact during the correction process.
Figure 1. An example Q3 Radar Only hourly QPE (a), the Q3 Local Gauge Bias Corrected radar QPE (b), and the associated GII (c) field. The products are valid at 0400UTC on 8 October 2016.