KOUN Case Study1820 October 2002: Heavy rain in southern OklahomaAuthor: Kevin Scharfenberg, CIMMS/OULast Updated: 1 November 2002 
Introduction 
In addition, rainfall accumulation estimation using a Z algorithm is subject to contamination from hail, radar miscalibration, anomalous propagation, ground clutter, and partial beam blockage. These factors may cause significant overestimation or underestimation of actual rainfall totals.
Polarimetric radars are capable of measuring the difference in propagation constants between horizontally and vertically polarized radar pulses over a given range, called the specific differential phase (K_{DP}). K_{DP} is directly related to the number concentration of rain drops in the radar volume, meaning there is a direct relationship between K_{DP} and rain rate.
Rainfall accumulation estimation using a K_{DP} algorithm is immune to contamination from hail, radar miscalibration, anomalous propagation, ground clutter, and partial beam blockage.
A heavy rainfall event in southern Oklahoma, between 100 and 200 km from the National Severe Storms Laboratory's polarimetric radar (KOUN), occurred between 18 and 20 October 2002. Polarimetric radar data were collected throughout the event without interruption. Rainfall accumulation estimation algorithms were run on the data in real time, and made available to forecasters at the Norman NWS WFO.
Reflectivity (Z) Rainfall Estimation Algorithm 
A verification chart can be constructed that shows how the algorithm estimation compares to corresponding Oklahoma Mesonet rainfall accumulation reports. This image shows how such verification charts are produced. Mesonet rainfall reports are plotted in the Y direction, while the corresponding radar algorithm estimation is plotted in the X direction. In this example, a Mesonet report of 1.00" total rainfall corresponds to a hypothetical radar algorithm estimate of 3.00". A linear least squares (L.L.S.) "best fit" line can be fitted to the data. If the algorithm estimates match the Mesonet reports exactly, the algorithm's "best fit" line will lie exactly over the "perfect" line. Data points to the left of the "perfect" line represent algorithm underestimates of Mesonet reports, while data points to the right represent algorithm overestimates.
For this event, the verification chart, shown in Figure 2 below, reveals the Z algorithm overestimated the reported rainfall for 24 of the 29 Mesonet reports. The algorithm overestimates became more severe for higher Mesonet rainfall totals.
Specific Differential Phase (K_{DP}) Rainfall Estimation Algorithm 


Algorithm estimate (in.) 


Conclusions 
It is clear that for this case, the algorithms using K_{DP} data significantly outperformed the algorithms using Z data, particularly in regions of heavy rainfall. In addition, while the Z algorithm showed a bias toward overestimation, the K_{DP} algorithm showed no consistent bias. Finally, while partial beam blockage hindered the Z algorithm, little corresponding beam blockage was noted in the K_{DP} algorithm output.
NWS WFO Norman forecasters were able to use these data in real time to determine that traditional Z/R algorithms were significantly overestimating rainfall accumulation near the Red River. "The (K_{DP}) data matched the Mesonet so well it wasn't necessary to make any significant subjective adjustments, unlike the Z/R products which significantly overestimated amounts," said NWS Norman meteorologist David Andra, who was on duty during the afternoon of 19 October. "I had greater confidence that flooding was not a serious problem."
Algorithm  Mean Absolute Error  Root Mean Squared Error 
Z  1.18"  1.99 
ZZ_{DR}  1.29"  2.20 
Z_{DR}K_{DP}  0.21"  0.63 
K_{DP}  0.26"  0.61 
Related Links 
Acknowledgements 