1. A dual-polarization QPE method based on the NCAR Particle ID algorithm Description and preliminary results
Michael J. Dixon1, J. W. Wilson1, T. M. Weckwerth1, D. Albo1 and E. J. Thompson2
1National Center for Atmospheric Research(NCAR), Boulder, Colorado
2Colorado State University (CSU), Fort Collins, Colorado
1NCAR is sponsored by the US National Science Foundation.
Thanks to Kyoko Ikeda and Andrew Newman of NCAR for helping to prepare the verification data sets.
9A.1 AMS 37th Conference on Radar Meteorology Norman, Oklahoma, USA

2. Dual-pol QPE algorithms use a variety of relationships to compute precipitation rate from radar variables.
The following relationships are used in this study:
But how do we combine these to produce a skillful estimator?

3. Some hybrid QPE algorithms are based on rules and decision thresholds
Flowchart describing the CSU-ICE algorithm (Cifelli et al., 2011).
Block diagram illustrating the rain-rate retrieval method using a variant of the Ryzhkov et al. (2005) approach and adapted for C-band. (Bringi et al. 2009).
The problem with this approach is that by selecting thresholds, we remove some of the valid decision space from consideration.

4. Alternative approach – use hydrometeor classification to select the appropriate rate relationship
As an alternative to the threshold-based hybrid techniques, a hydrometeor classification algorithm can be used to determine which rate relationship is appropriate (Giangrande and Ryzhkov 2008; Berkowitz et al. 2013).
This approach has the advantage that determination of the thresholds is not required in the QPE step because the hydrometeor classification algorithms on which they are based make use of fuzzy logic for the determination of the hydrometeor type (Vivekanandan et al. 1999; Lim et al. 2005; Park et al. 2009).

5. The NCAR PID algorithm classifies the scatterer type using fuzzy logic applied to the dual-polarization fields. (Vivekanandan et al., 1999)

6. Decision tree for NCAR HYBRID algorithm uses NCAR PID to select rate relationship
In the melting layer, measured reflectivity is reduced by 10 dBZ.

7. Decision tree for NCAR Weighted-PID algorithm uses NCAR PID to select rate relationship
Note: in mixed precip, measured reflectivity is
reduced by 10 dBZ.

8. Beam blockage algorithm
Uses the SRTM 30-m resolution digital elevation data from the space shuttle STS-99 mission. Takes account of standard atmospheric propagation effects and the convolution of the beam pattern with the terrain features.
Example – clutter at the S-Pol at the Front range site
Cumulative beam blockage map S-Pol at the Front range site

9. Decision tree for mapping QPE from aloft to the surface

10. Field test Plains Elevation Convection at Night (PECAN)
The PECAN project was centered on Kansas, and ran from the beginning of June to mid-July 2015.
The QPE system was run on a network of 16 NEXRAD radars, plus the NCAR S-Pol radar.
The RUC-RAPID model was used to provide temperature profiles for the PID algorithm.
The system was up and running prior to the start of PECAN, so the time period for this study is 2015/05/17 to 2015/07/16.

11. 17 radars of the S-band network used for the PECAN QPE product
The color scale shows the range from the closest radar

12. The orange rectangle is the primary PECAN study domain.
Example of large-scale convective system at PECAN. MRMS column-maximum reflectivity at 07:00 UTC on 2015/06/05.
The orange rectangle is the primary PECAN study domain.

13. Accumulation (mm) from NCAR HYBRID QPE for the 24-hour period ending at 00:00 UTC on 2015/06/06.

14. Data for these sites is available from NCDC.
Map of daily precipitation gauge sites for the QPE domain. Daily accumulation data for these sites is available from NCDC.
Data for these sites is available from NCDC.
For QPE verification, only stations within the orange rectangle are used.

15. Measured gauge precipitation amounts overlaid on the radar-derived QPE map, for 12:00 UTC (07:00 local) on 2015/06/05.
This is an example of the gauge measurements available for QPE verification. Gauge measurement values are shown in cyan.

16. Radar-based 24-hour QPE vs gauge-measured statistics 2015/05/17 – 2015/07/16
Method
N points
Correlation
Bias radar/gauge
NCAR HYBRID
21258
0.834
0.940
NCAR Weighted-PID
21311
0.826
1.108
R(Z)
21668
0.798
1.331
R(Z, ZDR)
21037
0.772
1.057
(a) NCAR HYBRID algorithm
(b) NCAR Weighted-PID algorithm

17. Radar-based 24-hour QPE vs gauge-measured statistics 2015/05/17 – 2015/07/16
Method
N points
Correlation
Bias radar/gauge
NCAR HYBRID
21258
0.834
0.940
NCAR Weighted-PID
21311
0.826
1.108
R(Z)
21668
0.798
1.331
R(Z, ZDR)
21037
0.772
1.057
(a) R(Z) estimator
(b) R(Z, ZDR) estimator

18. The weighted-PID algorithm overestimates (bias of 1
The weighted-PID algorithm overestimates (bias of 1.10) probably due to errors associated with the melting layer
Example of a widespread stratiform event passing over KDDC, the Dodge City NEXRAD at 09:45 UTC on 2015/05/22..

19. 24-hour QPE accumulation for this event, at 12:00 UTC on 2015/05/15, for the NCAR algorithms.
Accumulation over an event lifetime highlights problems in dealing with the melting layer.
(a) NCAR HYBRID algorithm.
(b) NCAR Weighted-PID algorithm.

20. 24-hour QPE accumulation for this event, at 12:00 UTC on 2015/05/15, for the R(Z) and R(Z, ZDR) four estimators.
Accumulation over an event lifetime highlights problems in dealing with the melting layer.
(c) R(Z) estimator.
(d) R(Z, ZDR) estimator.

21. Conclusions
Algorithms were developed to utilize the NCAR PID product for determining QPE aloft and at the surface.
The products were tested over an 8-week period during PECAN, using data from a network of 17 radars.
The results were verified against 24-hour precipitation accumulation data from NCDC.
The results are encouraging, showing good radar/gauge correlation and relatively minor biases.
The QPE formulations have some sensitivity to errors in the melting layer, this being more severe for the weighted-PID product.
Thank you