Quarterly Journal of the Royal Meteorological Society
Q. J. R. Meteorol. Soc. 139: 2233–2240, October 2013 B
A real-time automated convective and stratiform precipitation segregation algorithm in native radar coordinates Youcun Qi,a,b *Jian Zhangc Pengfei Zhanga a
Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK, USA b Nanjing University of Information Science and Technology, Nanjing, China c NOAA/OAR National Severe Storms Laboratory, Norman, OK, USA *Correspondence to: Y. Qi, NOAA, NSSL, 120 David L. Boren Blvd, Norman, OK 73072, USA. E-mail:
[email protected]
A new convective/stratiform (C/S) precipitation segregation algorithm was developed for applications with single radar volume scan data and in its native (spherical) coordinates. The new algorithm consists of two parts: the first is to find convective rainfall cores based on physical characteristics of different rainfall types, and the second is to delineate the full convective area through seeded region growing. The new scheme takes into account radar sampling characteristics and a variety of precipitation scenarios where the C/S delineation was relatively challenging. The new C/S delineation scheme has two impacts on radar quantitative precipitation estimation (QPE): (i) correctly separate convective and stratiform regions such that appropriate Ze –R relationships can be applied; (ii) correctly define the stratiform area such that a vertical profile of reflectivity (VPR) correction can be applied. The VPR correction is very important to reduce overestimation errors in radar QPEs associated with bright band. The new algorithm was tested on many events and showed improved performance over previous schemes, especially when handling strong bright band and melting graupels in stratiform precipitation. The new scheme was also tested in radar quantitative precipitation estimation (QPE) and it consistently reduced the root mean square error and mean absolute bias in the radar QPE when compared with gauges. Key Words:
radar QPE; VPR correction; MCSs
Received 24 May 2012; Revised 28 September 2012; Accepted 22 November 2012; Published online in Wiley Online Library 17 January 2013 Citation: Qi Y, Zhang J, Zhang P. 2013. A real-time automated convective and stratiform precipitation segregation algorithm in native radar coordinates. Q. J. R. Meteorol. Soc. 139: 2233–2240. DOI:10.1002/qj.2095
1. Introduction
systems (MCSs) where both precipitation regimes exist. The microphysical distinction between stratiform and convective A common methodology in the radar-based quantitative precipitation mainly lies in the magnitude of in-cloud precipitation estimation (QPE) is to derive precipitation vertical air motions and the time-scale of microphysical rates, R, from radar reflectivities, Ze , through empirical Ze – R precipitation growth processes as pointed out by Houghton relationships. Different raindrop size distributions (DSDs) (1968). Most stratiform precipitation falls from clouds that have different Ze – R relationships, and the accuracy of reach well above the 0◦ C height with quite small vertical air radar QPEs strongly depends on the choice of representative motions, and ice particles in upper levels of the cloud play Ze – R relationships (Xu et al., 2008). DSDs in convective and an important role in the precipitation processes. When the stratiform precipitation are very different, and segregation of ice particles melt they are marked on radar reflectivity the two types of precipitation is very important for obtaining images by a bright band (BB) of intense echoes in a accurate radar rainfall estimation in mesoscale convective horizontal layer about 1/2 km thick located just below the c 2013 Royal Meteorological Society
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0◦ C level. Convective precipitation processes differ sharply from stratiform processes. Since the strong updraughts in convective rainfall area are usually narrow and can loft large particles upward, radar echoes associated with active convection form well-defined vertical cores of maximum reflectivity. These are quite different from the horizontally oriented radar BB found at the melting level in stratiform precipitation. Separating convective and stratiform precipitation is also important for the vertical profile of reflectivity (VPR) correction, which is commonly practised in radar QPEs (e.g. Kitchen et al., 1994; Zhang and Qi, 2010; Zhang et al., 2012) to mitigate range-dependent errors due to radar sampling issues. Different types of precipitation have different VPRs and require separate VPR corrections for convective and stratiform precipitation. A few schemes have used the radar reflectivity BB to identify stratiform precipitation (Collier et al., 1980; Rosenfeld et al., 1995). However, there are some limitations for this method, for two reasons: (i) the radar beam widens with range from the radar, and only at the close range (50 dBZ) near the surface. Without the correct C/S classification, these c 2013 Royal Meteorological Society
areas would not be corrected for the BB (Figure 3(b3 )) and could result in significant overestimation in the radar QPE. The new scheme correctly assigned the BB areas as stratiform and facilitated the VPR correction. These cases showed two benefits of the C/S segregation in radar QPE: one is to facilitate the use of proper Z –R relationships, and another for the proper VPR correction of BB effects. The impact of the new C/S segregation scheme is further assessed quantitatively through the radar QPEs. Reflectivity fields are segregated into convective and stratiform precipitation, and the VPR correction is applied in stratiform areas as in ZQ10. Then the VPR corrected reflectivity field is converted into rain rate using two Ze – R relationships: one for convective (Ze = 300R1.4 ) and the other for stratiform precipitation (Ze = 200R1.6 ). The rain rates are aggregated into hourly rainfalls and compared to the surface gauge observations from the Hydrometeorological Automated Data System (HADS at http://www.nws.noaa.gov/oh/hads/). Figure 4 shows scatter plots of hourly radar rainfall estimates, one based on the ZQ10 C/S segregation scheme and the other on the new scheme, against gauge observations for one strong MCS event observed by two radars (KFFC – Atlanta, GA; and KJGX – Robins Air Force base, GA). The VPR correction was applied in both radar QPEs. It is clear that the radar QPE associated with ZQ10 had significant overestimations even though a VPR correction was applied. The overestimation was due to the facts that (i) strong BB/melting graupel areas were misclassified as convective and the VPR correction was not activated, and (ii) one mean VPR could not capture the horizontal variation of BB and melting graupel distributions in the whole radar domain. The new C/S separation algorithm significantly reduced the errors and the resultant QPE agreed much better with the gauge observations than the other QPE. A more extensive evaluation of the BB correction scheme was performed using eight heavy precipitation events from different regions and seasons in the USA (Table I). The data included 1731 hourly HADS gauges and 80 h of baselevel data from eight radars (Table I). Three statistic scores were used to assess the performance of the two separation schemes. I. Root-mean-square error (RMSE): 1/2 1 RMSE = (rk − gk )2 (6) N k=1,N
Here rk and gk represent the kth matching pair of the radar-derived and gauge-observed rainfall in radar scan domain; N represents the total number of matching gauge and radar pixel pairs. A matching pair of gauge and radar pixel is found when the following two criteria are met: (i) the gauge location is within the boundary of a 5◦ × 5 km radar bin: and (ii) both the radar estimate rk and the gauge observation gk are greater than 0. II. Relative mean absolute error (RMAE): 1
|rk − gk | N RMAE =
G=
k=1,N
G 1 gk N k=1,N
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(7a)
(7b)
A Real-Time Automated Segregation Algorithm in Native Radar Coordinates
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Figure 4. Scatter plots of hourly radar precipitation estimates versus gauge observations ZQ10 (blue dots) and new algorithm (red dots). Data are from (a) KFFC 0100–0800 UTC 5 April 2011 and (b) KJGX 0000–1200 UTC 5 April 2011. Table 1. Summary of the events. Event
Radar
No. of radar– gauge pairs
Time and date
Events summary
1
KJGX (32.68◦ N, 83.35◦ W) KFFC (33.36◦ N, 84.56◦ W) KLBB (33.65◦ N, 101.81◦ W) KICT (37.65◦ N, 97.44◦ W) KINX (36.18◦ N, 95.58◦ W) KFWS (32.57◦ N,97.31◦ W) KFDR (34.36◦ N, 98.97◦ W) KFWS (32.57◦ N, 97.31◦ W) 8 radars
339
0200–1200 UTC 5 April 2011
Strong BB behind a squall line, and squall line across radar site
340
0200–0800 UTC 5 April 2011
88 321
1000 UTC 15 March– 0400 UTC 16 March 2010 0600–1400 UTC 7 June 2010
360
0800–1400 UTC 7 June 2010
94
1700–2300 UTC 14 May 2010
152
1300–2300 UTC 14 May 2010
37
1200–2000 UTC 27 May 2008
1731 radar– gauge pairs
80 h
2 3
4
5 Total
Strong low and partial BB Weak convective rainfall embedded in stratiform rainfall, and then evolved as strong BB behind a squall line
Convective, melting snow aggregates in the trailing precipitation behind a squall line with high melting layer
Convective, melting snow aggregates in the trailing precipitation behind a squall line 5 events
4. Conclusion Here G is the averaged hourly gauge precipitation. III. Relative mean bias (RMB): 1 N
RMB =
(rk − gk )
k=1,N
G
(8)
Statistic scores from the eight events (Figure 5) showed that the new segregation algorithm consistently reduced the radar-derived QPE errors for all the events compared with ZQ10. The improvements were most significant for the three events (KJGX and KFFC 20110405, KFWS 20100514 and KFWS 20080527; Figure 5), where the stratiform rainfall was severely contaminated by strong BB or deep layer of melting graupel. A large improvement was also found for the cool season stratiform precipitation in the Great Plains (KLBB 20100315), where the BB is quite strong and very close to the ground. c 2013 Royal Meteorological Society
Many of the previous convective/stratiform (C/S) segregation schemes had based their convective precipitation identification on reflectivity intensity, which often result in misclassification of strong BB or melting graupel in stratiform regions as convective. Such misclassification can cause inaccurate radar QPEs. A new C/S segregation algorithm was developed for applications with single radar volume scan data and in its native (spherical) coordinates. The new C/S rainfall separation algorithm consists of two parts. The first part finds convective rainfall centres/cores based on physical characteristics of different rainfall types, and the second delineates the full convective rainfall area using the seeded region growing algorithm. The new scheme takes into account the radar sampling characteristics and a variety of precipitation scenarios, such as MCSs containing a strong BB and melting graupel area in the trailing stratiform or stratiform rain with very strong BB associated with a low melting layer. The new separation algorithm was tested on many events and showed much improved performance over Q. J. R. Meteorol. Soc. 139: 2233–2240 (2013)
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Figure 5. The (a) RMSE, (b) relative MAE and (c) relative mean bias scores for radar precipitation estimates after AVPR correction with ZQ10 (solid-star line) and with the new algorithm (dashed-circle line).
previous schemes in handling aforementioned situations where C/S segregation was relatively challenging. The new scheme was tested in radar QPEs where a precipitation typedependent VPR correction was applied. The results showed that with the new and improved C/S delineation the radar QPE accuracy was largely improved. The new C/S scheme is very efficient computationally and has been implemented in the real-time National Mosaic and QPE system (Zhang et al., 2011). Acknowledgements Major funding for this research was provided under NOAA’s Hydro-Meteorological Testbed (HMT) program and partial funding was provided under NOAA – University of Oklahoma Cooperative Agreement #NA17RJ1227. References Adams R, Bischof L. 1994. Seeded region growing. IEEE T. Pattern Anal. 16: 641–647. Benjamin SG, Schwartz BE, Koch SE, Szoke EJ. 2004. Supplement to:
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Q. J. R. Meteorol. Soc. 139: 2233–2240 (2013)
