Techniques

Techniques of Severe Convective
Weather Comprehensive Monitoring
Yongguang Zheng, Lin Yinjing, Zhu Wenjian, Lan Yu, Tang Wenyuan,
Zhang Xiaoling, Mao Dongyan, Zhou Qingliang, Zhang Zhigang
Severe Weather Prediction Center of
National Meteorological Center of CMA
China
6 Aug 2012
Some Severe Convective
Tornado,
5 Mayin China
Cases
Hailstones, 20
Nov. 2002,
Maoming,
Guangdong Prov
2006, Huadu,
Guangdong Prov
2002年
年11月
月20日
日
广东茂名冰雹
Lightning,
Heavy rain at
Jinan, Shandong
Prov., 2007
2006年
年5月
月5日广东花都龙卷
日广东花都龙卷 济南短时强降水
河南永城11级大风
河南永城 级大风
2009年
年6月
月3日河南大风强对流天气
日河南大风强对流天气
Severe Convective
Weather, 3 June 2009,
Henan Prov
High Winds, Yongcheng,
Henan Prov
Heavy Rain, 23 June
2011, Beijing
Max Rainfall 460mm
Beijing radar
Extreme Heavy Rain,
78
deaths
in
Beijing
21 July 2012, Beijing
Outline
• Objectives
• Data
• Monitoring Products
• Techniques
• Future work
• Summary
1 Objectives
• Basis of Forecasting
• Get Convective Weather State Rapidly
–
–
–
–
Current State
Convective Weather Process
Dataset of Convective Weather Cases
Study the Climatological Distribution
• Verify Convective Weather Forecast
– Qualitative Verification
– Quantitative Verification （TS score, etc)
2 Data Sources
•
•
•
•
•
Conventional surface observations
Severe Weather Reports（WS reports）
Automatic Weather Station Data
Cloud-ground Lightning Data
IR and WV TBB from Geostationary Satellite
(Fengyun-2)
• Radar reflectivity mosaic data
3 Monitoring Products
•
•
•
•
•
•
•
•
•
•
•
•
Cumuli
Thunderstorms
Cloud-ground lightning
Hail
Tornadoes
High winds
Thunderstorm high winds
Short-term heavy rain
Radar reflectivity
Convective storms (based on radar data)
Deep convective clouds
Mesoscale convective systems (MCS, based on the IR
satellite data)
• Different convective weather distribution
during recent 1, 3, 6, 12, or 24 hours
• Real-time Update
• Monthly, decade, and pentad distribution can
also be gotten
Monitoring Region
-China and surrounding areas
4 Techniques
• Quality control of automatic weather station data
• Extracting information and statistical technique
• Lightning density monitoring
• CTREC (Cartesian Tracking Radar Echoes by Correlation)
• TITAN (Thunderstorm Identification, Tracking, Analysis,
and Nowcasting)
• Identification of deep convective clouds
• Identification and tracking of MCS
4.1 Quality Control of AWS data
4.2 Extracting Convection Information
30dBZ
Extract Info from Radar Ref
50dBZ
14:00 不同阈值的识别结果
45dBZ
60dBZ
30dBZ during 12 Hours, 21 July 2012
Last for
more than
10 hours
4.3 CG Lightning density
monitoring
• Total CG lightning density in different periods
• Positive CG lightning density
• Negative CG lightning density
CG lightning density on 17 Apr 2011
12-Hour CG lightning density
1-Hour density and rainfall >=20mm/hr
0
50
4.4 CTREC (Cartesian Tracking
Radar Echoes by Correlation)
18BST 27 June 2011
4.5 TITAN (Thunderstorm Identification, Tracking,
Analysis, and Nowcasting, from NCAR)
16 BST 23 June 2011
4.6 Identification of deep
convective clouds
Criteron:
TBBWV-TBBIR1>-5℃
3 Threshold of TBB:
-32℃
-52℃
-72℃
4.7 Identification and tracking of MCS
Tracking
Identification
t+1
t
①
Nowcasting
t+2
t+1
t
t
①
①
②
③
②
t+2
③
t+3
-32℃
℃ TBB MCS
15 Arpril 2011 Guizhou Province
-52℃
℃ TBB MCS
Min TBB
1-hr Rainfal
17 Apr 2011 Guangdong Province
MCS Nowcasting 12 Aug 2011
5 Future Work
AWS data QC need to be further improved
Enhance MCS tracking method
Further can monitor the favorable conditions for
convective weather (such as dewpoint at surface,
convergence line)
6 Summary
• An operational system of convective weather
monitoring has been built at NMC of CMA
• The techniques are based on the multi-source
data (conventional, AWS, lightning, radar,
satellite).
• The monitoring products have played
important roles in the convective weather
forecasting and nowcasting operations at NMC
of CMA