0-12H VSR forecasting and nowcasting review and plan in CMA - PowerPoint PPT Presentation

0-12H VSR forecasting and nowcasting review and plan in CMA Xiaoling Zhang, Bo Yang, Yongguang Zheng, Jie Sheng, Yinjing Lin, Wenyuan Tang, Kanghui Zhou National Meteorological Center, China Meteorological Administration Page 1

Synoptic Systems over East Asia Cold air Meiyu Front plateau enforce D Subtropical High Low pressure of Monsoon D D Monsoon Somali Jet trough Typhoon Tropical conv. Clouds MJO propagate eastward South-east trade wind Mascarene High Australia High Page 2

Weather and Climate Systems Hundred Hundred Climate Clim ate chan change p ge proje rojecti ction Years Years Climate Global Change Deca Decadal dal clim climate ate pred predict iction on Ten Ten Years Years EI Nino Year Year Short-ter Shor term clim limate p te pred redictio ction Month Month Monsoo n Medium Medi um-range ange fo forecas ecast week week Weather Pressure Typhoon Shor Short-range for nge forecast cast day day Fronts Torrential rain Hour Hour Squall line Nowcasting/ Thunderstorm Very-short Tornado Minute Minute range Dust storm forecast 1km 10km 100km 1000km 10000km 100000km Small scale Small scale （＜ 2km 2km ） Mesoscale Mesoscale （ 2-200km 200km ） Synoptic Scale Synoptic Scale （ 200 200-10000km 10000km ） Global scale Global scale （＞ 10000km 10000km ） Page 3

Severe Convective Weather – Hail >5 mm – Thunderstorm gale> 17m/s – Tornado – Short-time rainstorm>20mm/h Short-time Rainstorm Tornado Squall-line Hail Jun.1 in Northeast China North China in Jul.19-20 Gauntry crane collapse in Guangdong Apr.13 Haerbin Hail in Jun.12 Beijing Jul.21 138.5mm/h （ 2300BJT Jul.19) Funing EF4 tornado in Jun.23

Contents  Severe convective weather characteristics and its operational forecasting in CMA  0-2h Nowcasting  2-12h Very-short-range forecast  Future Prospects

Contents  Severe convective weather characteristics and operational forecasting in CMA  0-2h Nowcasting  2-12h Very-short-range forecast  Future Prospects

1981-2010 annual frequency of severe convective weather Thunderstorm ST heavy rainfall >20mm/h Hail Thunderstorm gale

The high wind in 2016 vs 1981-2010 High frequency Jan.1-Jun.24 2016 Jan.1-Jun.24 1981-2010

Distribution and variation trend of EF2 tornados and above in 1961-2010 Distribution Variation trend From Wenjie Fan and Xiaoding Yu ， 2015 ）

Operational responsibility in CMA Warning signals Lightning Rainstorm Hail Gale 2 h 12 h 0 h SWPC MONITOR WATCH /NMC WARNING PROVINCE MONITOR WATCH WATCH CITY AND MONITOR WARNING COUNTY WARNING National level Local level

NMC 12h Forecast SCW location and intensity Local Office 0-2h Warning NMC 0-6h Watch

Contents  Severe convective weather characteristics and its operational forecasting in CMA  0-2h Nowcasting  2-12h Very-short-range forecast  Future Prospects In SWPC/NMC, the monitoring and Extrapolation techniques • mainly based on satellite and lightning data In local office mainly based on the radar data •

Severe Convective Weather monitor in NMC Lightning Hail High Wind Short-time heavy rainfall

Multiple data applied in a severe convective weather events in South China MCS identification and track Lightning intensity Radar mosaic imagine with satellite data 30dBz 30dBz 45dBz 45dBz Radar echo extracting Objective analysis based on Lightning monitor AWS data 60dBz 60dBz 50dBz 50dBz Ｐ \ θse \V

MCS recognition, track and extrapolation based on the FY satellites data extrapolation track recognition t+2 t+3 t+2 t+1 t+1 t t t ① ① ① ② ② ③ ③ 红外 1 亮温资料 较 “ 经典等值线追 踪算法 ” 高效 Mar ch in g-Sq u ar e 等值线追踪算法 指定最低识别亮温值及亮温 变化间隔（如以 -92 ℃为起 多阈值 点，以 2 ℃为间隔进行识别） 等值线识别 从最低识别亮温开始对逐级亮 MCS 边界 温识别得到的等值线判断包含 归并 关系，实现 MCS 边界的归并 计算 MCS 特征 边界阈值、边界点、特征阈值 并输出 的面积、平均亮温、椭圆率等

Recognition, track and extrapolation of the MCS based on the FY satellites data

0-3h QPF based on the MCS extrapolation MCSrec ecognition ognition and extra and extrapolation n beginning at 1800BJT Jul.21 2012 +hour +hourly preci y precipitation monit n monitor MCS extrapolation 180 minutes forecast 120 minutes forecast 60 minutes forecast Hourly precipitation monitor 0-3h QPF Obs. at 2000BJT Obs. at 2100BJT Obs. at 1900BJT

Quantity characteristics of Large –zone thunderstorm-gale-produced clouds temporal evolution of averaged IR BT VS the timing of the gale Thunderstorm gale likely occurred as the IR BT sharply dropped rather than to its minus

Thunderstorm cell recognition, track and Extrapolation based on the lightning data  Cell recognition by cluster analysis  Track and extrapolation by Calman filter  The algorithm not only Cell-1 Split recognizes and tracks Cluster analysis Cell-2 thunderstorm cell, but also distinguishes the Cell-3 split and merge of Cell-1 Merge Cell-3 thunderstorm. Cell-2

Case in Mar.20 2013 Integrated in SWPC operation platform

Tornado recognition and automatic warning in Jiangsu  Improved the Mesocyclone recognition algorithm Used in WSR-98D radar  Decreased FAR by diagnosing the intensity, bottom height and wind shear of a mesocyclone  Decreased FAR by pattern recognition TVS MC HIT 38% 61% FAR 16 PUP 24 / Total Num. by improved 40 71 algorithm Num. by PUP 47 168

Tornado recognition and automatic warning in Jiangsu • Integrated in SWATCH which is the operation platform of Jiangsu province bureau • Valid tornado-detected distance associated with typhoon and westerlies is 60km and 100km • A weak tornado was recognized from the mixed echo nearby Nantong at 1653BJT Aug.7 2014 • Funing EF4 Tornado was recognized ahead of ~15 min. in Jun.23 2016 --- Yuanyuan Zheng 2016

High wind recognition by radar radial velocity at 0.5 elevation angles  Used only in the area within 100km from the radar station  Automatic warning used to value the risk of the electric power system

Severe Weather Automatic System(SWAN) SWAN2.0 客户端  The main operation platform in CMA, used in many local offices  Some updating in 2015 • Version2.0 replaced Version 1.6 • significant change in the aspects of algorithm, data resource and function  3DVAR wind retrieval from Doppler radar  rain cluster recognition by QPE  enhanced very-short-range forecasting capability by integrated the SCW probability forecast based on NWP output Blue: 30min before Red: current SCIT Green: 1 hour later VIL

Contents  Severe convective weather characteristics and its operational forecasting in CMA  0-2h Nowcasting  2-12h Very-short-range forecast  Future Prospects

High resolution models  GRAPES-RAFS (10KM, 3h update, 30h valid time),and GRAPES-CR(3KM,12h update,48h valid time) in NPC/CMA  RMAPS(9/3KM, 3h update) in Beijing Bureau  SMB-WARMS(9/3KM,3h update) in Shanghai Bureau  GRAPES-RAFS(9/3KM,3h update) in Guangdong Bureau SMB-WARMS GRAPES-CR

Evaluated the high resolution model in warm season testbed  during 2013-2014  High resolution models would be useful in SCW forecast  SWPC began to develop 2-12h forecast techniques based on high resolution models in 2015 and test the 2-6h operational watch in warm season of 2015 and 2016 6h QPF TS in 18hours valid time beginning at 1200UTC during Jun.9-Jul.31 2014 HR Models

SWPC experimental watch in 2015-2016 warm season Thunderstorm gale and hail watch ST rainstorm watch SWPC watch and local office warning in Jul.14 2016

SWPS 0-12h Time-lagged hourly QPF ensemble prediction techniques prediction Based on GRAPES-RAFS sw ② intensity correction ① Weight decided by frequency matching by T Scores

QPF TS in June to August 2015 (red: after, blue: before) Obs. 5-10mm 10-15 15-20 20-25 25-30 GRAPES-RAFS prediction correction

SWPS 0-12h prediction Neighborhood calibration of the short-time techniques rainstorm and strong echo forecast （ Upscaling ） Fist ： intensity correction by frequency Second ： Averaged the space matching ， Finding the probability forecast and temporal grids threshold of ST rainstorm events (>20mm/h) Obs. Fore. Model forecast calibration Color dot; obs.

SWPS 0-12h Super ensemble based on the deterministic prediction techniques HR models Models ： GRAPES-RAFS and GRAPES-CR from NPC/CMA ， WRF from Shanghai bureau, Beijing Bureau and Nanjing University, GRAPES-MESO from Guangdong First ： Uniform resolution and interpolate to 0.05x0.05 1h Second ： intensity correction by frequency matching and 5-days sliding correction Third ： various weighting for the six models Output ： • 1/3/6h accumulated QPF every 3h • Every 3h updating ST rainstorm events prediction during 0-12h

Criterion of the ST rainstorm warning Super ensemble ST rainstorm forecast has been integrated in SWPC operational platform