The Progresses of THORPEX Activities in China

Transcription

1 The 4 th THORPEX Asia Science Workshop The Progresses of THORPEX Activities in China XU Xiaofeng Chairman of THORPEX China committee 31 October 2012, Kunming, China 1

2 OUTLINE Research focuses of THORPEX in China Mainline of THORPEX researches THORPEX China research group THORPEX research activities Field campaigns Demonstration projects Future tasks 2

3 Mainlines of THORPEX relative research in China To accelerate improvements in 1 day to 2 week high impact weather forecast in Eastern Asia, The THORPEX relative researches in China are emphasized on the numerical model development, current observation utilizing, user oriented interactive forecast system, etc. Mainline 1:Operational data assimilation improvement GRAPES data assimilation system improvement China Fengyun satellite data tuning and assimilation Flow dependent background error information utilizing and new data assimilation method development Sensitive area detection and adaptive observation Forecast sensitive to observations; The improvement in operational observation system & data assimilation techniques will finally feedback to GRAPES data assimilation system 3

4 Mainlines of THORPEX relative research in China Mainline 2: TIGGE data collection, application and ensemble prediction system development Ensemble prediction system development Beijing TIGGE archive center establishment and capability improvement Multi model ensemble data combination and downscaling Server weather probabilistic products Hydrology user oriented interactive rainfall forecast system TIGGE forecast demonstration system; The improvement in ensemble prediction and products will finally feedback to ensemble prediction system, EPS products, and user oriented interactive forecast system; Mainline3: Eastern Asia high impact weather system Dynamics and predictability of tropical cyclone, heavy rainstorm, 4

5 How did THORPEX China Committee achieve those goals? Operational centers and research units work together 5

6 Various field experiments, establish a bridge with THORPEX relative sciences THORPEX relative science researches and projects Researches are focused on several specific important directions Connect to WMO demonstration project, show the potential benefit of researches THORPEX China target: Eastern- Asia 1-14 days forecast improvement Beijing TIGGE archive center establish, data service, and improve Operational data assimilation and ensemble prediction systems improve Operational observation system improvement and tuning 6

7 OUTLINE Research focuses of THORPEX in China THORPEX research activities Advance of GRAPES data assimilation Research on new data assimilation scheme Sensitive area detection and adaptive observation Tuning of China Fengyun satellite observations Advance of ensemble prediction system TIGGE archive center & multi EPS combination Research on typhoon/rainstorm dynamics and predictability Field campaigns Demonstration projects Future tasks 7

8 Advance of GRAPES Global Forecast System (GFS) Observations assimilated Conventional obs. TEMP, SYNOP, SHIPS, AIRCRAFT AMV Unconventional obs. TOVS (NOAA15/16/17/18/19/METOP) GPS/RO (COSMIC) RADAR (wind and refractivity) GPS/PW FY-3A/FY-2E AIRS TC Bogus Daily sat. Obs. Received in CMA Daily Obs. Used in GRAPES_DAS To be assimilated: IASI ASCAT 8

9 Research on new data assimilation scheme 1. Expansion of variational data assimilation Type 1: Flow dependent background error variance/covariance information used in GRAPES three dimensional variational data assimilation (3DVAR) Type 2: Consider model constrain by much time saving methods: Research on DRP 4DVar data assimilation method with extended samples Research on 3DVAR with mapped observation Research on model Constraint 3DVAR 2. Ensemble Kalman Filter An orthogonal expansion of filtering function in localization EnKF long time running test and comparison 9

10 Research on sensitive area detection and adaptive observation 1. Sensitive area detection for adaptive observation Ensemble transform Kalman filter method (ETKF) Conditional Nonlinear Optimal Perturbations method (CNOP) 2. CNOP method development Method compare: adjoint method versus CNOP Calculation: EOF decomposition method to resolve CNOP will much faster and time saving 3. OSSE test for sensitive area detection methods With/without adaptive observation 10

11 Conditional Nonlinear Optimal Perturbations (CNOP)- forecast errors result from CNOP and LSV CNOP LSV CNOP resulted in larger forecast errors than those resulted from linear singular vector (LSV) sensitivity experiment more benefit obtained from the reductions of CNOPtype initial errors CNOP is applicable reduced degree benifit CNOP 49.4% 69.9% 86.4% LSV 15.3% 26.3% 46.5% 11

12 Typhoon OSSE adaptive observation experiments Typhoon Mirinae CNOP sensitive region and dropping sites LSV sensitive region and dropping sites Typhoon track forecast error relative forecast error improvement(right Typhoon Mirinae(2009) assimilation with/without drop sondes. 12

13 Typhoon OSSE adaptive observation experiments error with dropsondes (km) h 30h 36h 42h 48h 54h Nida 24-54h CNOP SVs relative difference (%) CNOP: -38.4% SVs: -21.7% Nida 24-54h error without dropsondes (km) forecast time (h) Typhoon Nida(2009) assimilation with/without drop sondes. Typhoon track forecast error (left) and relative forecast error improvement(right). 13

14 Tuning of Fengyun satellite observations 1. FY 3A/B MWTS microwave temperature vertical sounding FY 3A satellite data are seriously damaged by frequency shift and other instrument problems. Frequency shift means designed frequency at pre launch stage is not as same as real frequency on payload. Frequency shift is a common problem for microwave sounding data, tuning data will obtain a consistent data series. 2. FY 2E Atmosphere Moving Vector FY 2E AMV data high assign techniques are reviewed and improved. FY 2E AMV data quality is improved for Quality Index > FY 3A/B MWTS & FY 2E AMV data are ready for NWP data assimilation using. 14

15 FY 3A MWTS Frequency shift & Nonlinear radiance bias correction GRAPES Background Directly use FY 3A data (O B data, blue dots), and use FY 3A data after frequency shift correction and nonlinear radiance bias correction (O B data, red dots) 15

16 Frequency shift is a common problem MSU CH3 (54.96 GHz): NOAA-6 to NOAA-14 STD(FG_DEP) Frequency Shift / MHz Reference data is LBL model based on ERA-Interim fields 1 cycle per month: MSU CH3 data before (color dots) and after (blue) frequency shift correction, from NOAA-6 to NOAA-14 16

17 Improvement of FY 2/D/E atmosphere moving vector data quality Compare of FY-2E InfraRed AMV ( hpa, all samples) with Radio-sonde winds in (Unit: m/s) Compare of FY-2E InfraRed AMV ( hpa, all samples) with Radio-sonde winds in (Unit: m/s) 17

18 Improvement of FY 2/D/E Atmosphere Moving Vector (AMV) data quality IR AMVs monitoring from ECMWF, August 2011 (Quality Index > 0.8) AMV Bias FY2E Meteosat hpa hpa hpa AMV STD FY2E Meteosat hpa hpa hpa FY2E Meteosat7 18

19 Operational running for CMA REPS was implemented since 1 Jun 2011 working on REPS based on the WMO BO8RDP project. Jan.2006 working on REPS over China May 2009 Real time running. Jun 2010 Operational implementation Jun 2011 Configuration of REPS Model(WRF-ARW dynamical core) 15km grid space, 31 vertical levels 60h forecast 15 members BGM initial perturbation method multi- physics Forecast data was put into CMA TIGGE archive center every day 19

20 Global Ensemble Prediction versus Regional Ensemble Prediction CMA T213 EPS CMA Reg EPS 21 日 08 时 22 日 08 时 24h 观测降水分布 average:190.3mm peak:460.0mm Compare to global EPS, regional EPS can provide fine scale precipitation information, especially for local heavy rainfall. 20

21 TIGGE Archive Centres and Data Providers(9) UKMO CMC ECMWF CMA NCAR NCEP MeteoFrance KMA JMA CPTEC BoM Archive Centre Current Data Provider 21

22 Beijing TIGGE archive center: data archive & service Data archive Receive data since 1 November, More than 581.9TB data are archived by 31 October,2011. Global EPS system data exchange CMA exchange more than 52.9TB data, KMA 13.85TB, JMA 12.01TB ECMWF exchange more than 373.5TB data UK Met Office exchange more than 59.86TB data. On line data support Period update, on line support most recent 7 days data Data service On line monitoring and EPS comparing, 2.1TB (by June 2011). Off line data services, 45TB (June 2011). Shanghai EXPO, tropical cyclone data real time download support. TIGGE LAM project, CMA regional EPS data archive, 3.6G/per day. 22

23 Multi EPS typhoon track error (2010 all samples) Track Error (km) EPS mean typhoon track error CMA ECWMF UKMO JMA KMA NCEP CMC MEAN FAIR Observed frequency CMA ECWMF UKMO JMA KMA NCEP CMC MEAN FAIR Reliability FstHour Forecast Probability Multi-EPS combination results is the best! Compare single EPS typhoon track error with multi model EPS typhoon track error. Multi model EPS is more accuracy than any single EPS. 23

24 Multi-EPS precipitation combination and downscaling Multi-EPS combination is the best! 24h 48h BREM(Bias Removed Ensemble Mean) 72h 96h 2007:Jun 1 st Aug 27 th, 15-50N,90-140E, 24h,48h,72h,96h precipitation forecast ETS score ; Observation: TRMM 0.25X

25 Tropical cyclone and rain storm dynamics and predictability 1. Dynamic researches Tropical cyclone dynamic: Generation mechanism, kernel dynamic, typhoon induced precipitation South China heavy rainfall dynamic 2. Predictability researches Meiyu front predictability Meiyu front induced heavy rainfall adjoint sensitive research Typhoon adjoint sensitive research. 25

26 OUTLINE Research focuses of THORPEX in China THORPEX research activities Field campaigns South China Heavy Rainfall Experiments (SCHeREX) International radio sonde comparison filed experiment Observation experiment of mesoscale predictability on summer rainstrom around Beijing area. Tibetan Plateau relative field experimen Demonstration projects Future tasks 26

27 South China Heavy Rainfall Experiments SCHeREX Objectives: Obtaining meteorological data and describing 4 dimensional structure of meso scale systems; Developing gridded meso scale database, and improving heavy rainfall prediction by using this database. Experiment periods: ( ) Phase I (May 1 June 10): Experiments in the southern China (Zone A), including airbone drop sonde experiments Phase II (June 10 July 20): Experiments on the middle reaches of the Yangtze River valley (Zone B), Huai River valley (Zone C) and lower reaches of the Yangtze River valley (Zone D). 27

28 Experiment zone and equipment Equipments Experiment zone A B C D Tota l Doppler Radar Radio Sounding Station Mobil Radar Meteorological Station Automatic Weather Station (AWS) Boundary Layer Observation Wind Profiler Drop Sounding Aircraft 1 281

29 Goni Morakot Airplane path Route of the dropsonde aircraft between the Typhoons Goni and Morakot flying during UTC on August 7,

30 Application of drop sonde data, case study (a) (b) c c c c Wind vectors at 850 hpa (arrows) from the RAFS analysis at 0200 UTC on August 9, 2009 without (a) and with (b) the dropsonde data incorporated. Contours are wind velocity (unit: m s 1 ). Shadings are areas of strong winds with velocities larger than 16 m s 1. C denotes the cyclone center. 30

31 International radio sonde comparison filed experiment From 12July 3August,2010, WMO 8th international radio sonde comparison field experiment was hold in Guangdong 9 countries (China, U.S., Germany, Finland, South Africa, France, Switzerland, Japan, Korea ) 16 various kind radio sonde types 72 experiments 5 radio-sondes in one group 31

32 Temperature difference (K) Simultaneous daytime temperature differences [K], Yangjiang 2010 Systematic temperature bias in daytime Larger spread in high level temperature observations. Sun radiation bias correction scheme? Height (km) LM VAISALA DAQIAO CHANGF HUAYUN INTERMSA MODEM GRAW MEISEI ML JINYANG MEIS_REF 3THERM VAIS_REF 32

33 Beijing Mesoscale observing experiment and observing system Wind profiler Radar GPS/MET Radio Sounding Sensor-laden vehicle GPS sounding Robert aircraft synoptic key area h, t, u, v hpa, 200hpa Low vortex / trough Westerly trough Distribution of all participant station in observation experiment in 2009 summer. synoptic key area q, t, u, v hpa 33

34 Adaptive observation flow chart Phase I: Background Information collection Real time synoptic situation Numerical forecasting Forecaster analysis Phase II: Adaptive observation decision making What kind of synoptic system? When/how affect? Rainfall, position, move speed?, Phase III: Adaptive observation application Lowest level command (3 rd level): Observing team be informed 36h in advance Sounding team is on call Mobile observing team reach to target area Middle level command (2 nd level): Observing team be informed 24h in advance Sounding team start observing Mobile L-band radar start observing Robert aircraft is on call Highest level command(1 st level): Observing team be informed 2h in advance Robert aircraft team start observing 34

35 OUTLINE Research focuses of THORPEX in China THORPEX research activities Field campaigns Demonstration projects SWFDP TLFDP Future tasks 35

36 CMA support WMO Severe Weather Forecasting Demonstration Project in Southeast Asia (SWFDP SeA) Global centers include CMA, JMA, KMA. Products of disaster weather monitoring and forecast which are based on China global model numerical prediction system, the global ensemble prediction system, typhoon ensemble forecasting system and FY2 stationary satellite. Start from March 1 st,

37 WMO Typhoon Landfall Forecast Demonstration Project (WMO-TLFDP) The WMO-TLFDP has made significant progress in various aspects, which can be summarized as follows: Real-time TC forecast products are collected through various ways from a total of 13 agencies, including the TIGGE TC EPS products from 7 agencies. A real time verification platform of TC forecast has been set up. An evaluation system for TC forecast has been set up. A variety of TC forecast and verification products are disseminated through the WMO-TLFDP website ( A new landfall typhoon forecast flow based on the products of WMO-TLFDP has been experimentally set up in ECRMC/CMA, in which the TC EPS products are playing an important role. Based on a benefit assessment of TC forecast services in Shanghai from 2010 to 2011, the overall satisfaction factor is fairly high up to 82.3%. 37

38 Collaboration with the THORPEX NW Pacific Tropical Cyclone Ensemble Forecast RDP From TCEFP to TLFDP TC EPS products in CXML format Verification information on TC EPS products Training (to help forecasters prepared to use TC EPS products) From TLFDP to TCEFP Ideas or requirements on user-oriented TC EPS products Feedbacks on how and to what extent TC EPS products benefit the operational forecast and public service Training (to popularize the experience of using TC EPS products to broader users) 38

39 GIFS TIGGE typhoon CXML products support Shanghai World EXPO, EPS members from: CMA+ECMWF+UKMO+JMA+NCEP+CMC UTC UTC UTC UTC UTC UTC UTC UTC Typhoon 1013# GIFS-TIGGE CXML product example: Ensemble tracks and strike probabilistic products Initial time from to UTC, 240hour forecast, Color for strike probability; blue line for multi-eps members, green line for mean track, black for best track (observation). 39

40 OUTLINE Research focuses of THORPEX in China THORPEX research activities Field campaigns Demonstration projects Future tasks 40

41 THORPEX future research and development The aims of THORPEX programme will continuously focus on improving high impact weather forecasting in global, by extending reliable forecast range, encouraging to develop global ensemble prediction system combination method. The priority research fields: Key dynamics processes and predictability Numerical prediction required high quality initial condition and responding observation data coverage. Observation strategy and adaptive observation Data assimilation method, especially for high resolution Tropical organized convection, tropical cyclone,extratropical transition and tropical extratropical interaction 41

42 The future focus for THORPEX China committee The THORPEX relative research will focus on to accelerate improvements in 1 day to 2 week high impact weather forecast in Eastern Asia. Research will focus on: Efficiently use operational in situ/satellite observation system and data in numerical model, assess forecast sensitive to observations. To assess and design observation systems to match the requirement of numerical model system; To diagnose numerical model error resource, improve model dynamics and model physics. To improve ensemble prediction system, and multi model ensemble combination and downscaling method; To demonstrate hydrology user oriented interactive rainfall forecast system To combine field experiments data with THORPEX relative researches. 42

43 Thank you! 43