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Implementation of RRR in China

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Presentation on theme: "Implementation of RRR in China"— Presentation transcript:

1 Implementation of RRR in China
(Evaluate the impact of surface observations from China Automatic Surface Observation Network on numerical weather forecast) Jianxia Guo Meteorological Observation Center of CMA

2 Outline Background Implementation of the WMO RRR (The Rolling Review of Requirements) Process in china The on going work and the vision

3 Background Large of area Complex of topography
Diverse of weather systems Frequent of meteorological disasters Big challenge for meteorological observation and weather forecast (73°33′E~135°05′E,3°51′N~53°33′N)

4 National surface observation stations Doppler Weather Radars
Background The Current surface-based Network National surface observation stations 2423 Surface Observation AWS Marine met. Station 475 Agro-met. Observation 2075 Environmental Met. Obs. Upper Air Observation Weather Radar Network 181 Wind profile 69 GNSS/met Raidosond 120+3 Doppler Weather Radars 181

5 Issues How to make a integrated network in such a complex situation?
Establish a integrated view to the various observation networks. Have effective tools to handle the complex situation and various temporal and spatial scales observation. Develop and design the new observation or improve the existing observation based on the integrated view.

6 Outline Background Implementation of the WMO RRR (The Rolling Requirements Review) Process in China The on going work and the vision

7 EVALUATION OF REQUIREMENTS AGAINST SYSTEM CAPABILITIES

8 User requirements to Observation
Application areas Global numerical weather prediction; Regional numerical weather prediction; Synoptic meteorology; Nowcasting and very short-range forecasting; Seasonal and inter-annual forecasts; Atmospheric chemistry; Aeronautical meteorology; Climate variability; Climate change; Marine meteorology; Hydrology; Agricultural meteorology. Requirements statement (quantitative) Horizontal and vertical resolution; frequency (observation cycle); Timeliness (delay in availability) ; Accuracy (acceptable root mean square error and any limitations on bias). Maximum requirement Threshold requirement

9 Observing system capabilities analysis and review

10 The critical review Numerical model and Data assimilation are important and useful tool for critical review ; Make the critical review process objective; Establish a integrated view to the observing systems; Quantify the contribution of each observing systems to NWP; Target the observation area for special weather system; Assess the quality of the each observation; Produce new observation products; Design the observation network.

11 Practices Optimization of the reference climate observation network;
Team work, from 2009 to 2012. Optimization of the surface AWS network. Team work, on going since 2014.

12 Optimization of the reference climate observation network
The requirements: Representativeness (away from the local influence). Sustainable (lasting for a long time without interference). Accuracy (meet the GCOS requirements) The coverage (the network should cover the whole country).

13 Cont’ The capabilities data base
The surrounding environment detail from the field investments. The series of historical land-use type around the site of 50km from MODIS.

14 Cont’ The satellite images over the
site to calculate the proportion of the city area around the site.

15 Cont’ The layout of city developing plan of 20 or 30 years from the local government near the site. The observation data records. The information of the location, instruments, calibration etc.

16 Cont’ The critical review Single station assessment
The grades of the surrounding environs. The quality of the data series. The contribution of the representative area. The impregnability by the city developing plan.

17 Cont’ The network assessment The coverage cost.
The covariance between the chosen network with the background. Original netwrok Ratio of coverage optimized Station number Temperature covariance

18 Cont’ The results Degraded some stations from the climate reference station’s rank to the basic station’s rank; Upgraded some good stations from the basic or ordinary station’s rank; Established some new stations at the west part of china.

19 Optimization of the surface AWS network
National surface observation stations:2423 supported and managed by CMA Unified criteria and standards Regional automatic weather stations: 55680 supported by : CMA local government other organizations managed by: local Meteorological standards are not unified most of them lack of calibration Needs: NWP need more dense data than the national stations to evaluate the forecast results; Meso-scale severe weather need to be captured by sufficient and credible data.

20 RRR process 2016 2015 2014 Comprehensive analysis and decision-making
decision-making and statement Requirements and critical review 2 Requirements and critical review 1 Capability Comprehensive analysis and decision-making Requirements by NWP (impact study) The status of the existing AWS Requirements by severe weather capture 2016 2015 2014

21 Stage 1 (2014) Capabilities data base Collected metadata of AWS
Instruments factory , type approval, deployed time, calibration Running condition power supply,  communication mode, access Site Location, area, land cover, surrounding environs, thunder prevention Measurement elements Provincial and local meteorological services all over the country participated in. Greatly contribute to the judgment of data credible.

22 Stage 2 (2015) Requirements analysis 1 Weather systems analysis
Focus on 7 types of weather systems that may induce the severe weather.(low pressure ,convergence line, low vortex, low trough, front, shear line, the subtropical high) Scale, activity area High-impact /severe weather analysis Focus on 4 kinds of high-impact /severe weather (heavy rain, wind, hail and thunderstorm) Occurrence source, developing and weakening area, moving route and other sensitive areas. Forecasters from 31 province did the analysis. They selected AWS in the important areas according to the metadata.

23 Stage 3 (2016) NWP requirements analysis ( critical review)
Impact study OSEs, OSSEs, FSO Case study, period operational running test Density variation test for the economic selections. As the complex condition, different region may have different impact results. National and 8 regional NWP centers have joined in this work.

24 cont’ A technical experts team has been established. Exchange the progress and problems frequently. The technical detail has been determined by the participants. SMS completed 4 case studies and got some encouraged results. Dr. Sun Min will show the presentation soon. INSTITUTE/CMA NMC MOC north of China east of China south of China centre of China northeast of China southwest of China Xinjiang China Northwest of China  SCOPE Nationwide Beijing/Tianjin/Hebei/Shanxi/Neimenggu Jiangsu/Zhejiang/Anhui/Fujian/Jiangxi/Shandong/ Shanghai Guangdong /Guangxi/Hainan Hubei/Henan/Hunan Liaoning/Jilin/Heilongjiang … Sichuan/Yunnan/Guizhou/Tibet/Chongqing Xinjiang Gansu/Shaanxi/Qinghai/Ningxia QC GRAPES MDOS OPERATION MODEL WRF WHMM SWC-WARMS ASSIMILATION 3DVar 4DVar ADAS SENSITIVITY TEST osse+ ose density density+ batch  batch

25 Preliminary case study
Case:, ,12UTC ,00UTC Torrential rain caused by Low vortex shear observation 24h precipitation ,00UTC ,00UTC

26 Model Model:WRF_V3.6.1 Assimilation :WRFDA_V3.6.1, 3DVAR
Background: GSF Analysis 0.5〬*0.5〬 Period: ,12UTC~ 日,00UTC Centre of D01:(29〬N, 110〬E) Grid:171*171;253*241 Vertical :41 Resolution:27km,9km Steps:180s Physics :WRF double moment,6-class scheme New Grell scheme

27 experiments Experiments name Data of assimilation
GTS synop + radiosonde 1 TRUE with Regional AWS 2 EXP_NON_ASON without Regional AWS 3 EXP_NON_ASON_T Regional AWS without T 4 EXP_NON_ASON_P Regional AWS without P 5 EXP_NON_ASON_TD Regional AWS without TD 6 EXP_NON_ASON_V Regional AWS without UV

28 ,12UTC SNOP(6164,blue) Radio sound(401,red) ,12UTC Regional AWS(40308)

29 observation TRUE

30 EXP_NON_ASON EXP_NON_ASON_T EXP_NON_ASON_TD EXP_NON_ASON_P EXP_NON_ASON_V T>P>V>TD

31

32 Thanks for your attention! Any suggestions?


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