Extra Radiosonde Observations at 06UTC (2PM) in China Mainland and Their Impact Study on Mesoscale Model /Global Model XU Zhifang 1 CAO Yunchang 2 WANG Ruiwen 1 WANG Dan 1 GONG JianDong 1 ZHANG Lihong 1,3 TIAN Weihong 1 1 Numerical Weather Prediction Center of CMA 2 Meteorological Observation Center of CMA 3 Chengdu Institute of Plateau Meteorology of CMA SIXTH WMO WORKSHOP ON THE IMPACT OF VARIOUS OBSERVING SYSTEMS ON NWP

Outline Background Meso-scale model impact study –Observing System Simulation Experiments (OSSEs) –Observing System Experiments (OSEs) Global model impact study –Radiosonde impact diagnose –OSEs Discussion and conclusion

Beijing Rainfall Tianjing Rainfall Shaoguan Rainfall Lianyungang Rainfall Xiangshui Rainfall Shenyang Convective weather Wuhan Rainfall Guizhou Rainfall Xiangyang rainfall Hazard weather events (strong convective, heavy rainfall etc. ) generally happened in afternoon to mid-night, bring lives and property lost. Met-service demands …, more than “what’s the weather going to be ?

Frequency of short-duration heavy rainfall ≥ 20 mm/h (%). (Chen, 2013) SpringSummerAutumn Samples from April-September Climate background of short-duration heavy rainfall

Diurnal variations of frequencies of Short-duration heavy rainfall ≥20 mm/h (%) BT 08-14BT 14-20BT 20-02BT Diurnal variations of average frequencies of Short-duration heavy rainfall ≥20 mm/h, and temperature of brightness blackbody on cloud top (TBB) ≤ -52 ℃. Short-duration heavy rainfall frequencies are high in the period from afternoon to evening (14BT-20BT) in summer. (Chen, 2013) Diurnal variations of heavy rainfall

key questions in meso-scale model How much benefit can be obtained from extra radiosondes at 14BT (Beijing time) ? How many extra stations are needed? Balance the cost & benefit Radiosonde balloon type Expensive balloon to get observations to 10hpa or cheaper one in lower and middle troposphere Could unconventional observations replace extra radiosondes? eg, GPS/PW + AMVs + others

Exp1: without 06UTC radiosondes Exp2 : with 06UTC 80 stations radiosondes OSSE experiments Distribution of 80 Radiosonde Stations selected by forecasters Nature run from WRF model NCEP FNL for IC & BC Resolution: 10km, 50level Domain : E, 10-70N 12 hour spin-up Simulated observations : TEMP(80), synop, ships, AMVs, GPS/PW, airep, wind-profiler Obs errors: real data obs error No obs bias be considered

24h Accumulated Precipitation (Unit:mm) Nature Run (b) observation (c) Truth(WRF) (d) Truth(WRF) (a) Observation 00UTC UTC 21 June 00UTC UTC 22 June

Mean RMS error of 06UTC analysis (3 days average ) OSSE results

Left: Equal Threat Score (ETS); Right: Bias (initial: 06UTC19, 06UTC 20, 06UTC 21, June 2014) 6hour interval accumulated simulated rainfall (init:06UTC) Precipitation threshold (mm)

Left: Equal Threat Score (ETS);Right: Bias (initial: 12UTC19, 12UTC 20, 12UTC 21) 6hour interval accumulated simulated rainfall (init:12UTC)

Synoptic analysis Analysis of 500hpa geopotential height on 06 UTC 19 June 2014 Truth exp1 exp2

Truth exp1 exp2 Analysis of 700hpa wind field on 06 UTC 19June 2014 False vortex Convergence line

EXP1 EXP2 The 6h Accumulated Precipitation (Unit:mm) Solid line ： Truth ； Shaded ： simulated

Truthexp1 exp2 06UTC19 12UTC19 18UTC19 pressure longitude

Extra radio-sonde field experiments Period: 1-30 June of 2013, and 1-30 June of 2014 Stations: 120 stations in 2013, and 113 stations in 2014 of China Organization: China Meteorological Administration (CMA) Observing Time: 06UTC (14PM in Beijing time) (balloon released at 05:15 UTC) Observing height: m for P/T/RH, and m for U/V Cost : 1130 RMB per time per station (~US$200)

Schematic of GRAPES_RAFS configuration DATA ASSIMILATION GRAPES_RAFS （ Rapid Analysis & Forecast System ） obs data in 3h window; Basic configuration

OSE Experimenst Design Period:1-15 June 2014 Exp_14 : with 06UTC radiosonde (u,v,p,RH). Exp_no14: without 06UTC radiosonde. Exp_300hPa: with 06UTC radiosonde below 300hPa. Exp_nogps: with 06UTC radiosonde, without unconventional observation ( GPS/PW, AMVs, Radar VAD wind, GNSS/RO retrieval T,q ) Distribution of radiosonde stations Exp_NW: without 06UTC radiosonde at North-West area. Lower frequency for short-duration heavy rainfall Exp_NE: without 06UTC radiosonde at North-East area.

With & W/o 06UTC radiosonde experiment Verification: 12 UTC background VS radiosonde observation The Vertical profiles of RMSE (Red) and Bias(Blue) for background field verified against radiosonde observation at 12UTC P RH U V Solid: Exp_14 Dash: Exp_no14 Exp_14 worse Exp_14 better worse better

6h rainfall verification (against 2400 AWS station) Initialization ： 06UTC Initialization ： 12UTC ETS ETS

EXP_no14 Exp_14 3h (09UTC-12UTC) accumulated precipitation (Unit:mm) Solid line :observation shaded: simulated Forecast field on 12 UTC 1 June 2014; Shaded: Water vapor content at 850hPa; Solid line: geopotential height at 500hPa ; Wind barb: wind direction and speed at 850hPa Observation EXP_no14 Exp_14

With 06UTC radiosonde & with unconventional obs Verification: 12 UTC background VS radiosonde observation The Vertical profiles of RMSE (Red) and Bias(Blue) for background field verified against radiosonde observation at 12UTC Solid: Exp_nogps Dash: Exp_no14 P RH U V Exp_nogps worse Exp_nogps better Exp_nogps worse

6h rainfall verification Initialization ： 06UTC Initialization ： 12UTC ETS

P U V ETS Initialization ： 06UTC Accumulated 6h simulated rainfall verification Solid: Exp_14 Dash: Exp_300hpa With 06UTC radiosonde (top to 10hpa, or 300hpa) Verification: 12 UTC background VS radiosonde observation Exp-300hpa better Exp-300hpa better

With & W/o 06UTC radiosonde at NW/NE of China Verification: 12 UTC background VS RS observation U U V V P P Solid: Exp_14 Dash: Exp_NW Solid: Exp_14 Dash: Exp_NE The Vertical profiles of RMSE (Red) and Bias(Blue) for background field verified against radiosonde observation at 12UTC

ETS Accumulated 6h simulated rainfall verification Initialization ： 06UTC

~14 stations ~81 stations ~16 stations Solar radiation bias estimate O-A (ERA) June-August, 2014, 00/06/12UTC Sippican MK2 (USA) China

GRAPES forecast model Non-hydrostatic equations Terrain-following coordinate Arakawa-C(horizontal) and Charney Phillips(vertical) grid 60levels, model top at 32.5km Resolution 0.5°x 0.5 °. GRAPES-3DVar Observations assimilated : conventional data (radiosondes, synops, ships, AMV and aircraft), GNSS RO, radiances(NOAA15,16,18,19,METOP-A AMSU-A) Incremental digital filter initialization GRAPES (Global/Regional Assimilation PrEdiction System) Experiment Setup Experiment 0012: Control run, with all observations Experiment : Control run + 06UTCC radiosonde Experiment 0006 : 00UTC+06UTC radiosonde Cycling time: 1 st - 30 th, June, 2014 Verified against ERA-Interim and Radiosonde observation

Observation impact on analysis

500hPa 850hPa 700hPa Observations contribution Water vapor content 6h(00-06UTC) observing precipitation

850hPa 500hPa 200hPa 700hPa Observations contribution Wind

850hPa 500hPa 200hPa 700hPa Observations contribution Temperature

GRAPES ANA-ERA in East Asia at 06UTC Temp Heigh

GRAPES ANA-ERA at 500hpa in East Asia at 06UTC

Verificiation: GRAPES ANA-ERA in East Asia at 12 UTC

GRAPES ANA-ERA in East Asia at 18 UTC

B-O Pressure Bias and STD (against GRAPFS Ana) 06UTC 12UTC 18UTC00UTC pressure

GRAPES forecast in East Asia at 12 UTC

Verification CARD (Ref: FNL)

Discussion and conclusions RS data quality is the most important issues. Solar radiation bias is very likely the key reason for weakness of 6UTC radiosonde observation impact. Solar radiation bias correction scheme should rechecked for different RS type, black namelist isneeded. For troposphere, assimilating 06UTC radiosonde observations can improve forecast (eg. higher precipitation score and smaller RMSEs), especially for forecast initial from 06UTC. RS below 300hpa has similar impact for precipitation forecast. Unconventional observation data are not similar as 06UTC radiosonde observations. 06UTC RS need more research on their impact on meso-scale model and global model.

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