Overview of radar data assimilation at KMA: Status and Plan

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1 Overview of radar data assimilation at KMA: Status and Plan 2 November 2012 Eunha Lim and Hoon Park Numerical Model Development Division, KMA

2 Contents Status of radar DA Brief history of radar DA Configuration of radar DA in two operational models regional(kwrf) and local(ldaps) models Radar network and QC for assimilation Generating super-observation and defining error for LDAPS Recent studies Adding more elevations to improve rainfall forecast within 6 hours Introducing adaptive vertical grid to represent strong temperature inversions Future Plans Pre-processing and QC of radar data, DA, verification 2

3 History of radar DA Developing observation operator in MM5/WRF Radial Velocity Reflectivity Reflectivity RH Correction method ~ 12 ~ Set up the pre-processing system for v r, dbz Adapt radar data assimilation in RDAPS ( 05.5) Adapt radar data assimilation in KWRF ( 08.6) Develop new pre-processing system for v r, dbz ( 08.11) Develop the code to pre-process KMA radar data in UM Develop pre-processing system for rainrate for UM ( 09.1) Assimilate rainrate (radar+guage) through LHN in LDAPS ( 11.7) Assimilate v r in LDAPS ( 12.5) * Yellow components are lunched in operation mode 3

4 Configurations of radar DA Model Radar LDAPS( 12.5) KWRF( 08.6) Horizontal res. 1.5km 10km Top(Levels) 40km(L70) 20km(L38) DA(res.) 3dVar(3.0km) 3dVar(10km) Analysis freq. 3hrs 6hrs Data type Radial velocity Radial velocity, Reflectivity Coordinate (x,y,φ) (x,y, φ) Error STD + f(r) STD Resolution 15km ~ 40km Additional thinning near radar site Elevation angles o 2 elevations lower than 3.5 Limit of height - 8.5km Velocity dealiasing/routine -/no o all -/yes 4

5 Configurations of radars - 8 S-band radars, 2 C-band radars - 7 S-band radars except for GNG are assimilated GNG (S band) KSN (S band) BRI (C band) SSP, GSN(2, S) JNI (S band) GDK (S band) MYN (C band) KWK, PSN(2, S) YEAR Elv. Num./ Time(Min.) Nyquist Vel.(m/s) Band S S C S S S C S Range(Km) 280, , , , , , , 480 Moments DZ,CZ, VR,SW DZ,CZ, VR,SW DZ,CZ, VR,SW DZ,CZ, VR,SW CZ,VR,SW DZ,CZ, VR,SW CZ,VR,SW DZ,CZ, VR,SW Transmitter Klystron Klystron Klystron Klystron Klystron Klystron Klystron Klystron 5

diameter D=A When A>R (far from the radar) D=R When A<R (near the radar) D=A Very

6 Superobbing method circle resolution and its description Regular bin(r) and azimuthal(θ) spacing Cell size: R=3km, θ=3deg obs within a circle of arc length(a) in diameter D=A When A>R (far from the radar) D=R When A<R (near the radar) D=A Very close to the radar Only one data to each model grid Range < 3km No data Thinning resolution About 15km 6

7 Error for radial velocity Measurement error ~ 1m/s Superobbing error Definition The variance of the departure of the individual innovations from the mean innovation for the cell To weight the observation terms in the penalty function in the assimilation Representation error Definition The truth the turth seen by the model Assume Neither the observation(y) nor the model(x) is biased The observation operator(h) is linear Y H(X) 7

8 Error for radial velocity Representation error Plot of rep. error during 2 months for Cobbacombe radar 2 ( O B) Range(km) Figure 35 Evolution of the representativeness error with range in RadarWindImpact.doc(by Simonin) 8

9 Obs BackGr O-B Raw data - Jindo

10 Super observation - Jindo Super observation Error of super observation Including representation error 10

11 Experimental design Model: LDAPS Period: 18UTC 28 June ~ 00UTC 6 July 2012 Targets: 30 June, 5 July Forecast length: 24 hours every 6hr LBC: Global model(25km/l70), 1hr interval Experiments Name Elevation angles AVG 1) Remark OPER 2~3 (near 1.0 and 3.5 ) - Operational model MoreElevs 4~5 (+ 1: 3.5 ~ 6.0, +1: under 1.0 ) - Impact of adding more radial velocities MoreElevsAVG ο Impact of AVG 1) AVG: Adaptive Vertical Grid 11

12 Data types and distributions 18UTC 28 June 2012 (OPER) Sonde Surface Aircraft KMA 1 Scatwind METAR 16 Radial velocity Radial velocity (MoreElevs) 12

incr. over the East sea - Weaker northerly

13 Analysis increments - 1 OPER MoreElevs z=1553m Shade: π WIND - Stronger northeasterly incr. over the South sea - Stronger southwesterly incr. over the East sea - Weaker northerly incr. over near the east of Juju sea Exner pressure - No significant differences 13

14 Analysis increments - 2 MoreElevs MoreElevsAVG Shade: θ, z=93.3m Potential temperature - Higher incr. along the West coast - Lower incr. over the mountainous area Increases the horizontal gradient of temp. 14

15 Time series of RMSE and bias - v r Radial velocity Number of data RMSE(m/s) 3 2 Bias(m/s) -0.5 MoreElevs_bias (O-B) MoreElevs_bias (O-B) 1 MoreElevsAVG_bias (O-B) -1.0 OPER_bias (O-A) MoreElevs_bias (O-A) MoreElevsAVG_bias (O-A) ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' Radial wind Time(UTC) Time(UTC) OPER_RMSE (O-B) MoreElevs_RMSE (O-B) MoreElevsAVG_RMSE (O-B) OPER_RMSE (O-A) MoreElevs_RMSE (O-A) MoreElevsAVG_RMSE (O-A) RMSE - Reduced by about half of B-O - Fluctuation is slightly related with number of used data Bias - Close to zero after minimization - Fluctuation is not related with number of used data 0 ' ' ' ' ' ' ' ' Time(UTC) 15

16 Time series of RMSE and bias Sfc U MoreElevs_bias (O-B) MoreElevs_bias (O-B) MoreElevsAVG_bias (O-B) OPER_bias (O-A) MoreElevs_bias (O-A) MoreElevsAVG_bias (O-A) RMSE(m/s) 3 2 Bias(m/s) ' ' ' ' ' ' ' ' Surface U 2200 Time(UTC) -1.5 ' ' ' ' ' ' ' ' Time(UTC) synop(aws), buoy, METAR, scatwind Number of used data ' ' ' ' ' ' ' ' Time(UTC) RMSE - Reduced by about 30% of B-O - Diurnal cycle related with number of used data Bias - Close to zero after minimization except for several cases - Positive bias during 5 days 16

24hrs-accumulated rainfall OPER MoreElevs

Rainband A - Overall rainfall distribution

- Moved to south by adding more elevation

17 24hrs-accumulated rainfall OPER MoreElevs MoreElevsAVG IC: 06UTC 5 July 2012 A Rainband A - Overall rainfall distribution is well predicted by 3 exps. - Moved to south by adding more elevation angles center of heavy rainfall location is close to obs. - Moved to north by introducing AVG Rainband B - well forecasted by 3 experiments 273.0mm at Suwon B 17

18 Verification Rainfall Threshold: 5.0mm/3hr 1.25 OPER-ETS MoreElevs-ETS MoreElevsAVG-ETS 1.00 OPER-bias MoreElevs-bias MoreElevsAVG-bias 0.75 ETS Bias Against 76 surface stations Forecast time (hr) ETS - OPER has the best performance over almost whole period of forecast - Introducing AVG produces better rainfall forecast except for at the end of forecast period Bias - Underestimate rainfall - No distinct difference among 3 experiments 18

19 Verification wind at 10m 4 Wnd speed(1.5m): ~ (6hr int.) 1.0 RMSE (m/s) Forecast time (hr) Bias (m/s) OPER-RMSE(W) MoreElevs-RMSE(W) MoreElevsAVG-RMSE(W) OPER-bias(W) MoreElevs-bias(W) MoreElevsAVG-bias(W) RMSE - Higher score by adding more elevation angles - No difference by introducing AVG Bias - Positive bias for all experiments Overestimate surface wind by LDAPS - Similar score by adding more elevation angles 19

20 Verification wind speed at upper levels Wind: ~ (12hr interval) 4 sondes, first 6hr fcst Against 4 sondes over Korea peninsular - Interval: 12hrs - Forecast hour: 6hr Press sure (hpa) Bias/RMSE (m/s) RMSE - Larger RMSE except for at 850, 700hPa, especially 100hPa - Reducing RMSE by introducing AVG except for 700, 250hPa Bias - Slightly larger bias for all levels, especially at 1000hPa by adding more elevations - All negative bias except for at 1000hpa - Reducing bias significantly at 100hPa OPER-bias MoreElevs-bias MoreElevsAVG-bias OPER-RMSE MoreElevs-RMSE MoreElevsAVG-RMSE 20

21 Verification Wind at 10m according to different Initial times RMSE (m/s) OPER OPER, Wind(1.5m): ~ UTC Bias (m/s) OPER-RMSE(W00) OPER-RMSE(W06) OPER-RMSE(W12) OPER-RMSE(W18) OPER-bias(W00) OPER-bias(W06) OPER-bias(W12) OPER-bias(W18) Local time (hr) RMSE & Bias - Effects of DA last for hours: 00/06/12/18UTC: 3/6/3/4 hrs - All positive bias - Maximum bias near 17LST - Relatively larger error over day time 21

22 RMSE ( o C) Verification Temperature at 1.5m according to different Initial times OPER OPER, Temperature(1.5m): ~ Local time (hr) Bias ( o C) OPER-RMSE(T00) OPER-RMSE(T06) OPER-RMSE(T12) OPER-RMSE(T18) OPER-bias(T00) OPER-bias(T06) OPER-bias(T12) OPER-bias(T18) RMSE & bias - Effects of DA last about 6 hrs: 00/06/12/18UTC: 6/6/6/6 hrs Similar to DA cycle Longer lasting time than wind - Larger during day time, smaller during night time - Almost all positive bias, near to zero during night time - Time for the maximum error is about 14LST, except for 09LST (16LST) Need spin-up time to develop PBL 22

23 Summary for sensitivity tests Used number of radial velocities is about twice Analysis increment reflects the effect of additional wind and AVG Rainfall distributions(24h-accumulated) are not significantly different except for several cases However verification shows lower score AVG slightly mitigates the degradation of rainfall forecast Surface wind(10m) has positive bias and the impact of DA lasts less than 6hr Bias and RMSE are increased by adding more elevation angles Surface temperature(1.5m) has crucial benefit Maximum bias is about 1 o C during 14 ~16LST Minimum bias is less than 0.2 o C during 19~5LST 23

24 Future plans Radar data and QC Apply QC information of reflectivity(fuzzy) to pre-processor for KWRF Add GangNeung and NEXRAD radars Data assimilation Optimize the selection of elevation angles Analyze the O-B/O-A for each radar Apply to OPS (observation uncertainty) Change range dependent errors to each elevation of each radar Reintroduce LHN using radar-estimated rainfall produced using new algorithm Examine 1hr-cycling in 3dVar Investigate the possibility of applying 4dVar Further investigate the effect of AVG Verification Verify rainfall and shrface variables (T, wind, etc) against ~600 AWS 24

The Nowcasting Demonstration Project for London 2012

The Nowcasting Demonstration Project for London 2012 Susan Ballard, Zhihong Li, David Simonin, Jean-Francois Caron, Brian Golding, Met Office, UK Introduction The success of convective-scale NWP is largely