RAPIDS Operational Blending of Nowcasting and NWP QPF

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1 RAPIDS Operational Blending of Nowcasting and NWP QPF Wai-kin Wong and Edwin ST Lai Hong Kong Observatory The Second International Symposium on Quantitative Precipitation Forecasting and Hydrology 5-8 June

Observatory The Second International Symposium on

2 Lin et al (2004) 2

3 extrapolation effective in advective cases more effective in nonlinear moving; rapidly changing cases Nowcast High resolution NWP high resolution, rapidly updated seamless very-short-range QPF 3

4 RAPIDS Rainstorm Analysis and Prediction Integrated Data-processing System Nowcasting component SWIRLS 1-6 hr QPF by extending the linear extrapolation of radar echoes NWPcomponent Non-hydrostatic Model (NHM) 1 6 hr QPF by non-hydrostatic numerical model Hourly update 2 km resolution T+1 T + 6 hour forecast 4

of radar echoes NWPcomponent Non-hydrostatic Model (NHM) 1 6 hr QPF by

5 5

6 6

7 Extending SWIRLS F/C to 6 hr modified semi-lagrangian advection scheme (SLAS) Robert scheme (3 iterations) Bi-cubic Lagrangian interpolation Flux limiter (local max, min constraint) One-way nesting resolution 1.1 km -> 0.5 km Less dispersive Circulation preserved dz dt = Z t Z + u x = 0 TREC wind Forecast reflectivity TREC wind Up to 6 hr (6-min interval) Forecast reflectivity pure rotation Up to 6 hr (6-min interval) 7

8 Performance Verification data sets : 8 May 20 Dec 2005 Overall, similar performance For Amber, SLA slightly better than LE POD (SLA) POD (LE) 0.8 FAR (SLA) FAR (LE) Performance CSI (SLA) PIL (SLA) CSI (LE) PIL (LE) Amber Rain Red Rain Flood Landslip Warning Guidance Issued By SWIRLS 8

8 FAR (SLA) FAR (LE) Performance 0.7 0.6 0.5 0.

9 Non-hydrostatic Model (NHM) 5-km horizontal resolution 121x121 grid-points, 45 vertical levels (lowest at 10m ) Trial operation started in April 04 (two runs per day) and upgraded to hourly update in April 05 Initial : RSM + LAPS moisture Boundary : RSM KF + 3 ice bulk cloud microphysics 9

in April 04 (two runs per day) and upgraded to hourly update in April 05

10 Remote sensing data assimilated in HKO-LAPS Radar reflectivity and Doppler velocity Geostationary satellite imagery GPS PWV Radar derived echo movement (TREC wind) QuikSCAT retrieved wind 10

11 LAPS + NHM Initialized by ORSM 15 UTC 7 May 2004 T + 8 hr forecast Initialized by LAPS (no radar data) 11

12 Initialized by LAPS with radar and GPS data 12

13 Blending SWIRLS and NHM QPF QPF ( t) = (1 w( t)) QPF + w( t) SLAS QPF NHM 1. NHM rain pattern adjustment translation, rotation, deformation 2. Dynamical blending hyperbolic function with varying end points adjustment β α β α w( T ) = α + T 2 { 1+ tanh[ γ (3 9)] } α, β determined dynamically by past performance 13

Dynamical blending hyperbolic function with varying end points adjustment β α

14 Location adjustment Rigid and projective transforms J ( M ) = = = e 2 x, y [ I ( x', y') I( x, y) ] f [ I ( x', y') I( x, y) ] f 2 2 dxdy nonlinear coordinates transformation where x' y' = x M y I f ( x, y) I ( x, y) Input image reference image transformed image I f ( x', y') I f ( x, y) Szeliski (1996) 14

coordinates transformation where x' y' = x M y I f ( x, y) I ( x, y) Input

15 α and β derived from real-time verification of QPFs from NHM and SWIRLS β α γ 1 β α w( T ) = α + T 2 { 1 + tanh[ γ ( 3)] } 15

16 ν SWIRLS w( t) = ( t = 0; t = ν + ν SWIRLS NHM 6) ν = max( h( R1, R2 ), h( R2, R1 )) 2μR μ 2 1 R σ 2 R σ 1 R μ + μ σ + σ R 1 R 2 R 1 R 2 Measure of spatial error Intensity difference mean value of R 1 NHM QPF Radar rainfall analysis standard deviation of R 1 ν = 0 (perfect match) ν -> (totally mismatch) Venugopal et.al. (2005) 16

error Intensity difference mean value of R 1 NHM QPF Radar rainfall analysis

17 Re-visit of blending weight Frequency distribution of α and β Lost of pattern matching skill in SWIRLS SLA T+6 hr forecast 17

18 Re-visit of relocation vector NHM Forecast Rainfall Analysis min [ R NHM ( x, y) R an ( x u, y v)] 2 da 18

19 T + 1 hr T + 3 hr x - component y - component 19

20 T + 6 hr T + 9 hr x - component y - component 20

21 SWIRLS SLAS F/C NHM DMO F/C NHM F/C (rigid transformed) + Radar observation (verification) RAPIDS F/C SWIRLS good intensity F/C NHM good storm development F/C RAPIDS the best F/C 21

22 Red rainstorm 2 June

23 Red rainstorm on 2 June

24 T+5 and T+6 hr f/c starting at 06HKT SWIRLS SLA NHM DMO ACTUAL 24

25 NHM with phase correction RAPIDS ACTUAL 25

26 T+3 hr and T+4hr f/c starting from 08HKT SWIRLS SLA NHM DMO ACTUAL 26

27 NHM with phase correction RAPIDS ACTUAL 27

28 22 HKT 29 July 2005 (outer rain-bands due to TS Washi) Relocation of rainband based on projective transform Actual rainfall analysis at 22 HKT Relocation of rainband based on rigid transform 28

29 6-hr forecast from NHM 6-hr forecast from NHM Rigid transform 3-hr forecast from SWIRLS SLAS Projective transform 29

30 RAPIDS : Performance in 2005 More skillful rainfall forecast using blending Similar performance for two pattern adjustment schemes Grid-based (2km resolution) rain/no-rain verification Rainstorm cases in 2005 ~ 170 # of forecast 30

31 Concluding remarks RAPIDS combines QPF from SWIRLS: frequently update radar obs + improved advection forecast NHM: information on development of precipitation Optimal blending to add further skills in QPF using nowcast and NWP Future development Intensity change in SWIRLS QPF Improve the data assimilation system for NHM Regional phase correction guidance on potential precipitation development from other model forecast elements Probabilistic QPF 31

32 The End Thank you 32

Nowcasting of significant convection by application of cloud tracking algorithm to satellite and radar images

Nowcasting of significant convection by application of cloud tracking algorithm to satellite and radar images Ng Ka Ho, Hong Kong Observatory, Hong Kong Abstract Automated forecast of significant convection