Big Ensemble Data Assimilation

Transcription

1 October 11, 2018, WWRP PDEF WG, JMA Tokyo Big Ensemble Data Assimilation Takemasa Miyoshi* RIKEN Center for Computational Science *PI and presenting, Data Assimilation Research Team With many thanks to JMA UMD Weather-Chaos group JST CREST Big Data Assimilation project JAXA PMM Ensemble Data Assimilation project Japan s FLAGSHIP 2020 project RIKEN Data Assimilation Research Team

2 Who am I? B.S. from Kyoto U JMA administration (2y) JMA NWP (1.25y) UMD (2y, M.S. and Ph.D.) JMA NWP (3.5y) UMD (4y) RIKEN (~6y) speakers/takemasa-miyoshi/

3 RIKEN Center for Computational Science (R-CCS) Japan s flagship institute for computational science Missions: 1) Development & operation of the Japanese flagship supercomputer 2) Center of Excellence for research on computational science

4 RIKEN Center for Computational Science (R-CCS) Japan s flagship institute for computational science Missions: 1) Development & operation of the Japanese flagship supercomputer 2) Center of Excellence for research on computational science Post-K is under development FLAGSHIP2020 project

5

6

7

8

9 A simulated study using the T30/L7 SPEEDY AGCM (Miyoshi, Kondo, Imamura 2014)

10 Advantage of large ensemble 100 samples (Miyoshi, Kondo, Imamura 2014) samples Sampling noise reduced High-precision probabilistic representation

11 20 Histogram, Q, lev=1, 1982/02/01 06Z N, E

12 20 Histogram, Q, lev=1, 1982/02/01 06Z N, E

13 20 Skewness, Ps, 1982/02/01 06Z

14 20 Kurtosis, Ps, 1982/02/01 06Z

15 Non-Gaussianity(KLD), Ps, 1982/02/01 06Z

16 Non-Gaussianity(KLD), Ps, 1982/02/01 06Z >1000 members necessary for capturing Non-Gaussianity

17 RMSE Non-Gaussianity Surface-pressure RMSE (hpa) 20 (Kondo, Miyoshi 2016) Non-Gaussianity based on members Skewness Kurtosis Frequency of Non-Gaussianity [%] Larger errors Non-Gaussian regions

18 A real-world study using the NICAM (Miyoshi, Kondo, Terasaki 2015) NICAM-LETKF (Terasaki et al. 2015)

19 Correlation patterns (Q at ~100 hpa) 40 members Kondo, Miyoshi (2015) Localized (σ=400km) This is what we use for EnKF with 40 members. 11/8 00UTC after a week cycling

20 Correlation patterns (Q at ~100 hpa) Kondo, Miyoshi (2015) 40 members members 11/8 00UTC after a week cycling

21 Correlation patterns (Q at ~100 hpa) Kondo, Miyoshi (2015) 40 members members FLOW-DEPENDENT 11/8 00UTC after a week cycling

22 With subsets of samples Kondo&Miyoshi (2015)

23 To improve data assimilation 1-day fcst error = 5-day fcst error 80 members members w/o localization Implications to vertical localization for satellite data

24 Cover feature!

25

26 Only in 10 minutes!! (Courtesy of NICT) 17:30:16 17:32:16 17:34:16 17:36:16 17:38:16 17:40:16 17:42:16 17:44:16 10 km (height) 26

27 Phased Array Weather Radar (PAWR) 3-dim measurement using a parabolic antenna (150 m, 15 EL angles in 5 min) 100x more data! 10x more data in a 1/10 period 3-dim measurement using a phased array antenna (100 m, 100 EL angles in 30 sec) 27

28 Phased Array Radar (every 30 sec.) (Courtesy of NICT)

29 Pioneering Big Data Assimilation Era High-precision Simulations Future-generation technologies available 10 years in advance High-precision observations Mutual feedback

30 Revolutionary super-rapid 30-sec. cycle Obs data processing ~2GB Obs data processing ~2GB DA (4.5PFLOP) 380GB 3GB 30-sec. Ensemble forecasting (2.6PFLOP) 2.5TB DA (4.5PFLOP) 30-min. forecasting (1.6PFLOP) 380GB 3GB 30-sec. Ensemble forecasting (2.6PFLOP) 2.5TB D (4.5P 30-min. forecasting (1.6PFL Time (sec.) 120 times more rapid than hourly update cycles

31 9/11/2014 morning, sudden rain 8:00 8:05 8:10 8:15 8:20 8:25 8:30 8:35 8:40 8:45 8:50 8:55

32 9/11/2014, sudden local rain

33 9/11/2014, sudden local rain >40,000 views #9 of RIKEN channel

34 9/11/2014, sudden local rain

35 1-km-mesh, 1000-member LETKF T skewness at z=3845 m (Ruiz et al. in prep.) skewness Even 30-second update shows strong non-gaussianity with 1000 members. contours: 30 dbz reflectivity

36 What do we expect with rapid updates? based on Lorenz-model exp. (Teramura&Miyoshi 2016) Obs interval = 0.08 Obs interval = 0.25 Obs interval = 0.50 Κ 4 of PCA1 Κ 4 of PCA1 Κ 4 of PCA1 Light tailed Heavy tailed Κ 4 : 4th order cumulant kurtosis Scatter diag. Scatter diag. Scatter diag.

37 What do we expect with rapid updates? based on Lorenz-model exp. (Teramura&Miyoshi 2016) Obs interval = 0.08 Obs interval = 0.25 Obs interval = 0.50 Κ 4 of PCA1 Κ 4 of PCA1 Κ 4 of PCA1 Heavy tailed Frequent obsmore Gaussian Κ 4 : 4th order cumulant kurtosis Light tailed Scatter diag. Scatter diag. Scatter diag.

38 1-km-mesh, 1000-member LETKF T skewness at z=3845 m (Ruiz et al. in prep.) skewness Even 30-second update shows strong non-gaussianity with 1000 members. 30-sec. update may not be fast enough! contours: 30 dbz reflectivity

39 Non-Gaussianity and data assimilation frequency Comparison of KLD for different assimilation frequencies At 05:15 UTC (15 minutes after the end of the spin-up) w/ 1000 members, 1-km mesh T at 600 hpa 5 min DA T at 600 hpa 2 min DA T at 600 hpa 1 min DA Contours: 30 dbz T at 600 hpa 30 sec DA (Ruiz et al. in prep.)

40 Non-Gaussianity and data assimilation frequency Comparison of KLD for different assimilation frequencies. T 5 min - rain T 2 min - rain w/ 1000 members, 1-km mesh T 1 min - rain T 30 sec - rain Averaged area > 30 dbz (Ruiz et al. in prep.) more Gaussian with faster cycles

41 (Ruiz et al. in prep.)

42 30-min forecast: 15:10L 15:40L D4_1KM (deterministic) OBS after QC 30-sec DA cycle D4_1KM (deterministic) Lien et al. (in prep.) 5-min DA cycle

43 30-min forecast: 15:40L 16:10L D4_1KM (deterministic) OBS after QC 30-sec DA cycle 30-sec. update certainly helps. D4_1KM (deterministic) Lien et al. (in prep.) 5-min DA cycle

44 20-min forecast: 15:30L OBS after QC 30 sec Lien et al. (in prep.) 5 min (4D) 5 min (1/10 data)

45 20-min forecast: 15:30L OBS after QC 30 sec Lien et al. (in prep.) 2 min (4D) 5 min (4D) 5 min (1/10 data) 2 min (1/4 data) 5 min (1/10 data)

46 20-min forecast: 15:30L OBS after QC 30 sec Lien et al. (in prep.) 1 min (4D) 2 min (4D) 5 min (4D) 5 min (1/10 data) 1 min (1/2 data) 2 min (1/4 data) 5 min (1/10 data)

47 Meteorological Satellite Center (MSC) of JMA Himawari-8: a new generation geostationary meteorological satellite frequent, colorful, precise ~50x more data Every hour (30 min in NH) Every 10 min. 16UTC 2 to 13UTC 3 April 2015 MTSAT-2 (VIS) Every 1 hour 16UTC 2 to 13UTC 3 April 2015 Himawari-8 (True Color) Every 10 minutes (Courtesy of JMA)

48 Typhoon Soudelor (2015) The strongest western north Pacific typhoon in 2015 captured well by Himawari-8 Japan 8/4 900hPa

49 Himawari-8 impact NoHim8 Him8 Observation Brightness Temperature (K)

50 Himawari-8 impact on intensity fcst weak strong

51 Every 10 min. vs. 30 min. DA 30 min. cycle 10 min. cycle Observation Brightness Temperature (K)

52 Intensity forecast (30 min. vs. 10 min.) weak strong Assimilating every 10-min. is essential.

53 Moisture intensity The inner-core moisture error causes the intensity forecast error (Emanuel and Zhang 2017). More moist Less uncertain Him8 DA reduces a dry bias and ensemble spread (uncertainty) of moisture.

54 An idea of merging two scales Motivated by Buehner (2012), we construct analysis increments at high (h) and low (l) resolutions separately. = + Miyoshi and Kondo (2013)

55 Results are promising. Experiments with the T30L7 SPEEDY model (Molteni, 2003) Global-average RMSE Regular localization (700 km) Dual localization ( km) Mid-level U Low-level T Near-surface Q Surface pressure

56 Pushing the limits Big Data Big Simulations Big ensemble (10240 ensemble members) Rapid update (30-second update) High resolution (100-m mesh) Future NWP

57 Summary Scales Predictable range Update frequency