Recent activities on Big Data Assimilation in Japan

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Bertram Pitts
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1 August 17, 2014, WWOSC, Montreal, Canada Recent activities on Big Data Assimilation in Japan M. Kunii, J. Ruiz, K. Kondo, and Takemasa Miyoshi* RIKEN Advanced Institute for Computational Science *PI and presenting, S. Satoh, T. Ushio, H. Tomita, Y. Ishikawa, K. Bessho, H. Seko

Kondo, and Takemasa Miyoshi* RIKEN Advanced Institute for Computational

2 Global 870-m simulation (Miyamoto et al. 2013) JAMSTEC AORI (SPIRE Field3), RIKEN/AICS Visualized by Ryuji Yoshida

3 Computers getting more powerful With an Exa-scale supercomputer (~2020), we can afford 100 members of global 870-m simulation. Or a larger ensemble at a lower resolution? The Japanese 10-Peta-Flops K computer

4 10240-member SPEEDY-LETKF (Miyoshi and Kondo 2014) 20 members members

5 10240-member SPEEDY-LETKF (Miyoshi and Kondo 2014)

6 10240-member SPEEDY-LETKF (Miyoshi and Kondo 2014) Snapshots of ensemble-forecast histograms for Q [g/kg] at 2 locations (left and right) Larger ensemble certainly helps.

7 Toward next 20 years of DA Big Data Assimilation Era Exploding data Courtesy of S. Shima Big Data Enabling effective use Big Data High-resolution simulation More computational power High-resolution obs Advanced obs technology

8 Global Observing System Radar Aircraft Satellite Weather balloon Ship Buoy Surface station

9 Observation data (6-h period) (Courtesy of JMA) World s effort! (no border in the atmosphere)

10 Observation data (6-h period) (Courtesy of JMA) NWP has been pioneering Big Data science!

11 Next-generation geostationary satellite Himawari 8 will be launched in Himawari 9 will be launched in Super Rapid Scan every 30 seconds Full Disk 10 min. 2.5 min. Rapid Scan 30 sec. Super Rapid Scan (Courtesy of JMA)

Super Rapid Scan every 30 seconds Full Disk 10 min. 2.

12 Rapid scan effective for convections Typical lifetime of a convective system ~30 min. Satellite imagery captures developing convections. Chisholm, A. J. and Renick, J. H. (1972) Radar captures rain particles after the developing stage. (may be too late )

Satellite imagery captures developing convections. Chisholm, A.

13 Phased Array Radar (courtesy of NICT) Conventional Radar ~15 scan angles Every 5-10 minutes Phased Array Radar ~100 scan angles Every seconds

14 Conventional Radar (every 5 min.)

15 Phased Array Radar (every 30 sec.)

16 Two PAR in Kobe area NICT Kobe r = 60 km KOBE Osaka Univ.

17 New data: can we use live-camera images? 1. Reduced/extracted information (e.g., weather type, visibility) (challenge) Automated image processing 2. Simulating images from model outputs (challenge) precise 3-dimensional radiation model

18 Towards Big Data Assimilation High-resolution simulation Combination of next-generation technologies Big Data Assimilation Improving simulations High-resolution observation

19 Storm forecasting with Big Data Assimilation 5 people died in Kobe on July 28, 2008, due to local heavy rainfall Goal: 30-min forecasting of local severe weather through Big Data Assimilation innovations.

20 Revolutionary super-rapid 30-sec. cycle Phased Array Radar 1GB/30sec/2 radars Himawari 500MB/2.5min A 1. Quality Control A 2. Data Processing B 1. Quality Control B 2. Data Processing 130 sec Ensemble Forecast Simulations 2 PFLOP 2Ensemble Data Assimilation 2 PFLOP Analysis Data 2GB Ensemble シミュレーション Forecasts データデータ 200GB Ensemble シミュレーション Analyses データデータ 200GB 330 min Forecast Simulation 1.2 PFLOP Repeat every 30 sec. 120 times more rapid than the hourly Rapid Refresh 30 min Forecast 2GB

Data Processing 130 sec Ensemble Forecast Simulations 2 PFLOP 2Ensemble Data Assimilation 2 PFLOP Analysis Data 2GB

21 A lot of challenges to make it happen Obs data processing ~2GB Obs data processing ~2GB DA (2PFLOP) 200GB 2GB 30-sec. Ensemble forecasting (2PFLOP) 200GB DA (2PFLOP) 30-min. forecasting (1.2PFLOP) 200GB 2GB 30-sec. Ensemble forecasting (2PFLOP) 200GB D (2PF 30-min. forecasting (1.2PFL Time (sec.) Computing requirement: 250TFLOPS (effective) Equiv. to 1/4 of the K computer Challenges New DA algorithm for fast I/O Fast QC and data processing at observing sites

22 Case study: July 13, 2013, a disaster in Kyoto The top page of Yomiuri newspaper on 14 July, 2013

23 JMA NHM domain settings NHM 1km NHM 100m

24 Observed reflectivity at 5-km height JST JST JST

25 The first analysis (reflectivity at 2 km) Observation First guess Analysis

26 The third analysis (reflectivity at 2 km) Observation First guess Analysis

27 Future perspectives Explore a 30-sec. super-rapid DA cycle thorough innovating the Big Data Assimilation technology. Funded by Japanese Exa-scale supercomputer planned in 2020 May Tokyo 2020 be a good place to demonstrate?

28 Registration starts in mid September!

Big Data Assimilation Revolutionizing Weather Prediction

February 23, 2015, ISDA2015, Kobe Big Data Assimilation Revolutionizing Weather Prediction M. Kunii, J. Ruiz, G.-Y. Lien, K. Kondo, S. Otsuka, Y. Maejima, and Takemasa Miyoshi* RIKEN Advanced Institute