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9 months in San Diego

9 months in San Diego. Kei Yoshimura. Contents. Introduction Life in San Diego Recent Researches Global Downscale River Discharge Prediction. For Those who don’t know me…. 1978: Born in Nara (Ancient capital of Japan) 1996-2000: Warriors (Same as Kodama-kun)

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9 months in San Diego

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  1. 9 months in San Diego Kei Yoshimura

  2. Contents • Introduction • Life in San Diego • Recent Researches • Global Downscale • River Discharge Prediction

  3. For Those who don’t know me… • 1978: Born in Nara (Ancient capital of Japan) • 1996-2000: Warriors (Same as Kodama-kun) • 2000-: Musiake/Oki Lab (Classmate of Kumasaka and Kawamura) • 2002 Oct: MEng • 2006 Feb: PhD • 2006 Jun-: Scripps Institution of Oceanography by JSPS until 2008 May.

  4. Working Place • Climate Research Division, Scripps Institution of Oceanography, UCSD. • About 20 Researchers, 20 Students & PD • Each researcher has independent room (Like IIS) • Up to 2 students are in the same room. • IT are far behind than Oki-Lab.

  5. Some Differences • I don’t know any root password of computers. All IT related matter are done by specialists. • No monthly compulsory for seminar presentation. You take your own responsibility. • No cafeteria. Everyone need to go home to eat dinner.

  6. Recent Research Themes • Meso-scale Isotope Model • Development of Meso-scale isotope circulation model featuring processes in a typhoon system • Global Isotope Model • Inter-annual variations and trend analyses of precipitation and vapor isotopes with a Global Isotope Circulation Model and observations • Global Dynamical Downscale • Dynamical downscaling of global reanalysis with the Scale Selective Bias Correction using a Global Spectral Model • Real Time River Discharge Prediction • Development and verification of a predicting system of river discharge over Japan using JMA-MSM-GPV

  7. Global Dynamical Downscale • NCEP GSM is used. (Same as the NCEP/NCAR Reanalysis) • Cheaper way of High resolution global reanalysis. • Assimilation is very expensive. Highest resolution is T106 or so (~120km). • Effective downscale of many and/or large target areas. • Free from inconsistency of lateral boundaries.

  8. Spectrum Model Latitude: Gaussian Grid Longitude: Fourier series lat lon Spherical HarmonicsFunction Legendre Transform

  9. Scale Selective Nudging Nudgingweight, W Fourier series 1 √scale 600km Nudge scale Nudging Scale Forecast

  10. Nudging Variables and Flow • U, V and Surface Pressure • Nudge wavelengths more than 600km. • Temperature • Same as U and V, but less weighting function. • Humidity • Zonal mean Preparation stepTopographic Interpolation Dynamical Nudging R2(200km) Output(50km) Bi Input(T248) GSM

  11. Height (gpm) Difference from R2 With sfcP (intp’d R2) No sfcP +With gradual T

  12. 600km 200km 50km

  13. mm/day mm

  14. Verification with Observed Precip (GPCP), CorCoef of Daily Precip Signals T126 Downscaled R2 Globally, always Better Big improvement in tropics

  15. Surface Diagnostics in Japan mm/s mm/s

  16. Diurnal Pattern of Surface Wind m/s m/s m/s m/s m/s

  17. Further Studies • Validation. • HRPP, CRU(?), Satellite Wind, AMeDAS(?) • Application. • 50km dynamically consistent global dataset. Global Water Resources Simulation(?) • Isotope/Tracer Incorporation. • This Nudging is really the same to Reanalysis Forcing Water Circulation simulation.

  18. Real Time River Discharge Prediction • Today’s Earth, Today’s Japan, and Today’s Radar. • Below two are features of coming Suiko talk. • Sensitivity of Effective Flow Velocity • Empirical conversion function of River discharge. • Furthermore, Sakimura-kun’s index is being incorporated.

  19. Incorporation of Sakimura-Index • But, Test period of the Real Time Prediction is only 2003-04.

  20. Assumption • Systematic bias exists between MSM-TRIP’s and AMD-TRIP’s extreme events, so that peaks’ relative intensity are reproducible. If NDamd=10th in 29 year, assume NDmsm=10th.

  21. Calculation Process • Non-Exceeding Probability of the exponential distribution P: P=exp(-i/N)Xi=Xo+Ao*ln(l)-Ao*ln(ln(-P)) • Therefore,Xi=Xo+Ao*ln(M/i) • for i=1,n (largest data in 2003 and 2004, which are included in top 90 of 29-year), best-fitted Xo and Ao are calculated.

  22. Results 1/10 Probability MSM-2yr Difference AMeDAS-29yr 1/100 Probability AMD data are 30% or more larger than MSM MSM-2yr Difference AMeDAS-29yr

  23. Typhoon 0423, 2004/10/20 Sakimura, 2007

  24. Check Validity of the Assumption R of the Xo and Ao calculation. Also showing Linier relationship of Non-exceeding probability (ln(-P)) and corresponded discharge (Xi) Correlation between absolute Xi-msm and corresponded Xi-amd. Evaluation of the peak timing (Blue areas are well reproduced).

  25. It’s the end of my talk.Thanks!

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