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Researches in San Diego: Dynamics, Predictions, and Real-Time Insights

Dive into Kei Yoshimura's groundbreaking research on isotope models, dynamical downscaling, river discharge prediction, and global reanalysis. Discover the development of innovative predictions systems and the incorporation of cutting-edge techniques for real-time river discharge forecasts. Explore the nuances of climate studies through meticulous data analysis and empirical conversions. Uncover the correlations between different data sets and validation processes to enhance the accuracy of future predictions and simulations. Join the journey of discovery and innovation in climate research with Kei Yoshimura.

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Researches in San Diego: Dynamics, Predictions, and Real-Time Insights

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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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