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Data Assimilation for Atmospheric, Oceanicand Hydrologic Applications
Seon K. Park · Liang Xu (Eds.)
Data Assimilationfor Atmospheric, Oceanicand Hydrologic Applications
123
EditorsProf. Seon K. Park Dr. Liang XuEwha Womans University Naval Research LaboratoryDept. of Environmental 7 Grace Hopper AvenueScience & Engineering Stop 211-1 Daehyun-dong Monterey CA 93943-5502Seoul, Seodaemungu-gu USASeoul 120-750 [email protected] of [email protected]
ISBN: 978-3-540-71055-4 e-ISBN: 978-3-540-71056-1
DOI 10.1007/978-3-540-71056-1
Library of Congress Control Number: 2008935664
c© Springer-Verlag Berlin Heidelberg 2009
This work is subject to copyright. All rights are reserved, whether the whole or part of the material isconcerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting,reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publicationor parts thereof is permitted only under the provisions of the German Copyright Law of September 9,1965, in its current version, and permission for use must always be obtained from Springer. Violations areliable to prosecution under the German Copyright Law.
The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply,even in the absence of a specific statement, that such names are exempt from the relevant protective lawsand regulations and therefore free for general use.
Cover design: deblik, Berlin
Printed on acid-free paper
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springer.com
ToYoshi and KokoSASAKI
Two views of Yoshi lecturing 4/79, JML: Drawn by John M. Lewis, using a bamboopen and India ink, when he attended Yoshi’s lecture on variational data assimilationat University of Oklahoma in April 1979 (provided by Francois-Xavier Le Dimet).
Preface
Data assimilation (DA) has been recognized as one of the core techniques formodern forecasting in various earth science disciplines including meteorology,oceanography, and hydrology. Since early 1990s DA has been an important ses-sion topic in many academic meetings organized by leading societies such as theAmerican Meteorological Society, American Geophysical Union, European Geo-physical Union, World Meteorological Organization, etc.
Recently, the 2nd Annual Meeting of the Asia Oceania Geosciences Society(AOGS), held in Singapore in June 2005, conducted a session on DA under the ti-tle of “Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications.”This first DA session in the 2nd AOGS was a great success with more than 30 paperspresented and many great ideas exchanged among scientists from the three differentdisciplines. The scientists who participated in the meeting suggested making the DAsession a biennial event.
Two years later, at the 4th AOGS Annual Meeting, Bangkok, Thailand, the DAsession was officially named “Sasaki Symposium on Data Assimilation for Atmo-spheric, Oceanic and Hydrologic Applications,” to honor Prof. Yoshi K. Sasaki ofthe University of Oklahoma for his life-long contributions to DA in geosciences.At the 5th AOGS Annual Meeting, Busan, Korea, in June 2008, two special eventswere hosted along with the Symposium – “Special Lecture on Data Assimilation”and “Dinner with Yoshi.” The special lecture was presented by Prof. Sasaki with atitle of “Challenges in Data Assimilation.” More than 50 scientists participated inthe dinner and enjoyed talking with Yoshi.
Some papers of this volume are selected from the Symposium while others are in-vited. The first chapter of this book, titled “Sasaki’s Pathway to Deterministic DataAssimilation,” is contributed by John Lewis, one of Yoshi’s students. I. MichaelNavon provides a comprehensive review of data assimilation for numerical weatherprediction. Milija Zupanski, another student of Yoshi, addresses theoretical andpractical aspects of the ensemble data assimilation, especially for the maximumlikelihood ensemble filter. Francois-Xavier Le Dimet, who worked with Yoshi as apostdoctoral scientist and served as a co-convener of the Symposium, reviews vari-ational data assimilation in hydrology. Most notably, Yoshi himself has contributed
vii
viii Preface
a chapter, “Real Challenge of Data Assimilation for Tornadogenisis,” which intro-duces a new theory based on the entropic balance.
Through prominent contributions by other scientists in DA, this book has a goodmixture of collected papers in both theory and applications. Theoretical and method-ological aspects span variational methods, ensemble Kalman filtering, particle filter-ing, the maximum likelihood ensemble filter, representer method, genetic algorithm,etc., with applications to parameter estimation, radar/satellite assimilation, data as-similation for land surface/water balance modeling, oceanic and meteorological dataassimilation, adaptive (targeting) observations, and radar rainfall estimates. We hopethis book will be useful to individual researchers and graduate students as a refer-ence to the most recent research developments in the field of data assimilation. Weappreciate Jeffrey Walker of the University of Melbourne, Boon Chua of OregonState University, Lance Leslie of the University of Oklahoma, and Chun-Chieh Wuof National Taiwan University, who served as the co-convener of the Symposiumpreviously. We are very honoured to dedicate this book to Yoshi and his lovely wife,Koko.
June 2008 Seon K. ParkEwha Womans University, Seoul
Liang XuNaval Research Laboratory, Monterey
Contents
Sasaki’s Pathway to Deterministic Data Assimilation . . . . . . . . . . . . . . . . . . 1John M. Lewis
Data Assimilation for Numerical Weather Prediction: A Review . . . . . . . . 21Ionel M. Navon
Theoretical and Practical Issues of Ensemble Data Assimilationin Weather and Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Milija Zupanski
Information Measures in Ensemble Data Assimilation . . . . . . . . . . . . . . . . . 85Dusanka Zupanski
Real Challenge of Data Assimilation for Tornadogenesis . . . . . . . . . . . . . . . 97Yoshi K. Sasaki
Radar Rainfall Estimates for Hydrologic and Landslide Modeling . . . . . . 127Kang-Tsung Chang, Jr-Chuan Huang, Shuh-Ji Kao and Shou-Hao Chiang
High-Resolution QPE System for Taiwan . . . . . . . . . . . . . . . . . . . . . . . . . . . 147Jian Zhang, Kenneth Howard, Pao-Liang Chang, Paul Tai-Kuang Chiu,Chia-Rong Chen, Carrie Langston, Wenwu Xia, Brian Kaneyand Pin-Fang Lin
Assimilation of Satellite Data in Improving Numerical Simulationof Tropical Cyclones: Progress, Challenge and Development . . . . . . . . . . . 163Zhaoxia Pu
Diagnostics for Evaluating the Impact of Satellite Observations . . . . . . . . . 177Nancy L. Baker and Rolf H. Langland
Impact of the CHAMP Occultation Data on the Rainfall Forecast . . . . . . . 197Hiromu Seko, Yoshinori Shoji, Masaru Kunii and Yuichi Aoyama
ix
x Contents
Parameter Estimation Using the Genetic Algorithm in Termsof Quantitative Precipitation Forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219Yong Hee Lee, Seon Ki Park, Dong-Eon Chang, Jong-Chul Haand Hee-Sang Lee
Applications of Conditional Nonlinear Optimal Perturbationsto Ensemble Prediction and Adaptive Observation . . . . . . . . . . . . . . . . . . . . 231Zhina Jiang, Hongli Wang, Feifan Zhou and Mu Mu
Study on Adjoint-Based Targeted Observation of Mesoscale Lowon Meiyu Front . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253Peiming Dong, Ke Zhong and Sixiong Zhao
Ocean Data Assimilation: A Coastal Application . . . . . . . . . . . . . . . . . . . . . 269Xiaodong Hong, James A. Cummings, Paul J. Martin and James D. Doyle
Comparison of Ensemble-Based Filters for a Simple Model of OceanThermohaline Circulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293Sangil Kim
Preconditioning Representer-based Variational Data AssimilationSystems: Application to NAVDAS-AR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307Boon S. Chua, Liang Xu, Tom Rosmond and Edward D. Zaron
Cycling the Representer Method with Nonlinear Models . . . . . . . . . . . . . . . 321Hans E. Ngodock, Scott R. Smith and Gregg A. Jacobs
Implementation of the Ensemble Kalman Filter into a Northwest PacificOcean Circulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341Gwang-Ho Seo, Sangil Kim, Byoung-Ju Choi, Yang-Ki Cho and Young-HoKim
Particle Filtering in Data Assimilation and Its Application to Estimationof Boundary Condition of Tsunami Simulation Model . . . . . . . . . . . . . . . . . 353Kazuyuki Nakamura, Naoki Hirose, Byung Ho Choi and Tomoyuki Higuchi
Data Assimilation in Hydrology: Variational Approach . . . . . . . . . . . . . . . . 367Francois-Xavier Le Dimet, William Castaings, Pierre Ngnepieba and BaxterVieux
Recent Advances in Land Data Assimilation at the NASA GlobalModeling and Assimilation Office . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407Rolf H. Reichle, Michael G. Bosilovich, Wade T. Crow, Randal D. Koster,Sujay V. Kumar, Sarith P. P. Mahanama and Benjamin F. Zaitchik
Assimilation of Soil Moisture and Temperature Data into Land SurfaceModels: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429Nasim Alavi, Jon S. Warland and Aaron A. Berg
Contents xi
Assimilation of a Satellite-Based Soil Moisture Product into a Two-LayerWater Balance Model for a Global Crop Production Decision SupportSystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449John D. Bolten, Wade T. Crow, Xiwu Zhan, Curt A. Reynoldsand Thomas J. Jackson
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465
List of Contributors
Nasim Alavi Land Resource Science, University of Guelph, Guelph, ON, Canada,N1G 2W1, [email protected]
Yuichi Aoyama National Institute of Polar Research, 1-9-10 Kaga, Itabashi, Tokyo173-8515, Japan
Nancy L. Baker Marine Meteorology Division, Naval Research Laboratory,Monterey, CA 93943-5502, USA, [email protected]
Aaron A. Berg Department of Geography, University of Guelph, Guelph, ON,Canada, N1G 2W1, [email protected]
John D. Bolten Hydrology and Remotes Sensing Lab, U.S. Departmentof Agriculture, Agricultural Research Service, Beltsville, MD 20705 USA,[email protected]
Michael G. Bosilovich Global Modeling and Assimilation Office, NASA GoddardSpace Flight Center, Greenbelt, MD, USA, [email protected]
Williams Castaings Joint Research Center, Ispara, Italy, [email protected]
Dong-Eon Chang Numerical Model Development Division, Korea MeteorologicalAdministration, Seoul 156-720, Republic of Korea, [email protected]
Kang-Tsung Chang Kainan University, Taoyuan, Taiwan, [email protected]
Pao-Liang Chang Meteologogical Satellite Center, Central Weather Bureau,Taiwan
Chia-Rong Chen Meteologogical Satellite Center, Central Weather Bureau, Taiwan
xiii
xiv List of Contributors
Shou-Hao Chiang Department of Geography, National Taiwan University, Taipei,Taiwan
Paul Tai-Kuang Chiu Meteologogical Satellite Center, Central Weather Bureau,Taiwan
Yang-Ki Cho Department of Oceanography, Chonnam National University, Yongbong-dong, Buk-gu, Gwangju, Korea, 500-757, [email protected]
Byoung-Ju Choi Department of Oceanography, Kunsan National University,Miryong-dong, Gunsan, Korea, 573-701, [email protected]
Byung Ho Choi Department of Civil and Environmental Engineering,Sungkyunkwan University, Suwon, Gyeonggi 440-746, Korea, [email protected]
Boon S. Chua SAIC, Monterey, CA, USA; Marine Meteorology Division, NavalResearch Laboratory, Monterey, CA, USA
Wade T. Crow Hydrology and Remotes Sensing Lab U.S. Department ofAgriculture, Agricultural Research Service, Beltsville, MD 20705 USA,[email protected]
James A. Cummings Oceanography Division, Naval Research Laboratory, StennisSpace Center, MS 39529, USA
Peiming Dong Institute of Atmospheric Physics, Chinese Academy of Sciences,Beijing, 100029, China, [email protected]
James D. Doyle Marine Meteorology Division, Naval Research Laboratory,Monterey, CA 93943, USA
Jong-Chul Ha Forecast Research Laboratory, National Institute of MeteorologicalResearch, Korea Meteorological Administration, Seoul 156-720, Republic ofKorea, [email protected]
Tomoyuki Higuchi The Institute of Statistical Mathematics, Minato, Tokyo,106-8569, Japan; Japan Science and Technology Agency, Japan, [email protected]
Naoki Hirose Research Institute for Applied Mechanics, Kyushu University,Kasuga, Fukuoka 816-8580, Japan, [email protected]
Xiaodong Hong Marine Meteorology Division, Naval Research Laboratory,Monterey, CA 93943, USA
Kenneth Howard NOAA/OAR National Severe Storms Laboratory, Norman, OK,USA
List of Contributors xv
Jr-Chuan Huang Research Center for Environmental Changes, Academia Sinica,Taipei, Taiwan
Thomas J. Jackson Hydrology and Remotes Sensing Lab, U.S. Departmentof Agriculture, Agricultural Research Service, Beltsville, MD 20705 USA,[email protected]
Gregg A. Jacobs The Naval Research Laboratory, Stennis Space Center, MS39529, USA, [email protected]
Zhina Jiang State Key Laboratory of Severe Weather (LaSW), Chinese Academyof Meteorological Sciences, Beijing 100081, China, [email protected]
Brian Kaney Northeastern State University, Tahlequah, OK, USA
Shuh-Ji Kao Research Center for Environmental Changes, Academia Sinica,Taipei, Taiwan
Sangil Kim The College of Oceanic and Atmospheric Sciences, Oregon StateUniversity, Corvallis, OR 97331-5503, USA, [email protected]
Young-H. Kim Korea Ocean Research & Development Institute, Ansan, Korea,426-744, [email protected]
Randal D. Koster Global Modeling and Assimilation Office, NASA GoddardSpace Flight Center, Greenbelt, MD, USA, [email protected]
Sujay V. Kumar Science Applications International Corporation, Beltsville,Maryland, USA; Hydrological Sciences Branch, NASA Goddard Space FlightCenter, Greenbelt, MD, USA, [email protected]
Masaru Kunii Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki305-0052, Japan
Rolf H. Langland Marine Meteorology Division, Naval Research Laboratory,Monterey, CA 93943-5503, USA, [email protected]
Carrie Langston NOAA/OAR National Severe Storms Laboratory, CooperativeInstitute for Mesoscale Meteorological Studies, The University of Oklahoma,Norman, OK, USA
Francois-Xavier Le Dimet Laboratoire Jean-Kuntzmann, Universite de Grenobleand INRIA, Grenoble, France, [email protected]
xvi List of Contributors
Hee-Sang Lee Forecast Research Laboratory, National Institute of MeteorologicalResearch, Korea Meteorological Administration, Seoul 156-720, Republic ofKorea, [email protected]
Yong Hee Lee Forecast Research Laboratory, National Institute of MeteorologicalResearch, Korea Meteorological Administration, Seoul 156-720, Republic ofKorea, [email protected]
John M. Lewis National Severe Storms Laboratory and Desert Research Institute,2215 Raggio Prky, Reno, NV 89512-1095, USA, [email protected]
Pin-Fang Lin Meteologogical Satellite Center, Central Weather Bureau, Taiwan
Sarith P.P. Mahanama Global Modeling and Assimilation Office, NASAGoddard Space Flight Center, Greenbelt, MD, USA; Goddard Earth Sciences andTechnology Center, University of Maryland, Baltimore County, Baltimore, MD,USA, [email protected]
Paul J. Martin Oceanography Division, Naval Research Laboratory, Stennis SpaceCenter, MS 39529, USA
Mu Mu State Key Laboratory of Numerical Modeling for Atmospheric Sciencesand Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029, China, [email protected]
Kazuyuki Nakamura The Institute of Statistical Mathematics, Minato, Tokyo106-8569, Japan, [email protected]
Ionel M. Navon Department of Scientific Computing, The Florida State University,Tallahassee, Florida 32306-4120, USA, [email protected]
Pierre Ngnepieba Department of Mathematics, Florida A&M University,Tallahassee, Florida 32307, USA, [email protected]
Hans E. Ngodock The Naval Research Laboratory, Stennis Space Center, MS39529, USA, [email protected]
Seon Ki Park Department of Environmental Science and Engineering, EwhaWomans University, Seoul 120-750, Republic of Korea, [email protected]
Zhaoxia Pu Department of Meteorology, University of Utah, 135 S 1460 E, Rm.819, Salt Lake City, UT, USA, [email protected]
List of Contributors xvii
Rolf H. Reichle Global Modeling and Assimilation Office, NASA Goddard SpaceFlight Center, Greenbelt, MD, USA, [email protected]
Curt A. Reynolds International Production Assessment Division, U.S. Departmentof Agriculture, Foreign Agricultural Service, Office of Global Analysis, Washing-ton, DC 20250 USA, [email protected]
Tom Rosmond SAIC, Monterey, CA, USA; Marine Meteorology Division, NavalResearch Laboratory, Monterey, CA, USA
Yoshi K. Sasaki School of Meteorology, National Weather Center, The Universityof Oklahoma, Norman, OK 73072-7307, USA, [email protected]
Hiromu Seko Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki305-0052, Japan
Gwang-Ho Seo Department of Oceanography, Chonnam National University,Yong bong-dong, Buk-gu, Gwangju, Korea, 500-757, [email protected]
Yoshinori Shoji Meteorological Research Institute, 1-1 Nagamine, Tsukuba,Ibaraki 305-0052, Japan
Scott R. Smith The Naval Research Laboratory, Stennis Space Center, MS 39529,USA, [email protected]
Baxter Vieux University of Oklahoma, Norman, OK, USA, [email protected]
Hongli Wang State Key Laboratory of Numerical Modeling for Atmospheric Sci-ences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029, China, [email protected]
Jon S. Warland Land Resource Science, University of Guelph, Guelph, ON,Canada, N1G 2W1, [email protected]
Wenwu Xia NOAA/OAR National Severe Storms Laboratory, CooperativeInstitute for Mesoscale Meteorological Studies, The University of Oklahoma,Norman, OK, USA
Liang Xu Marine Meteorology Division, Naval Research Laboratory, Monterey,CA, USA
Benjamin F. Zaitchik Hydrological Sciences Branch, NASA Goddard SpaceFlight Center, Greenbelt, MD, USA, Department of Earth and Planetary Sciences,Johns Hopkins University, Baltimore, MD, USA, [email protected]
xviii List of Contributors
Edward D. Zaron Department of Civil and Environmental Engineering, PortlandState University, Portland, OR, USA
Xiwu Zhan Center for Satellite Applications and Research, National Oceanicand Atmospheric Administration, National Environmental Satellite, Data, andInformation Service, Camp Springs, MD 20746 USA, [email protected]
Jian Zhang NOAA/OAR National Severe Storms Laboratory and CooperativeInstitute for Mesoscale Meteorological Studies, The University of Oklahoma,Norman, OK 73072, USA, [email protected]
Sixiong Zhao Institute of Atmospheric Physics, Chinese Academy of Sciences,Beijing, 100029, China, [email protected]
Ke Zhong Institute of Atmospheric Physics, Chinese Academy of Sciences,Beijing, 100029, China, [email protected]
Feifan Zhou State Key Laboratory of Numerical Modeling for AtmosphericSciences and Geophysical Fluid Dynamics (LASG), Institute of AtmosphericPhysics, Chinese Academy of Sciences, Beijing 100029, China, [email protected]
Dusanka Zupanski Cooperative Institute for Research in the Atmosphere,Colorado State University, 1375 Campus Delivery, Fort Collins, CO 80523-1375,USA, [email protected]
Milija Zupanski Cooperative Institute for Research in the Atmosphere,Colorado State University, Fort Collins, CO 80523-1375, USA, [email protected]
Sasaki’s Pathway to DeterministicData Assimilation
John M. Lewis
Abstract Yoshikazu Sasaki developed the variational method of data assimilation,a cornerstone of modern-day analysis and prediction in meteorology. The gener-ation of this idea is tracked by analyzing his education at the University of Tokyoin the immediate post-WWII period. Despite austere circumstances—including lim-ited financial support for education, poor living conditions, and a lack of educationalresources—Sasaki was highly motivated and overcame these obstacles on his pathto developing this innovative method of weather map analysis. We follow the stagesof his intellectual development where information comes from access to his earlypublications, oral histories, letters of reminiscence, and biographical data from theUniversity of Tokyo and the University of Oklahoma. Based on this information,key steps in the development of his idea were: (1) a passion for science in his youth,(2) an intellectually stimulating undergraduate education in physics, mathematics,and geophysics, (3) a fascination with the theory of variational mechanics, and (4) a“bridge to America” and the exciting new developments in numerical weather pre-diction (NWP).
A comparison is made between Sasaki’s method and Optimal Interpolation (OI),a contemporary data assimilation strategy based on the work of Eliassen and Gandin.Finally, a biographical sketch of Sasaki including his scientific genealogy is foundin the Appendix.
1 View from Afar
In April 1947, Yoshikazu (“Yoshi”) Sasaki enrolled as an undergraduate in geo-physics at the University of Tokyo.1 Among every group of 1000 students whopassed through the rigorous gymnasium education in Japan, only one gained en-try into the hallowed institution (Schoppa 1991).
J.M. Lewis (B)National Severe Storms Laboratory and Desert Research Institute 2215 Raggio Prky,Reno, NV 89512-1095, USA, e-mail: [email protected]
1 Precise dates related to Sasaki’s life and career are found in the resume (Table 1).
S.K. Park, L. Xu, Data Assimilation for Atmospheric, Oceanic and Hydrologic 1Applications, DOI 10.1007/978-3-540-71056-1 1,c© Springer-Verlag Berlin Heidelberg 2009
2 J.M. Lewis
Table 1 Resume of Yoshikazu Sasaki
January 2, 1927: Born in Akita City, Akita PrefectureParents: Kosuke Sasaki and Itsu (Kosaka) SasakiSiblings: Toshifumi and Masahiro
April 1947: Entered University of Tokyo (U. Tokyo)
March 1950: Bachelors Degree (Geophysics), U. Tokyo
March 1955: D. Sc., U. Tokyo (Meteorology)
September 1956: Emigration to USA
1956–1960: Research Scientist, Texas A & M College
1960–present: University of Oklahoma (OU)1960 – Research Scientist/Adjunct Associate Professor1965 – Associate Professor1967 – Professor (Tenured)1974 – George Lynn Cross Research Professor1994 – George Lynn Cross Research Professor Emeritus
1973–1974: Research DirectorNaval Environmental Prediction Research Facility (NEPRF)Monterey, CA (Sabbatical Leave)
1980–1986: DirectorCooperative Institute for Mesoscale Meteorological StudiesOU and National Oceanic and Atmospheric Administration
Awards: Fujiwara Award (May 2000)Order of Sacred Treasure (May 2004)Oklahoma Hall of Fame for Higher Education (October 2004)
At the time of naturalization (U. S. citizenship) in 1973, Yoshikazu Sasaki changed his first nameto Yoshi Kazu.
At this time, less than two years after V-J day (Victory Over Japan; September 2,1945), the country lay in ruin and the American Occupation had commenced. Theoccupation would last for nearly seven years. Conditions for the students were notunlike those for the general population—an absence of adequate housing and a lim-ited supply of food. As recalled by Yoshimitzu Ogura, a fellow student at Universityof Tokyo:
First of all, I could not find a place to live, since so many houses were burned down. Ibrought my futon to the lab, and cleaned up the top of a big table every night to spreadmy futon and slept there. There were half a dozen homeless fellow young scientists in theGeophysical Institute. And I was hungry all the time. With an empty stomach, you cannotthink of science well.
(Y. Ogura 1992, personal communication)
Since geophysics was a sub-specialty within the physics department, Sasaki’s firstthree years of instruction followed the pattern for the typical physics student. Duringthe fourth year, the geophysics student was given courses in seismology, geomag-netism, oceanography, and meteorology (usually one lecture hour per week). Sasakiespecially enjoyed mathematics and has fond memories of the course in grouptheory, that important mathematical subject that is pervasive throughout physics,
Sasaki’s Pathway to Deterministic Data Assimilation 3
chemistry, and applied mathematics. Kunihiko Kodaira, a young assistant professortaught the course. As recalled by Yoshi:
Professor Kodaira often came to class late and sometimes the students would leave [beforehe arrived]2 you know. It was kind of strange to have so few students. But it was just amaz-ing how great a mathematician he was. I especially remember the first day of class whenhe came in late, bowed and then went to the blackboard and filled it with equations. It wasamazing.
(Y. Sasaki 1990, personal communication)
Several years after Sasaki took this course, Kodaira received the Fields Medal.3
After obtaining his bachelors degree in 1950, Sasaki joined the group of graduatestudents at the university’s Geophysical Institute. There was little financial supportfor these students and Sasaki earned money by tutoring high school/gymnasiumstudents as they prepared for the demanding university entrance exams. Class-mate Kikuro Miyakoda taught physics and geology-geophysics part-time at twohigh schools, but also tried to make money by dabbling in the stock market. AkioArakawa and Katsuyuki Ooyama served as upper-air observers aboard two JapanMeteorological Agency (JMA) ships off the east coast of Japan. The Japanese gov-ernment could not cut funding for these two destroyer-sized ships, the “X-RAY”and “TANGO”, since the observations were needed by the occupational forces(K. Ooyama 1992, personal communication). These jobs offered steady employ-ment, but the work assignments were demanding, especially in wintertime whenrough seas were the rule.
In the immediate post-war period, the meteorological community outside Japanwas making notable advances. Upper-air observations were being routinely col-lected over Europe and the United States, and this inspired research on the jetstream and hemispheric circulation. And, of course, a momentous change in the sci-entific environment was produced by the computer—labeled the “great mutation”by Edward Lorenz (1996).
The work that most stimulated students at the Geophysical Institute was JuleCharney’s paper on baroclinic instability (Charney 1947)—the dynamic underpin-ning for mid-latitude cyclone development. Although published in 1947, the stu-dents and chair professor of meteorology Shigekata Syono only became aware of ittwo years later.4 In those early years of occupation, a two-year delay in the trans-mission of information from the West was typical. Journals, if available at all, werehoused at the Hibiya Library in the heart of Tokyo near General MacArthur’s Head-quarters. And even if the journal could be located, obtaining photocopies of an arti-cle was a nontrivial task.
2 Information in brackets, [. . .], has been inserted by the author.3 The Fields Medal, the so-called “Nobel Prize” for mathematicians, was awarded to Kodaira in1954 for his major contribution to the theory of harmonic integrals. It was the first Fields Medalgiven to a mathematician in Japan.4 A scientific biography of Syono is found in Lewis (1993a).
4 J.M. Lewis
2 Connection to America
Inspired by Charney’s work, Kanzaburo Gambo, a research associate at theUniversity of Tokyo, began his own study of baroclinic disturbances and estab-lished modified criterion for the development of cyclones (Gambo 1950). This notewas sent to Charney at Princeton’s Institute for Advanced Study. Gambo received aswift and encouraging reply with an offer to join the celebrated group of researchersat Princeton. These researchers had recently made two 24-h numerical weather pre-dictions of transient features of the large-scale flow over the United States (Charneyet al. 1950; Platzman 1979; Nebeker 1995).
During his stay at Princeton from October 1952 to January 1954, Gambo fre-quently sent detailed reports to the staff and students at the Geophysical Institute.These reports stimulated the formation of a numerical weather prediction (NWP)group under the leadership of Syono (and Gambo after he returned to Japan in 1954).There were approximately 25 members of this group that included JMA employeesas well as the graduate students (See Fig. 1). They were, of course, without the bene-fit of computational power. In the absence of high-speed computation, the motivatedstudents made use of desk calculators; but more often, they performed the integra-tion of the governing equations via graphical methods. Science historian FrederikNebeker has explored the history of these graphical methods in a comprehensivemanner. We quote from Nebeker (1995, p. 167):
Fig. 1 Members of the Numerical Weather Prediction Group in Tokyo, Japan (ca. 1955). Listedfrom left to right: Front Row: Akio Arakawa (1), Kanzaburo Gambo (3), Akira Kasahara (7) BackRow: Syukuro Manabe (1), Katsuyuki Ooyama (4), Kikuro Miyakoda (11), Yoshio Kurahara(14), and Takio Murakami (17). Sasaki was absent when this picture was taken (Courtesy ofA. Kasahara)
Sasaki’s Pathway to Deterministic Data Assimilation 5
Fig. 2 This photograph shows faculty, students, and staff of the Geophysical Institute, Universityof Tokyo in the early 1950s. Yoshi Sasaki is crouched (second from the right), while KanzaburoGambo and Professor Syono are standing directly behind him (Gambo to the left and Syono to theright) (Courtesy of A. Kasahara)
One of the most important graphical techniques of the 1950s was the method devised byRagnar Fjørtoft (1952) for integrating the barotropic vorticity equation. As described inChapter 8 [of Nebeker (1995)], Fjørtoft was working in the Norwegian tradition of thegraphical calculus, and his 1952 paper may be said to have fulfilled Bjerknes’s program ofcalculating the weather graphically.
The students who were at the Geophysical Institute in the early 1950s view Gamboas a scientific hero. His altruistic efforts to build a bridge to the West are alwaysat the forefront of their thoughts as they retrospectively examine their own careers(Lewis 1993b). A photo of Gambo, Syono, and Sasaki is found in Fig. 2.
3 Track Prediction of Typhoons
Among the first contributions that came from the NWP group in Tokyo was apaper by Sasaki and Miyakoda on the track forecasting of typhoons (Sasaki andMiyakoda 1954). It is the first known publication on numerical prediction of ty-phoon/hurricane tracks.5 Twenty-four hour track predictions were made by par-titioning the geopotential field at 700 mb into a steering current (synoptic-scale
5 A historical review of hurricane track forecasting in the Atlantic Basin is found in DeMaria(1996).
6 J.M. Lewis
flow component) and a smaller-scale circular vortex to fit the geopotential in theimmediate vicinity of the typhoon. The structure of the circular vortex followed TedFujita’s empirical formula (Fujita 1952).6 By extracting the large-scale componentfrom the analyzed geopotential and combining this with the 24-h prediction of thegeopotential tendency (found from the graphical solution to the barotropic vorticityequation), the typhoon’s motion could be determined.
Sasaki and Miyakoda presented their paper at the UNESCO Symposium onTyphoons—held in Tokyo on November 9–12, 1954. Miyakoda has a vivid memoryof events surrounding this presentation. As he recalled:
Mr. Sasaki was very creative, and original thinker. We worked together in the university ondeveloping a method of typhoon tracks and presented our work on the international meetingin Tokyo. In that meeting, the scientists attending from US were H. [Herbert] Riehl, [FatherCharles] Deppermann [,S. J.], Bob Simpson, [Leon] Sherman, possibly J. [Jerome] Namias.This is the first international meeting after the Pacific War. This method had been used inJapan Meteorological Agency for more than 10 years since then. Much later, Dr. Y. [Yoshio]Kurihara developed a hurricane forecasting system in GFDL [Geophysical Fluid DynamicsLaboratory]. In this method, he first eliminates the typhoon and replaces it with a cleancircular systematic hurricane. In other words, the GFDL method of hurricane forecasts hassomething in common in this basic idea.
(Miyakoda 2007, personal communication)
Beyond its influence on operations at JMA, the work of Sasaki and Miyakoda stim-ulated research in the United States—first at the University of Chicago and later atthe U. S. Weather Bureau (USWB). Akira Kasahara, then a postdoctoral fellow atUniversity of Chicago, recalls this work:
When I joined the Platzman’s research project at University of Chicago in 1956, I decidedto extend the steering flow method of Sasaki and Miyakoda (1954) to suit for the numericalprediction of hurricanes using the electronic computer at the Joint Numerical Weather Pre-diction Unit in Suitland, MD [See Kasahara 1957]. Working with George Platzman, GeneBirchfield, and Robert Jones, we developed a series of numerical prediction models of hurri-cane tracks. Lester Hubert of the U. S. Weather Bureau took one of our prediction schemes,tested [it] operationally and published an article (Hubert 1959).
(A. Kasahara 2007, personal communication)
4 Sasaki’s Dissertation
Syono did not entrain students into his current interest as was the case with hiscontemporary C.-G. Rossby. George Platzman, a protege of Rossby’s, remembershis mentor as follows:
Rossby had a way of engaging you on an intellectual basis, to draw you out and to co-optyou into his current interest, not by using you but by transferring his enthusiasm to you.
(Platzman 1990, personal communication)
Syono expected the student to identify and find his own natural interest. He didnot discourage the students from working on problems that he had investigated,
6 At that time, Fujita was a professor of physics at Kyushu Institute of Technology.
Sasaki’s Pathway to Deterministic Data Assimilation 7
most notably those related to typhoons, but such problems were deemed no moreappropriate than a problem independently formulated by the student.
As an undergraduate in physics, Sasaki was captivated by the subject of vari-ational mechanics, that post-Newtonian branch of analytical mechanics developedby Leonhard Euler, Joseph Louis Lagrange, and William Rowan Hamilton. Histori-cally, this subject has been found so esthetically pleasing and utilitarian that wordslike “poetic”, “Shakespearian”, and “conspicuously practical” are used to describeit (Bell 1937; Lanczos 1970).7 Sasaki had seen its application in quantum mechan-ics and the theory of relativity, but as he said, “I was fascinated to find whether ornot there exists variational principle for meteorological phenomenon” (Sasaki 2007,personal communication). This research topic was far afield from other efforts at theGeophysical Institute, but “Professor Syono encouraged my “lone-wolf” approachand asked me to make informal presentations to him” (Sasaki 2007, personal com-munication). As elaborated upon by Sasaki:
There was minimal literature on the use of variational methods in fluids—some in HoraceLamb’s book on hydrodynamics [Lamb 1932] and some applications to irrotational fluidsin Bateman’s book [Harry Bateman, Caltech aerodynamicist; Bateman (1932)]. I had beenworking with Miyakoda on typhoon tracking and wanted to apply these variational ideas toa typhoon that would be idealized as a vortex. The Clebsch transformation [Clebsch 1857]is the key to this problem, and Syono wanted me to discuss the physical basis for the trans-formation. I learned a lot from these discussions, and I made good progress. The resultsfrom the work formed the major part of my dissertation research, which was published in1955 [Sasaki 1955].
(Sasaki 1990, personal communication)
Sasaki’s development of these ideas for geophysical fluid dynamics was a valu-able extension of the work accomplished by Bateman (1932, Sect. 2.52)—that is,Bateman’s case of irrotational flow in fixed coordinates was expanded to include thegeneralized hydro-thermodynamic equations on a rotating earth with application tothe problem of tracking the vorticity associated with a typhoon. Again, in the ab-sence of digital computers, Sasaki integrated the governing equations via graphicalmethods. Several years later, without knowledge of Sasaki’s work, the noted Scrippsphysicist/oceanographer Carl Eckart developed similar equations for the ocean-atmosphere system (Eckart 1960a, b). Eckart and Sasaki never exchanged thoughtson these nearly equivalent developments (Sasaki 2007, personal communication).
5 Variational Analysis: Sasaki (1958)8
In March 1950, the team at Princeton succeeded in producing the two 24-h numer-ical forecasts mentioned earlier. Forecasts were made using the quasi-geostrophicvorticity equation at 500 mb [See Platzman (1979) for a first-hand account of these
7 In Eric Temple Bell’s history, these superlative forms are found in his discussion of Lagrange’swork. This lofty language is found in Chap. 10 of Cornelius Lanczos’s book.8 Although this key paper was published in 1958, two years after Sasaki emigrated to the USA, thedevelopment of this idea was generated in Japan (Sasaki 2007, personal communication).
8 J.M. Lewis
events and Nebeker (1995) for a retrospective account]. This feat set the meteoro-logical world abuzz; yet there were skeptics—those who expressed doubt that NWPwould replace operational subjective forecasting. Nevertheless, by March 1954, ateam of meteorologists in Sweden produced the first operational NWP forecast forEurope and Scandinavia with the same dynamical equations used by the Prince-ton group (Wiin-Nielsen 1991). A little more than a year later, on 15 May 1955,operational NWP began in the USA, this time accounting for baroclinic structureby using a 3-level quasi-geostrophic model [reviewed in Thompson (1983) andNebeker (1995)].
Since upper-air observations worldwide are simultaneously collected every 12-h,the operational NWP models are initialized at these times. In the case of filteredmodels such as the quasi-geostrophic model, the initial conditions consist of anal-yses of the geopotential height on a network of grid points at one or more pressurelevels. Time constraints at the two major centers dictated development of computer-generated initial conditions for the expansive areas of North America on the onehand and Europe on the other [respectively, Gilchrist and Cressman (1955)/Cress-man (1959) and Bergthorsson and Doos (1955)]. These computer-produced analyseswere labeled “objective analyses” or “numerical weather map analyses”.
Into this milieu came Sasaki—fresh from his dissertation based on variationalmechanics. He appreciated the need for efficient computer-generated analyses inthe operational environment, yet from a more-dynamically based perspective, herealized that there was no guarantee that the operationally produced analyses wouldsatisfy the zeroeth-order constraint for the quasi-geostrophic model—i.e., the geost-rophic wind law. Generally there would be a discrepancy between the analyzedhorizontal gradient of geopotential and the horizontal gradient derived from thegeostrophic wind law and the observed wind. How to use both the wind and geopo-tential observations in accord with their accuracy to guarantee a dynamically con-sistent set of analyses was at the heart of his thought and contribution (Sasaki1958).
From his dissertation work, he knew that the governing equations for atmosphericmotion stemmed from minimizing the action integral, the time integrated differencebetween the kinetic and potential energy of the system (the Lagrangian). Now, in-stead of minimizing the Lagrangian, he could minimize the squared discrepanciesbetween the desired fields (the final analyses of wind and geopotential) and the ob-servations. Instead of integrating over time, integration over space was appropriatein this case. The approach contained the rudiments of several of the operationalmethods based on least squares [e.g., Panofsky (1949) and Gilchrist and Cress-man (1955)], yet it was more general—allowing for differential weighting of ob-servations while constraining the final analyzed fields to satisfy the constraint. Hewas most familiar with the mechanics of minimization under constraint. For thisproblem, governing analysis equations reduced to: (1) the solution of an ellipticequation in the geopotential forced by the vorticity (from the observed winds) andobserved geopotential, and (2) the direct substitution of this geopotential into thegeostrophic wind relations. Again, in the absence of computing machinery, Sasakisolved the equations via graphical methods.
Sasaki’s Pathway to Deterministic Data Assimilation 9
Sasaki’s approach seems so straightforward in retrospect, yet the magnitude ofthe contribution is ever so impressive in light of the fact that such methodologyhad never before been used in continuum mechanics. Most fundamentally, Sasaki’smethod is tied to Gauss’s monumental work of 1801 when Gauss developed themethod of least squares under constraint. Gauss’s work was driven by his desire toforecast the time and reappearance of the planetoid Ceres that came into conjunc-tion with the sun after only 42 days of observation. He needed to find the initialconditions, the celestial position and velocity of the planetoid at some point duringthe 42 day period of observation in order to predict its place and time of reappear-ance. Gauss’s work, however, was based on the gravitational mechanics of discreteobjects: the planetoid, the earth, and the sun. Sasaki was unaware of Gauss’s workon this subject (Gauss 1809, 1857) (Sasaki 2007, personal communication).
There was a strength and beauty in Sasaki’s 1958 contribution. He demonstratedits versatility not only with the geostrophic constaint, but also with the more-generalwind law called the “balance” equation, an equation that couples wind and geopo-tential for the larger-scale circulation of the atmosphere and is applicable in the trop-ics as well as in mid-latitudes (see Thompson 1980). Despite the solid theoreticalfoundation upon which the variational method rested, the computational demandsin the presence of time constraints at the operational centers made it untenable fordaily production of numerical map analyses in the 1950s–1960s.9
With the hope and promise of longer range weather prediction (beyond sev-eral days) that stemmed from Phillips’s successful numerical experiment linkedto hemispheric circulation (Phillips 1956), advances in general circulation model-ing using more-realistic models, improved high-speed computation [e.g., Smagorin-sky (1963)], and with the politico-scientific thrust that was aimed at acquisition ofglobal observations [addressed in Smagorinsky (1978)], the environment for appli-cation of Sasaki’s method was improving by the late-1960s.
In this environment, Sasaki rekindled his interest in objective analysis and ex-panded his view to encompass the temporal distribution of observations—in effect,he developed a four dimensional view of data assimilation that has come to be called4DVAR (4-dimensional variational analysis). His expansive approach to variationaldata assimilation is found in a trilogy of papers published in the December 1970issue of Monthly Weather Review (Sasaki 1970a, b, c). A photo of Sasaki shortlyafter completing this work is shown in Fig. 3.
6 Pathways to Operations
Sasaki’s 1958 contribution augmented by his 1970 papers established a solid dy-namical framework for data assimilation. With increased computational powerthat led to the inclusion of more physical processes in the models, there were
9 In the late 1960s, Odd Haug of Norwegian Weather Service made an effort to analyze surfacewind and pressure via Sasaki’s method. The author remembers reading the report, but despitediligent efforts by the Norwegian Meteorological Institute’s librarian, Greta Krogvold, the reportcannot be located.
10 J.M. Lewis
Fig. 3 With the National Severe Storms Laboratory in the background, Sasaki is shown standingon the site of Meteorology Department’s first home on the North Campus of the University ofOklahoma (1970) (Courtesy of Y. Sasaki)
attendant demands on the data assimilation—in essence, there was a need to im-prove the algorithms that searched for the optimal atmospheric state [See LeDimetand Talagrand (1986) for a review of these algorithms]. With these improvements,Sasaki’s variational approach became operational at the U. S. Navy’s Fleet Numer-ical Weather Center (FNWC) in 1971 [labeled the Global Band Analysis, coveringthe globe from 40S to 60N] (Lewis 1972). The generalized nonlinear thermal windequation, applicable over the tropics and mid-latitudes, was used as a weak con-straint. It remained operational until the mid-1990s.
Another form of variational analysis became operational at the National Meteo-rological Center (NMC) in the mid-1970s (Bergman 1975; McPherson 1975; and areview by Schlatter et al. 1976). This was labeled Optimum Interpolation (OI) andstemmed from the pioneering work of Arnt Eliassen (1954) and Lev Gandin (1965).Eliassen appears to be the originator of this idea in meteorology, but in his oral his-tory, he gives Gandin credit for developing the idea further (Eliassen 1990). Theywere both members of the World Meteorological Organization’s (WMO) committeeon network design in the late-1950s and that is where the discussion of this method-ology commenced.
The common ground for OI and Sasaki’s method rests on fundamental leastsquares problem formulation. The primary difference is that Sasaki’s approach is de-terministic whereas OI is stochastic. Determinism’s philosophy assumes that the fu-ture state of the system is completely determined by the present state. The evolution
Sasaki’s Pathway to Deterministic Data Assimilation 11
of the state is governed by causal relationships such as the Newtonian equations ofmotion. The stochastic philosophy rests on the assumption that there are intrinsicuncertainties in the governing dynamics.10
It is safe to say that both of these variational approaches play a prominent rolein the practice of data assimilation at the operational centers. A stimulating andenlightening discussion of current practice at these centers is found in Rabier andLiu (2003). The relative merits of the these strategies including factors that favorone method over the other are explored in Daley (1991), Tseng (2006), and Lewiset al. (2006).
7 Epilogue
Data assimilation, especially as viewed in the context of initializing dynamical pre-diction models, continues to present challenging problems. Compared to the dy-namical models of the 1950s–1960s, modern-day models exhibit a level or degreeof complexity that rivals L. F. Richardson’s phantasmagoric view of the atmosphereand its governing laws (Richardson 1922)11. Matching the ever-complex model out-put with measurements from conventional and new observational tools demandsexcellence in theory and practice. Rossby realized that it would take a team withexpertise in both arenas to produce an objective weather map, a map untouched byhuman hand. Pall Bergthorsson and Bo Doos were his choices, the practitioner andtheorist, respectively, and they delivered in splendid fashion. Nevertheless, “purists”like Bergthorsson’s mentor, the celebrated analyst Tor Bergeron, found it difficult toaccept the objective maps. Bergeron’s maps were not only artistic, they containedinformation that tacitly drew upon his years of experience as an observer and an-alyst (Saucier 1992, personal communication). Bergeron came to accept the NWPproduct, but he never accepted the objective map that provided the initial conditionfor the prediction model (Doos 2004, personal communication). To incorporate allof the “Bergeron-like” nuances into an objective map is probably impossible; yetrecent efforts by data assimilators like Jim Purser of National Center for Environ-mental Prediction (NCEP) hold promise for getting the “Bergeron-like” structuresinto analyses in the vicinity of discontinuities like fronts (Purser 2005).
The observational tools that now include the instruments aboard satellites inspace, the network of surveillance radars, and wind profilers, measure “surrogate”variables like spectral radiance, radar reflectivity, and turbulence, rather than thefundamental model variables—pressure, temperature, wind, and moisture. Thus,“model counterparts”—oft times complicated nonlinear functions of the modelvariables— must be used to make comparisons. And we must not forget that these
10 See Lewis and Lakshmivarahan (2008) for a historical review of data assimilation methods inthe context and time frame of Sasaki’s career.11 In the introduction of Dover’s reprint of Richardson (1922), Sidney Chapman said, “He[Richardson] told me that he had put all that he knew into this book.”
12 J.M. Lewis
observations stream into the system at variable times and places. To consistentlyconnect the model dynamics with these observations is a problem of Herculeandimension.
As computational power has increased over the last decades of the twentieth cen-tury, our ambitious demands on the data assimilation system have been met to acertain degree. Yet, pragmatism must be balanced by equally ambitious efforts tomore-fundamentally link dynamical law with observations. It was to this problemthat Yoshi Sasaki directed his energy in the 1950s. The austerity of the situationin Japan, essentially a lack of resources, drove him to examine the problem the-oretically. In short, there was an unexpected advantage from the virtues of adver-sity to use historian Arnold Toynbee’s phrase (Toynbee 1939). Further, he had a“lone wolf” approach to his research, a pathway or angle of attack that veered fromthe standard. He celebrated the alternate view in both his teaching and research.Like Gauss, he viewed the data assimilation /initialization problem deterministi-cally, and he set the stage for 4DVAR, four-dimensional variational data assimila-tion, the methodology that is currently used at five operational weather predictioncenters worldwide. It will become the standard for all centers in the future.
Acknowledgements I’m grateful to Professor Sasaki for his oral history in May 1990 that pro-vided the foundation for this story.
Oral histories and letters of reminiscence were received from the following individuals whereO indicates oral history and L a letter of reminiscence (date shown in parentheses).
Akio ArakawaL (June 1992)Pall BergthorssonL (June 2004; December 2007)Bo DoosL (June 2004)Kanziburo GamboL (June, September 1992)John Hovermale [Conversation] (March 1970)Akira Kasahara o;L (June 1991; June 1992, May 2007)Kikuro Miyakoda L (June 1992, May 2007)Yoshimitsu Ogura L (June 1992)Katsuyuki Ooyama o;L (March 1992; June, September 1992)George Platzman o (May 1990)Yoshi Sasaki L (June 2007)Walter Saucier L (September 1992; June 2007)
Information from these meteorologists facilitated reconstruction of events related to Sasaki’scareer; accordingly, I extend my heartfelt thanks to each of them.
Comments on an early version of the paper by Norman Phillips and George Platzman werevaluable. Akira Kasahara carefully checked the manuscript for accuracy of the recorded events inJapan (spelling of names, dates, times, and places).
For help in locating key historical papers, I commend Hiroshi Niino, Mashahito Ishihara, FrodeStordal, Trond Iversen, Greta Krogvold, and Fred Carr. Finally, Vicki Hall and Domagoj Podnarhelped me with the electronic submission process.
Appendix: Biographical Sketch
Sasaki’s resume is found in Table 1. Elaboration on several elements in the tablefollows and a discussion of his scientific genealogy is also included.
Sasaki’s Pathway to Deterministic Data Assimilation 13
Youthful Experiences
Since Yoshi Sasaki attended grade school, middle school, and high school fromthe mid-1930s through the end of WWII, there was disruption in his education.As he said, “...because of the irregular schedules, the study of science especiallydepended much upon individual study in Japan during these years” (Sasaki 2007,personal communication). His stimulation for science/mathematics came from threequarters: his father (an engineer), a 6th grade teacher, and a physics professor whowas a family friend.
Yoshi’s father gave him complete freedom to pursue the career of his choice. Bythe time he was in the 6th grade, his gift for mathematical thinking was apparent tohis grade-school teacher. This teacher asked Yoshi to serve as a “teaching assistant”in mathematics and “I was stimulated by this experience” (Sasaki 2007, personalcommunication).
A key event occurred in the early 1940s when the physics professor mentionedabove gave Yoshi a translation of Albert Einstein’s 1905 paper on special relativity.It was the philosophical ideas presented by Einstein—the majesty of observationand measurement in the various frames of reference—that touched him. It indeedwas a paper filled with “. . .text and little in the line of formal manipulations. In theend the startling conclusion was there, obtained apparently with the greatest of ease,and by reasoning which could not be refuted” (Lanczos 1965, p. 7). To this day,Sasaki exhibits excitement as he recollects this experience.
By his own admission, Yoshi was a romanticist—a child who reveled in the“... beauty of cloud patterns, floating and varying in the sky ... trying to find theprinciples of nature, and the elegant theories and models especially in physics”(Sasaki 2007, personal communication). With his youthful passion for science thatwas complemented by diligent study, he not only gained entrance into the Universityof Tokyo, but he was awarded a national scholarship for his undergraduate educationand he completed the bachelors degree in three years.
Emigration to the USA
Following in Gambo’s footsteps, about ten Japanese meteorologists from the Uni-versity of Tokyo permanently moved to the United States during the period fromthe mid-1950s through the mid-1960s (Lewis 1993b). Yoshi Sasaki, his wife Koko,and their infant son Okko came to America in 1956 on the Hikawa-maru. This ship,known as “Queen of the Pacific”, was the only mainstream Japanese passenger linerto survive WWII. Yoshi and Koko are shown aboard this passenger liner, three daysout from Yokohama on route to Seattle (Fig. 4).
Sasaki appeared to be heading for Johns Hopkins University or University ofChicago, but he eventually accepted a position at Texas A&M College. The decisionwas not entirely made on academic grounds; Okko’s poor health was central to thedecision. As remembered by Yoshi:
14 J.M. Lewis
Fig. 4 Sasaki and his wife Koko are shown aboard the trans-Pacific liner Mikawa-maru on theirway to America (1956) (Courtesy of Y. Sasaki)
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