Eugenia Kalnay Distinguished University Professor
Prior to coming to UMD, Eugenia Kalnay was Branch Head at NASA Goddard, and later
the Director of the Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction
(NCEP, formerly NMC), National Weather Service (NWS) from 1987 to 1997. During those ten years there
were major improvements in the NWS models' forecast skill. Many successful
projects such as the 60+years NCEP/NCAR Reanalysis (the paper on this Reanalysis has been cited over 10,000 times), seasonal and interannual dynamical
predictions, the first operational ensemble forecasting, 3-D and 4-D variational data assimilation,
advanced quality control, and coastal ocean forecasting. EMC became
a pioneer in both the fundamental science and the practical applications of
numerical weather prediction.
Current research interests
of Dr. Kalnay are in numerical weather prediction, data
assimilation, predictability and ensemble forecasting, coupled ocean-atmosphere
modeling and climate change and sustainability. Zoltan Toth and Eugenia Kalnay introduced the
breeding method for ensemble forecasting. She is also the author (with Ross
Hoffman and Wesley Ebisuzaki) of other widely used ensemble methods known as
Lagged Averaged Forecasting and Scaled LAF. Her book, Atmospheric Modeling,
Data Assimilation and Predictability (Cambridge University Press, 2003) sold
out within a year, is now on its fifth printing and was published in Chinese
(2005) and in Korean (2012). A second edition is in preparation. She has received
numerous awards, including the 2009 IMO Prize of the World Meteorological Organization, and
is a member of the UN Scientific Advisory Board (2013), the NOAA Scientific Advisory Board (2016) and other
Scientific Boards.
Please see ORCID, http://orcid.org/0000-0002-9984-9906, for a complete list of peer-reviewed publications
Work at UMD/AOSC
She worked with Drs.Shu-Chih Yang and Ming Cai on ensemble and data assimilation methods on
coupled ocean-atmosphere models using breeding (Cai et al, 2003, Yang et al,
2005, 2006, 2008, 2009), on the one and two-way interaction of the ocean and
the atmosphere (Pena et al., 2003, Pena and Kalnay, 2004). Kalnay and Cai
(2003) proposed a method (Observation minus Reanalysis trends, OMR) to estimate
impact of land-cover and land-use change in climate change. The OMR paper was
selected by Discovery Magazine as one of the top 100 science news of the year,
and many papers have since used OMR to conclude that Green is Cool.
E. Kalnay co-founded with J.
Yorke the Weather/Chaos Group at
UMCP, which discovered the presence of low dimensionality in unstable
regions of the atmosphere detected with breeding (Patil et al, 2002) and applied this result to
develop the Local Ensemble Kalman Filter (Ott et al. 2002, 2004), the Local
Ensemble Transform Kalman Filter (Hunt et al., 2007), and its extension to 4
dimensions (Hunt et al., 2004).
She directed more than 25 doctoral theses on data assimilation and related subjects (Carolyn Reynolds
(co-advisor with P. Webster, now at NRL), Zhao-xia Pu (Prof. at Utah), Carolina Vera (Prof. at U.
Buenos Aires), Matteo Corazza (U Genoa), DJ Patil (co-advisor with J Yorke, E Ott and B Hunt,
now Chief Data Scientist, US OSTP), Sim Aberson (co-advisor with F. Baer, NOAA), Malaquias Peña
(NCEP), Shu-Chih Yang (Prof. at NCU, Taiwan), Pablo Grunmann (co-advisor with Ferd Baer),
Takemasa Miyoshi (Prof at UMD, Data AssimilationTeam Leader at RIKEN, Japan), Chris Danforth (co-
advisor with Jim Yorke, Prof. at U Vt), Hong Li (Shanghai Typhoon Inst.), Junjie Liu (now at JPL), Jim
Jung (co-Advisor with John LeMarshall, now at NOAA), Mitch Goldberg (co-advisor with Zhanqing Li,
NESDIS), Ji-Sun Kang (Data Assim. Leader in KISTI, Korea), Matt Hoffman (with J Carton, now Prof.
at RIT), Juan J. Ruiz (co-advisor with C. Saulo, Prof. at UBA), Steve Penny (co-advisor with Jim Carton,
now at UMD and NCEP), Steve Greybush (now Prof. at PSU), Tamara Singleton (UMD, co-advisor with
Kayo Ide and Shu-Chih Yang, at J. Hopkins), Javier Amezcua (with Kayo Ide, now at U of Reading),
Safa Motesharrei (now at AOSC, UMD), Guo-Yuan Lien (now at RIKEN, Japan), Daisuke Hotta, (now
at Japan. Met. Service), Yongjing Zhao, Yan Zhou (NOAA), Adrienne Norwood (Johns Hopkins).
Since she presented a talk at NAS in2010 on Population and Climate Change, Eugenia has collaborated
with Safa Motesharrei and Jorge Rivas on the coupling with feedbacks the Human and the Earth Systems.
Their paper on the Human And Nature DYnamical (HANDY model, J. Ecological Economics, 2014)
http://www.sciencedirect.com/science/article/pii/S0921800914000615> has been the most downloaded paper
of the Journal every trimester since its publication.
Awards
- WMO/IMO Prize for 2009, see talk Population
and Climate Change: A Proposal (also in Spanish)
- Member of the National Academy of Engineering (1996)
- Foreign Member of the Academia Europaea (2000)
- Distinguished University Professor, UMD (2001)
- Eugenia Brin Professor in Data Assimilation (2008)
- Doctor Honoris Causa, University of Buenos Aires (2008)
- Corresponding member of the Argentine National Academy of Physical Sciences (2003)
- Fellow of AGU (2005), AAAS (2006), AMS (1983)
- UMD-wide Kirwan Award (2006)
- Robert E. Lowry Chair, School of Meteorology, U. of Oklahoma (1998)
- NASA medal for Exceptional Scientific Achievement (1981)
- Two Department of Commerce Gold and one Silver Medal
- Discovery Magazine selected her Nature paper as a top 100 science news of 2003 (see feature in International Association for Urban Climate newsletter)
- The Reanalysis paper of 1996 is the most cited paper in all geosciences (more that 10 thousand citations)
- Lorenz AGU 2012 Lecture
- 2015 AMS Joanne Simpson Mentorship Award for For effectively mentoring many early career scientists, with her unstinting generosity of time and attention in providing advice, encouragement, leadership, and inspiration
- 2015 AMS Honorary Member Award
- 2015: AMS Eugenia Kalnay Symposium
Publications
Please see ORCID, http://orcid.org/0000-0002-9984-9906, for a complete list of peer-reviewed publications
- Original Breeding Paper Tracton and Kalnay, 1993
- Original Breeding Paper Toth and Kalnay 93, BAMS
- Original Breeding Paper Toth and Kalnay, MWR 97
- NCEP-NCAR 40-Year Reanalysis Project paper 1
- NCEP-NCAR 50-Year Reanalysis Project paper 2
- Application of the Quasi-Inverse Method to Data Assimilation INV 3D-Var paper
- Oklahoma-Texas Drought of 1998: Origin and Maintenance Nature, 2000
- SAMEX paper Hou et al, 2001
- Breeding and the Errors of the Day Corazza et al, 2003
- Impact of Urbanization and Land-use Change on Climate Kalnay and Cai, Nature 2003
- Lifespan of coupled anomalies Pena et al, JoC, 2004
- Separating Fast and Slow Modes NPG 2004
- Inverse 3D-VAR to precondition 4Dvar Park and Kalnay, GRL, 2004
- Lorenz model is predictable Evans et al (2004) BAMS
- Ensemble forecasting and data assimilation: two problems with the same solution? ECMWF 2002 Predictability Book
- LETKF experiments with the NCEP global model Szunyogh et al, Tellus, 2005, in press
- 4-DimensionalEnsemble KalmanFilter, Tellus, 2004
- LocalEnsemble Kalman Filter Ott et al, 2004, Tellus
- MOS, Perfect Prog and Reanalysis Marzban, Sandgathe, Kalnay, MWR 2006
- Breeding in a coupled system Yang et al (JClim, 2006
- Data Assimilation as Synchronization of Truth and Model JAS 2006 paper
- Estimating and Correcting Global Weather Model Error Danforth et al, MWR-2007
- Harlim and Hunt (2006, link)
- Corazza et al 2007 (NPG)
- Estimating and correcting model errors> JAS 07
- Estimation of the Impact of Land Surface Forcings JGR NEW paper
- Impact of Vegetation Types on Surface Temperature Change Lim-Cai-Kalnay-Zhou (2007, JAMC-revised)
- Impact of Land-type on Surface Warming GRL Lim-Cai-Kalnay-Zhou paper
- 4D-Var or EnKF ? Tellus A (Oct07)
- 4D-Var or EnKF ? Discussion by Gustafsson
- 4D-Var or EnKF ? Response to Discussion
- JGR paper on Argentina's land change
- Application of coupled breeding to ensemble forecasting and data assimilation Yang et al., 2008, J.of C., revised
- Ensemble Kalman Filter: Current Status and Potential Book Chapter
- International Association for Urban Climate features our work
- Coarse analysis by weight interpolation in the LETKF Yang et al., 2009, QJRMS, revised
- Land-use and Land-cover impact on the US temperature trends Fall et al, 2009
- Data assimilation in a coupled system Ballabrera et al., 2009
- In the midst of chaos, good predictions
- Analysis sensitivity to obs and cross-validation
- Use of Breeding to Detect and Explain Instabilities in the Global Ocean
aux.material
- Accelerating spin-up in EnKF: Running in Place
- Handling nonlinearity and non-Gaussianity: Yang-Kalnay-Hunt
(in press, MWR)
- Comparison of methods to deal with model errors in EnKF
- Accelerating spin-up RIP,Kalnay-Yang, QJRMS, 2010
- Balance and Ensemble Kalman Filter Localization Techniques MWR, Greybush et al
2010
- Localization of Variables Kang et al., 2011, JGR
- Estimation of surface carbon fluxes with an advanced data assimilation methodology. JS Kang, E Kalnay, T Miyoshi, J Liu, I Fung - Journal of Geophysical Research: Atmospheres.
- Human and Nature DYnamical model (HANDY), J. Ecological Economics, HANDY Paper. The most downloaded paper of the journal.
- Elsevier Q&A on HANDY, (J. Ecological Economics), HANDY Paper Q&As
- The Guardian blog on the HANDY model had 40 million Google results in April 2014.
- Estimating and including observation-error correlations in data assimilation
T Miyoshi, E Kalnay, H Li. Inverse Problems in Science and Engineering 21 (3), 387-398
- Ensemble Forecast Sensitivity to Observations (EFSO): a simpler formulation, Kalnay et al. (Tellus, 2012)
- Ensemble Forecast Sensitivity to Observations (EFSO): Ota et al., 2013, application to NCEP observations. Tellus Ota et al.
- Effective Assimilation of Precipitation. Lien et al. 2013. Tellus.
- Ensemble transform Kalman-Bucy filters Amezcua et al (2013).
- Greybush, Steven J., R. J. Wilson, R. N. Hoffman, M. J. Hoffman, T. Miyoshi, K. Ide, T. McConnochie, and E. Kalnay, 2012: Ensemble Kalman Filter Data Assimilation of Thermal Emission Spectrometer Temperature Retrievals into a Mars GCM. J. Geophys. Res., 117, E11008, doi: 10.1029/2012JE004097.
- Greybush, Steven J., E. Kalnay, M. J. Hoffman, and R. J. Wilson, 2013a: Identifying Martian atmos- pheric instabilities and their physical origins using bred vectors. Q. J. R. Meteorol. Soc., 139, 639-653, doi: 10.1002/qj.1990.
- The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model SG Penny, E Kalnay, JA Carton, BR Hunt, K Ide, T Miyoshi, GA Chepurin. Nonlinear Processes in Geophysics 20 (6), 1031-1046
- Lyapunov, singular and bred vectors in a multi-scale system: an empirical exploration of vectors related to instabilities. A Norwood, E Kalnay, K Ide, SC Yang, C Wolfe - Journal of Physics A: Mathematical and Theoretical, 2013
Seminars and Presentations
- UN-Sci. Adv.Board, Berlin, Jan 2013
- ECMWF seminar October 2013
- GODAES/WGNE Invited Talk on Coupled Data Assimilation 19Mar2013
- GFDL Seminar 31Jan2013 LETKF and surface carbon fluxes
- AGU Lorenz Lecture December 2012
- Population and Climate Change @PSU December 2012
- Recent Developments in Data Assimilation @PSU December 2012
- Seminars at MIT, April 2011: Recent advances in EnKF,
Population and Climate Change
- Talk at WCRP, October 2011:Population and Climate Change (15 min)
- WMO-62 Executive Council Talk: Chaos-Predictability-EnKF
- WMO 15th Meeting of the Region III, Bogot, Talk on Predictability: what is scientifically feasible? (in Spanish)
- CO2 data assimilation and Reanalysis Baltimore, Nov 1 2010
- Mars: Hoffman et al., 2010, Eluszkiewicz et al., 2008, Greybush et al., PPT, May 2010
- Invited talk at MOCA-09 (Data assimilation)
- Seminar at NCEP - EnKF: Status and potential
- Simultaneous estimation of inflation and obs errors
- Six Lectures in Alghero, MSMM08: 1: (intro predict), 2: Tang/Adj Models-SVs, 3: BV applications, 4: EKF&EnKF, 5: New ideas to improve EnKF, 6: 4D-Var and EnKF
- Two lectures in Puerto Rico: 1. Reanalyses; 2. Impact of Land Use on Climate Change
- Lidar Workshop: Adaptive observations
- AMS 2007 presentations (ppt.pdf): Li-Kalnay-Online-Estimation-Inflation&ObErro$
Liu-Kalnay-AdaptiveObservations, Liu-AssimHumidity, Li-AIRSretrievals, Li-Model-Errors, Yang-QGcomparison-4DVarEnKFHybrid3DVar, Kalnay-ArakawaSymposium (Breeding-A simple tool for complex dynamics);Kalnay-Li-Miyoshi:Inflation-ObErrorsEstimation (extended abstract)
- IUGG 2003 Sushi lecture
- 50th NWP Symposium.
- SAMSI Talk: 4D-Var or EnKF.
- 50thNWPSymposium: Ensemble forecasting and data assimilation, two problems with the same solution?
- LEKF at UMd (Szunyogh et al 2005)
- Yang ESSIC Seminar 2006
- MIT-Seminar 2005
- Shu-Chih Yang, Kalnay and Cai (PPT)
- ENSO Instability Derived with BreedingPowerpoint
- AMS 2002 Use of Breeding in DataAssimilation (Corazza et al, 2002); Lyapunov and BredVectors (Kalnay et al 2002); (Powerpoint); Low dimensionality paper (Patil et al, PRL)
- Keeping the bred vectors young (Powerpoint)
- One-Way and Two-Way ocean-atmosphere coupling (Pena et al)
- Ensemble forecasting and data assimilation seminar at NCAR (powerpoint)
- Seminars on data assimilation: Istvan Szunyogh, Ibrahim Hoteit, Takemasa Miyoshi, Kayo Ide, Szunyogh (CSCAMM)
- Some Opportunities for DERF (NASA GSFC, April 2002)
- Predict. Workshop, ECMWF Sept 2002, paper1; , paper2
- Joel Susskind seminar 05/12/05
Student Dissertations
- Pablo Grunmann Variational Data Assimilation of Soil Moisture Information (2005)
- Shannon Sterling The Impact of Anthropogenic Global Land Cover Transformation on the Land-Atmosphere Fluxes of the Water (2005)
- Shu-ChihYang Bred Vectors in the NASA NSIPP Global Coupled Model and their Application to Coupled Ensemble Predictions and Data Assimilation (2005)
- Takemasa Miyoshi Ensemble Kalman Filter Experiments with a Primitive-Equation GLobal Model (2005)
- Chris Danforth Making Forecasts for Chaotic Processes in the Presence of Model Error (2006)
- Hong Li Simultaneous estimation of inflation and observational errors (2007)
- Junji Liu Adaptive obs, obs sensitivity, obs impact w/o adjoint, and assimilation of humidity (2007)
- Matt Hoffman Ensemble Data Assilimation and Breeding in the Ocean, Chesapeake Bay, and Mars (2009)
- Ji-Sun Kang Carbon Cycle Data Assimilation Using a Coupled Atmosphere-Vegetation Model and the Local Ensemble Transform Kalman Filter (2009)
- Tamara Singleton Data Assimilations Experiments with a Simple Coupled Ocean-Atmospheric Model (2011)
- Stephen Penny Data Assimilation of the Global Ocean Using the 4D Local Ensemble Transform Kalman Filter (4D-LETKF)and the Modular Ocean Model(2011)
- Yongjing Zhao Breeding Analyis and Growth and Decay In NonLinear Waves and Data Assimilation and Predictability in the Martian Atmosphere (2014)
- Daisuke Hotta Proactive Quality Control Based on Ensemble Forecast Sensativity to Observations (2014)
Courses (syllabus, notes and required readings)
AOSC614
- Syllabus for METO614
- SPEEDY (Junjie Liu Tutorial)
- SPEEDY files(Junjie)
- Book typos and corrections
- A few more typos
- Ch1 Intro&Overview
- Evolution of Forecast Skill
- Ch2.1 Governing Eqs
- Ch2.2 Eqs Motion Sphere
- Ch2.3 Wave Oscillations
- Ch2.4 Filtering Approx
- Ch2.5 SWE-Filtering
- Ch2.6 Vertical Coords
- Ch3.1 PDE's Well Posed
- Ch3.2.1 Finite Diffs Stab
- Ch3.2.2 Leap-Frog Table Semi-implicit
- Williams (2009)
- RAW filter & HOMEWORK
- RAW Figure
- RAW filter on SPEEDY discretization-Spectral models
- Ch3.3.1 & Space discretization-Spectral models
- Ch3.3.3 & Hong
- Ch3.3.5 Hong
- Ch3.3.5, 3.3.6, and 3.4 JLiu
- Nested models BC (Martini)
- Ch4 JLiu
- Chapter 5 Lecture Intro to Data Assim1
- Chapter 5 Lecture Intro to Data Assim2
- Steve Greybush toy DA model examples
- 5.4.4 DVarShu Chih
- 5.5 EnKF Hong
- Recent Advances In EnKF (JCSDA/NCEP seminar)
- Takemasa Miyoshi's LETKF, SPEEDY: Google code
- Junjie Liu Data Assimilation Package: Tutorial
- Junjie Liu Data Assimilation Package: Codes and Data (download)
- Junjie Liu Data Assimilation Package: Miyoshi 3D-Var doc
- Amezcua Lorenz63: Lorenz63 (zip file)
- Chapter 6 (Predictability): 1:intro predict)
- Chapter 6 (Predictability): 2: Tang/Adj Models SVs
- Chapter 6 (Predictability): 3: BV applications
- Dresden: BV-SV-LV-4D-Var
- Ch6.1
- Ch6.2
- Ch6.3
- QGSV
- Ch6.4
- Ch6.5
- Palmer ENStm
AOSC630
- Outline
- review of prob., Bayes Notes 1
- Ji-Sun Kang Class 1
- Debra Baker Class 2
- Exploratory Notes 2
- Param. Prob. Distr. Notes 3
- hypothesis testing Notes 4
- Guayaquil Table
- Regression Notes 5
- Regression Notes 6
- Multiple Regression Notes 7
- Statistical Prediction Notes 8
- ANOVA
- MOS, Adaptive Regression/KF Notes 9 (Updated Mar 4, 2016)
- Time Series, Markov Chains Notes 10
- Markov Prediction of SST Xueetal 2000
- Applications M. Pena
- MOS/NBM Lecture: Mark Antolick
- Time Series, AR, ARMA Notes 11
- Time Series, Frequency, Fourier Transforms Notes 12
- Time Filters, Lanczos Notes 13
- Lanczos Code (matlab, Greybush)
- Neural Networks
Krasnopolsky 2013
- Application of Neural NetTianleYuan
- Intro to EOFs Notes 14
- EOF code (matlab, Greybush)
- EOF example from Mars (Greybush)
- Coupled Fields, SVD Notes 15
- Cluster Analysis, Hong Li Notes 16
- Huug Vanden Dool: Chapter 2 (pptx)
- Huug Vanden Dool: Chapter 4 (pptx)
- Huug Vanden Dool: Chapter 5 (pptx)
- Huug Vanden Dool: Chapter 7 (pptx)
- Huug Vanden Dool: Chapter 8 (pptx)
- Huug Vanden Dool: CPC predictions (ppt)
- Application of Wavelets to Statistical Forecasting Webster-Hoyos
- Tangborn 2010: Wavelets
- Malaquías Peña: Ensembles 101 (2016)
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